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Attribute reduction of hybrid decision information systems (HDISs) is a significant research area within the field of machine learning. Due to the presence of nominal attributes, it is difficult to accurately measure the distance between objects in HDISs, which often results in poor attribute reduction for these systems. Rough set theory (RST) is a crucial tool for attribute reduction, but it requires computation of upper and lower approximations, which often leads to computational difficulties. In response to the aforementioned issues, this paper proposes a fast attribute reduction algorithm for HDISs based on fuzzy conditional information entropy that utilizes an iterative model and matrix operations. Firstly, a novel measurement of the distance between nominal attribute values is defined using decision attributes. Subsequently, fuzzy conditional information entropy is calculated from the perspective of “the attribute values is fed back to the attribute set” and its properties are provided. Additionally, an iterative attribute reduction model and difference matrix are established, and two new matrix operations are introduced. Finally, an iterative attribute reduction algorithm is provided. The results of experiments and statistical tests on fifteen UCI datasets, including three large datasets, demonstrate that the proposed algorithm is more effective and efficient than nine state-of-the-art algorithms. This paper not only addresses the issue of difficulty in measuring the distance between nominal attribute values but also significantly improves the computational efficiency of attribute reduction algorithms based on RST, making it possible for them to be applied to large datasets. [ABSTRACT FROM AUTHOR]

During coal mining, sudden inrushes of water from the floor pose significant risks, seriously affecting mine safety. This study utilizes the 3602 working face of the Chenmanzhuang coal mine as a case study, and the original influencing factors were downscaled using principal component analysis (PCA) to obtain four key evaluation factors: water inflow, aquiclude thickness, water pressure, and exposed limestone thickness. The rough set theory (RST) was applied to determine the weights of the four main influencing factors as 0.2, 0.24, 0.36, and 0.2; furthermore, 19 groups of comprehensive values were calculated using the weighting method, and a water inrush risk assessment was conducted for several blocks within the working face. The results are presented as a contour map, highlighting various risk levels and identifying the water inrush danger zone on the coal seam floor. The study concludes that water inrush poses a threat in the western part of the working face, while the eastern area remains relatively safe. The accuracy and reliability of the model are demonstrated, providing a solid basis and guidance for predicting water inrush. [ABSTRACT FROM AUTHOR]

Introduction: Although there are several psychological theories on bipolar disorders (BD), the empirical evidence on these theories through experimental studies is still limited. The current study systematically reviews experimental methods used in studies on the main theories of BD: Reward Hypersensitivity Theory (RST) or Behavioral Activation System (BAS), Integrative Cognitive Model (ICM), Positive Emotion Persistence (PEP), Manic Defense theory (MD), and Mental Imagery (MI). The primary aim is to provide an overview of the used methods and to identify limitations and suggest areas of improvement. Methods: A systematic search of six databases until October 2023 was conducted. Study selection involved two independent reviewers extracting data on experimental study design and methodology. Results: A total of 84 experimental studies were reviewed. BAS and RST were the most frequently studied theories. The majority of these experimental studies focus on mechanisms of reward sensitivity. Other important elements of the reviewed theories, such as goal setting and—attainment, situation selection (avoidance or approach), activation, affective/emotional reactivity, and regulatory strategies, are understudied. Self‐report and neuropsychological tasks are most often used, while mood induction and physiological measures are rarely used. Conclusion: There is a need for more consensus on the operationalization of psychological theories of mania. Standardization of test batteries could improve comparability among studies and foster a more systematic approach to experimental research. Research on affective (activated) states is still underrepresented in comparison with studies on trait vulnerabilities. [ABSTRACT FROM AUTHOR]

Background: Improving overall and individual health literacy is a major focus of national initiatives in China and similar initiatives globally. However, few studies have examined the identification and improvement of individual health literacy levels, especially among patients. Purpose: To develop an interpretable method with decision rules to assess the health literacy levels of male patients and identify key factors influencing health literacy levels. Methods: Using a convenience sampling method, we conducted on-site surveys with 212 male patients of a hospital in China from July 2020 to September 2020. The questionnaire was developed by the Ministry of Health of the People's Republic of China. A total of 206 of the completed surveys were ultimately included for analyses in this study. The rough set theory was used to identify conditional attributes (ie, key factors) and decision attributes (ie, levels of health literacy) and to establish decision rules between them. These rules specifically describe how different combinations of conditional attributes can affect health literacy levels among men. Results: Basic knowledge, concepts, and health skills are important in identifying whether male patients have health literacy. Health skills, scientific health concepts, healthy lifestyles and behaviors, infectious disease prevention and control literacy, basic medical literacy, and health information literacy can be identified as cognitive behaviors with varying degrees of health literacy among patients. Conclusion: This model can effectively identify the key factors and decision rules for male patients' health literacy. Simultaneously, it can be applied to clinical nursing practice, making it easier for hospitals to guide male patients to improve their health literacy. [ABSTRACT FROM AUTHOR]

Relevant studies have shown that methane gas has a close relationship with seismic activity. The concentration of methane released within a tectonic zone can reflect the intensity status of tectonic activities, which is important for seismic monitoring. In this study, the January 2020 Xinjiang Jiashi earthquake was taken as the research object, and the mature Robust Satellite Technique (RST) algorithm was used to characterize the L3-level methane product data from the hyperspectral sensor, Atmospheric Infrared Sounder (AIRS), installed on the Earth Observing System (EOS) AQUA satellite at the monthly scale, 8-day scale and daily scale. An analysis of the spatial and temporal distribution of methane was carried out for before and after the earthquake based on the 3D structural condition of the gas, and the 3D structural conditions of the 8-day scale were introduced. An 8-day scale 3D structural condition was introduced and migration validation was performed, and the results showed that (1) the seismic methane anomaly-extraction process proposed in this study is feasible; (2) the 3D contour features indicated that the methane anomalies that occurred before the Jiashi earthquake were caused by geogenic emissions; (3) the anomaly-extraction algorithm from this study did not extract the corresponding anomalies in the non-seismic year, which indicated that the anomaly-extraction algorithm of this study has some degree of feasibility; and (4) the migrated validation of the Wenchuan earthquake of May 2008 further suggested that methane anomalies at the time of the Wenchuan earthquake were caused by the earthquake. [ABSTRACT FROM AUTHOR]

This research was carried out to compare selected forecasting methods, such as the following: Artificial Neural Networks (ANNs), Classification and Regression Trees (CARTs), Chi-squared Automatic Interaction Detector (CHAID), Fuzzy Logic Toolbox (FUZZY), Multivariant Adaptive Regression Splines (MARSs), Regression Trees (RTs), Rough Set Theory (RST), and Support Regression Trees (SRTs), in the context of determining the temperature of brine from vertical ground heat exchangers used by a heat pump heating system. The subject of the analysis was a public building located in Poland, in a temperate continental climate zone. The results of this study indicate that the models based on Rough Set Theory (RST) and Artificial Neural Networks (ANNs) achieved the highest accuracy in predicting brine temperature, with the choice of the preferred method depending on the input variables used for modeling. Using three independent variables (mean outdoor air temperature, month of the heating season, mean solar irradiance), Rough Set Theory (RST) was one of the best models, for which the evaluation rates were as follows: CV RMSE 21.6%, MAE 0.3 °C, MAPE 14.3%, MBE 3.1%, and R2 0.96. By including an additional variable (brine flow rate), Artificial Neural Networks (ANNs) achieved the most accurate predictions. They had the following evaluation rates: CV RMSE 4.6%, MAE 0.05 °C, MAPE 1.7%, MBE 0.4%, and R2 0.99. [ABSTRACT FROM AUTHOR]

This work presents the results of the research on the thermal state of the road surface measured by means of a mobile road condition sensor. A 15 km route circumnavigating the city centre and used by urban traffic was taken as the research area. Sixteen test runs were performed under summer and winter conditions. An analysis of the locations at which the rate of change of surface temperature had extreme values between consecutive test runs (which lasted around 25 min) was carried out. Based on that, critical sections of the test route were identified, and thus points characterised by readings deviating significantly from the mean values. Based on the thermal mapping methodology, an analysis of the rate of surface cooling was carried out. It was shown to be characterised by temporal (as well as spatial) non-uniformity. The rate of change of surface and air temperatures was calculated as a function of time, which was equal to −2.22 and −1.97°C/h, respectively. During the analysis, it came to light that hourly intervals represent the optimal frequency for thermal monitoring of a road network. Exceptions to the above are the aforementioned sections selected on the basis of the mobile measurements, in which permanent monitoring of road conditions via ESS stationary systems are recommended.

Outlier detection is an important topic in data mining. An information system (IS) is a database that shows relationships between objects and attributes. A real-valued information system (RVIS) is an IS whose information values are real numbers. People often encounter missing values during data processing. A RVIS with the miss values is an incomplete real-valued information system (IRVIS). Due to the presence of the missing values, the distance between two information values is difficult to determine, so the existing outlier detection rarely considered an IS with the miss values. This paper investigates outlier detection for an IRVIS via rough set theory and granular computing. Firstly, the distance between two information values on each attribute of an IRVIS is introduced, and the parameter λ to control the distance is given. Then, the tolerance relation on the object set is defined according to the distance, and the tolerance class is obtained, which is regarded as an information granule. After then, λ-lower and λ-upper approximations in an IRVIS are put forward. Next, the outlier factor of every object in an IRVIS is presented. Finally, outlier detection method for IRVIS via rough set theory and granular computing is proposed, and the corresponding algorithms is designed. Through the experiments, the proposed method is compared with other methods. The experimental results show that the designed algorithm is more effective than some existing algorithms in an IRVIS. It is worth mentioning that for comprehensive comparison, ROC curve and AUC value are used to illustrate the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a systematic review of studies published between 2010 and 2022 couched within the theoretical-methodological framework of Rhetorical Structure Theory (RST). Using "Publish or Perish" software, we extracted from Web 760 works related to RST and, considering the number of citations, we analyzed the first 100 results that were organized and described based on their abstracts. For didactic purposes, we classified these studies into the following criteria: (i) works that couldn't be analyzed due to accessibility issues; (ii) works focusing on theorization and the description of various linguistic phenomena; (iii) studies using corpus creation and exploration; and (iv) investigations on computational applications in Natural Language Processing (NLP). In addition, among the data collected, we conducted a brief analysis of RST works developed by Brazilian researchers. As a result, we present an overview of RST studies in the last decade, allowing for the creation of research programs that consider the projects already developed and the advances of the area in Brazil and worldwide. [ABSTRACT FROM AUTHOR]

Outlier detection has been broadly used in industrial practices such as public security and fraud detection, etc. Outlier detection from various perspectives against different backgrounds has been proposed. However, most of outlier detection consider categorical or numerical data. There are few researches on outlier detection for set-valued data, and a set-valued information system (SVIS) is a proper way of tackling the problem of missing values in data sets. This paper investigates outlier detection for set-valued data based on rough set theory (RST) and granular computing (GrC). First, the similarity between two information values in an SVIS is introduced and a variable parameter to control the similarity is given. Then, the tolerance relations on the object set are defined, and based on this tolerance relation, θ-lower and θ-upper approximations in an SVIS are put forward. Next, the outlier factor in an SVIS is presented and applied to various data sets. Finally, outlier detection method for set-valued data based on RST and GrC is proposed, and the corresponding algorithms are designed. Through numerical experiments based on UCI, the designed algorithm is compared with six other detection algorithms. The experimental results show the designed algorithm is arguably the best choice under the context of an SVIS. It is worth mentioning that for a comprehensive comparison, we use two criteria: AUC value and F-1 measure, to show the superiority of the designed algorithm. [ABSTRACT FROM AUTHOR]

Topological Data Analysis (TDA) is an approach to handle with big data by studying its shape. A main tool of TDA is the persistence diagram, and one can use it to compare data sets. One approach to learn on the similarity between two persistence diagrams is to use the Bottleneck and the Wasserstein distances. Another approach is to fit a parametric model for each diagram, and then to compare the model coefficients. We study the behavior of both distance measures and the RST parametric model. The theoretical behavior of the distance measures is difficult to be developed, and therefore we study their behavior numerically. We conclude that the RST model has an advantage over the Bottleneck and the Wasserstein distances in sense that it can give a definite conclusion regarding the similarity between two persistence diagrams. More of that, a great advantage of the RST is its ability to distinguish between two data sets that are geometrically different but topologically are the same, which is impossible to have by the two distance measures. [ABSTRACT FROM AUTHOR]

On 2 January 2022, an earthquake of Ms 5.5 occurred in Ninglang County, Lijiang City, the earthquake-prone area of northwestern Yunnan. Whether this earthquake caused significant deformation and thermal anomalies and whether there is a relationship between them needs further investigation. Currently, multi-source remote sensing technology has become a powerful tool for long-time-series monitoring of earthquakes and active ruptures which mainly focuses on single crustal deformation and thermal anomaly. This study aims to reveal the crustal deformation and thermal anomaly characteristics of the Ninglang earthquake by using both Interferometric Synthetic Aperture Radar (InSAR) and Robust Satellite Techniques (RST). First, Sentinel-1A satellite SAR data were selected to obtain the coseismic deformation field based on Differential InSAR (D-InSAR), and the Small Baseline Set InSAR (SBAS-InSAR) technique was exploited to invert the pre- and post-earthquake displacement sequences. Then, RST was used to extract the thermal anomalies before and after the earthquake by using Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (MODIS LST). The results indicate that the seismic crustal deformation is dominated by subsidence, with 23 thermal anomalies before and after the earthquake. It is speculated that the Yongning Fault in the deformation area is the main seismogenic fault of the Ninglang earthquake, which is dominated by positive fault dip-slip motion. Meanwhile, the seismic fault system composed of NE- and NW-oriented faults is an important factor in the formation of thermal anomalies, which are accompanied by changes in stress at different stages before and after the earthquake. Moreover, the crustal deformation and seismic thermal anomalies are correlated in time and space, and the active rupture activities in the region produce deformation accompanied by changes in thermal radiation. This study provides clues from remote sensing observations for analyzing the Ninglang earthquake and provides a reference for the joint application of InSAR and RST for earthquake monitoring. [ABSTRACT FROM AUTHOR]

The world today faces water scarcity issues, especially in developing countries. This situation is further aggravated under arid and semiarid climates with the water demand increasing and limited rainfall events. The study was conducted in the Riyadh metropolitan area of Saudi Arabia. The study area has a hot arid desert climate. Therefore, there is an urgent need for harvesting rainwater to confront the increasing water demand. This research aims to identify the potential rainwater harvesting (PRWH) suitable sites based on the multi-criteria decision support system by the spatial analytic hierarchy process, with the aid of the integration of geographic information systems and remote sensing techniques. Mapping PRWH was carried out using the thematic layers of the slope, soil texture, land use and land cover (LULC), precipitation, and potential runoff coefficient (PRC). The study findings revealed that Riyadh has four hydrologic soil groups (HSGs), namely A, B, C, and D groups, and the percentage area is 2%, 26%, 3%, and 71%, respectively. The slope classes are flat (< 2%), sloping (2–8%), strongly sloping (> 8–15%), moderately steep (> 15–30%), and mountainous (> 30%). The LULC layers are barren lands, agricultural lands, urban, and roads. The precipitation has been distributed into five classes namely very low (5.9%), low (10.1%), medium (13.2%), high (13.5%), and very high (57.3%) of the total investigated area. The PRC values were distributed in five levels namely very low (< 0.2), low (0.2–0.3), medium (> 0.3–0.5), high (> 0.5–0.7), and very high (> 0.7–1), where about 83% of the capital faces high and very high PRC values. The percentage area of PRWH suitability sites is unsuitable (0.4%), poor (0.8%), moderate (13.3%), good (47.5%), and excellent (38%) of the total entire area. More than 85% of Riyadh has good and excellent suitability for PRWH. This study is tantamount to a powerful tool for identifying the PRWH suitability sites in arid and semiarid regions to confront the water demand increase. [ABSTRACT FROM AUTHOR]

Outlier detection is a process to find out the objects that have the abnormal behavior. It can be applied in many aspects, such as public security, finance and medical care. An information system (IS) as a database that shows relationships between objects and attributes. A real-valued information system (RVIS) is an IS whose information values are real numbers. A RVIS with missing values is an incomplete real-valued information system (IRVIS). The notion of inner boundary comes from the boundary region in rough set theory (RST). This paper conducts experiments directly in an IRVIS and investigates outlier detection in an IRVIS based on inner boundary. Firstly, the distance between two information values on each attribute of an IRVIS is introduced, and the parameter λ to control the distance is given. Then, the tolerance relations on the object set are defined according to the distance, by the way, the tolerance classes, the λ-lower and λ-upper approximations in an IRVIS are put forward. Next, the inner boundary under each conditional attribute in an IRVIS is presented. The more inner boundaries an object belongs to, the more likely it is to be an outlier. Finally, an outlier detection method in an IRVIS based on inner boundary is proposed, and the corresponding algorithm (DE) is designed, where DE means degree of exceptionality. Through the experiments base on UCI Machine Learning Repository data sets, the DE algorithm is compared with other five algorithms. Experimental results show that DE algorithm has the better outlier detection effect in an IRVIS. It is worth mentioning that for comprehensive comparison, ROC curve and AUC value are used to illustrate the advantages of the DE algorithm. [ABSTRACT FROM AUTHOR]

Uncertainty measurement (UM) gives a brand-new perspective on attribute reduction in an information system (IS). Interval-valued data is a kind of very vital data in rough set theory (RST). Rough set model based on tolerance relations can be considered to deal with interval-valued data. However, these kinds of tolerance relations have deficiencies when they are used in fuzzy rough computation. This paper studies new UM for an interval-valued information system (IVIS) and considers its attribute reduction. Firstly, a novel fuzzy symmetry relation on the object set of an IVIS is established based on “The similarity between information values that is fed back to the attribute set”. Secondly, $\lambda $ -information granules on the basis of a fuzzy symmetry relation are obtained. Then, four UMs for an IVIS are investigated. Next, numerical experiments and statistical tests are used to evaluate the performance of the proposed UMs. Moreover, attribute reduction in an IVIS is studied and the relevant algorithms are proposed. Finally, clustering analysis on the reduced IVIS is conducted. Experimental results indicate that the proposed algorithms are effective based on evaluation indicators of clustering performance. This paper provides a novel viewpoint for the establishment of fuzzy symmetry relation and attribute reduction algorithms.

The world's supply of water is drying up. Particularly in arid and semiarid regions, farmers are being forced to use low-quality brackish and drainage water for irrigation. Proposed solutions for getting an adequate water supply include seawater desalination and groundwater. This study develops a site suitability map for future desalination plants in Saudi Arabia as an arid desert region. The study has been conducted through the integration of remote sensing techniques, geographic information system, multi-criteria evaluation, and spatial analytic hierarchy process, using satellite imagery, a digital elevation model, and Saudi saline water conversion corporation data. The results show that 29 Saudi desalination plants distributed along the east and west coast service will be needed to supply water to 46 cities populated by 18.2 million people. Yet, at present, only eight minor plants are distributed on the east coast. Site suitability levels were classified into five categories: very low (covering 16% of the area), low (77%), medium (2.7%), high (2.3%), and very high (2%). Generally, the seaside sites are the most suitable for constructing desalination plants due to the gentle slope. Also, these regions have available sites for construction. These findings will help decision-makers plan for future desalination plants and improve the management of the current plants. The methodology used in this work can also be a useful approach for other studies. [ABSTRACT FROM AUTHOR]

Information system (IS) is a significant model in the field of artificial intelligence. Information structure is not only a research direction in the field of granular computing (GrC), but also an important method to study an IS. A multiset-valued information system (MVIS) refers to an IS where information values are multisets. A MVIS can be seen as a model that is the result of information fusion of multiple categorical ISs. This model helps deal with missing values in the dataset. This paper studies information structures in a MVIS on the view of GrC and consider their application for uncertainty measurement (UM). First of all, some notions of multisets and probability distribution sets (PDSs) are proposed. Naturally, relationships between multisets and PDSs are researched. Then, the concept of a MVIS based on the notion of multisets is given, and the internal structure of a MVIS is revealed by an incomplete information system (IIS). Furthermore, tolerance relations in a MVIS are defined by using Hellinger distance, and tolerance classes are obtained to construct the information structures of a MVIS. Considering the association of information structures, relationships between information structures are raised from the two aspects of dependence and separation. Moreover, some properties between information structures are provided by using information distance and inclusion degree. Finally, four UMs as the applications of information structures are investigated, and comprehensive experiments on several datasets demonstrate the feasibility and superiority of the proposed measures. These results will be helpful for establishing a framework of GrC in a MVIS and studying UM. [ABSTRACT FROM AUTHOR]

Discourse parsing is an important research area in natural language processing (NLP), which aims to parse the discourse structure of coherent sentences. In this survey, we introduce several different kinds of discourse parsing tasks, mainly including RST-style discourse parsing, PDTB-style discourse parsing, and discourse parsing for multiparty dialogue. For these tasks, we introduce the classical and recent existing methods, especially neural network approaches. After that, we describe the applications of discourse parsing for other NLP tasks, such as machine reading comprehension and sentiment analysis. Finally, we discuss the future trends of the task. [ABSTRACT FROM AUTHOR]

A set-valued information system (SVIS) is the generalization of a single-valued information system. A SVIS with missing information values is called an incomplete set-valued information system (ISVIS). This paper focuses on studying uncertainty measurement for an ISVIS with application to attribute reduction. First, the similarity degree between information values on each attribute is presented in an ISVIS. Then, the tolerance relation induced by each subsystem is given and rough approximations based on this relation is considered. Next, some tools to measure the uncertainty of an ISVIS are put forwarded. Moreover, the validity of the proposed measures is analyzed from the statistical point of view. Finally, information granulation and information entropy are applied to attribute reduction, the incomplete rate is adopted, and the effectiveness under different incomplete rates is analyzed and verified by k-means clustering algorithm and Mean Shift clustering algorithm. [ABSTRACT FROM AUTHOR]

Attribute reduction in an information system (IS) is an important research topic in rough set theory (RST). This paper investigates attribute reduction for incomplete heterogeneous data based on information entropy. Information entropy in an incomplete IS with heterogeneous data (IISH) is first defined. Then, some derived notions of information entropy, such as joint information entropy, conditional information entropy, mutual information entropy, gain and gain ratio in an incomplete decision IS with heterogeneous data (IDISH), are presented. Next, information entropy is applied to perform attribute reduction in an IDISH. Two attribute reduction algorithms, based on gain and gain ratio, respectively, are proposed. Finally, in order to illustrate the feasibility and efficiency of the proposed algorithms, experimental analysis is carried out and comparisons are done. It is worth mentioning that the incomplete rate is used to deal with incomplete heterogeneous data. [ABSTRACT FROM AUTHOR]

Abstract Background Signal fidelity depends on protein–protein interaction–‘hubs’ integrating cues from large interactomes. Recently, and based on a common secondary structure motif, the αα-hubs were defined, which are small α-helical domains of large, modular proteins binding intrinsically disordered transcriptional regulators. Methods Comparative structural biology. Results We assign the harmonin-homology-domain (HHD, also named the harmonin N-terminal domain, NTD) present in large proteins such as harmonin, whirlin, cerebral cavernous malformation 2, and regulator of telomere elongation 1 to the αα-hubs. The new member of the αα-hubs expands functionality to include scaffolding of supra-modular complexes mediating sensory perception, neurovascular integrity and telomere regulation, and reveal novel features of the αα-hubs. As a common trait, the αα-hubs bind intrinsically disordered ligands of similar properties integrating similar cellular cues, but without cross-talk. Conclusion The inclusion of the HHD in the αα-hubs has uncovered new features, exemplifying the utility of identifying groups of hub domains, whereby discoveries in one member may cross-fertilize discoveries in others. These features make the αα-hubs unique models for decomposing signal specificity and fidelity. Using these as models, together with other suitable hub domain, we may advance the functional understanding of hub proteins and their role in cellular communication and signaling, as well as the role of intrinsically disordered proteins in signaling networks. Video Abstract

This research presents an effective method for automatically segmenting brain tumors using the proposed Optimized Thresholded Difference (OTD) and Rough Set Theory (RST). The tumor area is determined using the proposed two-level segmentation algorithm. The first level i.e., an overlay image is created, which is the intensity average of all the pixels of the brain area that were segmented in the initial stage. Then the second level, in which the process of the thresholded difference is applied between the brain area and the overlay image depending on the specified threshold. Features are extracted from the segmented images using the Gray-Level Co-occurrence Matrix (GLCM). To improve performance, an RST is employed with the extracted features. The completely automated methodology is validated using Figshare open dataset. [ABSTRACT FROM AUTHOR]

There are several soft computing methods in use to predict rainfall. Mainly two features were included to increase rainfall prediction: (1) using data pre-processing procedure and (2) using a modular approach. The projected pre-processing approach incorporated Moving Average (MA) and Singular Spectrum Analysis (SSA). The method of modular was poised of Support Vectors Regression l (SVR) models or the Rough Set Technique (RST). In the development for forecasting of rainfall, the RST was initially applied for the data- preprocessing. Modular models used to pre-process the preparation data into three distinct subsets (level-1, level-2, and level-3) from the scales of the trained data, and lastly, two SVRs were used in the level-2 and level-3 subsets. In contrast, Artificial Neural Network (ANN) or SVR was concerned with predicting and was useful in training the data. Each day's rate of downpour proof, the level-1 subset tended to be modelled by the ANN because it was overwhelming in the training data. ANN is beneficial for huge-scale samples training because of its corresponding information processing configuration. This work explores the use of hybridization technique of RST and time series, where RST helps to find the essential attribute from the collection of raw meteorological data set then analyzing the important characteristic using time series and various classifiers. There are several prediction techniques available to predict based on meteorological data set to suitably predict a meteorological phenomenon. In this work, our objective is to find the most frequent meteorological phenomena which affects our state most. Purposefully, we consider the RST to predict the most significant meteorological phenomena by using the RST and predict the rainfall per day basis. Hybrid soft computing techniques and RST are used in the present work. We validate our claim using the Chi-square test. [ABSTRACT FROM AUTHOR]

Uncertainty measurement (UM) provides a new perspective on feature selection in an information system (IS). The intent of this paper is to measure the uncertainty of a set-valued information system (SVIS) from the perspective of "The similarity between information values is fed back to the feature set" and consider its application in feature selection. Based on the similarity between information values, fuzzy symmetry relations on the object set of an SVIS are first established. Secondly, θ -information granules based on the fuzzy symmetry relations are obtained. Thirdly, four UMs for an SVIS, including θ -information granulation ( G θ ), θ -information entropy ( H θ ), θ -rough entropy ( E r θ ) and θ -information amount ( E θ ), are proposed. Moreover, numerical experiments and statistical tests to evaluate the performance of the proposed measurements are carried out. Finally, an application in feature selection for an SVIS is given and the corresponding algorithms based on G θ and H θ are presented, clustering analysis on the reduced SVIS is conducted. The experimental results show that the proposed algorithms are effective according to three evaluation indicators of clustering performance. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the rhetorical structure of the results and discussion section of scientific papers of the colleges of Life Sciences and Exact, Technological and Multidisciplinary Sciences. In addition to investigating rhetorical relationships, clues (linguistic signals) that signal the relationships are also investigated. The theoretical-methodological model adopted is the Rhetorical Structure Theory (RST), a descriptive theory that studies the organization of texts, characterizing the relations held between its parts. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) installed with a gripper is an effective and robust way to grab the wanted object from inaccessible locations. In this study, we develop a novel control mechanism to regulate the nonlinear dynamics of the aerial manipulator. In this research, hex-rotor UAV is chosen in order to fulfill the mission requirement in terms of size and weight of the object. It is equipped with a manipulator and the gimbal-based camera that will help to see the desired object and then transport it. The aerial vehicle has six-degrees-of-freedom (6-DOF) and the installed manipulator has 4-DOF which in total makes the 10-DOF aerial manipulator vehicle. At the time of clutching the desired object to eliminate or reduce the external noise, and stabilize the dynamic behavior of the aerial manipulator, we need a robust and efficient controller. To solve the aforementioned problems, this study develops a hybrid control mechanism that tracks and controls the altitude and attitude of UAV after clutching the desired object. The main contribution of this study is to design a control mechanism that includes Model Reference Adaptive Control with an Integrator (MRACI) in conjunction with regulation, pole-placement and tracking (RST) control algorithm. On one hand, the simulation results using MATLAB demonstrate the efficiency of the proposed control mechanism. On the other hand, to cross verify the validity of the proposed control algorithm, we perform the experiment by clutching the desired object at hovering and normal flight operation. [ABSTRACT FROM AUTHOR]

Prediction of the flash floods in ungauged or poorly gauging watershed is one of the challenging tasks in the field of hydrology and needs implication of advanced techniques to obtain the reliable results. In this study, an innovative artificial intelligence-based rough set theory (RST) was used to retrieve missing hydro-meteorological data which were utilized to build a forecast model to predict the flood event in an ungauged watershed in Pakistan (Thor Nullah). The RST-based forecast model was calibrated for 1986 to 2004 and tested for 2008 to 2016. The result showed that 9 out of 10 forecasting objects were predicted precisely. Basin data model technique along with rainfall–runoff (R.F-R.O) model and RST forecasting model was used to estimate the peak discharge of flood event occurred in 2015. The modeled peak discharge (1152 m3 s−1) was compared with the field observation-based highest flood marks (HFMs—1189 m3 s−1), which showed slight discrimination due to indeterminate model calibration sparse rain gauge density. Moreover, flood inundation map showed high flood risk to the 80% localities with a flood depth of 0.1–1.67 m in locality. Overall, this study suggested a reliable use of RST for data mining and flood modeling; however, the absence of adequate flow data at study site limits the reliability of R.F-R.O model calibration. Moreover, based on the array of flood hazard simulation studies, provision of channelization and cross-drainage works is suggested to protect the catchment against floods and debris brought down through catchment. [ABSTRACT FROM AUTHOR]

Background: Signal fidelity depends on protein–protein interaction–'hubs' integrating cues from large interactomes. Recently, and based on a common secondary structure motif, the αα-hubs were defined, which are small α-helical domains of large, modular proteins binding intrinsically disordered transcriptional regulators. Methods: Comparative structural biology. Results: We assign the harmonin-homology-domain (HHD, also named the harmonin N-terminal domain, NTD) present in large proteins such as harmonin, whirlin, cerebral cavernous malformation 2, and regulator of telomere elongation 1 to the αα-hubs. The new member of the αα-hubs expands functionality to include scaffolding of supra-modular complexes mediating sensory perception, neurovascular integrity and telomere regulation, and reveal novel features of the αα-hubs. As a common trait, the αα-hubs bind intrinsically disordered ligands of similar properties integrating similar cellular cues, but without cross-talk. Conclusion: The inclusion of the HHD in the αα-hubs has uncovered new features, exemplifying the utility of identifying groups of hub domains, whereby discoveries in one member may cross-fertilize discoveries in others. These features make the αα-hubs unique models for decomposing signal specificity and fidelity. Using these as models, together with other suitable hub domain, we may advance the functional understanding of hub proteins and their role in cellular communication and signaling, as well as the role of intrinsically disordered proteins in signaling networks. A-H2aprvKcDfzYTUKY2TJm Video Abstract [ABSTRACT FROM AUTHOR]

Electronic media have been extensively used as a replacement for printed media, and learning and teaching using electronic media have become increasingly popular. However, there is still a concern regarding the use of electronic media for learning in terms of its effectiveness. This study aimed to examine if electronic media are more effective for learning compared with printed media, and we focused on the performance of short-memory tasks and brain activity in subjects while completing the tasks. Eleven Japanese subjects in their twenties performed a reading span task (RST) using both a printed medium and tablet computer. While performing the task, their brain activity was measured using near-infrared spectroscopy (NIRS). The average values of the RST performance scores were almost the same for printed and tablet media. However, the NIRS measurement results showed higher activity in the frontal lobe of the brain while the subjects performed the task on the tablet compared with the printed medium. The results of the questionnaire answered by the subjects indicated that they felt there was better visibility when performing the RST on the electronic medium than on the printed medium. The questionnaire results also showed that the levels of tiredness and concentration were almost the same for both media. These results suggest that the use of electronic media for learning can be more effective in terms of brain activity and visibility. [ABSTRACT FROM AUTHOR]

This work aims to investigate the use of the element “portanto” in the Brazilian and African varieties of Portuguese, identifying the semantic values of such element, based on the categories listed by Lopes, Pezatti and Novaes (2001). In addition, it proposes to analyze the rhetorical relations established by this term, based on the Rhetorical Structure Theory (RST).

The aim of this research was to study the genetic structure of the Afghan Pika’s (Ochotona rufescens) populations in Northern Khorasan province in order to determine their isolation rate. A total of 122 samples from four sample groups (Ghorkhod, Golol-Sarani, Salouk and Sarigol) were selected and the genotypic features were detected using 7 microsatellite loci. The results showed that all of the loci were subject to polymorphism and the allele ranged from 2 – 7. Significant Fst and Rst values were found among the populations based on the AMOVA test. Based on the Assignment Test, more than 90 percent of the individuals of the populations belonged to their original population (only 10 percent of the individuals belonged to other populations). A Paired comparison of genetic differentiation between the populations revealed significant deferences among them. The results of the Prichard model grouping showed that the samples collected in this study were approximately 7 groups. The results of AMOVA analysis revealed a significant genetic structure among different populations. Also, the majority of the variance is related to the variance within the population. There seems to be a different but small genetic structure among the studied populations.

Background There is a rich body of literature describing the links between behavioral activation/inhibition, personality and trait affectivity. The sensitivity of the behavioral activation system (BAS) is related to extraversion and positive affect, while the sensitivity of the behavioral inhibition is related to neuroticism and negative affect. Besides the strong links observed, it is unclear what the causal relationships between these constructs are. Participants and procedure The aim of this study was to provide indirect support for the theoretical accounts postulating the causal direction from behavioral sensitivity through personality to affectivity. Using data from two measurement occasions, two sets of models were specified to test those predictions. A total of 286 social sciences and humanities students participated in the study for course credit or financial reimbursement. Results Our results provide limited support for the proposed causal direction, but only some of the direct links were observed to be significantly different from zero. Extraversion and positive affect showed a reciprocal pattern of influences, while the sensitivity of the behavioral inhibition system (BIS) predicted neuroticism. Conclusions The results presented here give limited support to the proposed direction of relationships between the three sets of affective constructs investigated in this study. BIS and BAS sensitivities and personality measures showed higher stability in the one-month period compared to positive and negative affectivity.