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In this paper, we address ambiguity, intended as a characteristic of any data expression for which a unique meaning cannot be associated by the computational agent for either lack of information or multiple interpretations of the same configuration. In particular, we will propose and discuss ways in which a decision-support classifier can accept ambiguous data and make some (informative) value out of them for the decision maker. Towards this goal we propose a set of learning algorithms within what we call the three-way-in and three-way-out approach, that is, respectively, learning from partially labeled data and learning classifiers that can abstain. This approach is based on orthopartitions, as a common representation framework, and on three-way decisions and evidence theory, as tools to enable uncertain and approximate reasoning and inference. For both the above learning settings, we provide experimental results and comparisons with standard Machine Learning techniques, and show the advantages and promising performances of the proposed approaches on a collection of benchmarks, including a real-world medical dataset.

Rough set models on two universes are important generalizations of Pawlak’s classical model. In most of the two-universe models, the upper and lower approximations are constructed based on inclusion relation. We generalize inclusion relation to the general evaluation function and define the models of three-way decision on two universes. As an important class of evaluation functions, subsethood measures are considered. We compare our models with other five existing two-universe models in rough set theory and point out that the model of three-way decision on two-universe unifies the five two-universe models. Besides, properties of the two-universe model of three-way decision are also given. More importantly, we propose an approach to computing the pair of thresholds α and β. Our approach is based on the maximum value of the accuracy measure with respect to tri-partitions of a universe induced by all thresholds.

The trisecting-acting-outcome (TAO) model of three-way decisions comprises three components, i.e., constructing a trisection, strategies for action, and measuring the outcome. Existing research seldom involves the third component, i.e., measuring the effectiveness of the outcome. A previous study provides a strategy select method by analyzing benefits or costs in the movement-based three-way decision. However, we often choose a strategy depend on the probability or distribution of three regions instead of the benefits or costs. In this paper, we propose a probabilistic movement model of three-way decision and give a strategy selection mechanism based on information entropy. Conditional entropy and cross entropy are used to select strategies by measuring these probabilities according to the user's preferences and structural features of the three partitions. It selects the action or strategy which has the maximum probability of desirable movements based on the minimization of the conditional entropy. It also selects the action or strategy which has the least difference between actual probabilistic distributions and expected probabilistic distribution based on the minimization of the cross entropy. We give two examples to illustrate the practicality and effectiveness of the strategy selection method in the probabilistic movement model of three-way decision.

In order to use three-way decision (TWD) to solve multiple attribute decision making (MADM) problems, in this article, a new TWD model with intuitionistic fuzzy numbers (IFNs) is proposed. First of all, we define the relative loss functions to demonstrate some features of loss functions in TWDs, which is the basis for future research. Then, based on the correlation between the loss functions and the IFNs, we get the relative loss functions based on IFNs. At the same time, the classification rules of the TWDs are discussed from different viewpoints, including the thresholds and their properties. Aiming at MADM problems with unreasonable values, a new integrated method of relative loss functions is established to obtain a fairer loss integration result of alternatives. In addition, considering that there are no decision attributes and only condition attributes in MADM, we use grey relational degree to calculate the condition probability. In the end, a novel TWD model is proposed to solve MADM problems with IFNs, and a practical example on selecting suppliers is used to demonstrate its effectiveness and practicability.

The methods of mining incomplete data based on characteristic sets or characteristic relation have been intensively studied in recent years. With the development of related research, many modifications of the definition of characteristic relation have been proposed. However, few of them can be used for decision rule induction due to the so-called definability problem. In this paper, by using the wide sense of the three-way decision theory, we extend the notions of characteristic relation and characteristic set to the systems with four types of characteristic relations and characteristic sets, respectively. Then we study the probabilistic approximations based on the extended characteristic set system. Moreover, we extend the maximal characteristic neighborhood system into four types of maximal characteristic neighborhood systems, and investigate the probabilistic approximations based on them. Finally, we generalize the definition of local definability for incomplete data processing. In particular, several existing methods based on characteristic sets are integrated into our model, and the new types of characteristic relations proposed by us seem more practical than the classical one in some aspects. In this way, our model illustrates the effectiveness and comprehensiveness of thinking in threes.

Conflict analysis, which plays an important role in our society, has attracted more and more attentions. However, the existing conflict analysis models are not effective enough to evaluate the inconsistency degree of cliques that have more than two individuals. Besides, for each clique, its allied, conflict and neutral attributes have not been explicitly defined and thoroughly explored. Therefore, due to the lack of enough explicit clues, we cannot further formulate some strategies manipulating the conflict degrees of related cliques to realize the specific objectives. Last but not least, one-vote veto is seldom considered, although it plays a vital role in many fields, such as venture capital and United Nations Security Council resolutions. In order to solve these problems, we resort to three-way concept analysis and describe a clique by using the intent of a specific concept. On the basis of the obtained specific concepts, we derive the allied, conflict and neutral attributes of cliques, and further make decisions and explore the maximal coalitions and minimum conflict sets. Finally, in order to cater dynamic environment, we also describe an incremental algorithm for conflict analysis.

Three-way concept lattices can supply much more information than classical concept lattices since they contain the positive information and negative information between objects and attributes simultaneously. Taking advantage of this, the rules acquisition for formal decision contexts from the perspective of three-way concept lattices are discussed, and the results are compared with the common decision rules based on classical concept lattices. Firstly, the definition of object-induced three-way consistence of a formal decision context is presented. Then, positive decision rules and negative decision rules are proposed for an object-induced three-way consistent formal decision context. Furthermore, we give semantic explanation for these rules, and compare them with common rules obtained from a strongly consistent lattice . In parallel, the similar issues are investigated from the perspective of attributes, including the definition of attribute-induced three-way consistence of a formal decision context, rules acquisition based on an attribute-induced three-way consistent formal decision context, and rules comparison. Finally, the relationships among the three types of consistence, namely, object-induced three-way consistence, attribute-induced three-way consistence, and existing strongly consistence are discussed to make the contents compact. Some theoretical examples are given to illustrate the main results of this paper.

The conventional clustering approaches are not very effective in dealing with clusters having overlapping regions. The three-way clustering (3WC) is an effective and promising approach in this regards. A key issue in 3WC is the determination of thresholds which plays a crucial and important role in accurate estimation of the overlapping region. In this article, we propose different variance based criteria for determining the thresholds. In particular, we examine the variance or spread in evaluation function values of objects contained in the three regions obtained with 3WC of objects. An algorithm called 3WC-OR is introduced that considers the optimization of the proposed criteria for determining effective thresholds by incorporating approaches such as genetic algorithms and game-theoretic rough sets. Experimental results on five UCI datasets indicate that the proposed algorithm significantly improves results on datasets with overlapping clusters and provide comparable results on datasets with non-overlapping clusters.

In this study, detailed surface reaction mechanisms for CO/O2 system on Pt/Al2O3 and Rh/Al2O3 have been developed based on the measurements of gaseous and surface species. The conventional conversi...

Three-way catalytic converter (TWC) is considered as the most effective solution to reduce emissions in new electric fuel injection engines. The usage of the TWC on carburetor system will not promo...

In this paper, we study convex systems under the frame of three-way decision theory. Firstly, we give the notion of three-way convex systems. We obtain an equivalence characterization of three-way convex systems. Then, we propose the concept of three-way fuzzy convex systems in term of L-convex systems. Some related examples are presented. Finally, we establish a one-to-one correspondence between three-way convex systems and three-way fuzzy convex systems.

The three way catalyst NOx trap catalyst (TWC-NT) is consisted of a conventional three way catalyst (TWC) compositions and NOx storage material (NSM) to mitigate NOx breakthrough occurred during the fuel cut event to improve fuel economy of stoichiometric (A/F = 14.6) gasoline powered vehicles. Typical fuel cut operation generates very lean exhaust mixture that oxidizes the catalyst surface in TWC system, and NOx is slipped on the oxygen rich catalyst surface when the engine is switched back to the stoichiometric air to fuel ratio. The fully batch formulated TWC and TWC-NT catalyst were prepared and aged on engine dynamometer equivalent to 150,000 miles at underfloor location. The aged catalysts was evaluated at LEV-3 SULEV30 passenger car to check their effects on NOx efficiency, fuel saving and N2O suppression ability. The results revealed TWC-NT showed higher NOx removal performance by 71 % with multiple the fuel cuts and O2 purge period through the thermally enhanced NOx storage and reduction function, and 2 % higher fuel economy on FTP-75 cycle than the conventional TWC. Also the catalyst had good N2O suppression ability by the removal of NOx as the main reactant of N2O formation in the exhaust gas reactions over catalyst.

Sequential three-way decision (S3WD) is a multi-stage decision-making progress that emulates human cognition. S3WD employs coarse-to-fine information granularity and uses various types of costs to determine thresholds to make appropriate decisions. To date, however, few existing models discuss the construction of granules or consider costs in the decision process. This paper describes efforts to develop a granularity-driven sequential three-way decision model to address these two issues, and handle dual-constraint satisfaction problems considering both decision process and decision result costs. A key feature of the model is the incorporation of information granularity into the decision-making process. The model also incorporates a cost structure that accounts for the costs of both the decision process and the decision result. Using this model, we design two algorithms to minimize the cost of the decision process or the cost of the decision result. Our experimental results validate the effectiveness of the algorithms and the viability of the new model.

Credit scoring is a crucial task within risk management for any company in the financial sector. On the one hand, it is in the self-interest of banks to avoid approving credits to customers who probably default. On the other hand, regulators require strict risk management systems from banks to protect their customers and, from “too big to fail institutions”, to avoid bankruptcy with negative impacts on an economy as a whole. However, credit scoring is also expensive and time-consuming. So, any possible method, like three-way decisions, to further increase its efficiency, is worth a try. We propose a two-step approach based on three-way decisions. Customers whose credit applications can be approved or rejected right away are decided in a first step. For the remaining credit applications, additional information is gathered in a second step. Hence, these decisions are more expensive than the ones in the first step. In our paper, we present a methodology to apply three-way decisions with probabilistic rough sets for credit scoring and an extensive case study with more than 7000 credit applications from Chilean micro-enterprises.

In the study, non-deterministic information systems-Apriori-based (NIS-Apriori-based) rule generation from table data sets with incomplete information, SQL implementation, and the unique characteristics of the new framework are presented. Additionally, a few unsolved new research topics are proposed based on the framework. We follow the framework of NISs and propose certain rules and possible rules based on possible world semantics. Although each rule τ depends on a large number of possible tables, we prove that each rule τ is determined by examining only two τ-dependent possible tables. The NIS-Apriori algorithm is an adjusted Apriori algorithm that can handle such tables. Furthermore, it is logically sound and complete with regard to the rules. Subsequently, the implementation of the NIS-Apriori algorithm in SQL is described and a few new topics induced by effects of NIS-Apriori-based rule generation are confirmed. One of the topics that are considered is the possibility of estimating missing values via the obtained certain rules. The proposed methodology and the environment yielded by NIS-Apriori-based rule generation in SQL are useful for table data analysis with three-way decisions.

A model of decision-theoretic three-way approximations of fuzzy sets that can calculate a pair of interpretable thresholds (α, β) by combining shadowed sets with three-way decisions was established by Deng. In that model, the significant contribution is that the value 0.5, which denotes a membership grade with the highest uncertainty, is used to replace the uncertain region with the interval [0,1] in shadowed sets. From the principle of the minimum decision cost, although the lowest overall cost can be achieved precisely in some data distributions based on the value 0.5, there is a difference between the obtained overall cost and the least cost in some cases. Therefore, in this paper, based on Deng’s model, the concept of a general three-way approximation of a fuzzy set is proposed to replace 0.5 with a variable value c(0

Neighborhood Covering (NC) is the union of homogeneous neighborhoods and provides a set-level approximation of data distribution. Because of the nonparametric property and the robustness to complex data, neighborhood covering has been widely used for data classification . Most existing methods directly classify data samples according to the nearest neighborhoods. However, the certain classification methods strictly classify the uncertain data and may lead to serious classification mistakes. To tackle this problem, we extend traditional neighborhood coverings to fuzzy ones and thereby propose a Three-Way Classification method with Fuzzy Neighborhood Covering (3WC-FNC). Fuzzy neighborhood covering consists of membership functions and forms an approximate distribution of neighborhood belongingness. Based on the soft partition induced by the memberships of fuzzy neighborhood coverings of different classes, data samples are classified into Positive (certainly belonging to a class), Negative (certainly beyond classes) and Uncertain cases. Experiments verify that the proposed three-way classification method is effective to handle the uncertain data and in the meantime reduce the classification risk.

Three-way granular computing is a paradigm of thinking and information processing in three granules. We introduce a framework of three-way granular computing based on a hexagon induced by a trisection of a set. This enables us to unify rough-set concept analysis and formal concept analysis. For an object granule (i.e., a subset of a universal set of objects), we construct three attribute granules. Conversely, for an attribute granule (i.e., a subset of a universal set of attributes), we construct three object granules. Rough-set concept analysis uses one kind of trisections to study four types of disjunctive formal concepts and the associated concept lattices. Formal concept analysis uses another kind of trisections to study four types of conjunctive formal concepts and the associated concept lattices. The eight concept lattices can be grouped into two classes such that four lattices in each class are isomorphic.

In recent years, multi-view clustering algorithms have shown promising performance by combining multiple sources or views of datasets. A problem that has not been addressed satisfactorily is the uncertain relationship between an object and a cluster. Thus, this paper investigates an active three-way clustering method via low-rank matrices that can improve clustering accuracy as clustering proceeds for the multi-view data of high dimensionality. We adopt a three-way clustering representation to reflect the three types of relationships between an object and a cluster, namely, belong-to definitely, uncertain and not belong-to definitely. We construct the consensus low-rank matrix from each weighted low-rank matrix by taking account of the diversity of views, and give the method to solve the optimization problem of objective function based on the improved augmented Lagrangian multiplier algorithm. We suggest an active learning strategy to learn important informative pairwise constraints after measuring the uncertainty of an object based on the entropy concept. The experimental results conducted on real-world datasets have validated the effectiveness of the proposed method.

Natural gas vehicles (NGVs), as alternatives to gasoline and diesel vehicles, have been rapidly and widely promoted in the past 20 years. The three-way catalyst (TWC) technology is one of the dominant technologies for gaseous emission control of NGVs. However, it is still a great challenge to purify the exhaust gas of NGVs to meet increasingly strict emission limits, particularly for CH4 and NOx. This review is focused on the recent research advances in TWCs for NGVs, including the technical fundamentals, classic components (active phases, supports, and promoters), novel preparations of catalysts, resistance to thermal and chemical aging, current challenges and future perspectives. Particular attention is given to Pd-based catalysts as typical commercial TWCs, and potential alternatives which have competitive three-way catalytic performance are also highlighted.

Eight field trials (2 in 2016, 3 in 2017, 3 in 2018) were conducted in farmers’ fields with heavy infestations of GR Conyza canadensis (Canada fleabane, horseweed or marestail) to evaluate glyphosate (900 g ae ha-1) plus saflufenacil (25 g ai ha-1), 2,4-D ester (500 g ai ha-1) or paraquat (1100 g ai ha-1) applied preplant (PP) as 2-way tankmixes, or in 3-way tankmixes with sulfentrazone (140 g ai ha-1), flumioxazin (107 g ai ha-1) or metribuzin (400 g ai ha-1) for the glyphosate-resistant (GR) C. canadensis control in GR soybean. Glyphosate plus saflufenacil applied PP controlled GR C. canadensis as much as 90%. The addition of sulfentrazone, flumioxazin or metribuzin to the tankmix provided as much as 93%, 96% and 97% control of GR C. canadensis, respectively. Glyphosate plus 2,4-D ester applied PP provided as much as 59% control of GR C. canadensis. The addition of sulfentrazone, flumioxazin or metribuzin to the tankmix provided as much as 60%, 59% and 91% control of GR C. canadensis, respectively. Glyphosate plus paraquat applied PP provided as much as 85% control of GR C. canadensis. The addition of sulfentrazone, flumioxazin or metribuzin to the tankmix provided as much as 88%, 89% and 98% control of GR C. canadensis, respectively. Density and biomass reductions of GR C. canadensis with herbicides evaluated followed the same pattern as weed control evaluations. GR C. canadensis interference reduced soybean yield 66%. Reduced GR C. canadensis interference with the preplant herbicides evaluated provided soybean yield similar to the weed-free control. Results from this study show that glyphosate plus saflufenacil, glyphosate plus 2,4-D ester or glyphosate plus paraquat tankmixed with metribuzin can provide effective control of GR C. canadensis in GR soybean.

Autoencoder network is an efficient representation learning method. In general, a finer feature set obtained from autoencoder leads to a lower error rate and lower total misclassification cost. However, the network is usually trained for a long time to obtain a finer feature set, leading to a high time cost and total cost. To address this issue, a Sequential Three-Way Decision (S3WD) model is developed to balance the misclassification cost and the time cost in autoencoder based classifications and decisions. To implement the tradeoff strategy, it is necessary to extract a multi-granular feature set. In the network, the associated discriminative information in the extracted features increases with training epochs, which constructs a multi-granular feature structure. An autoencoder-based multi-granular feature description definition is presented. Based on the definition, an autoencoder composed of restricted Boltzmann machines is adopted to extract the multi-granular features. Then, a new cost-sensitive S3WD model is proposed, which aims to find the optimal granule level with the lowest total cost. Finally, the experiments demonstrate the effectiveness of the proposed approaches.

In the study, a method of solving ANOVA problems based on an unbalanced three-way mixed effects model with interaction for data when factors A and B are fixed, and factor C is random was presented, and the required EMS was derived. Under each of the appropriate null hypotheses, it was observed that none of the derived EMS was unbiased for the other. Unbiased estimators of the mean squares were determined to test hypotheses. With the unbiased estimators, appropriate F-statistics as well as their corresponding pseudo-degrees of freedom were obtained. The theoretical results presented in the paper were illustrated using a numerical example.

Three-way decisions provide a trisecting-and-acting framework for complex problem solving. For a cost-sensitive decision-making problem under multiple levels of granularity, sequential three-way decisions have come into being. Within this framework, how to act upon the three pair-wise disjoint regions is the most important issue. To this end, we propose a generalized model of sequential three-way decisions via multi-granularity in this paper. Subsequently, we adopt the typical aggregation strategies to implement the following five kinds of multigranulation sequential three-way decisions—the weighted arithmetic mean multigranulation sequential three-way decisions, the optimistic multigranulation sequential three-way decisions, the pessimistic multigranulation sequential three-way decisions, the pessimistic-optimistic multigranulation sequential three-way decisions and the optimistic-pessimistic multigranulation sequential three-way decisions. Furthermore, we discuss the rightness and rationality of the five kinds of multigranulation sequential three-way decisions and also analyze the relationships and differences between them. Finally, the experimental results demonstrate that the first four different multigranulation sequential three-way decisions are effective. These models will accelerate and enrich the development of multigranulation three-way decisions.

Three-way decisions (3WD) is an important methodology in solving problems with uncertainty. A systematic analysis on three-way based uncertainty measures is conducive to the promotion of three-way decisions. Meanwhile, confusion matrix, with multifaceted views, serves as a fundamental role in evaluating classification performance. In this paper, confusion matrix is endowed with semantics of three-way decisions. A collection of measures are thus deduced and summarized into seven measure modes. We further investigate the formulation of three-way regions from a measure driven view. To satisfy the preferences of stakeholder, two different objective functions are formulated, and each of them can include different combinations of measures. To demonstrate the effectiveness, we generate probabilistic three-way decisions for a wealth of datasets. Compared with Gini coefficient based and Shannon entropy based objective functions, our model can deduce more satisfying three-way regions.

La-loaded Al2O3 (La/Al2O3) is a practical support for three-way catalysis (TWC) reactions. Although it has been reported that the addition of La to Al2O3 results in improved thermal stability to re...

Cluster ensemble has emerged as a powerful technique for combining multiple clustering results. To address the problem of clustering on large-scale data, this paper presents an efficient three-way cluster ensemble approach based on Spark, which has the ability to deal with both hard clustering and soft clustering. First, this paper proposes the framework of three-way cluster ensemble based on Spark inspired by the theory of three-way decisions, and develops a distributed three-way k-means clustering algorithm. Then, we introduce the concept of cluster unit, which reflects the minimal granularity distribution structure agreed by all the ensemble members. We also introduce quantitative measures for calculating the relationships between units and between clusters. Finally, we propose a consensus clustering algorithm based on cluster units, and we devise various three-way decision strategies to assign small cluster units and no-unit objects. The experimental results using 19 real-world data sets validate the effectiveness of the proposed approach from different indices such as ARI, ACC, NMI and F1-Measure. The experimental results show that the proposed approach can effectively deal with large-scale data, and the proposed consensus clustering algorithm has a lower time cost and does not sacrifice the clustering quality.

Strategically combining four structured domains creates the first ever three-way topological energy-splitter; remarkably, this is only possible using a square, or rectangular, lattice, and not the graphene-like structures more commonly used in valleytronics. To achieve this effect, the two mirror symmetries, present within all fully-symmetric square structures, are broken; this leads to two nondistinct interfaces upon which valley-Hall states reside. These interfaces are related to each other via the time-reversal operator and it is this subtlety that allows us to ignite the third outgoing lead. The geometrical construction of our structured medium allows for the three-way splitter to be adiabatically converted into a wave steerer around sharp bends. Due to the tunability of the energies directionality by geometry, our results have far-reaching implications for applications such as beam-splitters, switches and filters across wave physics.

In the framework of multi-way data analysis, this paper presents symmetrical and non-symmetrical variants of three-way correspondence analysis that are suitable when a three-way contingency table is constructed from ordinal variables. In particular, such variables may be modelled using general recurrence formulae to generate orthogonal polynomial vectors instead of singular vectors coming from one of the possible three-way extensions of the singular value decomposition. As we shall see, these polynomials, that until now have been used to decompose two-way contingency tables with ordered variables, also constitute an alternative orthogonal basis for modelling symmetrical, non-symmetrical associations and predictabilities in three-way contingency tables. Consequences with respect to modelling and graphing will be highlighted.

This work was supported by the Spanish Government, Ministry of Economy and Competitiveness (PGC2018-095868-B-I00)., At intersections, drivers need to infer which ways are allowed by interpreting mandatory and/or prohibitory traffic signs. Time and accuracy in this decision-making process are crucial factors to avoid accidents. Previous studies show that integrating information from prohibitory signs is generally more difficult than from mandatory signs. In Study 1, we compare combined redundant signalling conditions to simple sign conditions at three-way intersections. In Study 2, we carried out a survey among professionals responsible for signposting to test whether common practices are consistent with experimental research. In Study 1, an experimental task was applied (n = 24), and in Study 2, the survey response rate was 17%. These included the main cities in Spain such as Madrid and Barcelona. Study 1 showed that inferences with mandatory signs are faster than those with prohibitory signs, and redundant information is an improvement only on prohibitory signs. In Study 2, prohibitory signs were those most frequently chosen by professionals responsible for signposting. In conclusion, the most used signs, according to the laboratory study, were not the best ones for signposting because the faster responses were obtained for mandatory signs, and in second place for redundant signs., Spanish Government, Ministry of Economy and Competitiveness PGC2018-095868-B-I00

Mobile Computing is a new paradigm aimed at providing users with constant network connectivity regardless of their location. Computer networking removed the physical barrier between computers, allowing users connected to the system from one computer to access resources on another. This paved the path for distributed computing, in which a group of connected computers works together to achieve a common goal. However, the access points to such a distributed system were still tethered machines and a user did not have the flexibility to move while accessing such a system. Mobile computing expands distributed computing in the direction of making a system's services available to a user regardless of their location or, more significantly, their location changes. Wireless networking removed the requirement to be linked to a cable, static infrastructure in order to benefit from distributed system services. A cable infrastructure of static hosts (representing a conventional distributed system) connects areas of wireless coverage ('cells') to mobile computers in a distributed system with mobile hosts.

This study uses ship-based weather radar observations collected from research vessel Investigator to evaluate the Australian weather radar network calibration monitoring technique that uses spaceborne radar observations from the NASA Global Precipitation Mission (GPM). Quantitative operational applications such as rainfall and hail nowcasting require a calibration accuracy of ±1 dB for radars of the Australian network covering capital cities. Seven ground-based radars along the western coast of Australia and the ship-based OceanPOL radar are first calibrated independently using GPM radar overpasses over a 3-month period. The calibration difference between the OceanPOL radar (used as a moving reference for the second step of the study) and each of the seven operational radars is then estimated using collocated, gridded, radar observations to quantify the accuracy of the GPM technique. For all seven radars the calibration difference with the ship radar lies within ±0.5 dB, therefore fulfilling the 1 dB requirement. This result validates the concept of using the GPM spaceborne radar observations to calibrate national weather radar networks (provided that the spaceborne radar maintains a high calibration accuracy). The analysis of the day-to-day and hourly variability of calibration differences between the OceanPOL and Darwin (Berrimah) radars also demonstrates that quantitative comparisons of gridded radar observations can accurately track daily and hourly calibration differences between pairs of operational radars with overlapping coverage (daily and hourly standard deviations of ∼ 0.3 and ∼ 1 dB, respectively).

Three-way decisions, as a better way than two-way decisions, has played an important role in many fields. As an extension of semiconcept, preconcept constitutes a new approach for data analysis. In contrast to preconcept, formal concept or semiconcept are too conservative about dealing with data. Hence, we want to further apply three-way decisions to preconcept. In this work, we introduce three-way preconcept by an example. This new notion combines preconcept with the assistant of three-way decisions. After that, we attain a generalized double Boolean algebra consisting of three-way preconcept. Furthermore, we give two form operators, approximation operators from lattice and set equivalence relation approximation operators, respectively. Finally, we present a conclusion with some summary and future issues that need to be addressed.

Citizenship pressure has recently been a hot topic in organizational citizenship behavior research since it aids in understanding the driving mechanism of organizational citizenship behavior. However, previous research has revealed discrepancies in the connection. This article develops a theoretical model of the impact of citizenship pressure on organizational citizenship performance based on expectancy theory. A leader–employee paired questionnaire was used to evaluate the hypotheses. The results indicate that organizational citizenship performance is positively influenced by citizenship pressure. The connection between organizational citizenship performance and citizenship pressure is positively moderated by transformational leadership. The better the transformational leadership, the greater the influence of citizenship pressure on organizational citizenship performance. Furthermore, the impact of transformational leadership on the link between citizenship pressure and organizational citizenship performance is dependent on the political skill of employees. When individuals with strong political skill encounter transformational leadership, the relationship between citizenship pressure and organizational citizenship performance is minimal. On the contrary, this relationship is enhanced when personnel with limited political skill are confronted with transformational leadership.

This is an entry for the publication titled "Three-way decision and conformal prediction: Isomorphisms, differences and theoretical properties of cautious learning approaches". You can cite the paper as: Andrea Campagner, Federico Cabitza, Pedro Berjano, Davide Ciucci. Three-way decision and conformal prediction: Isomorphisms, differences and theoretical properties of cautious learning approaches. Information Sciences, Volume 579, 2021, Pages 347-367. This entry contains both a pre-publication version of the paper, as well as the only non-previously available dataset used in the study. This dataset, in particular, contains observations for 325 patients, of which 124 have been annotated by two different clinicians. The legend of the labels is as follows: 0 Normal sagittal alignment 1 Cervical kyphosis 2 Thoracic kyphosis 3 Thoracolumbar kyphosis 4 Lumbar kyphosis 5 Lower lumbar kyphosis 6 Global kyphosis 7 Pelvic kyphosis While the features are the following: PI: pelvic incidence PT: pelvic tilt SS: sacral slope LL: lumbar lordosis LLL: lower lumbar lordosis TLK: thoraco-lumbar kyphosis TK: thoracic kyphosis SVA: sagittal vertical axis T1PA: T1 pelvic angle sex: 0=female; 1=male type of X-ray: 0=EOS; 1=traditional X-ray&nbsp