Adaptive Resilience of Complex Safety-Critical Sociotechnical Systems: Toward a Unified Conceptual Framework and Its Formalization

Resilience is commonly understood as the capacity for a system to maintain a desirable state while undergoing adversity or to return to a desirable state as quickly as possible after being impacted. In this paper, we focus on resilience for complex sociotechnical systems (STS), specifically those where safety is an important aspect. Two main desiderata for safety-critical STS to be resilient are adaptive capacity and adaptation. Formal studies integrating human cognition and social aspects are needed to quantify the capacity to adapt and the effects of adaptation. We propose a conceptual framework to elaborate on the concept of resilience of safety-critical STS, based on adaptive capacity and adaptation and how this can be formalized. A set of mechanisms is identified that is necessary for STS to have the capacity to adapt. Mechanisms belonging to adaptive capacity include situation awareness, sensemaking, monitoring, decision-making, coordination, and learning. It is posited that the two mechanisms required to perform adaptation are anticipation and responding. This framework attempts to coherently integrate the key components of the multifaceted concept of STS Equationsadaptive resilience. This can then be used to pursue the formal representation of Equationsadaptive resilience, its modeling, and its operationalization in real-world safety-critical STS.

The effects of malapportionment on economic development

Does the unequal formal representation of people in legislatures (“malapportionment”) affect development? Answering this question is critical for assessing the welfare costs of malapportionment. We argue that representation might spur development as the desire for reelection incentivizes legislators to provide for their districts, and as voters hold politicians to account for doing so. Since this is the case, malapportionment might cause unequal development. Using data from India, we show that a 10% increase in representation causes a 0.6% increase in night lights, a frequently used proxy for development. Reapportionment, or the equalization for representation, attenuates this effect. Consistent with the theory, the effect of representation is larger in districts with legislators and voters that are able to hold the executive to account.

Using multivalued logic for qualitative data analysis

The paper considers a logical approach to data analysis to solve the classification problem. The studied data is a set of objects and their features. As a rule, this is scattered heterogeneous information and it is not enough for a reasonable application of probabilistic models. Therefore, logical algorithms are considered, which under certain conditions may be more adequate. For an expressive formal representation of the relationship between objects and their attributes, multivalued logic is used, and the number of values depends on a specific attribute. Therefore, a system of operations is proposed for variables with different domains of definition. As a result, a decisive function is built, which is a classifier of objects that are present in the studied data. The properties and capabilities of this function are analyzed. It is shown that the logical function, which is a conjunction in the space of rules connecting given objects with their characteristic features, uniquely characterizes the initial data, divides the subject area into classes, possesses modifiability properties, meets the requirements of completeness and consistency in a given area. The paper also proposes an algorithm for its implementation.

OntoSLAM: An Ontology for Representing Location and Simultaneous Mapping Information for Autonomous Robots

Autonomous robots are playing an important role to solve the Simultaneous Localization and Mapping (SLAM) problem in different domains. To generate flexible, intelligent, and interoperable solutions for SLAM, it is a must to model the complex knowledge managed in these scenarios (i.e., robots characteristics and capabilities, maps information, locations of robots and landmarks, etc.) with a standard and formal representation. Some studies have proposed ontologies as the standard representation of such knowledge; however, most of them only cover partial aspects of the information managed by SLAM solutions. In this context, the main contribution of this work is a complete ontology, called OntoSLAM, to model all aspects related to autonomous robots and the SLAM problem, towards the standardization needed in robotics, which is not reached until now with the existing SLAM ontologies. A comparative evaluation of OntoSLAM with state-of-the-art SLAM ontologies is performed, to show how OntoSLAM covers the gaps of the existing SLAM knowledge representation models. Results show the superiority of OntoSLAM at the Domain Knowledge level and similarities with other ontologies at Lexical and Structural levels. Additionally, OntoSLAM is integrated into the Robot Operating System (ROS) and Gazebo simulator to test it with Pepper robots and demonstrate its suitability, applicability, and flexibility. Experiments show how OntoSLAM provides semantic benefits to autonomous robots, such as the capability of inferring data from organized knowledge representation, without compromising the information for the application and becoming closer to the standardization needed in robotics.

Connecting Building Design with the Digital Factory by Design Languages to Explore Different Solutions

In engineering, design decisions in one domain exhibit multiple consequences in other domains. These consequences result from the often more or less hidden coupling between the different design domains. In order to examine these consequences, models need to be created. In practice, this is challenging due to the exchange of data between different engineering domains, since different software applications are often used and the effort involved with manual model creation. In this paper, we explore the use of graph-based design languages in a Model-Based Systems Engineering (MBSE) approach to link the digital factory with building design. We also show that the use of a common formal representation based on the Unified Modeling Language (UML) supports the interoperability between the two domains. Finally, we demonstrate how the engineering knowledge for the preliminary design of a factory building can be formally described using graph-based design languages and how the production line of the digital factory can then be used as an input to automatically create valid preliminary designs for the factory building.1

Análisis Automático de Modelos de Variabilidad : el Proceso SeVaTax

Una línea de productos software provee de una plataforma común flexible, de manera que permita adaptarse a las diferentes necesidades de productos dentro de un rango de requerimientos establecido. Dicha flexibilidad se logra mediante la identificación, definición y posterior configuración de lo que se conoce como Variabilidad. Los modelos de variabilidad, como cualquier otro artefacto software, están sujetos a un proceso de análisis para detectar y (posiblemente) resolver errores e incompatibilidades. Esto lleva a la existencia de un proceso de análisis de variabilidad, que presta especial atención al momento de definición y uso de la variabilidad. Existen hoy día, propuestas que presentan diferentes métodos y/o herramientas para realizar un análisis automatizado de la variabilidad. Sin embargo, muchas de ellas se enfocan en sólo un tipo de modelo como entrada y/o sólo disponen de algunos escenarios de validación para controlar. A su vez, muy pocas proponen correcciones o identifican exactamente dónde se encuentran las anomalías o inconsistencias en los modelos. Entonces, se hace necesario mejorar este proceso de validación y su soporte, evaluando el rendimiento durante esa validación. En este sentido, esta Tesis propone el proceso llamado SeVaTax, que toma como entrada modelos de variabilidad (uno o más), generando una representación formal que permite analizar un conjunto de escenarios de validación mayor y proporciona un nivel diferente de respuestas, incluso proponiendo algunas acciones específicas para corregir los modelos. Se proponen dieciocho escenarios de validación, que son experimentalmente validados desde dos puntos de vista: (1) la exactitud de los resultados en términos de los errores que SeVaTax permite identificar; y (2) el cubrimiento, que muestra el grado en que el conjunto de escenarios está cubierto por otros enfoques con herramientas similares. A software product line supplies a common and flexible platform, which allows to adaptto different needs of products from a range of established requirements. Such a flexibility is achieved through the identification, definition and configuration of what is called Variability. Variability models, like any other software artifact, are subjected to an analysis process to detect and (possibly) solve errors and incompatibilities. This fact leads to the existence of a process called variability analysis, which pays special attention to the variability definition and use. Nowadays, several approaches propose different methods and/or tools to automatically analyzing variability. However, many of these approaches only focus on one type of model as input, and/or only show some validation scenarios to control. In addition, few approaches propose corrections, or identify where the anomalies or inconsistencies are. Therefore, there is a need of improving the analysis process as well as its support, assessing their performance during validation. In this sense, this Thesis proposes the SeVaTax process, which takes variability models (one or more) as inputs, generates a formal representation that allows to analyze a larger set of validation scenarios, and gives a different level of responses to validation – including corrections in some cases. Eighteen validation scenarios are proposed, which are experimentally validated form two viewpoints: (1) accuracy, in terms of errors that SeVaTax identifies; and (2) covering, that shows the degree in which the set of scenarios is covered by similar proposals in the literature.

Applying reinforcement learning to plan manufacturing material handling

Applying machine learning methods to improve the efficiency of complex manufacturing processes, such as material handling, can be challenging. The interconnectedness of the multiple components that make up real-world manufacturing processes and the typically very large number of variables required to specify procedures and plans within them combine to make it very difficult to map the details of such processes to a formal mathematical representation suitable for conventional optimization methods. Instead, in this work reinforcement learning was applied to produce increasingly efficient plans for material handling in representative manufacturing facilities. Doing so included defining a formal representation of a realistically complex material handling plan, specifying a set of suitable plan change operators as reinforcement learning actions, implementing a simulation-based multi-objective reward function that considers multiple components of material handling costs, and abstracting the many possible material handling plans into a state set small enough to enable reinforcement learning. Experimentation with multiple material handling plans on two separate factory layouts indicated that reinforcement learning could consistently reduce the cost of material handling. This work demonstrates the applicability of reinforcement learning with a multi-objective reward function to realistically complex material handling processes.

Complex q -Rung Orthopair Fuzzy N -Soft Sets: A New Model with Applications

This research article expands the formal representation of human thinking to a most generalized hybrid theory, namely, complex q -rung orthopair fuzzy N -soft set. It is able to capture a great deal of graded imprecision and vagueness, which so often appear together in human interpretations. This model renders a parameterized mathematical tool for the ranking-based fuzzy modeling of two-dimensional paradoxical data. To that purpose, the proposed theory integrates complex q -rung orthopair fuzzy sets with the parametric structure of N -soft sets. The framework that arises captures information beyond the confined space of complex intuitionistic fuzzy N -soft sets and complex Pythagorean fuzzy N -soft sets, with the assistance of a parameter q . We establish the basic set-theoretical operations of this model and prove some of its fundamental properties. The Einstein and other elementary algebraic operations on complex q -rung orthopair fuzzy N -soft values shall be introduced to broaden the mathematical toolbox of this field. Its relationships with contemporary approaches shall demonstrate its outstanding flexibility. Moreover, we establish two competent multicriteria decision-making algorithms that capture the nuances of periodical inconsistent data. Their feasibility shall be demonstrated with an explicit application to the selection of optimum aerospace technology required for the economic development of the Mexican space agency. A comparative analysis of both strategies with the prevailing techniques substantiates their rationality. In addition, we illustrate this comparative study with an explicative bar chart that shows the compatibility of their outcomes. Finally, we examine the functionality of the proposed model and compare it with alternative theories.

Geometry of gene regulatory dynamics

Embryonic development leads to the reproducible and ordered appearance of complexity from egg to adult. The successive differentiation of different cell types that elaborate this complexity results from the activity of gene networks and was likened by Waddington to a flow through a landscape in which valleys represent alternative fates. Geometric methods allow the formal representation of such landscapes and codify the types of behaviors that result from systems of differential equations. Results from Smale and coworkers imply that systems encompassing gene network models can be represented as potential gradients with a Riemann metric, justifying the Waddington metaphor. Here, we extend this representation to include parameter dependence and enumerate all three-way cellular decisions realizable by tuning at most two parameters, which can be generalized to include spatial coordinates in a tissue. All diagrams of cell states vs. model parameters are thereby enumerated. We unify a number of standard models for spatial pattern formation by expressing them in potential form (i.e., as topographic elevation). Turing systems appear nonpotential, yet in suitable variables the dynamics are low dimensional and potential. A time-independent embedding recovers the original variables. Lateral inhibition is described by a saddle point with many unstable directions. A model for the patterning of the Drosophila eye appears as relaxation in a bistable potential. Geometric reasoning provides intuitive dynamic models for development that are well adapted to fit time-lapse data.