On the double Roman bondage numbers of graphs

For a graph [Formula: see text], a double Roman dominating function (DRDF) is a function [Formula: see text] having the property that if [Formula: see text] for some vertex [Formula: see text], then [Formula: see text] has at least two neighbors assigned [Formula: see text] under [Formula: see text] or one neighbor [Formula: see text] with [Formula: see text], and if [Formula: see text] then [Formula: see text] has at least one neighbor [Formula: see text] with [Formula: see text]. The weight of a DRDF [Formula: see text] is the sum [Formula: see text]. The minimum weight of a DRDF on a graph [Formula: see text] is the double Roman domination number of [Formula: see text] and is denoted by [Formula: see text]. The double Roman bondage number of [Formula: see text], denoted by [Formula: see text], is the minimum cardinality among all edge subsets [Formula: see text] such that [Formula: see text]. In this paper, we study the double Roman bondage number in graphs. We determine the double Roman bondage number in several families of graphs, and present several bounds for the double Roman bondage number. We also study the complexity issue of the double Roman bondage number and prove that the decision problem for the double Roman bondage number is NP-hard even when restricted to bipartite graphs.

An Integrated Fuzzy Fault Tree Model With Bayesian Network-based Maintenance Optimization of Complex Equipment in Automotive Manufacturing

Knowledge-based approaches are useful alternatives to predict the Failure Probability (FP) coping with the insufficient data, process integrity, and complexity issue in manufacturing systems. This study proposes a Fault Tree Analysis (FTA) approach as proactive knowledge-based technique to estimate the FP based maintenance planning with subjective information from domain experts. However, the classical-FTA still suffers from uncertainty and static structure limitations which poses a substantial dilemma in predicting FP. To deal with the uncertainty issues, a Fuzzy-FTA (FFTA) model was developed by statistical analysing the effective attributes such as experts' trait impacts, scales variation and, assorted membership and defuzzification functions. Besides, a Bayesian Network (BN) theory was conducted to overcome the static limitation of classical-FTA. The results of FFTA model revealed that the changes in decision attributes were not statistically significant on FP variation while BN model considering conditional rules to reflect the dynamic relationship between events had more impact on predicting FP. After all, the integrated FFTA-BN model was used in the optimization model to find the optimal maintenance intervals according to estimated FP and the total expected cost. As a practical example, the proposed model was implemented in a semi-automatic filling system in an automotive production line. The outcomes could be useful for upgrading the availability and safety of complex equipment in manufacturing systems.

Main stages of development of architectural science. Organization, methodology and updating of theoretical knowledge in the field of architecture and design

The paper focuses on research of the background and basic elements of complexity issue in architectural theory, and in the context of design practice. It also brings forth the question of introduction of complexity in didactic and education programs implemented in universities in architecture as a field of study. Paper contains critical analysis of limitations of traditional approach to architectural practice based mainly on intuition and confronts it with integrated design ideas. These ideas contribute to the development of wider application of the theory of complexity.

Towards a UMLsec-Based Proctored Examination Model

Electronic examination systems are becoming increasingly complex and intensive to develop with the introduction of virtual invigilator in proctored examinations. In order to address this complexity issue, there is evident need to have a global model that is extensible. Modeling software systems enables developers to better understand the system they are building and offers opportunities for simplification and reuse. This article presents an extension to UMLsec, by introducing three (3) new stereotypes which were added to the UMLsec Profile for a Proctored e-Exam model. The model was validated and converted to a platform specific model using the Java stereotype available on Papyrus. This enabled the model generate Java classes which can be used for the implementation of a secure proctored e-exam system. The model allows developers with little or no knowledge in security to use the model to build proctored e-exam systems and to incorporate all known security requirements. The model can also be extended to accommodate new security solutions for e-exam systems as they are discovered.

Solving Time Complexity Issue in Copy-Move Forgery Detection Thru Pre-processing Techniques

Copy-move forgery detection (CMFD) has become a popular an important research focus in digital image forensic. Copy-move forgery happens when a region in an image is copied and paste into the same image. Apart from the main problem of detection robustness and accuracy, CMFD is struggle with time complexity issue. One of the options to resolve this problem was by including pre-processing step in CMFD pipeline. This paper reviews on the importance of pre-processing step, and available techniques in reducing time complexity of copy-move forgery detection. An experiment using discrete wavelet transform (DWT) as a pre-processing technique was carried out to evaluate the performance of adopting pre-processing technique in CMFD pipeline. The experimental result has shown a significant reduction in processing time with some trade off to detection accuracy.

Prescriptive Data-Analytical Modeling of Laser Powder Bed Fusion Processes for Accuracy Improvement

Laser powder bed fusion (LPBF) has the ability to produce three-dimensional lightweight metal parts with complex shapes. Extensive investigations have been conducted to tackle build accuracy problems caused by shape complexity. For metal parts with stringent requirements, surface roughness, laser beam positioning error, and part location effects can all affect the shape accuracy of LPBF built products. This study develops a data-driven predictive approach as a promising solution for geometric accuracy improvement in LPBF processes. To address the shape complexity issue, a prescriptive modeling approach is adopted to minimize geometrical deviations of built products through compensating computer aided design models, as opposed to changing process parameters. It allows us to predict and control a wide range of shapes starting from a limited set of measurements on basic benchmark geometries. An error decomposition and compensation scheme is developed to decouple the influence from different error components and to reduce the shape deviations caused by part geometrical deviation, laser beam positioning error, and other location effects simultaneously via an integrated modeling and compensation framework. Experimentation and data collection are conducted to investigate error sources and to validate the developed modeling and accuracy control methods.

Striking circadian neuron diversity and cycling of Drosophila alternative splicing

Although alternative pre-mRNA splicing (AS) significantly diversifies the neuronal proteome, the extent of AS is still unknown due in part to the large number of diverse cell types in the brain. To address this complexity issue, we used an annotation-free computational method to analyze and compare the AS profiles between small specific groups of Drosophila circadian neurons. The method, the Junction Usage Model (JUM), allows the comprehensive profiling of both known and novel AS events from specific RNA-seq libraries. The results show that many diverse and novel pre-mRNA isoforms are preferentially expressed in one class of clock neuron and also absent from the more standard Drosophila head RNA preparation. These AS events are enriched in potassium channels important for neuronal firing, and there are also cycling isoforms with no detectable underlying transcriptional oscillations. The results suggest massive AS regulation in the brain that is also likely important for circadian regulation.

A Projection Bias in Frequent Subgraph Mining Can Make a Difference

The aim of the frequent subgraph mining task is to find frequently occurring subgraphs in a large graph database. However, this task is a thriving challenge, as graph and subgraph isomorphisms play a key role throughout the computations. Since subgraph isomorphism testing is a hard problem, subgraph miners are exponential in runtime. To alleviate the complexity issue, we propose to introduce a bias in the projection operator and instead of using the costly subgraph isomorphism projection, one can use a polynomial projection having a semantically-valid structural interpretation. This paper presents a new projection operator for graphs named AC-projection, which exhibits nice theoretical complexity properties. We study the size of the search space as well as some practical properties of the projection operator. We also introduce a novel breadth-first algorithm for frequent AC-reduced subgraphs mining. Then, we prove experimentally that we can achieve an important performance gain (polynomial complexity projection) without or with non-significant loss of discovered patterns in terms of quality.

DYNAMIC SYSTEM APPROACH IN PSYCHOLOGY: PROPOSITION AND APPLICATION IN THE STUDY OF EMOTION, APPRAISAL AND COGNITIVE ACHIEVEMENT

Psychological processes are difficult to be studied due to their complexity. The dynamic system approach shows itself as a good tool for psychology to deal with this complexity issue. We propose two fundamental contributions of the dynamic system approach to psychology and apply it in the study of achievement emotions, appraisal and cognitive achievement. Two hypotheses were investigated: 1) More than one correlation pattern between test achievement, appraisal and emotion will be found; 2) Test achievement, appraisal and emotion form a dynamic system which will be explained by a latent variable that is dependent on the previous state of the system. A sample of thirteen students from seventh to ninth grades performed an inductive reasoning test, appraised their achievement, and declared their emotional valences (from extreme positive to extreme negative). Each variable was measured in 20 different occasions. One correlation matrix of each individual was generated and seven qualitative profiles were identified. Then four different states of relations between the variables were identified through a hidden Markov model. The two hypotheses were not refuted. It’s concluded that the dynamic system approach brings new possibilities to the study of psychological processes. Key words: achievement emotion, appraisal, cognitive achievement, dynamic system approach, methodology.