Design and Implementation of a Metadata Repository about UML Class Diagrams. A Software Tool Supporting the Automatic Feeding of the Repository

Model-Driven Engineering is largely recognized as the most powerful method for the design of complex software. This study deals with the automated archival of metadata about the content of UML class diagrams (a particularly relevant category of models) into a pre-existing repository. To define the structure of the repository, we started from the definition of a UML metamodel. From the latter, we derived the schema of the metadata repository. Then, a parser was developed that is responsible for extracting the useful information from the XMI file about class diagrams and enters it as metadata into the repository. The parser has been implemented as a Java web interface, while the metadata repository has been implemented as a PostgreSQL database based on the JSONB data type. The metadata repository is thought to support modelers in the initial phase of the process of the development of new models when looking for artifacts to start from. The schema of the metadata repository and the Java code of the parser are available from the authors.

Photoelectron Spectroscopy in Molecular Physical Chemistry

Photoelectron spectroscopy has long been a powerful method in the toolbox of experimental physical chemistry and molecular physics. Recent improvements in coincidence methods, charged-particle imaging, and electron energy resolution have...

Synthesis of oxindoles bearing a stereogenic 3-fluorinated carbon center from 3-fluorooxindoles

3,3-Disubstituted oxindole bearing a stereogenic 3-fluorinated carbon center is a privileged structural motif present in many bioactive molecules. The straightforward functionalization of 3-fluorooxindoles constitutes a powerful method for the synthesis...

Making Reflective Equlibrium Precise: A Formal Model

Reflective equilibrium (RE) is often regarded as a powerful method in ethics, logic, and even philosophy in general. Despite this popularity, characterizations of the method have been fairly vague and unspecific so far. It thus may be doubted whether RE is more than a jumble of appealing but ultimately sketchy ideas that cannot be spelled out consistently. In this paper, we dispel such doubts by devising a formal model of RE. The model contains as components the agent’s commitments and a theory that tries to systematize the commitments. It yields a precise picture of how the commitments and the theory are adjusted to each other. The model differentiates between equilibrium as a target state and the dynamic equilibration process. First solutions to the model, obtained by computer simulation, show that the method allows for consistent specification and that the model’s implications are plausible in view of expectations on RE. In particular, the mutual adjustment of commitments and theory can improve one’s commitments, as proponents of RE have suggested. We argue that our model is fruitful not only because it points to issues that need to be dealt with for a better understanding of RE, but also because it provides the means to address these issues.

Development of 3D convolutional neural network to recognize human activities using moderate computation machine

Human activity recognition (HAR) is recently used in numerous applications including smart homes to monitor human behavior, automate homes according to human activities, entertainment, falling detection, violence detection, and people care. Vision-based recognition is the most powerful method widely used in HAR systems implementation due to its characteristics in recognizing complex human activities. This paper addresses the design of a 3D convolutional neural network (3D-CNN) model that can be used in smart homes to identify several numbers of activities. The model is trained using KTH dataset that contains activities like (walking, running, jogging, handwaving handclapping, boxing). Despite the challenges of this method due to the effectiveness of the lamination, background variation, and human body variety, the proposed model reached an accuracy of 93.33%. The model was implemented, trained and tested using moderate computation machine and the results show that the proposal was successfully capable to recognize human activities with reasonable computations.

Biosynthesis and metabolic engineering of 1-hydroxyphenazine in Pseudomonas chlororaphis H18

Background 1-Hydroxyphenazine (1-OH-PHZ) is a phenazine microbial metabolite with broad-spectrum antibacterial activities against a lot of plant pathogens. However, its use is hampered by the low yield all along. Metabolic engineering of microorganisms is an increasingly powerful method for the production of valuable organisms at high levels. Pseudomonas chlororaphis is recognized as a safe and effective plant rhizosphere growth-promoting bacterium, and faster growth rate using glycerol or glucose as a renewable carbon source. Therefore, Pseudomonas chlororaphis is particularly suitable as the chassis cell for the modification and engineering of phenazines. Results In this study, enzyme PhzS (monooxygenase) was heterologously expressed in a phenazine-1-carboxylic acid (PCA) generating strain Pseudomonas chlororaphis H18, and 1-hydroxyphenazine was isolated, characterized in the genetically modified strain. Next, the yield of 1-hydroxyphenazine was systematically engineered by the strategies including (1) semi-rational design remodeling of crucial protein PhzS, (2) blocking intermediate PCA consumption branch pathway, (3) enhancing the precursor pool, (4) engineering regulatory genes, etc. Finally, the titer of 1-hydroxyphenazine reached 3.6 g/L in 5 L fermenter in 54 h. Conclusions The 1-OH-PHZ production of Pseudomonas chlororaphis H18 was greatly improved through systematically engineering strategies, which is the highest, reported to date. This work provides a promising platform for 1-hydroxyphenazine engineering and production. Graphical Abstract