Explore the vast range of titles available.

Background Feature extraction of protein sequences is widely used in various research areas related to protein analysis, such as protein similarity analysis and prediction of protein functions or interactions. Results In this study, we introduce FEGS (Feature Extraction based on Graphical and Statistical features), a novel feature extraction model of protein sequences, by developing a new technique for graphical representation of protein sequences based on the physicochemical properties of amino acids and effectively employing the statistical features of protein sequences. By fusing the graphical and statistical features, FEGS transforms a protein sequence into a 578-dimensional numerical vector. When FEGS is applied to phylogenetic analysis on five protein sequence data sets, its performance is notably better than all of the other compared methods. Conclusion The FEGS method is carefully designed, which is practically powerful for extracting features of protein sequences. The current version of FEGS is developed to be user-friendly and is expected to play a crucial role in the related studies of protein sequence analyses.

ContextSoftware engineering is a social and collaborative activity. Communicating and sharing knowledge between software developers requires much effort. Hence, the quality of communication plays an important role in influencing project success. To better understand the effect of communication on project success, more in-depth empirical studies investigating this phenomenon are needed.ObjectiveWe investigate the effect of using a graphical versus textual design description on co-located software design communication.MethodTherefore, we conducted a family of experiments involving a mix of 240 software engineering students from four universities. We examined how different design representations (i.e., graphical vs. textual) affect the ability to Explain, Understand, Recall, and Actively Communicate knowledge.ResultsWe found that the graphical design description is better than the textual in promoting Active Discussion between developers and improving the Recall of design details. Furthermore, compared to its unaltered version, a well-organized and motivated textual design description–that is used for the same amount of time–enhances the recall of design details and increases the amount of active discussions at the cost of reducing the perceived quality of explaining.

In this study, the authors envisage the neutrosophic number from various distinct rational perspectives and viewpoints to give it a look of a conundrum. They focused and analysed neutrosophic fuzzy numbers when indeterminacy and falsity functions are dependent on each other, which serves an indispensable role for the uncertainty concept. Additionally, the idea of cylindrical neutrosophic single-valued number is focused here, when the indeterminacy and falsity functions are dependent to each other using an influx of different logical and innovative graphical representation. They also developed the score and accuracy function for this particular cylindrical neutrosophic single-valued number and analysed some real-life problems like networking critical path model problem and minimal spanning tree problem of operation research field when the numbers are in cylindrical neutrosophic ambiance. They also introduced a multi-criterion group decision-making problem in this cylindrical neutrosophic domain. This noble thought will help us to solve a plethora of daily life problems in the neutrosophic arena.

Protein sequence comparison remains a challenging work for the researchers owing to the computational complexity due to the presence of 20 amino acids compared with only four nucleotides in Genome sequences. Further, protein sequences of different species are of different lengths; it throws additional changes to the researchers to develop methods, specially alignment-free methods, to compare protein sequences. In this work, an efficient technique to compare protein sequences is developed by a graphical representation. First, the classified grouping of 20 amino acids with a cardinality of 4 based on polar class is considered to narrow down the representational range from 20 to 4. Then a unit vector technique based on a two-quadrant Cartesian system is proposed to provide a new two-dimensional graphical representation of the protein sequence. Now, two approaches are proposed to cope with the varying lengths of protein sequences from various species: one uses Dynamic Time Warping (DTW), while the other one uses a two-dimensional Fast Fourier Transform (2D FFT). Next, the effectiveness of these two techniques is analyzed using two evaluation criteria—quantitative measures based on symmetric distance (SD) and computational speed. An analysis is performed on five data sets of 9 ND4, 9 ND5, 9 ND6, 12 Baculovirus, and 24 TF proteins under the two methods. It is found that the FFT-based method produces the same results as DTW but in less computational time. It is found that the result of the proposed method agrees with the known biological reference. Further, the present method produces better clustering than the existing ones.

The use of written or graphic representations is essential in mathematics. Graphic representations are mainly used and researched as instruments for problem solving. There is a gap in research for interventions that use learner-generated graphic representations as documents for reflection processes for promoting the development of children's graphic representation competences. This is the focus of the study presented here. The study examines to what extent such an intervention has an effect on how the children take into account a mathematical structure in their self-generated graphic representations, how they ensure a mathematical matching with the word problem, and what degree of abstraction they choose. Additionally, the effect on the solution rates is investigated. The results show that children in the intervention group more frequently pay attention to a mathematically appropriate structure, compared with children in the control groups. This result is statistically significant. At the same time, children keep the degree of abstraction relatively constant. Solution rates improve continuously, but the difference is not significant.

We present a basketball training visualization method – Motion Time Cylinder (MTC) designed to help novice players learn basketball techniques more easily. The basketball player can select their preferred movement to learn and visualize the body and joint movements in detail. Extensive visualization as a graphical assistant help supports the player in understanding 3D movements from the visual patterns. At the same time, players can understand the coordination and spatial relationship when playing the movement. The visualization principle is based on the mapping of body movement into the graphic of clock-based division locations. The movements can then be represented by the translation pattern between the frontal and transverse planes of cylinder spaces. As a result, the movement can be visualized by rendering the translation pattern base on the clock from a specific perspective, for example, an orthogonal view. This paper explains the visualization principles of the mapping and pattern as well as the graphical representation based on user perception

In this paper, we introduce the notion of resolvable networks. A resolvable network is a digraph of subnetworks, where subnetworks may overlap, and the inner structure of subnetworks are not interesting from the viewpoint of the network. There are two special subnetworks, Source and Sink, with the following properties: there is no incoming edge to Source, and there is no outgoing edge from Sink. Any resolvable network can be represented by a satisfiability problem in Boolean logic (shortly, SAT problem), and any SAT problem can be represented by a resolvable network. Because of that, the resolution operation is valid also for resolvable networks. We can use resolution to find out or refine the inner structure of subnetworks. We give also a pessimistic and an optimistic interpretation of subnetworks. In the pessimistic case, we assume that inside a subnetwork, all communication possibilities are represented as part of the resolvable network. In the optimistic case, we assume that each subnetwork is strongly connected. We show that any SAT problem can be visualized using the pessimistic interpretation. We show that transitivity is very limited in the pessimistic interpretation, and in this case, transitivity corresponds to resolution of clauses. In the optimistic interpretation of subnetworks, we have transitivity without any further condition, but not all SAT problems can be represented in this case; however, any such network can be represented as a SAT problem. The newly introduced graphical concept allows to use terminology and tools from directed graphs in the field of SAT and also to give graphical representations of various concepts of satisfiability problems. A resolvable network is also a suitable data structure to study, for example, wireless sensor networks. The visualization power of resolvable networks is demonstrated on some pigeon hole SAT problems. Another important application field could be modeling the communication network of an information bank. Here, a subnetwork represents a dataset of a user which is secured by a proxy. Any communication should be done through the proxy, and this constraint can be checked using our model.

This text results from research developed in the Postgraduate Program in Education at the Tiradentes University (Unit), in partnership with the University of Aveiro, Portugal, in 2019 and 2020. The objective sought to describe how the Visualization of Data (VD) is represented in the analysis of qualitative data with the support of Qualitative Data Analysis Software (QDAS). To achieve this objective, we reached the inclusion/exclusion criteria. Seven software frequently used today, trying to understand the most frequent representations of HV in QDAS, their structuring, and how they can contribute to the phases of organisation and analysis in a scenario that can vary from small to large amounts of data. The results show that the QDAS can help the researcher visualise the qualitative data analysed with transparency through data visualisation representations that stood out in tables, charts, maps, and representations with movements. During the analysis, it was also observed that each software offers representations in different ways. The type of user/researcher interaction with the generated representations has been an exclusive phenomenon of digital technologies, which visually improves how scientific production knowledge can better circulate knowledge production.

Electrical anisotropy is a property of the Earth materials that can be studied through electromagnetic geophysical methods, such as magnetotellurics. It consists of the electrical conductivity changing with the orientation and being mathematically characterized by the conductivity tensor. In order to better understand the conductivity tensor and provide more effective tools for quantitatively analyzing the conductivity tensor of anisotropic structures, three graphical representations for symmetric tensors using ellipsoids, Mohr circles and geometric forms are presented. The ellipsoid representation can be applied to indicate the strength of the anisotropy in different directions. The Mohr circle provides a graphic representation of a tensor as a function of the rotation of the coordinate system. For the geometric forms, one-dimensional (1-D), two-dimensional (2-D) and three-dimensional (3-D) sheet models with given parameters (sizes and resistivities of the constituent prisms), the macroscopic anisotropic conductivity may be calculated using the closed-form mathematical formulas. These three graphical representations have different abilities for revealing information on the conductivity tensor. Four synthetic examples involving uniaxial or biaxial anisotropic conductivity structures are examined in the principal axis coordinate system to investigate the advantages and disadvantages of the graphical displays.

A knowledge graph enables the structured representation of process knowledge. Traditional knowledge graphs typically represent process fact knowledge by depicting relations between entities. However, higher-order knowledge, such as causality, coupling, and rationale among process facts, should be addressed. The Process Hyper-relational knowledge graph (PHKG) was proposed to address these shortcomings. It comprises three layers: a concept layer representing process concept knowledge, an instance layer representing process fact knowledge, and a hyper-relationship layer representing higher-order knowledge linking process facts. Employing a semi-automatic construction method, a hyper-relation knowledge graph was created with 1, 602 entities, 2, 509 entity relationships, and 231 pairs of hyper-relationships. A process knowledge reasoning algorithm has also been developed to enable applications to reason about process knowledge.