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Figure 2 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept

Figure 2. – Three images of organisms obtained by cropping images of lots; from left to right: Chalinidae (Porifera), Polyclinidae (Chordata), Hormatidae (Cnidaria).

Figure 3 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept

Figure 3. – Image of a batch of macro-invertebrate bycatch organisms from Kerguelen Exclusive Economical Zone (Poker 4 survey, 2017), including corals, a crinoïd, an ophiurid, a sea urchin and a brachiopoda; organisms are incomplete and have been quickly spread out over a small plate to take the picture.

Figure 6 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept

Figure 6. – Example of detection and classification obtained with an image including an Ophiuroid, a piece of coral and a sea star with network 2; red squares and annotations have been provided by the computer with no human action.

Figure 5 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept

Figure 5. – Example of detection and classification obtained with an image including Ascidians and a sea star with network 2; red squares and annotations have been provided by the computer with no human action.

Fig. 4 in Algal genomics perspective: the pangenome concept beyond traditional molecular phylogeny and taxonomy

Fig. 4. The pangenome concept based on a comparison of gene inventory. Colored squares indicate commonly shared or newly acquired genes between species or populations.

Fig. 1 in Algal genomics perspective: the pangenome concept beyond traditional molecular phylogeny and taxonomy

Fig. 1. Phase-contrast microscopy images of diverse algal taxa. A. Rhodella maculata CCMP736 (Rhodophyta). B. Dixoniella grisea CCMP1916 (Rhodophyta). C. Emiliania huxleyi (Haptophyta). D. Diacronema lutheri LIMS-PS-0073 (Haptophyta). E. Proteomonas sulcata (Cryptophyta). F. Rhinomonas nottbecki (Cryptophyta). G. Coolia monotis (Alveolata). H. Sungminbooa australiensis (Pelagophyceae; Stramenopiles). I. Halamphora pseudohyalina (Bacillariophyceae; Stramenopiles). J. Navicula avium (Bacillariophyceae; Stramenopiles). K. Thalassiosira gravida (= T. rotula; Bacillariophyceae; Stramenopiles). L. Ditylum sol (Bacillariophyceae; Stramenopiles). Multifocus light microscopy images were merged, and white balances were properly adjusted by Adobe Photoshop and Illustrator (scale bars: A-F, and H-J = 15 μm; G, and K = 40 μm; L = 100 μm).

Fig. 3 in Algal genomics perspective: the pangenome concept beyond traditional molecular phylogeny and taxonomy

Fig. 3. Major photosynthetic algal lineages in the eukaryote Tree of Life (eToL). The eToL is reconstructed based on previous studies (Burki et al., 2019; Keeling and Burki, 2019; Strassert et al., 2019; Bhattacharya and Price, 2020; Sibbald and Archibald, 2020).

Fig. 2. The red algal phylogenomic approaches. A. Concatenated multigene phylogeny using 170 in Algal genomics perspective: the pangenome concept beyond traditional molecular phylogeny and taxonomy

Fig. 2. The red algal phylogenomic approaches. A. Concatenated multigene phylogeny using 170 plastid genes (Muñoz-Gómez et al., 2017). B. Concatenated multigene phylogeny using 4,777 nuclear genes (Lee et al., 2019). C. Intertwining phylogenetic network tree of red algal plastid and nuclear multigene phylogenies.

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Fig. 2 Micromorphological differences between Drepanocladus longifolius and D in Do Antarctic populations represent local or widespread phylogenetic and ecological lineages? Complicated fate of bipolar moss concepts with Drepanocladus longifolius as a case study

Fig. 2 Micromorphological differences between Drepanocladus longifolius and D. capillifolius. Alar cells of D. longifolius a, b—from Lyall 47, Falkland Islands. Alar cells of D. capillifolius c―from Nelson 4262, USA, Wyoming (KRAM), d―from isolectotype of Hypnum capillifolium var. fallax Renauld, Canada, Quebec. Scale bar 100 μm

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Fig. 1 in Do Antarctic populations represent local or widespread phylogenetic and ecological lineages? Complicated fate of bipolar moss concepts with Drepanocladus longifolius as a case study

Fig. 1 Geographical distribution of the studied accessions and detected genetic lineages corresponding to Drepanocladus longifolius (blue dots) and D. capillifolius (green triangles) according to present circumscription.

The Fundamentals Regarding the Usage of the Concept of Interface for the Modeling of the Software Artefacts-Figure 4. The artefact during the initial structuring stage

The IT developer elaborates the detailed structure of the artefact, while considering several aspects: — The resources that are necessary to the artefact in order to accomplish its mission; — The artefact’s resistance to the changes regarding the functional requirements; — The artefact’s resistance to the technological changes; — The reasonably priced integration of the artefact in the structure of the host system; — The assurance of a reasonable reusability coefficient of the artefact during the struc- turing process of other artefacts; — The flexibility of the relations that exist among the components of the artefact; — The flexibility of the artefact’s connections with the host system.

Figure 3. The artefact as it exists as a black box-The Fundamentals Regarding the Usage of the Concept of Interface for the Modeling of the Software Artefacts

Consequently, the artefact as it exists as a black box can be represented according to the representation in Figure 3. It can be noticed that the artefact as it exists as a black box begins to interact with the environment. Two main categories of interfaces may be utilized by any artefact in order to interact with the environment: — Human Computer Interfaces (HCI); — Shared Resource Interfaces (SRI).

Figure 1. Visual and synthetic representation of the modelling process in the software industry-The Fundamentals Regarding the Usage of the Concept of Interface for the Modeling of the Software Artefacts

The experience that is accumulated regarding the modelling paradigms in the software engineering is impressive. Thus, the software engineering recognizes modelling paradigms like object orientation, aspect orientation, component orientation, service orientation, agent orientation. In one form or another, these paradigms prove their ex- cellence in certain types of IT projects. At the same time, these paradigms reveal their objective limits when they are used to engineer the real world software systems. Every modelling paradigm represents, in fact, a modality to represent the real world using a specific formal framework. The specificity of the formal framework is defined from both a syntactic and semantic perspective. The  formal syntactic framework of a paradigm refers to the  concepts that are  used  by the  paradigm in  order to represent the  real  world,  but  also  to the recommended principles that allow  for these concepts to interact in a correct and  efficient manner. Both the concepts and the principles benefit from a formal representation that ultimately favours communication as a secondary modelling lever inside the IT projects. Every syntactic artefact of a paradigm can be associated with a certain real world semantics, which it abstracts. As a consequence, considering that the real world continuously enhances its semantic potential, the syntactic constructs that are favoured by the paradigm may become problematic.

The Fundamentals Regarding the Usage of the Concept of Interface for the Modeling of the Software Artefacts-Figure 2. The UML representation of the artefact as it exists as a metaphor

The accumulation of energy that exists in each ingenious metaphor is progressively released, thus contributing to the transformation of a theoretical promise into effective reality. The artefact successively goes through several maturation stages, as the creator is preoccupied with obtaining an as precise and as close as possible description of the artefact as it exists as a metaphor. The completion of these successive stages is achieved through a methodic abstraction process, while leaving open the possibility to innovate and  targeting three main objectives: — broadening the abstraction scope; — adding new details; — detecting and eliminating abstraction errors.

Figure 1. Mind mapping of Concepts and Versions of Micro learning (Hug, 2005)-Micro Learning: A Modernized Education System

The methods of micro learning are in line with the way that the learner’s brain naturally takes in information, so that the body does not get stressed-out. One of the salient features of micro learning is that it allows the user to find exactly what he or she is looking for. When the mind focuses on a particular question, it is the most open to receiving that answer (www.digitalpromise.org/microcredentials dated on 10/10/2015). It allows the learner’s brain to explore its own curiosity and its own patterns.

BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 6. The general conception of our asynchronous system BCI (offline - online) for reinforcement of a joystick movement

Once the motor imagery is identified, a command may be associated to this mental task in order to control a machine (Prataksita et al., (2014)) (Guger et al., 1999). In this work, we constructed a new Simuhnk/MathWork model to translate on-line the EEG signals into low-level commands. Fig. 6 shows our experimental EEG-based BCI System

BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 1. The whole-building switch concept for the power lines of standby devices

68 million houses in North America and Europe will be smart by 2019 (Kurkinen, 2016) with a compound annual growth rate of 37 % and 61 %, respectively. The smart equipment is usually installed together with an upgrade (e.g. aluminum wires are replaced by copper ones) of the power grid. In this case, additional power lines for standby devices are cabled, and the WBS concept is applied using one power switch only (see figure 1). For instance, the Songle high-power relay T90  can control the whole building electricity with load up to 30 A using NodeMcu Lua ESP8266 WiFi and/or Arduino Uno / Mega boards.

BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 3. The unified hardware unit based on NodeMcu Lua ESP8266 WiFi development board, ACS712T ELC-30A current sensor, and relay SRD-05VDC-SL-C

The software consists of two parts, low-level Arduino sketches and high-level C# Windows form appplication. They are connected using the open-source message MQTT broker Mosquitto.11 Every hardware unit has the unique identifier and commands to control the relay. The MQTT topic “/VPP/Relays” is used by subscribers and publishers. The number “50” sent from C# Windows form (it equals number “2” sent from the standard Mosquitto publisher) is a command to switch on the second relay, “51” (“3”) – to switch off, respectively. The prototype was developed with one root controller and two descendant relays. The commands are as follows: “52” (“4”) / “53” (“5”) – to switch on / off the first relay, “54” (“6”) / “55” (“7”) – to switch on / off the third relay, respectively. This solution is similar to the one presented in [22], but ACS712T ELC-30A current sensor and ESP8266WiFi.h library are applied here. In addition, other commands, e.g. “56” (“8”) to get the value of the current in the 3rd segment, are in use as well.

BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 5. An example of smart lighting using NodeMcu Lua ESP8266 ESP-12 WiFi board

for different purposes together with switching on/off relays, e.g. to control the motors, to acquire the data from sensors. It allows developing multifunctional smart systems. For instance, the smart lighting unit is created using NodeMcu Lua ESP8266 ESP-12 WiFi board, Arduino light sensor, and relay SRD-05VDC-SL-C, which controls the power supply of the lamp. Figure 5 shows a simplified example of smart lighting, where the lamp is represented by eight 5 mm light-emitting diodes (LEDs).

BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 4. Screen shot of the C# Windows form app

The screen shot of the C# Windows form app is shown in figure 4. The text field on the left side includes numbers from 2 to 7, which are commands to control the states of relays.