Difference between regular programming and visual programming.
\r\n\tAn update on clinical manifestations, their assessment, monitoring, and imagiology, including peripheral arthritis, enthesopathy, and extra-articular findings, and, the differential diagnosis with other diseases which evolves with axial and peripheral calcifications will be provided.
\r\n\r\n\t
\r\n\tAn important component of this book must be dedicated to the more recent treatments namely with biologic therapies but focusing also on new small molecule inhibitors and experimental therapies.
Chronic obstructive pulmonary disease (COPD) is induced by mainly tobacco smoking. The patients of COPD complained of cough, sputa, or exertional dyspnea. Compared to the patients with bronchial asthma, COPD has an extremely poor prognosis. Approximately 3,200,000 COPD patients died worldwide in 2015 [1]. The mortality is estimated to be ≥8-fold that of bronchial asthma. In this chapter, we review the role of bacterial infection on the pathogenesis of COPD, with a particular focus on
COPD is a relatively common disease characterized by respiratory symptoms and airflow limitation, usually caused by smoking. A mixture of small airway disease and parenchymal destruction caused airflow limitation. COPD patients complained of cough, dyspnea, and sputum production. Acute exacerbation frequently occurs after an acute respiratory infection. The acute exacerbation leads to more severe airflow limitation [2]. Sometimes, COPD patients are accompanied with bronchiectasis. These patients caused more frequent acute exacerbations and severe airway obstruction. Also, these patients demonstrated pathogenic microorganisms and mortality [3]. At present, there is no universally effective treatment for COPD. Some treatments just relieve the symptoms, while others could slow the progression of the disease. The most effective treatment is stopping smoking. Bronchodilators are the mainstay of available treatment options for COPD. There are several types of bronchodilators. Long-acting beta agonist (LABA), long-acting muscarinic antagonist (LAMA), or a combination of these agents is used for COPD patients, since long-acting medication is preferred. For the patients complicated with bronchial asthma, known as asthma-COPD overlap (ACO), inhaled glucocorticoids (ICSs) can be used. Also, ICSs can be used for patients with frequent COPD exacerbation. Short-acting beta agonists (SABAs) and short-acting muscarinic anticholinergics (SAMAs) are used to relieve symptoms, such as exertional dyspnea. Since theophylline can be given orally, it can be used for patients who cannot inhale medications. Advanced COPD patients with hypoxemia are administered with long-term oxygen therapy (LTOT). Rehabilitation programs are also important. They can be effective; however, they do not improve the outcome. Lung volume reduction surgery is performed in select patients with COPD; however, the candidate patients are limited. Also, lung transplantation is rarely done because of lack of donors [4]. Antioxidants, mucolytics, antiproteases, and antifibrotics are sometimes used; however, these drugs are not a mainstay for COPD. Anti-inflammatory drugs such as phosphodiesterase 4 inhibitors are used to reduce airway inflammation. Also, kinase inhibitors, chemokine receptor antagonists, innate immune mechanism inhibitors, and statins are developing [5]. However, these new treatments are still insufficient to demonstrate the evidence for caring COPD. As such, a novel therapy for COPD is required.
\nNumerous studies have characterized the lung microbiome of healthy adult subjects using BAL samples. The most common phyla consistently observed have been
Acute exacerbation of COPD is defined as a worsening of the respiratory condition, such as coughing, sputum production, and dyspnea, beyond daily physiological fluctuations and requiring additional treatment. COPD patients with acute exacerbation document a worse quality of life as well as decrease of pulmonary function. Finally, COPD patients with acute exacerbation result in lower mortality [9]. Acute exacerbation mainly occurs due to airway infections. The relationship between COPD and
The relationship between disease progression and
The pathogenesis of COPD is considered to be chronic airflow limitation results from an abnormal inflammatory response to the inhaled particles and gasses in the lung in susceptible smokers. There is a famous hypothesis that a protease-antiprotease imbalance leads to the progression of the destruction of alveoli [2]. Alveolar cell loss through apoptosis might contribute to the pathogenesis [29, 30, 31]. There are many animal models for COPD, including elastase, cigarette, inhaled gasses, and gene-targeted models. As COPD models, administration of papain or porcine pancreatic elastase model is well known [32]. Neutrophil elastase and proteinase-3, but not non-elastolytic enzymes, such as bacterial collagenase, caused COPD-like changes [33, 34, 35]. Cigarette smoking is a major factor inducing the development of COPD. Long-term cigarette smoking caused macrophage-predominant inflammation and airspace enlargement in animal models similar to those found in humans [36]. Potential mechanisms include high concentrations of reactive oxygen species (ROS) [37], oxidative stress [38], and matrix metalloproteinase (MMP)-12 [39, 40]. Inhaled stimuli, such as sulfur dioxide [41, 42], nitrogen dioxide [43], and oxidant [44], have been shown to induce COPD-like lesions in animal models. Ultrafine particles, such as silica, coal dust, diesel exhaust particles, and cadmium, induced focal emphysema [45, 46], chronic airway inflammation [47], and interstitial fibrosis with enlargement of the airspaces [48]. Alveolar wall apoptosis without the accumulation of inflammatory cells, resulting in emphysematous changes, was attained by active caspase-3 [31, 49]. Prednisolone also causes emphysematous changes through apoptosis [50]. Apoptosis in the alveoli resulted in airspace enlargement was also attained by a block of vascular endothelial cell growth factor (VEGF) receptor-2 [51]. Ceramide production, as the second messenger lipid, was induced by apoptosis. Ceramide played a role in induction of inflammation by the blockade of apoptosis by a VEGF receptor antibody. Since ceramide administration provoked the expression of MMP-12, it was considered to be a link between excessive apoptosis and inflammation [52]. Several gene-targeted models demonstrated COPD-like changed; however, the changes were developmental abnormalities rather than the destruction of mature lung tissue. Tight-skin mice [53, 54], pallid mice [55], and beige mice [56] were reported to be models. COPD mimic models by genetically altered techniques have been reported such as the overexpression of collagenase in the lung of transgenic mice [57], the deficiency of the microfibrillar component fibulin-5 and the deficiency of platelet-derived growth factor A (PDGF-A) [58, 59], epithelial restricted integrin αvβ6 knockout mice [60], fibroblast growth factor (FGF) receptor (FGFR)-3, and FGFR-4-double knockout mice [61]. Mice lacking the surfactant protein D (SP-D) gene [62] and the tissue inhibitor of metalloproteinase-3 (TIMP-3) gene [63] showed COPD-like lesions. These gene-targeted mice provided useful information for understanding the pathogenesis of COPD. However, none of these mice showed the same pathologic changes as those seen with human COPD.
\nChronic inflammation also plays a pivotal role in its development. Administration of lipopolysaccharide (LPS) to the lungs induced severe inflammation and resulted in airspace enlargement [64, 65]. COPD-like changes, such as goblet cell metaplasia in the larger airways, thickening of the airway walls, and irreversible alveolar enlargement, were attained by repeated administration of LPS [66, 67]. Tumor necrosis factor (TNF)-alpha is a proinflammatory cytokine induced by stimuli such as LPS. We reported that TNF-alpha overexpression mice in the lungs demonstrated pulmonary emphysema-like changes [68, 69]. MMP activation induced alveolar enlargement [68]. Chronic inflammation by TNF-alpha overexpression is considered to play an important role in the development of COPD. Several reports have found that the overexpression of inflammatory cytokines, such as IL-13 and IFN-gamma, resulted in pathologic changes mimicking human COPD [70, 71, 72]. These reports also supported the hypothesis that chronic inflammation in the lungs leads to lung tissue destruction, a hallmark of pulmonary emphysema, and chronic bronchitis.
\nWe therefore investigated the role of chronic inflammation in the pathogenesis of COPD.
Histology of lungs after chronic
Pulmonary physiology after chronic
Schematic illustrations of COPD-like changes induced by chronic
Recently, macrolide treatment has been reported to protect against acute exacerbation. Among some subjects with COPD, taking azithromycin daily for 1 year, when added to the usual treatment regimen, reduced the frequency of exacerbations and improved the quality of life [81]. Macrolides are known to modulate the inflammation, the so-called immunomodulatory effect, besides antimicrobial effects. In Japan, macrolides are used to treat chronic infection with
We appreciate the assistance of Dr. Brian Quinn for editing the English usage.
\nThe authors declare that they have no conflicts of interest (COI).
In this modern era, there has been a lot of development and upgradation in the field of Internet of things (IoT). The development has been actively growing in various sectors, for instance, industries, healthcare, education, agriculture, just to name a few. Consequently, it creates more employment opportunities. Thus, IoT should come into intensive usages with very careful awareness. The development of IoT programs can be done in various ways into the society. Although there have been a plenty of IoT development and applications, only very few academic institutions throughout the world are fully IoT equipped in their laboratories.
Since 2019, the COVID-19 pandemic has resulted in shutdown of educational institutions across the world. Globally, over 1 billion children are out of the classroom. As a result, education has changed dramatically with the distinctive rise of e-learning, whereby teaching is undertaken remotely and on digital platforms. While lecture sessions have been quite well organized, the laboratory sessions might be still challenging and dependent on the types of laboratories. To overcome this problem for an IoT design class, a virtual laboratory can be implemented. With a virtual laboratory, a laboratory session can be launched with flexibility of time. Self-learning process can be achieved by becoming more self-determining. It also supports collaborative group works and helps us develop critical thinking skills while doing individual assignments. The most important advantage is that it can be accessed from any location through Internet. Thus, any materials from the Internet can also be used. Nevertheless, there are some challenges in the virtual laboratory, which includes setting up the virtual laboratory environment, lack of social interaction between students and instructors, accessibility to various laboratory required technologies, software requirement and software inconsistency, student’s issues while teaching simultaneously, etc. A visual programming language (VPL) is any programming language that lets users create programs by manipulating program elements graphically rather than by specifying them textually. A VPL allows programming with visual expressions, spatial arrangements of text and graphic symbols used either as elements of syntax or secondary notation. Many VPLs known as dataflow programming are based on the idea of boxes and arrows, where boxes or screen objects are treated as entities connected by arrows, lines, or arcs representing relations. Different prototyping boards have its own programming language, such as C, Python, Java, and similar. To develop an IoT system, at least one of these programming languages is required. However, it is fortunate that during these days, several VPLs have been developed to help us start programming without knowing the programming language. These IoT visual programming tools have a user-friendly approach of programming. It has a graphical user interface (GUI), where the user can just drag and drop moving code blocks and execute a simple piece of logic.
Node-RED VPL is an open-source software, which is a flow-based development tool originally developed by International Business Machines (IBM) for wiring hardware devices together with application programming interface (API) and few online services as part of the IoT. Node-RED can be used flexibly under the Apache2 license. Some developed their own services based on Node-RED, while others changed to their own user interface (UI) and deployed it as built-in. It can be established as a platform where we can publish our own developed node so that anyone can use it. The open-source software is an alternative to interact with the class. Students can practice, study, and understand with basic engineering skills or even develop an IoT-based system with artificial intelligence (AI) capabilities, e.g., prediction, regression, clustering, and classification. It can be installed locally on a personal computer (PC) or a laptop. With its simplicity for learning and using as well as several built-in entities, it can be used in evolving innovative platforms providing ability to assign code to all interfaces with less coding complexity. In addition, it supports several IoT prototyping board such as Arduino, Raspberry, and Android as well as cloud-based platforms.
The objectives of the book chapter are as follows.
To describe the environment and features of Node-RED software with its abilities of numerous functions.
To deliver a complete outlook on each function of Node-RED that can be applied for an IoT-based virtual laboratory.
To talk over the suitability and rightness of machine learning and deep learning centered explanations in numerous practical fields.
To arrange for a broad opinion on machine learning algorithms, which can be put to build-up the abilities of a data-driven approach.
To highlight and summarize the possible study for smart systems including cloud technology.
To bring out the virtual awareness/importance of IoT including laboratory sessions.
The rest of the book chapter is organized as follows. Section 2 presents the past related existing works on VPL and NodeRED. In Section 3, the step-by-step methodology of the work is described in detail, which includes getting started, configuration, utilization, and machine learning (ML) package for Node-RED. The experimentational examples are provided along with the corresponding results in Section 4. Section 5 discusses about the results. Lastly, the conclusion is drawn in Section 6.
IoT has been playing an important role throughout the globe. It is a combination of both software and hardware tools. With the support of VPL, IoT can be applied into our day-to-day essential things. According to the survey by Ray in 2017, there were 13 VPLs for the usage and for the upgradation of IoT [1]. The VPL is classified into two types, namely open-source and proprietary-source with the four following main features, i.e., programming atmosphere, license, project source, and platform support [1]. Students can use open-source VPL such as Node-RED to learn and implement an IoT system or device with basic engineering skills and low-code programming. It helped many programmers to develop new software [2, 3]. Node-RED was initiated and was developed by IBM for connecting hardware devices with web-based editors [2, 3]. It was then applied further in the field of IoT. Rajalakshmi and Shahnasser came with a problem while making a cloud-system for IoT devices [4]. This is difficult to update the firmware by reinstalling the devices. To solve this issue, Node-RED was used without reinstalling the device and changing the programming code with quick setup [4]. In 2018, a model based on LoPy, which is a MicroPython triple-network development platform doubling up as a long-range (LoRa) Nano gateway, was connected to a system that uses Node-RED for interfacing with a local actuator and the external data with protocols [5, 6]. Their IoT development could be done in fog/clouds. The introduction of the IoT in education allows Internet-based communications to occur between things, sensors, and actuators. This has improved educational institutions [7, 8, 9, 10, 11, 12, 13, 14]. In 2015, Giang et al. implemented a distributed Node-RED (D-NR) framework for building various types of IoT applications, which can work efficiently with simple designing process and less time [15]. Abdel-Basset et al. could make an efficient framework by new ideas with lower cost and greater security [16]. The advantages in maximizing IoT became more for the institutions, i.e., bringing out an affluent knowledge, better quality of working efficiency, and gaining real-time experiences [17]. In addition, they could keep track record of all the university resources with secured data accessibility. Using an open-source platform such as Node-RED, it helps for the creation of new ideas, i.e., linking up with many other specialized courses, for instance, information and communication technologies (ICTs), embedded system design, humanities, agricultural, just to name a few, yielding a good IoT program of study for students to learn and make research [16]. Another model was proposed by Marquez et al. as an IoT educational platform for virtual academic communities [17]. In 2020, Torres et al. used VPL with Node-RED to improve their IoT system by reducing the time taken for the development, i.e., reducing the number of failed attempts while deploying an IoT system [18]. Home automation consisting of water heater, cooling systems, electrical outlets was raised all over the IoT using cloud-platform where Node-RED is used to make fast setup for remote monitoring and control of data with a mutual communication [19]. In 2021, David et al. worked on indoor crop agriculture by monitoring parameters such as humidity, temperature, and light intensity with the help of IBM-Bluemix, which is the IBM open cloud platform providing mobile and web developers access to IBM software for integration security transaction and other key functions [20]. The data was transformed into Node-RED platform through a mobile application for tracking purpose of the farmers. Thuluva et al. made a solution for interoperability problem for IoT semantic web technologies in industrial field using semantic Node-RED models with feasibility and scalability approach [21]. A rapid and low-cost IoT prototype was developed by Ferencz and Domokos within less time on Node-RED using the combination of various cycle power plant dataset [22]. A low-power wide area network (LPWAN) technology called LoRa with its medium access control (MAC) layer protocol called LoRaWAN was deployed by Fox et al. in 2019, and these end devices interact with a gateway using Node-RED connection to an IBM-IoT platform [23]. Node-RED was applied and analyzed by Olsson and Eric in terms of modeling and security with misuse of API and providing security guarantees [24]. Clerissi et al. made a model for testing and developing IoT platforms using Node-RED with the functional behavior and the static view of the system. The class diagrams were used by testing, defining, and generating in java script using a Mocha test framework [25]. Proper regulations and rules for all the Node-RED developers were given by Clerissi et al. [26]. They are about the comprehensibility issues, which can be used to increase efficiency by reducing errors and time to complete tasks.
VPL is any programming language that lets users create program by manipulating program elements graphically rather than by specifying them textually [27, 28]. A VPL allows programming with visual expressions, spatial arrangements of text and graphic symbols used either as elements of syntax or secondary notation. For instance, many VPLs known as dataflow or diagrammatic programming are based on the idea of “boxes and arrows,” where boxes or other screen objects are treated as entities, connected by arrows, lines, or arcs representing relations [29, 30, 31]. There are several different VPLs, which can be divided into different fields of applications, such as education (23 languages), multimedia (26 languages), video games (18 languages), systems (34 languages), automation (4 languages), data warehousing (8 languages), legacy (5 languages), and miscellaneous (10 languages) [29, 30, 31]. The difference between a regular programming and visual programming, in terms of type, nature, flexibility, speed, efficiency, interface, learning complication, space usage, and example, is shown in Table 1.
No | Context | Regular programming | Visual programming |
---|---|---|---|
1 | Type | Use only text | Use only graphics |
2 | Nature | Not user-friendly | User-friendly |
3 | Customizable | Very high | Moderate |
4 | Flexibility | High | Low |
5 | Speed | Very high | Low |
6 | Efficiency | Very high | Moderate |
7 | Interface | Not good | Great |
8 | Learning Complication | Take time | Easy to learn |
9 | Space Usage | Less | High |
10 | Example | Python, Java, etc. | Drakon, Helix, etc. |
Difference between regular programming and visual programming.
Node-RED is preinstalled in many devices, such as Raspberry Pi, Intel, Fujitsu, just to name a few. Moreover, there are several different cloud services including Cisco, Nokia, IBM, Hitachi, etc., using Node-RED as a VPL and a dataflow programming.
This section describes the complete scenario of methodology process. As can be seen in Figure 1, the first stage is installation, which involves the three following types, i.e., local machine, Raspberry Pi, and cloud services. After the correct installation, the Node-RED is configured involving all the basic nodes required to make a workflow with deployment activities. Next, the utilization is the main stage in which the development of the flows, handling errors of all the core nodes are explained. Accordingly, when the utilization process is completed, any type of experiments can be implemented with the expected and accurate outcomes.
Block diagram representing methodology workflow.
Getting started by installing Node-RED can be done on various criteria, which are explained as follows.
Running on a local machine for Windows 7 and above
Windows 7 and above versions have the capability of installing Node.js, Node-RED, and node package manager (npm) using command prompt (cmd) or Powershell. The procedure is described as follows.
Node.js (version 14.x long term support (LTS) or above) is installed.
All the local administrators are given their rights.
Cmd or Powershell is opened and the following code is run to check if it is installed correctly or not installed.
“node –version; npm –version” (Powershell),
“node –version && npm –version” (cmd)
Node-RED is installed by the command “npm install -g –unsafe-perm node-red.”
As soon as installed, the cmd and type “node-red” is opened. As an output, a terminal log of Node-RED is displayed on the screen.
Running on a Raspberry Pi or any other IoT module
Buster is the supported version for Node-RED at present for all the Raspberry Pi Operating Systems (OS).
The subsequent command is used to install node.js, npm and node-red on any Raspberry Pi.
“bash<(curl-sLhttps://raw.githubusercontent.com/node-red/linuxinstallers/ master/deb/update-nodejs-and-nodered)”.
(This command is used on any Debian-based OS like Ubuntu, DietPI.)
“bash<(curl-sLhttps://raw.githubusercontent.com/node-red/linux-installers/master/rpm/update-nodejs-and-nodered)”.
(This command is used on any Red-Hat packet management (RPM) based OS like Red Hat, Fedora, CentOS, Oracle Linux.)
Running on different cloud services (IBM cloud, Amazon web services, Microsoft Azure)
Node-RED is used on different cloud-based services with many features depending on the user or the client requirements.
For International Business Marketing (IBM) cloud: It is highly virtualized with high power, storage, networking, security, data management, analytics, developer tools, IoT, and integration and migration of virtual servers.
For Microsoft Azure: It is also having the same abilities like IBM cloud with some extra added features such as replacing as an supplement for many other on-premise virtual servers with best recovery support.
For Amazon web services (AWS): It has various new features when compared with many other cloud-services for developing new innovative smart devices, where it includes all the required API with less cost for the third-party usage.
This section describes about various types of configurations that are needed for the deployment of each different application with default parameters in the default file directory.
Normal application: Entire structure is loaded by default settings file, which is a built-in source.
Embedded application: It is passed into property called RED.init(), where embedded.
Run-time configuration: It defines the time-value of each node during the deployment.
Logging configuration: Only console logging is supported in Node-RED.
Node configuration: It is in the hierarchical format used for the application deployment.
External module configuration: It defines about the run-time handling external npm modules and decides whether the editor allows new node modules to be installed such as the function node to have their own dynamic configured dependencies.
Editor configuration: It is a set of files with different coding styles with various text editor plugins.
adminAuth: It permits security for user in the editor and admin API.
paletteCategories: It describes the sequence of types in the palette. By default, the pattern is subflow, common, function, network, sequence, parser, storage.
The run-time configuration is explained as follows.
Flow file: It is used to store the flows.
Userdir: It is used to store user data, credentials, and library data.
Nodesdir: It is used to search additional installed nodes.
Uihost: It is an interface to listen all connection on IPv4.
Uiport: It is a port to serve ui editor.
Httpadminroot: It is the root url, which contains both API and editor UI.
httpAdminAuth: It allows HTTP validation on the editor UI.
httpAdminMiddleware: It is an array of all functions, which is added to all admin routes.
httpNodeRoot: It is the node root for all the urls that run HTTP at all endpoints.
httpNodeAuth: It enables HTTP Basic Authentication
httpRoot: It enables the root url to run on both admin and node endpoints through overriding httpAdminRoot and httpNodeRoot value.
https: It permits https.
httpStaticAuth: It supports basic confirmation and validation of HTTP with the static content.
httpNodeCors: It is source distribution for the nodes, which are responsible for HTTP endpoints with cross-origin authentication.
httpNodeMiddleware: It permits custom processing. For example, validation is required for the node.
Various stages of logging configuration used in Node-RED are described as follows.
Fatal: Errors that make application unworkable are tracked.
Error: Tracked errors for requests and fatal errors.
Warn: Record of the problems about non-fatal and fatal errors.
Info: Tracked information of application, warnings, error, and fatal errors.
Debugging (Debug): Tracked information, which is more verbose than information, warnings, error, and fatal errors.
Tracing (Trace): Tracks about all complete logging, debugging, info, warnings, error, and fatal errors.
The node configuration is explained as follows.
Function Node: It is for gathering bits and pieces to attach into universal functions.
functionExternalModules: It allows adding additional modules that are available to the function.
Debug Node: Any message directed to the debug sidebar tab with maximum size and characters.
MQTT Nodes: If the link is misplaced, how much time to pause in milliseconds before trying to connect.
Serial Nodes: How much time to pause in milliseconds before making an effort to revive into serial port
socketReconnectTime: How much time to pause in milliseconds before trying to connect again.
socketTimeout: How much time to pause in milliseconds before scheduling out any port.
The important nodes used for the basic functioning of the Node-RED are called “core nodes.” The main six types of core nodes are explained as follows.
Inject: It starts any flow manually by clicking the inject button. It can be at required intervals or any time within the editor.
Debug: It is used to show messages in the Debug sidebar in the editor, and the control on the node can be used to permit or restrict its outcome.
Function: It permits JavaScript code to run beside the messages delivered from it.
Change: It is used to transform message properties and fix context properties without changing a Function node with multiple operations such as set, change, move, delete.
Switch: It permits messages to be routed into different divisions of a flow by means of calculating set of instructions compared with each message with rules values, sequence, expression, otherwise property.
Template: It is used to produce text by message properties to fill the template.
It is the section in which all the necessary placement and arrangement of the nodes are done for the successful execution on the Node-RED.
Flow structure: It helps us organize flows, approaches for splitting into smaller, reusable components, and how to modify them to make use in different platforms.
Message design: It helps how to design messages to create nodes and flows, which can work together with any number of nodes and are easier to maintain.
Documenting flows: It helps about making or providing documentation on what tools and techniques Node-RED provides.
It is described as tracing out the bug or an error, which helps to reduce the developing time and correct the errors easily. The different types of errors on Node-RED are explained as follows.
Logging error: It displays the error with the date and time of the error and the node, which is noted as an error.
Catchable error: It will not be logged, but it informs about run-time error. Then, the Catch node will be used to produce a flow which can handle it.
Sub-flow error: It will not be logged but it informs about run-time error. Then, the Catch node will be used to produce a flow, which can handle it.
Uncatchable errors: If the error is written in the log, then a message is seen in the Debug sidebar and log outcome. But creation of a flow is not possible to handle it.
Uncaught errors: It causes the Node-RED run-time to shut down and cannot be controlled in the flow as they are produced by bugs in nodes.
Status changing errors: It is used to control modifications in node position by including the position property which provides the data about the position with the node that caused the incident.
This node-red-contrib-machine learning module for Node-RED contains a set of nodes offering machine learning functionalities. Such nodes have a python core that takes advantage of common ML libraries such as SciKit-Learn and Tenserflow. Classification and outlier detection can be performed using this package.
These flows create a dataset, train a model, and then evaluate it. Models, after training, can be used in real scenarios to make predictions.
Flows and test datasets are available in the “test” folder. We need to make sure that the paths specified inside nodes’ configurations are correct before trying to execute the program. “node-red” can be run from the folder “.node-red/node-modules/node-red-contrib-machine-learning” and the paths will be automatically correct. The flow shown in Figure 2 loads a csv file, shuffles it, and creates a training and a test partition.
Flow for loading a csv file, shuffling it, and creating a training and a test partition.
The flow shown in Figure 3 loads a training partition and trains a “decision tree classifier” and then saves the model locally.
Flow for loading a training partition and training a “decision tree classifier”.
The flow shown in Figure 4 loads a test partition and evaluates a previously trained model.
Flow for loading a test partition and evaluating a trained model.
Figure 5 shows the flow of how to use a trained model during deployment. Data is received via mqtt, predictions are made and then sent back.
Flow for showing how to use a trained model during deployment.
In this section, various types of experiments related to IoT are provided. ESP32, which is a microcontroller chip manufactured by Espressif Systems, is applied. It consists of a low-cost and a low-power chip with features such as Wi-Fi (IEEE802. 11 b/g/n), Bluetooth, and built-in antenna. The distributed hash table (DHT11) is a basic, ultralow-cost digital temperature and humidity sensor. It uses a capacitive humidity sensor and a thermistor to measure the surrounding air and spits out a digital signal (using an 8-bit microcontroller unit (MCU)) on the data pin. An Arduino integrated development environment (IDE) is used to write and upload programs to the Arduino compatible boards supporting the languages C and C++. Message queuing telemetry transport (MQTT), which is a lightweight messaging protocol, is used on such a small microcontroller that allows messaging between device to cloud and cloud to device with supporting several IoT devices. By using MQTT commands can be sent to control outputs, data can be read and published from sensors. Therefore, communications between multiple devices can be established. We can send a command with a client to control outputs, or we can read data from a sensor and publish it to a client.
The procedure of this task is described as follows.
The ESP32 is connected to the DHT11 sensor as shown in Figure 6 (GND TO GND, 3.3 V TO VCC, GPIO4 TO DATA PIN).
If the connections are correct, then a light present on the DHT11 sensor is turned on.
The DHT11 sensor is programmed into the Arduino integrated development environment (IDE) and uploaded into the ESP32 to show the corresponding temperature and humidity.
If the required packages are absent, then the Arduino IDE shows an error while uploading the code.
After the code is successfully uploaded, we can click on the serial monitor to see the corresponding temperature and humidity readings of the DHT11 sensor.
The Node-RED software is installed into the system. Then, the local hosting address of the Node-RED is open. This can be run on any browsers.
The required nodes, i.e., dashboard and serial port, are installed.
A flow is created as shown in Figure 7.
A serial is inserted into the port node, one function node for temperature and another function node for humidity, which display the DHT11 sensor data.
To get the representation, two-gauge nodes for temperature and humidity are inserted and connected to their respective function nodes.
Finally, the overall workflow is deployed.
ESP32 connection with DHT11 temperature sensor (serial port).
Node-RED serial port connection for temperature and humidity.
The procedure of this task is described as follows.
The ESP32 is connected to the DHT11 sensor as shown in Figure 8 (GND TO GND, 3.3 V TO VCC, GPIO4 TO DATA PIN).
If the connections are correct, then a light present on the DHT11 sensor will be turned on.
The DHT11 sensor is programmed into the Arduino (IDE) and uploaded into the ESP32 to show the temperature and humidity.
If the required packages are absent, then the Arduino IDE shows an error while uploading the code.
After the code is successfully uploaded, click on the serial monitor to see the temperature and humidity readings of the DHT11 sensor.
The Node-RED software is installed into the system. Then, the local hosting address of the Node-RED is open. This can be run on any browsers.
The required nodes, i.e., dashboard, MQTT-in, and MQTT-out nodes, are installed.
A flow is created as shown in Figure 9.
Two MQTT-in nodes, one for temperature and another for humidity, are inserted.
This extracts the sensor data from the ESP32 through the MQTT broker service and gets into the Node-RED platform with the help of the local-host ip address.
To display this data, the one gauge is connected to the temperature node and the other is connected to the humidity node.
Finally, the overall workflow is deployed.
ESP32 and DHT11 temperature sensor (MQTT broker service).
MQTT broker service for temperature and humidity with node-RED.
In this example, a user interface (UI) has a major role for making an interface between the user and the Node-RED dashboard by the means of a system camera and a UI-table. Figure 10 shows the procedure of this task, which is described as follows.
The Node-RED software is installed into the system. The local hosting address of the Node-RED is opened. This can be run on any browser.
The required nodes, i.e., random, dashboard, UI-table, UI-webcam, tfjs-coco-ssd, tf-model, tfjs-node (tf = tensorflow), are installed.
We connect the nodes by inserting button node = 2 (capture and clear), change node = 4 (set msg.capture, set msg.filename, set msg.image, set msg.payload), UI-webcam node = 1, UI-table node = 1, tfjs-coco-ssd = 1, debug node = 2 (msg.payload), file node = 1.
We check whether the camera of the PC/laptop is working or not.
After deploying successfully, we go to the hosting ip address followed by /ui, which opens another new webpage showing the output of the live web camera.
Node-RED flow for live camera capture and object detection using machine learning (ML).
The object detection flow recognizes objects in an image and annotates objects with bounding boxes. An image can be loaded from a built-in camera, the file system, or by injecting the default image. We need to make sure that we have the node-red-contrib-browser-utils package installed for all these input nodes to work. This flow uses three of the custom nodes mentioned above (tf-function, tf-model, and post-object-detection). The loaded image is passed into the preprocessing node as msg.payload. The msg object is a JavaScript object that is used to carry messages between nodes. By convention, it has a payload property containing the output of the previous node. The preprocessing function node is an example of tf-function that directly calls the tf.node.decodeImage method with the predefined tf variable. The node produces a Tensor4D image representation as the payload and then passes it to the COCO SSD lite node, which is an instance of the tf-model custom node. This loads the COCO-SSD lite model.json from an external URL and runs inference on the model.
The result of the model goes through the postprocess node that returns an object array containing bbox, className, and score properties. The objects node combines an additional property, complete that is set to true, to the msg with the image object. Then, the bounding-box node draws bounding boxes on the input image and displays it in the browser.
Once after the successful deployment, we go to the hosting ip address followed by:1880/ui, which opens another new webpage representing the gauge readings of the temperature and the humidity of the DHT11 sensor. The temperature and humidity readings are directly sent from the serial port to the Node-RED directly. Figure 11 shows an example of the dashboard showing temperature and humidity from the DHT11 sensor (serial port).
Dashboard of temperature and humidity from DHT11 sensor (serial port).
Once after the successful deployment, go to the hosting ip address followed by:1880/ui, which opens another new webpage representing the gauge readings of the temperature and the humidity of the DHT11 sensor. Temperature and humidity readings are sent from the sensor to MQTT broker (as a cloud), then finally sent to the Node-RED as a third-party service. Figure 12 shows an example of the dashboard showing temperature and humidity from the DHT11 sensor (MQTT broker service).
Dashboard of temperature and humidity from DHT11 sensor (MQTT broker service).
When the deployment is successful, go to the hosting ip address followed by:1880/ui, which opens another new webpage asking for the permission to allow camera (Press allow). Then, we can see the live camera working and press the capture button to capture. It detects the object with the help of TensorFlow analysis node as earlier describe and the output can be seen on the debug panel with the accuracy score of its correctness. The examples of the single object and the dual object detection are shown in Figures 13 and 14, respectively.
Debug message showing single object class and accuracy score.
Debug message showing dual object class and accuracy score.
From the given examples, they can also be made visible on a mobile phone for both iOS and Android. It is done through remote access just by installing the application named “RemoteRED” from the mobile app store. The steps are explained as follows.
Install the Remote-RED node in the Node-Red.
Open the Remote-RED settings and configure it to get the QR code.
Open the Remote-RED application from the mobile phone and scan the QR code.
Wait for a minute as it is asynchronous to get updated on the mobile phone.
Now we can see the same output of the Node-RED on the mobile phone Remote-RED application as shown in Figure 15.
This process is the same and it works for any Node-RED workflow.
Connecting to remote-RED application (iOS/android).
The results show that simulated systems developed using the Node-RED can be deployed in the real world without changing the system parameters. Moreover, the simulated system can be connected with hardware platforms easily. VPL-based hands-on tools are effectively smoothing the beginner’s learning curve on IoT. The stranded network protocols and IoT protocols are easy to understand using hands-on experience and deploy in the Node-RED environment. Node-RED system models are easy to debug compared with the traditional programming debug methods. The platform works well in both Internet and Intranet mode as all the components are virtually presented in Node-RED. After all, the laboratory instructors can effectively convey the basics of network and IoT concepts to the students through a VPL language including the Node-RED.
The book chapter described a complete outlook of Node-RED that can be applied for an IoT based virtual laboratory. The configuration, the flow development, the requirements, and the usage of the Node-RED were explained with respect to handling all the various types of errors. We modeled, implemented, and tested the IoT virtual laboratory using Node-RED. The implemented virtual laboratory system is currently serving for flexible postgraduate programs and broadcasting completely online. Students obtain great encouragement and motivation toward virtual IoT hands-on practices as they can manage their own and convenient time and a location. The virtual laboratory concept utilizes the available hardware (Wi-Fi IEEE802. 11 b/g/n routers, classical Bluetooth hardware, and system-on-chip (SoC) like MCU) at the student location. The virtual IoT laboratory concept was proved to help students to learn faster than a classical theory class or a video-recorded lesson. With the simplicity of Node-RED and its built-in entities a few examples, which can be used for an IoT-based virtual laboratory, were done to evolve innovative platforms with less coding complexity. It provides flexibility such that the remote laboratory can run on several operating systems or on a mobile application. The proposed solution is platform-independent, and therefore, it can be implemented on low-cost hardware for smaller systems, and the clients can run on mobile devices. The first and second examples are typical scenarios in IoT on Node-RED platform including Arduino and third-party cloud service, whereas the third example is live camera capture with object detection capability. Finally, it was also shown about how Node-RED can be used as a mobile application remotely. After the completion of the book chapter, the readers are supposed to be able to develop IoT systems using VPL-Node-RED, integrate IoT with Node-RED, to design various workflows for IoT on Node-RED, develop real-time IoT applications, and apply security features for IoT and Node-RED while using cloud-based services. Hence, IoT with Node-RED has the capability to change the entire education system, which makes better learning with good interaction and flexibility.
The variety of IoT devices in the future is expected to dramatically grow. Our Node-RED IoT platform was designed to be extendible. To model these future scenarios, we expect to support new IoT device types by using the generic framework for creating new devices. An example of such devices could be wearables that generate movement data. This type of data can be emitted at high volumes but small size. Another device type, which is expected, is an IoT device that is configurable at runtime. This would give us the ability to change the behavior of the device according to an operation plan. An example of this type of device would be a smart home thermostat. Furthermore, we also want to improve the intelligence of our smart testing framework providing machine learning libraries that can continuously learn from past data to improve prediction accuracy. Finally, empirical research can be done collecting a large amount of data in order to know the impact of virtual IoT laboratories on the students with their involvement as it helps the instructors to improve their laboratory sessions further.
The authors declare no conflict of interest.
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Pampanin and Magne O. Sydnes",authors:[{id:"139987",title:"Dr",name:null,middleName:null,surname:"Sydnes",slug:"sydnes",fullName:"Sydnes"},{id:"143899",title:"Dr.",name:"Daniela",middleName:null,surname:"Pampanin",slug:"daniela-pampanin",fullName:"Daniela Pampanin"}]},{id:"40697",doi:"10.5772/51040",title:"Selective Removal of Heavy Metal Ions from Waters and Waste Waters Using Ion Exchange Methods",slug:"selective-removal-of-heavy-metal-ions-from-waters-and-waste-waters-using-ion-exchange-methods",totalDownloads:19319,totalCrossrefCites:36,totalDimensionsCites:94,abstract:null,book:{id:"2549",slug:"ion-exchange-technologies",title:"Ion Exchange Technologies",fullTitle:"Ion Exchange Technologies"},signatures:"Zbigniew Hubicki and Dorota Kołodyńska",authors:[{id:"42116",title:"Dr.",name:"Dorota",middleName:null,surname:"Kołodyńska",slug:"dorota-kolodynska",fullName:"Dorota Kołodyńska"},{id:"141883",title:"Prof.",name:"Zbigniew",middleName:null,surname:"Hubicki",slug:"zbigniew-hubicki",fullName:"Zbigniew Hubicki"}]},{id:"33450",doi:"10.5772/37583",title:"Measurement of the Nanoscale Roughness by Atomic Force Microscopy: Basic Principles and Applications",slug:"measurement-of-the-nanoscale-roughness-by-atomic-force-microscopy-basic-principles-and-applications",totalDownloads:21263,totalCrossrefCites:20,totalDimensionsCites:89,abstract:null,book:{id:"2282",slug:"atomic-force-microscopy-imaging-measuring-and-manipulating-surfaces-at-the-atomic-scale",title:"Atomic Force Microscopy",fullTitle:"Atomic Force Microscopy - Imaging, Measuring and Manipulating Surfaces at the Atomic Scale"},signatures:"R.R.L. De Oliveira, D.A.C. Albuquerque, T.G.S. Cruz, F.M. Yamaji and F.L. Leite",authors:[{id:"1164",title:"Dr.",name:"Fabio",middleName:"Lima",surname:"Leite",slug:"fabio-leite",fullName:"Fabio Leite"},{id:"136651",title:"MSc.",name:"Ricardo",middleName:null,surname:"De Oliveira",slug:"ricardo-de-oliveira",fullName:"Ricardo De Oliveira"},{id:"136652",title:"M.Sc.",name:"Diego",middleName:"Aparecido Carvalho",surname:"Albuquerque",slug:"diego-albuquerque",fullName:"Diego Albuquerque"},{id:"136653",title:"Prof.",name:"Tersio",middleName:null,surname:"Cruz",slug:"tersio-cruz",fullName:"Tersio Cruz"},{id:"136657",title:"Prof.",name:"Fabio",middleName:null,surname:"Yamaji",slug:"fabio-yamaji",fullName:"Fabio Yamaji"}]},{id:"63161",doi:"10.5772/intechopen.80495",title:"Modelling of Adsorption Kinetic Processes—Errors, Theory and Application",slug:"modelling-of-adsorption-kinetic-processes-errors-theory-and-application",totalDownloads:3942,totalCrossrefCites:27,totalDimensionsCites:85,abstract:"Adsorption has become a competitive method in the field of wastewater and air treatment. Adsorption kinetics is one of the main factors that must be understood before the applicability of any adsorbent. In every adsorption process, linear or non-linear analysis of the kinetics is applied. The goodness of fit index (coefficient of correlation or sum of squares) is applied to access the best model. The usage of linear or non-linear from of the adsorption kinetics has an impact on the distribution of error function. Almost in every adsorption study, linear forms have been used to conclude the best kinetic model that influence the adsorption mechanism—which might be an error. Therefore, this review highlights the mistakes in the usage of linear and non-linear models. The applicability of the adsorption kinetics in wastewater treatment is also illuminated.",book:{id:"7486",slug:"advanced-sorption-process-applications",title:"Advanced Sorption Process Applications",fullTitle:"Advanced Sorption Process Applications"},signatures:"George William Kajjumba, Serkan Emik, Atakan Öngen, H. Kurtulus Özcan\nand Serdar Aydın",authors:[{id:"26340",title:"Dr.",name:"Kurtulus",middleName:null,surname:"Ozcan",slug:"kurtulus-ozcan",fullName:"Kurtulus Ozcan"},{id:"250972",title:"Associate Prof.",name:"Serdar",middleName:null,surname:"Aydin",slug:"serdar-aydin",fullName:"Serdar Aydin"},{id:"251149",title:"Dr.",name:"Serkan",middleName:null,surname:"Emik",slug:"serkan-emik",fullName:"Serkan Emik"},{id:"251150",title:"Dr.",name:"Atakan",middleName:null,surname:"Öngen",slug:"atakan-ongen",fullName:"Atakan Öngen"},{id:"251152",title:"Mr.",name:"George William",middleName:null,surname:"Kajjumba",slug:"george-william-kajjumba",fullName:"George William Kajjumba"}]},{id:"62303",doi:"10.5772/intechopen.79374",title:"Modified Titanium Dioxide for Photocatalytic Applications",slug:"modified-titanium-dioxide-for-photocatalytic-applications",totalDownloads:3989,totalCrossrefCites:34,totalDimensionsCites:75,abstract:"Titanium dioxide (TiO2) has been widely used as a photocatalyst in many environmental and energy applications due to its efficient photoactivity, high stability, low cost, and safety to the environment and humans. However, its large band gap energy, ca. 3.2 eV limits its absorption of solar radiation to the UV light range which accounts for only about 5% of the solar spectrum. Furthermore, the photocatalytic activity of TiO2 is also limited by the rapid recombination of the photogenerated electron-hole pairs. When used in water treatment applications, TiO2 has a poor affinity toward organic pollutants, especially hydrophobic organic pollutants. Several strategies have been employed to reduce its band gap energy, its electron-hole recombination rates as well as enhance its absorption of organic pollutants. In this chapter, we review some of the most recent works that have employed the doping, decoration, and structural modification of TiO2 particles for applications in photocatalysis. Additionally, we discuss the effectiveness of these dopants and/or modifiers in enhancing TiO2 photoactivity as well as some perspective on the future of TiO2 photocatalysis.",book:{id:"7478",slug:"photocatalysts-applications-and-attributes",title:"Photocatalysts",fullTitle:"Photocatalysts - Applications and Attributes"},signatures:"John Moma and Jeffrey Baloyi",authors:[{id:"250026",title:"Dr.",name:"John",middleName:null,surname:"Moma",slug:"john-moma",fullName:"John Moma"},{id:"250963",title:"Mr.",name:"Jeffrey",middleName:null,surname:"Baloyi",slug:"jeffrey-baloyi",fullName:"Jeffrey Baloyi"}]}],mostDownloadedChaptersLast30Days:[{id:"68496",title:"Moisture Sorption Isotherms and Isotherm Model Performance Evaluation for Food and Agricultural Products",slug:"moisture-sorption-isotherms-and-isotherm-model-performance-evaluation-for-food-and-agricultural-prod",totalDownloads:1960,totalCrossrefCites:6,totalDimensionsCites:13,abstract:"Moisture sorption characteristics of agricultural and food products play important roles in such technological processes as drying, handling, packaging, storage, mixing, freeze-drying and other processes that require the prediction of food stability, shelf life, glass transition and estimation of drying time and texture and prevention of deteriorative reactions. They are useful in the computation of thermodynamic energies of moisture in the products. An understanding of moisture sorption phenomena in products, moisture sorption isotherm (MSI) determination techniques and moisture sorption isotherm model evaluation procedures would be useful in the development or selection, modeling and controlling as well as optimization of appropriate processes to make for enhanced efficiency. The phenomena addressed in this chapter are equilibrium moisture content (EMC)-water activity (aw) relationships and MSI types, temperature influence on isotherms and occurrence of moisture sorption hysteresis. MSI measurement techniques highlighted are the gravimetric, vapor pressure manometric (VPM), hygrometric and inverse gas chromatographic and the use of AquaLab equipment. Commonly used moisture sorption isotherm models (BET, GAB, modified GAB, Hailwood-Horrobin, modified Hailwood-Horrobin, modified Halsey, modified Henderson, modified Chung-Pfost and modified Oswin) were selected, and their evaluation procedures using moisture sorption data were outlined. Static gravimetric technique involving the use of saturated salt solution appears to be the most widely used and recommended method of determining the EMC of agricultural and food products. Most of the MSI models can be fitted to moisture sorption data thorough linearization by logarithmic transformation, while others can be solved using such expression as second-order polynomial. Model goodness of fit can be determined using standard (SE) error of estimate, coefficient of determination (R2), mean relative percentage deviation (P) and fraction explained variation (FEV). The acceptance of a model depends on the nature of its residual plots. A model is considered acceptable if the residual plots show uniform scatter around the horizontal value of zero showing no systemic tendency towards a clear pattern. A model is better than another model if it has lower SE, lower P, higher R2 and higher FEV. Although it appears as if a generalized MSI model is yet to exist, it is recommended that the Ngoddy-Bakker-Arkema (NBA) model should be given thorough going and extensive testing on the MSI of different categories of food as it could prove true to its generalized model posture due to the fundamental nature of its derivation.",book:{id:"8012",slug:"sorption-in-2020s",title:"Sorption in 2020s",fullTitle:"Sorption in 2020s"},signatures:"Ndubisi A. Aviara",authors:[{id:"303694",title:"Prof.",name:"Ndubisi",middleName:null,surname:"Aviara",slug:"ndubisi-aviara",fullName:"Ndubisi Aviara"}]},{id:"63788",title:"Disinfection Methods",slug:"disinfection-methods",totalDownloads:3200,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"Water must be made safe to drink, and an important step in ensuring water safety is disinfection. Disinfectants are added to water to kill disease-causing microorganisms. Ground water sources can be disinfected by “The Water Treatment Rule,” which requires public water systems for disinfection. Chlorination, ozone, ultraviolet light, and chloramines are primary methods for disinfection. However, potassium permanganate, photocatalytic disinfection, nanofiltration, and chlorine dioxide can also be used. Organic material is naturally present in water. Certain forms of chlorine can react with these organic materials and result in the formation of harmful by-products; the U.S. Environmental Protection Agency has anticipated maximum levels for these contaminants.",book:{id:"7478",slug:"photocatalysts-applications-and-attributes",title:"Photocatalysts",fullTitle:"Photocatalysts - Applications and Attributes"},signatures:"Muhammad Saqib Ishaq, Zobia Afsheen, Amjad Khan and Amjad\nKhan",authors:[{id:"228353",title:"Dr.",name:"Muhammad Saqib",middleName:null,surname:"Ishaq",slug:"muhammad-saqib-ishaq",fullName:"Muhammad Saqib Ishaq"},{id:"246559",title:"Dr.",name:"Zobia",middleName:null,surname:"Afsheen",slug:"zobia-afsheen",fullName:"Zobia Afsheen"},{id:"246561",title:"Mr.",name:"Amjad",middleName:null,surname:"Khan",slug:"amjad-khan",fullName:"Amjad Khan"},{id:"271289",title:"Dr.",name:"Amjad",middleName:null,surname:"Khan",slug:"amjad-khan",fullName:"Amjad Khan"}]},{id:"41887",title:"Microbial Techniques for Hydrocarbon Exploration",slug:"microbial-techniques-for-hydrocarbon-exploration",totalDownloads:6504,totalCrossrefCites:3,totalDimensionsCites:6,abstract:null,book:{id:"2351",slug:"hydrocarbon",title:"Hydrocarbon",fullTitle:"Hydrocarbon"},signatures:"M.A. Rasheed, D.J. Patil and A.M. Dayal",authors:[{id:"143475",title:"Dr",name:"Mohammed Abdul",middleName:null,surname:"Rasheed",slug:"mohammed-abdul-rasheed",fullName:"Mohammed Abdul Rasheed"},{id:"144630",title:"Dr.",name:"Dayal",middleName:null,surname:"Anurodh",slug:"dayal-anurodh",fullName:"Dayal Anurodh"}]},{id:"58999",title:"The DFT+U: Approaches, Accuracy, and Applications",slug:"the-dft-u-approaches-accuracy-and-applications",totalDownloads:4446,totalCrossrefCites:20,totalDimensionsCites:41,abstract:"This chapter introduces the Hubbard model and its applicability as a corrective tool for accurate modeling of the electronic properties of various classes of systems. The attainment of a correct description of electronic structure is critical for predicting further electronic-related properties, including intermolecular interactions and formation energies. The chapter begins with an introduction to the formulation of density functional theory (DFT) functionals, while addressing the origin of bandgap problem with correlated materials. Then, the corrective approaches proposed to solve the DFT bandgap problem are reviewed, while comparing them in terms of accuracy and computational cost. The Hubbard model will then offer a simple approach to correctly describe the behavior of highly correlated materials, known as the Mott insulators. Based on Hubbard model, DFT+U scheme is built, which is computationally convenient for accurate calculations of electronic structures. Later in this chapter, the computational and semiempirical methods of optimizing the value of the Coulomb interaction potential (U) are discussed, while evaluating the conditions under which it can be most predictive. The chapter focuses on highlighting the use of U to correct the description of the physical properties, by reviewing the results of case studies presented in literature for various classes of materials.",book:{id:"6193",slug:"density-functional-calculations-recent-progresses-of-theory-and-application",title:"Density Functional Calculations",fullTitle:"Density Functional Calculations - Recent Progresses of Theory and Application"},signatures:"Sarah A. Tolba, Kareem M. Gameel, Basant A. Ali, Hossam A.\nAlmossalami and Nageh K. Allam",authors:[{id:"175824",title:"Dr.",name:"Nageh",middleName:"K.",surname:"Allam",slug:"nageh-allam",fullName:"Nageh Allam"},{id:"398157",title:"Dr.",name:"Sarah A.",middleName:null,surname:"Tolba",slug:"sarah-a.-tolba",fullName:"Sarah A. Tolba"},{id:"398158",title:"Dr.",name:"Kareem M.",middleName:null,surname:"Gameel",slug:"kareem-m.-gameel",fullName:"Kareem M. Gameel"},{id:"398162",title:"Dr.",name:"Basant A.",middleName:null,surname:"Ali",slug:"basant-a.-ali",fullName:"Basant A. Ali"},{id:"398163",title:"Dr.",name:"Hossam A.",middleName:null,surname:"Almossalami",slug:"hossam-a.-almossalami",fullName:"Hossam A. Almossalami"}]},{id:"40233",title:"Ammonia as a Hydrogen Source for Fuel Cells: A Review",slug:"ammonia-as-a-hydrogen-source-for-fuel-cells-a-review",totalDownloads:9263,totalCrossrefCites:15,totalDimensionsCites:39,abstract:null,book:{id:"2795",slug:"hydrogen-energy-challenges-and-perspectives",title:"Hydrogen Energy",fullTitle:"Hydrogen Energy - Challenges and Perspectives"},signatures:"Denver Cheddie",authors:[{id:"141157",title:"Dr.",name:"Denver",middleName:null,surname:"Cheddie",slug:"denver-cheddie",fullName:"Denver Cheddie"}]}],onlineFirstChaptersFilter:{topicId:"86",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81502",title:"Investigation of Synthesis Methods for Improved Platinum-Ruthenium Nanoparticles Supported on Multi-Walled Carbon Nanotube Electrocatalysts for Direct Methanol Fuel Cells",slug:"investigation-of-synthesis-methods-for-improved-platinum-ruthenium-nanoparticles-supported-on-multi-",totalDownloads:14,totalDimensionsCites:0,doi:"10.5772/intechopen.104541",abstract:"This book chapter reports on various catalyst synthesis methods (impregnation, polyol, modified polyol, and microwave-assisted modified polyol methods) to determine which method would result in the most electrochemically active platinum-ruthenium (PtRu) electrocatalyst supported on multi-walled carbon nanotubes (MWCNTs) for methanol oxidation reaction in an acidic medium. Different techniques were used to characterize the synthesized catalysts, including the high-resolution transmission electron microscope used for morphology and calculating particle sizes, and X-ray diffraction for determining crystalline sizes. The electroactive catalyst surface area, ECSA of the electrocatalysts was determined using cyclic voltammetry (CV), while the electroactivity, electron kinetics, and stability of the electrocatalysts towards methanol oxidation were evaluated using CV, electrochemical impedance spectroscopy, and chronoamperometry, respectively. The microwave-assisted modified polyol method produced the PtRu/MWCNT electrocatalyst with the most enhanced electrocatalytic activity compared to other PtRu/MWCNT catalysts produced by the impregnation, polyol, and modified polyol methods.",book:{id:"10381",title:"Electrocatalysis and Electrocatalysts for a Cleaner Environment - Fundamentals and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/10381.jpg"},signatures:"Adebare Nurudeen Adewunmi, Sabejeje Akindeji Jerome, Su Huaneng and Lindiwe Eudora Khotseng"},{id:"79547",title:"Nickel Foam Electrode with Low Catalyst Loading and High Performance for Alkaline Direct Alcohol Fuel Cells",slug:"nickel-foam-electrode-with-low-catalyst-loading-and-high-performance-for-alkaline-direct-alcohol-fue",totalDownloads:149,totalDimensionsCites:0,doi:"10.5772/intechopen.100287",abstract:"Nickel foam has a unique three-dimensional (3-D) network structure that helps to effectively utilize catalysts and is often used as an electrode support material for alkaline direct alcohol fuel cells. In this chapter, first, the effect of nickel foam thickness on cell performance is explored. The results show that the thickness affects both mass transfer and electron conduction, and there is an optimal thickness. The thinner the nickel foam is, the better the conductivity is. However, the corresponding three-dimensional space becomes narrower, which results in a partial agglomeration of the catalyst and the hindrance of mass transfer. The cell performance of 0.6 mm nickel foam electrode is better than that of 0.3 and 1.0 mm. Secondly, to fully exert the catalytic function of the catalyst even at a lower loading, a mixed acid-etched nickel foam electrode with lower Pd loading (0.35 mg cm−2) is prepared then by a spontaneous deposition method. The maximum power density of the single alkaline direct ethanol fuel cell (ADEFC) can reach 30 mW cm−2, which is twice the performance of the hydrochloric acid treated nickel foam electrode. The performance improvement is attributed to the micro-holes produced by mixed acids etching, which enhances the roughness of the skeleton and improves the catalyst electrochemical active surface area.",book:{id:"10381",title:"Electrocatalysis and Electrocatalysts for a Cleaner Environment - Fundamentals and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/10381.jpg"},signatures:"Qian Xu, Jiajia Zhang and Chunzhen Yang"},{id:"77862",title:"Characterization, Photoelectric Properties, Electrochemical Performances and Photocatalytic Activity of the Fe2O3/TiO2 Heteronanostructure",slug:"characterization-photoelectric-properties-electrochemical-performances-and-photocatalytic-activity-o",totalDownloads:109,totalDimensionsCites:0,doi:"10.5772/intechopen.98759",abstract:"The Fe2O3/TiO2 nanocomposite was synthesized on FTO subtract via hydrothermal method. The crystal structure, morphology, band structure of the heterojunction, behaviors of charge carriers and the redox ability were characterized by XRD, HR-TEM, absorption spectra, PL, cyclic voltammetry and transient photocurrent spectra. The as-prepared Fe2O3/TiO2 photocatalysts with distinctive structure and great stability was characterized and investigated for the degradation of methylene blue (MB) dye in aqueous solution. The ability of the photocatalyst for generating reactive oxygen species, including O2− and.OH was investigated. It was revealed that the combination of the two oxides (Fe2O3 and TiO2) nano-heterojunction could enhance the visible response and separate photogenerated charge carriers effectively. Therefore, the remarkable photocatalytic activity of Fe2O3/TiO2 nanostructures for MB degradation was ascribed to the enhanced visible light absorption and efficient interfacial transfer of photogenerated electrons from to Fe2O3 to TiO2 due to the lower energy gap level of Fe2O3/TiO2 hybrid heterojunctions as evidenced by the UV–Vis and photoluminescence studies. The decrease of the energy gap level of Fe2O3/TiO2 resulted in the inhibition of electron–hole pair recombination for effective spatial charge separation, thus enhancing the photocatalytic reactions. Based on the obtained results, a possible mechanism for the improved photocatalytic performance associated with Fe2O3/TiO2 was proposed. The Fe2O3/TiO2 nanocomposite has a specific capacity of 82 F.g−1 and shows a higher capacitance than Fe2O3.",book:{id:"10381",title:"Electrocatalysis and Electrocatalysts for a Cleaner Environment - Fundamentals and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/10381.jpg"},signatures:"Salah Kouass, Hassouna Dhaouadi, Abdelhak Othmani and Fathi Touati"},{id:"76150",title:"Heterogeneous Electrocatalysts for CO2 Reduction to Value Added Products",slug:"heterogeneous-electrocatalysts-for-co-sub-2-sub-reduction-to-value-added-products",totalDownloads:222,totalDimensionsCites:1,doi:"10.5772/intechopen.97274",abstract:"The CO2 that comes from the use of fossil fuels accounts for about 65% of the global greenhouse gas emission, and it plays a critical role in global climate changes. Among the different strategies that have been considered to address the storage and reutilization of CO2, the transformation of CO2 into chemicals and fuels with a high added-value has been considered a winning approach. This transformation is able to reduce the carbon emission and induce a “fuel switching” that exploits renewable energy sources. The aim of this chapter is to categorize different heterogeneous electrocatalysts which are being used for CO2 reduction, based on the desired products of the above mentioned reactions: from formic acid and carbon monoxide to methanol and ethanol and other possible by products. Moreover, a brief description of the kinetic and mechanism of the CO2 reduction reaction) and pathways toward different products have been discussed.",book:{id:"10381",title:"Electrocatalysis and Electrocatalysts for a Cleaner Environment - Fundamentals and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/10381.jpg"},signatures:"M. Amin Farkhondehfal and Juqin Zeng"},{id:"74671",title:"C-H Activation/Functionalization via Metalla-Electrocatalysis",slug:"c-h-activation-functionalization-via-metalla-electrocatalysis",totalDownloads:223,totalDimensionsCites:0,doi:"10.5772/intechopen.95517",abstract:"In conventional methods, C−H activations are largely involved in the use of stoichiometric amounts of toxic and expensive metal & chemical oxidants, conceding the overall sustainable nature. Meanwhile, undesired byproducts are generated, that is problematic in the scale up process. However, electrochemical C−H activation via catalyst control strategy using metals as mediators (instead electrochemical substrate control strategy) has been identified as a more efficient strategy toward selective functionalizations. Thus, indirect electrolysis makes the potential range more pleasant, and less side reactions can occur. Herein, we summarize the metalla-electrocatalysis process for activations of inert C−H bonds and functionalization. These Metalla-electrocatalyzed C−H bond functionalizations are presented in term of C−C and C−X (X = O, N, P and halogens) bonds formation. The electrooxidative C−H transformations in the presence of metal catalysts are described by better chemoselectivities with broad tolerance of sensitive functionalities. Moreover, in the future to enhance sustainability and green chemistry concerns, integration of metalla-electrocatalysis with flow and photochemistry will enable safe and efficient scale-up and may even improve reaction times, kinetics and yields.",book:{id:"10381",title:"Electrocatalysis and Electrocatalysts for a Cleaner Environment - Fundamentals and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/10381.jpg"},signatures:"Guilherme M. Martins, Najoua Sbei, Geórgia C. Zimmer and Nisar Ahmed"},{id:"74780",title:"Recent Trends in Development of Metal Nitride Nanocatalysts for Water Electrolysis Application",slug:"recent-trends-in-development-of-metal-nitride-nanocatalysts-for-water-electrolysis-application",totalDownloads:252,totalDimensionsCites:1,doi:"10.5772/intechopen.95748",abstract:"Nanocatalysts for sustainable water electrolysis is strongly desirable to promote the commercialization of H2 as the alternate clean energy source for the future. The goal is cheaper hydrogen production from sea and low grade water by minimizing the energy consumption and using low cost cell components & non-noble metal catalysts. The conductivity of metal nitrides and their ability to carry out Hydrogen Evolution Reaction and Oxygen Evolution Reaction at relatively low overpotential render these one of the frontline candidates to be potentially utilized as the catalyst for low cost H2 production via electrolysis. In this chapter, the potential of metal nitride catalyst towards fulfilling the above objective is discussed. The synthesis of various metal nitride catalysts, their efficiency towards electrode half reactions and the effectiveness of these class of nanocatalyst for electrolysis of sea water is elaborated. A review of recent literature with special reference to the catalyst systems based on non-noble metals will be provided to assess the likelihood of these nanocatalyst to serve as a commercial grade electrode material for sea water electrolysis.",book:{id:"10381",title:"Electrocatalysis and Electrocatalysts for a Cleaner Environment - Fundamentals and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/10381.jpg"},signatures:"Akhoury Sudhir Kumar Sinha and Umaprasana Ojha"}],onlineFirstChaptersTotal:8},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:320,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:133,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:16,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. 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