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Viscous/inviscid interaction methods employing velocity decomposition have been explored for reducing computational costs in external flow problems. For this, viscous simulations are made in reduced computational domains encompassing the rotational flow region. The coupling of potential and viscous solutions is made on a boundary of negligible vorticity, assessed by means of sampling lines normal to the body wall and the wake. For bluff bodies, sampling lines at the rear tend to be submerged in rotational regions, making it difficult to build the coupling boundary. For overcoming this difficulty, it is proposed in this work to use a transpiration auxiliary surface to represent the influence of the body and the wake. The null vorticity criterion for finding the coupling boundary is also proposed. The proposals are tested for laminar flows around a square, a circular cylinder and a semi-ellipse. The obtained results are compared with those for extended domains. The velocity decomposition with an auxiliary surface is able to reproduce with satisfactory accuracy the flow fields in the reduced domain as well as the drag coefficient on the bodies.

Massive Open Online Massive Open Online Courses (MOOCs) have been transitioning slowly from being completely open and without clear recognition in universities or industry, to private settings through the emergence of Small and Massive Private Online Courses (SPOCs and MPOCs). Courses in these new formats are often for credit and have clear market value through the acquisition of competencies and skills. However, the endemic issue of academic dishonesty remains lingering and generating untrustworthiness regarding what students did to complete these courses. In this case study, we focus on SPOCs with academic recognition developed at the University of Cauca in Colombia and hosted in their Open edX instance called Selene Unicauca. We have developed a learning analytics algorithm to detect dishonest students based on submission time and exam responses providing as output a number of indicators that can be easily used to identify students. Our results in two SPOCs suggest that 17% of the students that interacted enough with the courses have performed academic dishonest actions, and that 100% of the students that were dishonest passed the courses, compared to 62% for the rest of students. Contrary to what other studies have found, in this study, dishonest students were similarly or even more active with the courseware than the rest, and we hypothesize that these might be working groups taking the course seriously and solving exams together to achieve a higher grade. With MOOC-based degrees and SPOCs for credit becoming the norm in distance learning, we believe that if this issue is not tackled properly, it might endanger the future of the reliability and value of online learning credentials.

A new methodology for the aerothermodynamic design of radial-inflow turbines used in organic Rankine cycles is presented. Initially, a fully detailed one-dimensional analysis was performed, where the real gas properties of R123 are calculated using the REFPROP© software. The developed model was validated against the most recent studies. Next, a 3D CFD analysis of the nozzle and the rotor was carried out using ANSYS-CFX® software. The 3D CFD method considered the influence of the rotor shroud curvature, which strongly influences the performance characteristics. The variations of this parameter allowed us to obtain uniform velocity contours, better blade loading and avoided the flow separation observed in the first attempt geometry. A comparison between the 1D mean-line and 3D CFD approach showed good agreement, validating the reliability of the two approaches. Also, an analysis of the 3D flowfield for different pressure ratios was performed to verify the quality of the method. The developed methodology has proven to be an appropriate tool for the radial-inflow ORC turbines design, which integrates uni- and three-dimensional models, for the prediction and improvement of the performance characteristics. The integration of these two approaches results in a novel procedure, since some phenomena associated with the three-dimensional characteristics of flow cannot be analyzed in detail in the one-dimensional analysis and are only possible to detect by a complete 3D CFD study.

Recommendation systems have overcome the overload of irrelevant information by considering users&rsquo, preferences and emotional states in the fields of tourism, health, e-commerce, and entertainment. This article reviews the principal recommendation approach documents found in scientific databases (Elsevier&rsquo, s Scopus and Clarivate Web of Science) through a scientometric analysis in ScientoPy. Research publications related to the recommenders of emotion-based tourism cover the last two decades. The review highlights the collection, processing, and feature extraction of data from sensors and wearables to detect emotions. The study proposes the thematic categories of recommendation systems, emotion recognition, wearable technology, and machine learning. This paper also presents the evolution, trend analysis, theoretical background, and algorithmic approaches used to implement recommenders. Finally, the discussion section provides guidelines for designing emotion-sensitive tourist recommenders.

Lung cancer is one of the dangerous diseases that cause huge cancer death worldwide. Early detection of lung cancer is the only possible way to improve a patient’s chance for survival. A Computed Tomography (CT) scan used to find the position of tumor and identify the level of cancer in the body. The current study presents an innovative automated diagnosis classification method for Computed Tomography (CT) images of lungs. In this paper, the CT scan of lung images was analyzed with the assistance of Optimal Deep Neural Network (ODNN) and Linear Discriminate Analysis (LDA). The deep features extracted from a CT lung images and then dimensionality of feature is reduced using LDR to classify lung nodules as either malignant or benign. The ODNN is applied to CT images and then, optimized using Modified Gravitational Search Algorithm (MGSA) for identify the lung cancer classification The comparative results show that the proposed classifier gives the sensitivity of 96.2%, specificity of 94.2% and accuracy of 94.56%.

The primary function of a coronary care unit is to monitor the rhythm of cardiac patients. The cardiac abnormality of the patients should be identified during the early stage. The heart sound recoding is the phonocardiogram (PCG) signal. Stethoscopes are commonly used to hear the heart sound for diagnosis. In common, the heart murmurs can be caused by a high blood pressure, heart attack, pregnancy, rheumatic fever, anemia or thyrotoxicosis. This study analyzes PCG signals spectrally with different windows with signal-to-noise ratio (SNR) computations. The two PCG signals of normal and abnormal subjects in each were analyzed in this study. The normal subject produce clear “lub” and “dub” sounds where the abnormal subject produce a kind of whistling or swishing sound in middle which may be due to the problem of septal defect in a heart. The spectral leakage reduction by different windows helps to increase the SNR value. Simulation results of SNR with different windows are obtained using Matlab R2016b tool.

Blood vessel generation is an essential process for tissue formation, regeneration, and repair. Notwithstanding, vascularized tissue fabrication in vitro remains a challenge, as current fabrication techniques and biomaterials lack translational potential in medicine. Naturally derived biomaterials harbor the risk of immunogenicity and pathogen transmission, while synthetic materials need functionalization or blending to improve their biocompatibility. In addition, the traditional top-down fabrication techniques do not recreate the native tissue microarchitecture. Self-assembling ultrashort peptides (SUPs) are promising chemically synthesized natural materials that self-assemble into three-dimensional nanofibrous hydrogels resembling the extracellular matrix (ECM). Here, we use a modular tissue-engineering approach, embedding SUP microgels loaded with human umbilical vein endothelial cells (HUVECs) into a 3D SUP hydrogel containing human dermal fibroblast neonatal (HDFn) cells to trigger angiogenesis. The SUPs IVFK and IVZK were used to fabricate microgels that gel in a water-in-oil emulsion using a microfluidic droplet generator chip. The fabricated SUP microgels are round structures that are 300-350 μm diameter in size and have ECM-like topography. In addition, they are stable enough to keep their original size and shape under cell culture conditions and long-term storage. When the SUP microgels were used as microcarriers for growing HUVECs and HDFn cells on the microgel surface, cell attachment, stretching, and proliferation could be demonstrated. Finally, we performed an angiogenesis assay in both SUP hydrogels using all SUP combinations between micro- and bulky hydrogels. Endothelial cells were able to migrate from the microgel to the surrounding area, showing angiogenesis features such as sprouting, branching, coalescence, and lumen formation. Although both SUP hydrogels support vascular network formation, IVFK outperformed IVZK in terms of vessel network extension and branching. Overall, these results demonstrated that cell-laden SUP microgels have great potential to be used as a microcarrier cell delivery system, encouraging us to study the angiogenesis process and to develop vascularized tissue-engineering therapies.

Sensor devices that act in the IoT architecture perception layer are characterized by low data processing and storage capacity. These reduced capabilities make the system ubiquitous and lightweight, but considerably reduce its security. The IoT-based Food Traceability Systems (FTS), aimed at ensuring food safety and quality, serve as a motivating scenario for BIoTS development and deployment, therefore, security challenges and gaps related with data integrity are analyzed from this perspective. This paper proposes the BIoTS hardware design that contains some modules built-in VHDL (SHA-256, PoW, and SD-Memory) and other peripheral electronic devices to provide capabilities to the perception layer by implementing the blockchain architecture’s security requirements in an IoT device. The proposed hardware is implemented on FPGA Altera DE0-Nano. BIoTS can participate as a miner in the blockchain network through Smart Contracts and solve security issues related to data integrity and data traceability in an Blockchain-IoT system. Blockchain algorithms implemented in IoT hardware opens a path to IoT devices’ security and ensures participation in data validation inside a food certification process.

In medium-sized cities in developing countries, transit services without dedicated lanes have issues related to route compliance, schedules, speed control, and safety. An efficient way for dealing with this issue is the use of Information and Communication Technologies (ICT), to implement a Fleet Management and Control Systems (FMCS). Such implementation can be performed using Intelligent Transportation Systems (ITSs), which allow integration of services and adequate standardization. This article features: (a) a literature review, related to FMCS based on ITS and enabling technologies, (b) design of the ITS architecture of an FMCS, and (c) some advances in the development of the proposed FMCS in a Colombian city (Popayán). The results of the literature review allowed identifying the most important requirements of FMCS in order to design the ITS architecture and build a prototype featuring the suggested technologies. Finally, some experiments were performed to evaluate the operation of the developed prototype. The results showed evidence of adequate operation in sending and receiving messages from and to four prototypes developed for the vehicles, also complying with the established requirements of location, tracking, exchanged data, and security. This allows continuing the development of the proposed FMCS, with some adjustments.

Breast cancer is the most commonly occurring cancer in women worldwide. While mammography remains the gold standard in breast cancer screening, ultrasound is an important imaging modality for both screening and cancer diagnosis. This paper presents a novel method for the detection of breast lesions in ultrasound images using texton filter banks, local configuration pattern features, and classification, without employing any segmentation technique. The developed method was able to accurately detect and classify breast lesions and achieved an accuracy, sensitivity, specificity, and positive predictive value of 96.1%, 96.5%, 95.3%, and 97.9%, respectively. The paradigm that we describe may, therefore, be useful as an effective tool to detect breast nodules during screening and in whole breast imaging, enabling clinicians to focus on images where a lesion is already known to be present. The developed method may also serve as a component for automatic breast nodule detection, and, when found, for the subsequent classification between lesion type benign versus malignant.

Making an accurate diagnosis of nasopharyngeal carcinoma (NPC) disease is a challenging task that involves many parties such as radiology specialists often times need to delineate NPC boundaries on various tumor-bearing endoscopic images. It is a tedious and time-consuming operation exceedingly based on doctors and experience of radiologist. NPC has complex and irregular structures which makes it difficult to diagnose even by an expert physician. However, the diagnosis accuracy results of such methods are still insignificant and need improvement in order to manifest robust solution. The study aim is to develop and propose a new automatic classification of NPC tumor using machine learning techniques and feature-based decision-level fusion scheme from endoscopic images. We have implemented the fusion of the three image texture-based schemes (local binary patterns, the first-order statistics histogram properties, and histogram of gray scale) at the decision level and tested the performance of this scheme using the same experimental setup in the previous section for simple score-level fusion, but for comparison, We used the classifiers methods which are support vector machines (SVM), k-nearest neighbors’ algorithm, and artificial neural network (ANN). The results demonstrate that the majority rule for decision-based fusion is outperformed considerably by the single best performing feature scheme (FFGF) for the SVM classifier, but for the ANN and KNN classifier it is significantly outperformed by each of the components features. The classifiers approaches were listed a high accuracy of 94.07%, the sensitivity of 92.05%, and specificity of 93.07%.

Brain tumor diagnosis is a challenging and difficult process in view of the assortment of conceivable shapes, regions, and image intensities. The pathological detection and identification of brain tumor and comparison among normal and abnormal tissues need grouped scientific techniques for features extraction, displaying, and measurement of the disease images. Our study shows an improved automated brain tumor segmentation and identification approach using ANN from MR images without human mediation by applying the best attributes toward preparatory brain tumor case revelation. To obtain the exact district region of brain tumor from MR images, we propose a brain tumor segmentation technique that has three noteworthy improvement focuses. To begin with, K-means clustering will be utilized as a part of the principal organization in the process of improving the MR image to be marked in the districts regions in light of their gray scale. Second, ANN is utilized to choose the correct object in view of training phase. Third, texture feature of brain tumor area will be extracted to the division stage. With respect to the brain tumor identification, the grayscale features are utilized to analyze and diagnose the brain tumor to differentiate the benign and malignant cases. According to the study results demonstrated that: (1) enhancement adaptive strategy was utilized as post-processing in brain tumor identification; (2) identify and build an assessment foundation of automated segmentation and identification for brain tumor cases; (3) highlight the methods based on region growing method and K-means clustering technique to select the best region; and (4) evaluate the proficiency of the foreseen outcomes by comparing ANN and SVM segmentation outcomes, and brain tumor cases classification. The ANN approach classifier recorded accuracy of 94.07% with line assumption (brain tumor cases classification) and sensitivity of 90.09% and specificity of 96.78%.

Application of artificial intelligence in Bio-Medical image processing is gaining more and more importance in the field of Medical Science. The bio medical images, has to go through several steps before the diagnosis of the disease. Firstly, the images has to be acquired and preprocessing has to be done and the data has to be stored in memory. It requires huge amount of memory and processing time. Among the preprocessing steps, edge detection is one of the major step. Edge detection filters the unwanted details in the image, and preserves the edges of the image, which describe the boundary of the image. In biomedical application, for the detection of the diseases, it is very essential to have the boundary detail of the acquired image of the organ under observation. Thus it is very essential to extract the edges of the images. Power is one of the main parameters that have to be considered while dealing with biomedical instruments. The biomedical signal processing instruments should be capable of operating at low power and also at high speed. In order to segregate the images into different levels or stage, we use convolutional neural networks for classification. By having a hardware architecture for image edge detection, the computational time for pre-processing of the image can be reduced, and the hardware can be a part of acquisition device itself. In this paper a low-power architecture for edge detection to detect the biomedical images are presented. The edge detection output are given to the system, which will diagnose the diseases using image classification using convolutional neural network. In this paper, Sobel and Prewitt, algorithms are used for edge detection using 180 nm technology. The edge detection algorithms are implemented using VLSI, and digital IC design of the architecture is presented. The algorithms for edge detection is co-simulated using MATLAB and Modelsim. The architecture is first simulated using CMOS logic and new method using domino logic is presented for low power consumption.

Alcoholism is an adverse situation that changes the functioning of important part of nervous system which is neuron. This changes the functional behavior of alcoholic person. The diagnosis of this state is done with the help of EEG signals which gets modified with the electrical activity of the brain. The EEG data sets used in this paper are taken from the University of California at Irvine, Irvine, knowledge discovery and databases. A review on how the EEG signals get affected by the consumption of alcohol and the extraction of features from these signals help to differentiate alcoholic and uninfluenced people with the help of graphical user interface (GUI) is presented in this paper. GUI is an interface that showcases the features extracted from the raw EEG data and classifies the two different classes. This is achieved with the help of sample entropy, approximate entropy, mean, and standard deviation of raw EEG data collected from the electrodes frontal polar, frontal, and central. This GUI system is economical and efficient which is used as a proper clinical decision support system by clinicians and also helps rehabilitation centres in getting to know about the subject. Quadratic SVM gives a highest accuracy of 95% for the detection of alcoholic EEG signal.

The online telemedicine systems are helpful since they provide timely and effective healthcare services. Such online healthcare systems are usually based on sophisticated and advanced wearable and wireless sensor technologies. A rapid technological growth has improved the scope of many remote health monitoring systems. Here, the researchers employed a cloud-based remote monitoring system for observing the health status of the patients after monitoring their heart rate variability. This system was developed after considering many factors like the ease of application, costs, accuracy, and the data security. Furthermore, this system was also conceptualized to act as an interface between the patients and the healthcare providers, thus ensuring a two-way communication between them. The major aim of this paper was to provide the best healthcare monitoring services to the people living in the remote areas, which was otherwise very difficult owing to the small doctor-to-patient ratio. The researchers also analyzed their monitoring system using two different databases. First comes from MIT Physionet database i.e., the MIT-BIH sinus rhythm and the MIT-St. Petersburg. While the second database was collected after monitoring 30 people who were asked to use these wearable sensors. After analyzing the performance of the proposed scheme, the obtained results for accuracy, sensitivity, and specificity were 99.02%, 98.78%, and 99.17%, respectively. The achieved results concluded that the proposed system was quite reliable, robust, and valuable. Also, the data analysis revealed that this system was very convenient and ensured data security. In addition, this developed monitoring system generated warning messages, directed towards the patients and the doctors, during some critical situation.

This paper presents the design of the Internet of Things (IoT)-based greenhouse traceability model for the tracking and recordkeeping of seedlings and other agricultural products in the germination and growth stages. Traced products are generally of high quality and high commercial value, making the voluntary adoption of traceability processes for the market in processed products and trade in fresh products more common today. The transmission of diseases to humans, along with the cases of chemical poisoning, provided the motive for changes in trade relations between the countries and in the manner of assessing consumer safety. The model allows the tracking of variables, such as luminosity, humidity, temperature, and water consumption, thereby revealing overall water use, growth patterns of the plants, and the timeline for harvest of produce. The system enables automated control of the indoor environment of the greenhouse using an irrigation system or temperature control and presents the main outline of internal traceability of agricultural products from seed to final produce. By means of an IoT platform, this greenhouse design finally facilitates a novel analysis of the behavior of a number of species that comprise the local agriculture in one region of Colombia.

Brain signal processing is important for not only the physiologist doing analysis investigation, but also for the clinician inspecting patients, biomedical engineer who is responsible for collecting, processing, and interpreting the electroencephalogram signals by modeling systems and algorithms for their manipulations. The abundant materials on the subject of brain signal/image processing are scattered in different scientific, technological and physiological journals, international conference proceedings, and also in various databases. Therefore, it is altogether a difficult, too time-consuming, and much tiresome work, exclusively to the newcomers in this field. Therefore, this paper focuses on providing the list of popular databases available belonging to the neurological signals, brain signal/image collections, and so on. The count and the kinds of attacks across the networked computer systems have hiked the significance of computer network security. At present, network administrators use to inspect, examine, scrutinize, review, and analyze the network traffic to figure out what is going on and to set up a prompt response in the event of an identified attack. This paper analyzes the different sweep techniques such as Ping sweep, TCP sweep, and Null sweep on the popular databases about the brain signal/image collections. The results of the Ping sweep support status, TCP sweep times, and Null scan times on different servers are discussed finally.

The computation of fast Fourier transform (FFT) plays a vital role in all fields of science and engineering, especially in the medical domain. Spectral results of the data convey more information to the clinicians to take handy decisions during the diagnosis. If a problem happens during the computation of FFT, the incorrect results misinterpret the information and hence the suggestions and decisions by practitioners would be the useless one moreover, it may yield harmful result to the patients. This paper deals with how to recover the spectrum from the faulty one with the heart sound case reports. The heart sound recording is the phonocardiogram (PCG) signal. Stethoscopes are commonly used to hear the heart sound for diagnosis. The two PCG signals of normal and abnormal subjects in each were analyzed in this paper. The normal subject produce clear lub and dub sounds, where the abnormal subject produce a kind of whistling or swishing sound in middle which may be due to the problem of septal defect in a heart. This paper analyzes four PCG signals spectrally with fault and the recovered ones along with the execution time results in each using Matlab ${\mathrm {R2016b}}$ tool.

In this paper, we address the problem of confidentiality of keyframes, which are extracted from diagnostic hysteroscopy data using video summarization. We propose an image color coding method aimed at increasing the security of keyframes extracted from diagnostic hysteroscopy videos. In this regard, we use a 2-D logistic map to generate the cryptographic keys sequences, which relies on mixing and cascading the orbits of the chaotic map in order to generate the stream keys for the encryption algorithm. The encrypted images produced by our proposed algorithm exhibit randomness behavior, providing a high-level of security for the keyframes against various attacks. The experimental results and security analysis from different perspectives verify the superior security and high efficiency of our proposed encryption scheme compared to other state-of-the-art image encryption algorithms. Furthermore, the proposed method can be combined with mobile-cloud environments and can be generalized to ensure the security of cloud contents as well as important data during transmission.

The focal and non-focal epilepsy is seen to be a chronic neurological brain disorder, which has affected $\approx ~60$ million people in the world. Hence, an early detection of the focal epileptic seizures can be carried out using the EEG signals, which act as a helpful tool for early diagnosis of epilepsy. Several EEG-based approaches have been proposed and developed to understand the underlying characteristics of the epileptic seizures. Despite the fact that the early results were positive, the proposed techniques cannot generate reproducible results and lack a statistical validation, which has led to doubts regarding the presence of the pre-ictal state. Various methodical and algorithmic studies have indicated that the transition to an ictal state is not a random process, and the build-up can lead to epileptic seizures. This study reviews many recently-proposed algorithms for detecting the focal epileptic seizures. Generally, the techniques developed for detecting the epileptic seizures were based on tensors, entropy, empirical mode decomposition, wavelet transform and dynamic analysis. The existing algorithms were compared and the need for implementing a practical and reliable new algorithm is highlighted. The research regarding the epileptic seizure detection research is more focused on the development of precise and non-invasive techniques for rapid and reliable diagnosis. Finally, the researchers noted that all the methods that were developed for epileptic seizure detection lacks standardization, which hinders the homogeneous comparison of the detector performance.

Due to the significant advancement of the Internet of Things (IoT) in the healthcare sector, the security, and the integrity of the medical data became big challenges for healthcare services applications. This paper proposes a hybrid security model for securing the diagnostic text data in medical images. The proposed model is developed through integrating either 2-D discrete wavelet transform 1 level (2D-DWT-1L) or 2-D discrete wavelet transform 2 level (2D-DWT-2L) steganography technique with a proposed hybrid encryption scheme. The proposed hybrid encryption schema is built using a combination of Advanced Encryption Standard, and Rivest, Shamir, and Adleman algorithms. The proposed model starts by encrypting the secret data; then it hides the result in a cover image using 2D-DWT-1L or 2D-DWT-2L. Both color and gray-scale images are used as cover images to conceal different text sizes. The performance of the proposed system was evaluated based on six statistical parameters; the peak signal-to-noise ratio (PSNR), mean square error (MSE), bit error rate (BER), structural similarity (SSIM), structural content (SC), and correlation. The PSNR values were relatively varied from 50.59 to 57.44 in case of color images and from 50.52 to 56.09 with the gray scale images. The MSE values varied from 0.12 to 0.57 for the color images and from 0.14 to 0.57 for the gray scale images. The BER values were zero for both images, while SSIM, SC, and correlation values were ones for both images. Compared with the state-of-the-art methods, the proposed model proved its ability to hide the confidential patient's data into a transmitted cover image with high imperceptibility, capacity, and minimal deterioration in the received stego-image.

Neurological signal processing is of significance not only the physiologist doing research and the clinician investigating patients but also to the biomedical engineer who is needed to collect, process, and interpret the physiological signals by prototyping systems and algorithms for their manipulations. While it is a fact that there does hold immense stuff (material) on the subject of digital neurological signal processing, however, it is dispersed in various scientific, technological, and physiological journals, databases also in various international conference proceedings. Consequently, it is a quite hard, more time-consuming, and often tiresome job, especially to the stranger to the domain. Hence, this study concentrates on how much time would require to access the databases belong to the brain signal/image collections, neurological signals, etc. The sixteen US-based Servers, ten UK-based Servers, and the five Servers from other countries are included in this study. Mainly, the domain name system, hyper text transfer protocol, and the Internet control message protocol query/response times are analyzed using a popular packet sniffer called Wireshark.

We perform experiments on an active granular material composed of individually-driven, spinning disks confined within a circular arena. Small bumps at the outer edges of the disks provide a variable amount of interparticle coupling in the form of geometric friction. The disks each spin counter-clockwise, but undergo a transition in their collective circulation around the center of the arena, from a clockwise orbit to a counter-clockwise orbit, as a function of packing fraction φ. We identify that, unlike for vibrated granular gases, the particles' velocity distributions are Gaussian over a large range of φ. By fitting the speed distribution to a Maxwell-Boltzmann distribution, we identify a temperature-like parameter which is a universal function of φ; this parameter is also equal to the mean translational energy of the particles. We quantify the collective circulation via its solid-body-like rotation rate, and find that this is a universal function centered around a critical packing fraction. In addition, the ratio of orbital kinetic energy to spin kinetic energy is also a universal function for non-zero geometric friction. These findings highlight the important role of both the type of driving and the interparticle interactions (here, geometric friction) in controlling the collective behavior of active granular systems.

The present study describes the development of a preliminary design of a rotor for a radial turbine operating in an organic Rankine cycle. An optimization algorithm is applied to the preliminary design in order to obtain a better configuration of the geometric parameters that provides good quantification of the efficiency in the turbine, a priori, since the application of optimization processes applied to three-dimensional problems consume a lot of computational resources. The strategy makes it possible to obtain an optimized geometry to obtain flow field analyzes by applying computational fluid dynamics techniques. The working fluid R236fa was used for comparison with the literature, as it presents a positive slope of the saturation curve, and thus it is possible to work with lower temperatures. The R245fa working fluid is more suitable to the operating conditions of the proposed cycle, allows an overpressure in the condenser and allows higher levels of system efficiency. The losses at the rotor nozzle were initially modeled using a mean line design approach. The preliminary design was implemented in a commercial code Matlab®, as well as the optimization algorithm, CRSA (Controlled Random Search Algorithm), and the real gas formulations were used based on the NIST REFPROP® database. The present study is presented under three work routes: i) Development of the preliminary design methodology for a radial turbine that operates with ORC producing 50 kW of power, in order to compare with other methodologies presented in the literature. The results were compared with results observed in the literature, and demonstrate agreement between the reference geometry and the thermodynamic parameters. The total-total efficiencies of the reference turbine designs were 76.23% (R236fa) and 79.28% (R245fa); ii) Optimization by CRSA of the preliminary design of a radial turbine developed on the basis of flow coefficient and load coefficient correlations. A three-dimensional analysis of the flow through the blade section using computational fluid dynamics was performed in the final optimized design to confirm the preliminary design and subsequently analyze its characteristics. The optimization focused on the R245fa working fluid. Although several optimized preliminary designs are available in the literature with efficiency levels of up to 90%, the preliminary design choices made will only be valid for machines operating with ideal gases, that is, exhaust gases typical of an air-breathing combustion engine. For machines operating with real gases, such as organic working fluids, the design options need to be rethought and a preliminary design optimization process must be introduced. As an important result observed, an efficiency of 82.4% was obtained in the final design of the radial turbine operating with R245fa after the optimization process.

Wireless sensor networks (WSNs) combine sensing, computation, and communication for automated data collection, processing, and telemetry. WSNs use spatially distributed devices for applications in which scalable connectivity and energy efficiency are increasingly important. Thus, WSNs are a key technology for the Internet of Things (IoT), whether consumer and industrial, connecting billions of devices over the Internet for applications including health care, transportation, process analysis, and environmental assessment, to name a few. However, wireless connectivity comes at the expense of power.

Hydrokinetic energy generation is a renewable source that captures kinetic energy from river or tidal currents. Little physical construction is required, and environmental impacts are reduced. The identification of potential projects and definition of turbine arrangement to consider the wake dissipation generated by the flow on the rotor are two extremely important points for successful implantation of hydrokinetic farms. The present study presents a methodology for evaluating the length of the hydrokinetic rotor wakes in natural channels through the use of computational fluid dynamics techniques and the actuator disk model. These techniques were applied in this study on a portion of the Brazilian Amazon river with real measured data on velocity, curvature and geometry in the field. The wake length obtained was between 7 and 9 diameters, varying according to the operation point of the turbine. A study of the hydrokinetic arrangement for the three turbines was also carried out and an electrical power of 18.3 kW was obtained. The outcome and results of the study provided insight on hydrokinetic turbine wakes in rivers which may aid in the development of future studies, thus resulting in a greater diffusion of hydrokinetic power plants.

This work presents an optimization study for designing a shrouded turbine diffuser. The goal of the study was to obtain the geometry of the hydrofoil shaped diffuser which maximizes the hydraulic power of a turbine with a given set of constraints. The optimization process has been based on an automatic cycle of geometry modification, meshing and CFD analysis of the flow field, including a loaded diffuser (actuator disk). Bezier curves were implemented to describe the diffuser section, which allowed exploring non pre-existent hydrodynamic shapes for the shroud, and the optimization process has been driven by the Simulated Annealing algorithm. The optimal diffuser was also simulated with various conditions of disk loading and considering different tip clearances between the disk and the shroud. The results have showed that the optimum diffuser achieves a power coefficient of approximately 118%. Also, this methodology provides the data to the rotor design process (flow rate and pressure drop), and can be used as a first step to define the preliminary characteristics of the whole equipment based on an optimized pre-design. Considerations about the optimal actuator disk loading for a shrouded turbine considering viscous effects and turbulence, tip clearance effects and rotor design guidelines are also discussed.

A control method that rejects disturbances affecting a declared critical dynamics in a MIMO-PID decentralized system is presented. Through coordinated set points displacement, some noncritical control loops are detuned. To avoid instability caused by the detuning, the selection of allowable set points is made through a collaborative optimization algorithm that provides robustness to control. The benefits and challenges of acting on set points to stabilize systems are discussed. Collaborative control concepts and definitions proposed by this scheme are explained. An analysis of the effects achieved on a single loop due detuning in the other loops is made. The proposed structure of collaborative control and results acting on a MIMO-PID process controlling the critical concentration on a CSTR type reactor are detailed.

This paper presents a methodology for the aerodynamic design of reversible axial-flow rotors of jet fans, usually used for ventilation of road tunnels. Due to the low hub-to-tip ratio, typical of this kind of rotors, a particular type of non-free vortex is adopted to solve the radial equilibrium equation, in order to reduce the twist in the blades when compared to the free-vortex case. Values of the deviation and incidence angles, lift and profile drag coefficients, were interpolated from the literature data, for elliptical profiles with maximum aspect ratio of 8%. In order to reliably use these data in the computational routine, written in FORTRAN, for obtaining the main geometric and operational characteristics, a technique of scattered data interpolation was utilized. Radial basis functions (RBF) of type thin plate spline (TPS) were thus used for this purpose. This function showed to be efficient for the iterative procedure in the aerodynamic design of the rotor blades. Two reversible axial-flow rotors, formed by elliptical profiles, were finally obtained with the proposed methodology. The first profile resulted from the free-vortex condition and the second one from the non-free vortex condition. Both rotors have the same dimensions and the same number of blades, but differ in the stagger angle of each profile forming the blades. Through the use of the commercial code FLUENT, the main aerodynamic performance characteristics of these two rotors were determined, for a specific rotation, within a wide range of operation conditions. The non-free vortex rotor showed a higher maximum efficiency when compared to the free-vortex one, which was located in the region of stability of the rotor. On the contrary, the free-vortex rotor maximum efficiency was located in the region of instability of the rotor.

La aparición de nuevas alternativas de interacción puede llevar a generar videojuegos cada vez más interesantes; este es el caso de la interacción con el teléfono móvil, especialmente la tecnología NFC. Sin embargo, no existe un referente para conocer qué tipos de videojuegos serían los más apropiados para desarrollar con NFC. Por tanto, este artículo aporta una revisión de varias fuentes para determinar el estado actual de videojuegos con NFC para futuras referencias de generadores de contenido y emprendedores en el campo de los videojuegos; como contribuciones se tiene una revisión en varias fuentes de nivel comercial, bibliográfico y de tiendas de aplicaciones para generar el estado actual y el esquema de posibles videojuegos con los cuales NFC podría interactuar, determinando las categorías de videojuegos más apropiadas para desarrollar con dicha tecnología y proponiendo algunas características que NFC permite implementar. The emergence of new interaction alternatives can result in increasingly interesting videogames; this is the case of the interaction with the mobile phone, especially NFC technology. However, there is not a model to get to know the types of videogames that would be more appropriate to be developed with NFC. Therefore, this article provides a review of several sources to determine current state of videogames with NFC for future reference of content generators and entrepreneurs in the field of videogames; as a contribution, there is a review of several commercial, bibliographic sources and applications shops to generate current state and a scheme of potential videogames with which NFC could interact, by determining categories of the most appropriate videogames to be developed with such a technology and proposing some features that can be implemented with the NFC.

We describe the design and metamodel-based optimization of an ORC radial-inflow turbine for low-grade thermal energy applications using R245fa as a working fluid. Real gas properties were used in 1D and 3D CFD models, which agreed closely in predictions of total-to-total efficiency and net power. However, 3D analysis of the 1D design revealed non-uniform rotor blade loading. The rotor was optimized using Modefrontier® to maximize efficiency at the design point. The optimization method integrates CFD simulations, response surfaces constructed from Gaussian radial basis function and the NSGA II genetic algorithm. The wrap angle (θ) and the displacement in the X- and Y-directions of the intermediate control point of the shroud Bezier curve were chosen as the design variables. The performance characteristics and the 3D flow field at design and off-design operating conditions showed that the optimization resulted in better blade loading and higher efficiencies over the entire operating range studied. The new RBF-based metamodel method thus optimizes the performance of the radial turbine while taking into account the detailed 3D flow field at low computational cost.

Recommender systems have been based on context and content, and now the technological challenge of making personalized recommendations based on the user emotional state arises through physiological signals that are obtained from devices or sensors. This paper applies the deep learning approach using a deep convolutional neural network on a dataset of physiological signals (electrocardiogram and galvanic skin response), in this case, the AMIGOS dataset. The detection of emotions is done by correlating these physiological signals with the data of arousal and valence of this dataset, to classify the affective state of a person. In addition, an application for emotion recognition based on classic machine learning algorithms is proposed to extract the features of physiological signals in the domain of time, frequency, and non-linear. This application uses a convolutional neural network for the automatic feature extraction of the physiological signals, and through fully connected network layers, the emotion prediction is made. The experimental results on the AMIGOS dataset show that the method proposed in this paper achieves a better precision of the classification of the emotional states, in comparison with the originally obtained by the authors of this dataset. This research project is financed by theGovernment of Colombia, Colciencias and the Governorateof Boyacá

The privacy of patients is jeopardised when medical records and data are spread or shared beyond the protected cloud of institutions. This is because breaches force them to the brink that they start abstaining from full disclosure of their condition. This type of condition has a negative effect on scientific research, patients and all stakeholders. A blockchain-based data sharing system is proposed to tackle this issue, which employs immutability and autonomy properties of the blockchain to sufficiently resolve challenges associated with access control and handle sensitive data. Our proposed system is supported by a Discrete Wavelet Transform to enhance the overall security, and a Genetic Algorithm technique to optimise the queuing optimization technique as well. Introducing this cryptographic key generator enhances the immunity and system access control, which allows verifying users securely in a fast way. This design allows further accountability since all users involved are already known and the blockchain records a log of their actions. Only when the users’ cryptographic keys and identities are confirmed, the system allows requesting data from the shared queuing requests. The achieved execution time per node, confirmation time per node and robust index for block number of 0.19 s, 0.17 s and 20 respectively that based on system evaluation illustrates that our system is robust, efficient, immune and scalable.

This paper presents a cascade optimization methodology for representing axial turbomachinery profiles of series NACA-65. In geometrical parameterization scheme of the cascade the following are considered as design variables: the stagger angle, cascade pitch, and camber line. For the calculation of the flow, the panel method of Hess and Smith with modifications is used to introduce the viscous effects by calculating the boundary layer. This methodology used a viscous/inviscid interaction and has as advantage the low computational cost and being favorable for the optimization process. The maximization of the relationship between the lift and the drag coefficient was defined as objective function and was added a penalty function, given by the minimum deflection angle of the flow in the cascade. For the optimal solution, a controlled random search algorithm is used. On the other hand, is also constructed a response surface by interpolation of radial basis functions to obtain comparative analysis of the solutions. Results of pressure distributions and aerodynamic coefficients are also obtained for the optimal cascades.

Massive Open Online Courses (MOOC) have been considered an “educational revolution”. Although these courses were designed to reach a massive number of participants, Higher Education institutions have started to use MOOCs technologies and methodologies as a support for educative traditional practices in what has been called Small Private Online Courses (SPOCs) and Massive Private Online Courses (MPOCs) according to the proportion of students enrolled and the teachers who support them. A slightly explored area of scientific literature is the possible correlations between performance and learning styles in academic value courses designed to be offered in massively environments. This article presents the results obtained in the MPOC “Daily Astronomy” at University of Cauca in terms of the possible associations between learning styles according to Kolb and the results in the evaluations and the activity demonstrated in the services of the platform that hosted the course.

Internet of Things (IoT) is connecting billions of devices to the Internet. These IoT devices chain sensing, computation, and communication techniques, which facilitates remote data collection and analysis. wireless sensor networks (WSN) connect sensing devices together on a local network, thereby eliminating wires, which generate a large number of samples, creating a big data challenge. This IoT paradigm has gained traction in recent years, yielding extensive research from an increasing variety of perspectives, including scientific reviews. These reviews cover surveys related to IoT vision, enabling technologies, applications, key features, co-word and cluster analysis, and future directions. Nevertheless, we lack an IoT scientometrics review that uses scientific databases to perform a quantitative analysis. This paper develops a scientometric review about IoT over a data set of 19,035 documents published over a period of 15 years (2002–2016) in two main scientific databases (Clarivate Web of Science and Scopus). A Python script called ScientoPy was developed to perform quantitative analysis of this data set. This provides insight into research trends by investigating a lead author’s country affiliation, most published authors, top research applications, communication protocols, software processing, hardware, operating systems, and trending topics. Furthermore, we evaluate the top trending IoT topics and the popular hardware and software platforms that are used to research these trends.

Low-power technologies and communication protocols have enabled pervasive monitoring and the Internet of Things (IoT) for diverse applications. Yet, many wide-reaching use-cases such as shipping and industrial IoT (IIoT) demand next-generation energy-efficiency for wireless sensor networks (WSNs). The enormous growth requirements in this and related fields are not easily met by current state-of-the-art prototypes and comparisons are nontrivial among dozens of differentiating characteristics and impact factors. This work ideates a lightweight densely-scalable network of sensor nodes that must survive on a coin-cell battery for 15 days minimum, sending minute-to-minute alerts for remote analytics and control. This end-to-end solution enables status tracking throughout the shipping lifecycle with a quick-deploy WSN to detect damage/theft in near-real-time. This work contributes a scalable WSN (1) communication protocol, (2) software development kit, and (3) reference hardware. This full-stack system offers a self-configuring, customizable WSN for low-duty-cycle pervasive monitoring in a small, low-cost form-factor.

Field Programmable Gate Array (FPGA) is a general purpose programmable logic device that can be configured by a customer after manufacturing to perform from a simple logic gate operations to complex systems on chip or even artificial intelligence systems. Scientific publications related to FPGA started in 1992 and, up to now, we found more than 70,000 documents in the two leading scientific databases (Scopus and Clarivative Web of Science). These publications show the vast range of applications based on FPGAs, from the new mechanism that enables the magnetic suspension system for the kilogram redefinition, to the Mars rovers&rsquo, navigation systems. This paper reviews the top FPGAs&rsquo, applications by a scientometric analysis in ScientoPy, covering publications related to FPGAs from 1992 to 2018. Here we found the top 150 applications that we divided into the following categories: digital control, communication interfaces, networking, computer security, cryptography techniques, machine learning, digital signal processing, image and video processing, big data, computer algorithms and other applications. Also, we present an evolution and trend analysis of the related applications.

Background: Machine learning researches algorithms that allow a machine to learn about resolving problems in different application domains. Due to the wide number of machine learning applications, it is necessary for newcomers to the field to have alternatives to explore this field faster. Methods: In this paper, we present a science mapping analysis on the machine learning research in the period 2007-2017. This study was develop using the CiteSpace tool based on results from Clarivate Web of Science. This analysis shows how the field has evolved, by highlighting the most notable authors, institutions, keywords, countries, categories, and journals. Results: The results provide information on trends and possibilities in the near future, particularly in areas such as health, biology and banking, where machine learning is a valuable tool to generate solutions. Conclusions: Machine learning is being widely studied, and several institutions in countries like the USA and China constantly generate machine learning based solutions. Diseases, such as cancer or Alzheimer’s disease, studies in biology, such as the protein molecule, virtual reality, commerce, smartphones, and ubiquitous computing, are all fields where machine learning contributes to resolving problems.

Silk is known as the queen of textiles due to its softness, durability, and luster. This textile is obtained from cocoons spun by larvae known as the silkworm. The combined effect of both temperature and humidity, determines the satisfactory growth of the silkworms and the production of good quality cocoons. For that rea- son, we propose a new prototype for silkworm incubators that monitors environmental conditions, created with Raspberry Pi due to its capabilities, features, and low cost. The prototype monitors the temperature, humidity, and luminosity in a silkworm incubator. The monitoring data are collected and saved on file hosting service, Google Drive, for subsequent analysis. Preliminary tests were gathered using the silkworm incubator of University of Cauca, Colombia.

There is a lack of datasets in Colombia that characterize the nutritional components and other similar information about food items. This study describes a structured process to develop datasets that captures the preferences and purchases of food items by a selected group of people. The datasets would classify products according to their sodium and sugar content. The outcome of this structured process would include three datasets, each with a different focus: the first contains data on food preferences, the second contains the purchase history according to the invoices obtained, and the third contains characteristics of the food items such as its brand, category, sodium and sugar content levels, among others.

Many different types of branching have been developed, such as bifurcation, trifurcation, and manifolds, among others. These configurations are used in penstocks to transport water from surge tanks to power houses in order to feed several turbines at the same time. This arrangement allows for smaller assembly costs in comparison with independent penstock systems. Nevertheless, such installations can generate higher head losses in the system in comparison with single systems. This study focuses on the quantification of these head losses as a function of volumetric flow rate using Computational Fluid Dynamics (CFD) and later validated with previously published results. To determine the coefficient of head losses three mesh settings were analyzed: hexahedral, tetrahedral and hybrid, for both a steady state and transitory flow. Based on the literature, the k-omega) turbulence model was used, with refinement to elements near the wall to check y+. To the simulation transitory, the SAS model was used for analysis of the instability in the trifurcation. (C) 2018 Elsevier Ltd. All rights reserved.