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Background: The respiratory tract harbors a variety of microbiota, whose composition and abundance depend on specific site factors, interaction with external factors, and disease. The aim of this study was to investigate the relationship between COVID-19 severity and the nasopharyngeal microbiome. Methods: We conducted a prospective cohort study in Mexico City, collecting nasopharyngeal swabs from 30 COVID-19 patients and 14 healthy volunteers. Microbiome profiling was performed using 16S rRNA gene analysis. Taxonomic assignment, classification, diversity analysis, core microbiome analysis, and statistical analysis were conducted using R packages. Results: The microbiome data analysis revealed taxonomic shifts within the nasopharyngeal microbiome in severe COVID-19. Particularly, we observed a significant reduction in the relative abundance of Lawsonella and Cutibacterium genera in critically ill COVID-19 patients (p

In Colombia, coffee futures contracts represent essential financial agreements that allow producers and buyers to establish prices, quality, and conditions for future transactions in the coffee market. Despite the evident benefits of stability and predictability, this practice faces significant sustainability challenges that threaten its long-term viability. One of the reasons is the significant lack of transparency in the supply chain. Farmers, affected by abrupt price fluctuations and adverse weather conditions such as the El Niño phenomenon, experience an increase in market prices, leading to the non-delivery of the final product, and contract breaches as they find better prices in the local market. In this context, smart contracts emerge as a promising technological solution to address these problems. These contracts enable the verification of each step in the process, from harvest to final sale, within a blockchain. Therefore, this research designs a smart contract managed through a platform called SmartBeanFutures, which records the clauses of futures contracts using the IERC721 framework, allowing the generation of a unique and non-repeatable asset. It aims to sell, promote, and manage coffee sale prices during the agreement’s signing, creating a transparent environment for chain actors. This proposal undergoes evaluation in a test environment, providing farmers access to the designed platform. Following the validation of the proposal, it was identified that over 74% would use this type of contract in their agricultural processes, highlighting that implementing this technology contributes to eliminating intermediaries in the chain and gives farmers more control over their participation in the market.

The Indoor Environmental Quality (IEQ) combines thermal, visual, acoustic, and air-quality conditions in indoor environments and affects occupants’ health, well-being, and comfort. Performing continuous monitoring to assess IEQ is increasingly proving to be important, also due to the large amount of time that people spend in closed spaces. In the present study, the design, development, and metrological characterization of a low-cost multi-sensor device is presented. The device is part of a wider system, hereafter referred to as PROMET&O (PROactive Monitoring for indoor EnvironmenTal quality & cOmfort), that also includes a questionnaire for the collection of occupants’ feedback on comfort perception and a dashboard to show end users all monitored data. The PROMET&O multi-sensor monitors the quality conditions of indoor environments thanks to a set of low-cost sensors that measure air temperature, relative humidity, illuminance, sound pressure level, carbon monoxide, carbon dioxide, nitrogen dioxide, particulate matter, volatile organic compounds, and formaldehyde. The device architecture is described, and the design criteria related to measurement requirements are highlighted. Particular attention is paid to the calibration of the device to ensure the metrological traceability of the measurements. Calibration procedures, based on the comparison to reference standards and following commonly employed or ad hoc developed technical procedures, were defined and applied to the bare sensors of air temperature and relative humidity, carbon dioxide, illuminance, sound pressure level, particulate matter, and formaldehyde. The next calibration phase in the laboratory will be aimed at analyzing the mutual influences of the assembled multi-sensor hardware components and refining the calibration functions.

Data centers in higher education institutions, as well as those of large corporations, face challenges in terms of traffic flow management. In some cases, due to the limited hardware resources used for this purpose, and in others, despite having enough high-performance equipment, the centers lag behind when the traffic flow grows exponentially due to the memory limitations of the devices, which slows down the network performance. The contribution of this investigation work is the implementation of a classifying elephant and mice system using machine learning techniques for the early detection with the first flow based on the dynamic calculation of the threshold, according to the input parameters of the final system. In the first instance, training algorithms are used to determine the best performance, then the proposed algorithm determines the model with the best prediction, obtained from the supervised learning algorithm trained in off line mode. Finally in the phase of online prediction, the algorithm is capable of predicting with high precision the type of traffic in terms of the input flow, and updates in a dynamic way the threshold to determine whether the traffic is elephant or mice. With this information the network hardware can decide then to route the flows according to their characterization. According to the results, the model that best generates predictions is the decision tree with a 100% confidence level.

Abstract Introduction The global prevalence of diabetes in 2019 in adults was estimated to be 9.3%. This study developed in Ecuador, for the first time, instruments to assess patient‐reported outcomes and experiences. Methods The Experiences of the Person with Diabetes (EPD) Questionnaire is a diabetes‐specific instrument. A mixed‐methods study was conducted. First, a qualitative item development phase that included four focus groups and six semi‐structured interviews with patients was conducted in different rural and urban areas of Ecuador to obtain information on culture, beliefs, demographics, diet and social perspectives. A second quantitative phase for psychometric validation was carried out in primary care settings of rural and urban areas of Ecuador. Results Forty‐two and four hundred and eighty‐nine participants were included in each phase, respectively. The item development phase resulted in a questionnaire of 44 items (23 for perceived outcomes and 21 for experiences). In the validation study, most participants were women (58%) and from urban areas (57%). Exploratory factor analysis revealed three dimensions for each instrument. Outcomes instrument dimensions were symptoms and burnout, worries and fears and social limitations. Experiences instrument dimensions were information, patient‐centred care and care delivery. Cronbach's α values of the total score and dimensions were high, ranging between .81 and .93 in both instruments. Confirmatory factor analysis showed an acceptable fit of the data. Conclusion The EPD Questionnaire is probably the first instrument developed to assess patient‐reported experiences and perceived outcomes in a middle‐income country that included patients to capture all dimensions relevant for the intended population. Its psychometric properties are robust and could provide valuable information for clinicians and policymakers in the region. Patient or Public Contribution The development of these instruments has taken into consideration patients and the public since their conception. A qualitative approach gathered relevant information related to the cultural, social and economic burden of different populations in Ecuador. Before validation, a pilot test was carried out with users of the National Health Services to obtain their perspectives and insights of the developed instrument. Finally, during the data analysis, we have given special consideration to social variables such as rural and urban populations.

Summary: The derivation of cardiomyocytes from human pluripotent stem cells (hPSCs) is a powerful tool to investigate early cardiogenesis and model diseases in vitro. Here, we present an optimized protocol to obtain contracting hPSCs-derived cardiomyocytes using a ready-to-use kit. We describe steps for hPSC culture and differentiation to cardiomyocytes including the identification of key parameters such as starting cell confluency and temperature. We then detail immunofluorescence, flow cytometry, and the quantification of cardiomyocytes' calcium spikes using live imaging.For complete details on the use and execution of this protocol, please refer to Astro et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Recently there has been an increase in the use of Blockchain technology for multiple purposes; one of them has been food traceability. This technology has increased quality control, safety, and reliability. So, the producers are looking for better ways to trace the products at any supply chain stage to ensure their quality. A smart contract is a transaction protocol that execute automatically when a predefined set of conditions are met. In this paper, we propose a smart contract to monitor the status of the coffee beans in the transport and storage stages with data validation. Using the Hyperledger Fabric Blockchain tool, we deploy a test network of two actors, also known as organizations. The organizations come together to form a channel in the network. Each has a valid identity that helps them verify their signatures over any transaction. We selected JavaScript to write our proposed smart contract for experimental and evaluation purposes. To evaluate the smart Contract, we use Hyperledger Caliper, obtaining an average throughput of 10.4tps and average latency of 0.7s, being fast enough to be used in a real environment, considering the current control conditions of the coffee beans.

Summary: The histone demethylase KDM1A is a multi-faceted regulator of vital developmental processes, including mesodermal and cardiac tube formation during gastrulation. However, it is unknown whether the fine-tuning of KDM1A splicing isoforms, already shown to regulate neuronal maturation, is crucial for the specification and maintenance of cell identity during cardiogenesis. Here, we discovered a temporal modulation of ubKDM1A and KDM1A+2a during human and mice fetal cardiac development and evaluated their impact on the regulation of cardiac differentiation. We revealed a severely impaired cardiac differentiation in KDM1A−/− hESCs that can be rescued by re-expressing ubKDM1A or catalytically impaired ubKDM1A-K661A, but not by KDM1A+2a or KDM1A+2a-K661A. Conversely, KDM1A+2a−/− hESCs give rise to functional cardiac cells, displaying increased beating amplitude and frequency and enhanced expression of critical cardiogenic markers. Our findings prove the existence of a divergent scaffolding role of KDM1A splice variants, independent of their enzymatic activity, during hESC differentiation into cardiac cells.

Human pluripotent stem cells (hPSCs) constitute a valuable model to study the complexity of early human cardiac development and investigate the molecular mechanisms involved in heart diseases. The differentiation of hPSCs into cardiac lineages in vitro can be achieved by traditional two-dimensional (2D) monolayer approaches or by adopting innovative three-dimensional (3D) cardiac organoid protocols. Human cardiac organoids (hCOs) are complex multicellular aggregates that faithfully recapitulate the cardiac tissue’s transcriptional, functional, and morphological features. In recent years, significant advances in the field have dramatically improved the robustness and efficiency of hCOs derivation and have promoted the application of hCOs for drug screening and heart disease modeling. This review surveys the current differentiation protocols, focusing on the most advanced 3D methods for deriving hCOs from hPSCs. Furthermore, we describe the potential applications of hCOs in the pharmaceutical and tissue bioengineering fields, including their usage to investigate the consequences of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV2) infection in the heart.

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.

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.

Teaching is an activity that requires understanding the class’s reaction to evaluate the teaching methodology effectiveness. This operation can be easy to achieve in small classrooms, while it may be challenging to do in classes of 50 or more students. This paper proposes a novel Internet of Things (IoT) system to aid teachers in their work based on the redundant use of non-invasive techniques such as facial expression recognition and physiological data analysis. Facial expression recognition is performed using a Convolutional Neural Network (CNN), while physiological data are obtained via Photoplethysmography (PPG). By recurring to Russel’s model, we grouped the most important Ekman’s facial expressions recognized by CNN into active and passive. Then, operations such as thresholding and windowing were performed to make it possible to compare and analyze the results from both sources. Using a window size of 100 samples, both sources have detected a level of attention of about 55.5% for the in-presence lectures tests. By comparing results coming from in-presence and pre-recorded remote lectures, it is possible to note that, thanks to validation with physiological data, facial expressions alone seem useful in determining students’ level of attention for in-presence lectures.

The Internet of Things (IoT) opens opportunities to monitor, optimize, and automate processes into the Agricultural Value Chains (AVC). However, challenges remain in terms of energy consumption. In this paper, we assessed the impact of environmental variables in AVC based on the most influential variables. We developed an adaptive sampling period method to save IoT device energy and to maintain the ideal sensing quality based on these variables, particularly for temperature and humidity monitoring. The evaluation on real scenarios (Coffee Crop) shows that the suggested adaptive algorithm can reduce the current consumption up to 11% compared with a traditional fixed-rate approach, while preserving the accuracy of the data.

Tourist traceability is the analysis of the set of actions, procedures, and technical measures that allows us to identify and record the space–time causality of the tourist’s touring, from the beginning to the end of the chain of the tourist product. Besides, the traceability of tourists has implications for infrastructure, transport, products, marketing, the commercial viability of the industry, and the management of the destination’s social, environmental, and cultural impact. To this end, a tourist traceability system requires a knowledge base for processing elements, such as functions, objects, events, and logical connectors among them. A knowledge base provides us with information on the preparation, planning, and implementation or operation stages. In this regard, unifying tourism terminology in a traceability system is a challenge because we need a central repository that promotes standards for tourists and suppliers in forming a formal body of knowledge representation. Some studies are related to the construction of ontologies in tourism, but none focus on tourist traceability systems. For the above, we propose OntoTouTra, an ontology that uses formal specifications to represent knowledge of tourist traceability systems. This paper outlines the development of the OntoTouTra ontology and how we gathered and processed data from ubiquitous computing using Big Data analysis techniques.

The collection of physiological data from people has been facilitated due to the mass use of cheap wearable devices. Although the accuracy is low compared to specialized healthcare devices, these can be widely applied in other contexts. This study proposes the architecture for a tourist experiences recommender system (TERS) based on the user’s emotional states who wear these devices. The issue lies in detecting emotion from Heart Rate (HR) measurements obtained from these wearables. Unlike most state-of-the-art studies, which have elicited emotions in controlled experiments and with high-accuracy sensors, this research’s challenge consisted of emotion recognition (ER) in the daily life context of users based on the gathering of HR data. Furthermore, an objective was to generate the tourist recommendation considering the emotional state of the device wearer. The method used comprises three main phases: The first was the collection of HR measurements and labeling emotions through mobile applications. The second was emotional detection using deep learning algorithms. The final phase was the design and validation of the TERS-ER. In this way, a dataset of HR measurements labeled with emotions was obtained as results. Among the different algorithms tested for ER, the hybrid model of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks had promising results. Moreover, concerning TERS, Collaborative Filtering (CF) using CNN showed better performance.

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.

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.

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 R2016b tool.

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.

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.

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.

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.

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.

Many affected counties have had experienced a shortage of personal protective equipment (PPE) during the coronavirus disease (COVID-19) pandemic. We aimed to investigate the needs of healthcare professionals and the technical difficulties faced by them during the initial outbreak. A cross-sectional web-based survey was conducted among the healthcare workforce in the most populous cities from three Latin American countries in April 2020. In total, 1,082 participants were included. Of these, 534 (49.4%), 263 (24.3%), and 114 (10.5%) were physicians, nurses, and other professionals, respectively. At least 70% of participants reported a lack of PPE. The most common shortages were shortages in gown coverall suits (643, 59.4%), N95 masks (600, 55.5%), and face shields (569, 52.6%). Professionals who performed procedures that generated aerosols reported shortages more frequently (p

Radio frequency identification (RFID) is widely used in several contexts, such as logistics, supply chains, asset tracking, and health, among others, therefore drawing the attention of many researchers. This paper presents a review of the most cited topics regarding RFID focused on applications, security, and privacy. A total of 62,685 records were downloaded from the Web of Science (WoS) and Scopus core databases and processed, reconciling the datasets to remove duplicates, resulting in 40,677 unique elements. Fundamental indicators were extracted and are presented, such as the citation number, average growth rate, and average number of documents per year. We extracted the top topics and reviewed the relevant indicators using a free Python tool, ScientoPy. The results are discussed in the following sections: the first is the Applications Section, whose subsections are the Internet of Things (IoT), Supply Chain Management, Localization, Traceability, Logistics, Ubiquitous Computing, Healthcare, and Access Control; the second is the Security and Privacy section, whose subsections are Authentication, Privacy, and Ownership Transfer; finally, we present the Discussion section. This paper intends to provide the reader with a global view of the current status of trending RFID topics and present different analyses from different perspectives depending on motivations or background.

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.

Recommendation systems have overcome the overload of irrelevant information by considering users’ 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’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.

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’ navigation systems. This paper reviews the top FPGAs’ 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.

La publicidad ha sido durante años una de las herramientas más valiosas del mercadeo a través de un enfoque principalmente masivo, generalizado y vertical entre clientes y anunciantes. No obstante, una nueva corriente conocida como publicidad ubicua marca una evolución en el concepto clásico hacia entornos más interactivos, personalizados y horizontales que busca mejorar la eficiencia y el impacto de la publicidad convencional. Gracias al apoyo de tecnologías emergentes que se sustentan en la evolución de los Smartphones y los Smart TV, el potencial de la publicidad ubicua es indudable, lo cual la ha convertido en un terreno fértil de investigación. El presente artículo presenta un modelo conceptual que condensa las áreas de investigación más relevantes relacionadas con el despliegue de publicidad en entornos de computación ubicua soportados en esquemas de cooperación Smart TV – Smartphone.

A new generation of public displays demands high interactive and multiscreen features to enrich people’s experience in new pervasive environments. Traditionally, research on public display interaction has involved mobile devices as the main characters during the use of personal area network technologies such as Bluetooth or NFC. However, the emergent Smart TV model arises as an interesting alternative for the implementation of a new generation of public displays. This is due to its intrinsic connection capabilities with surrounding devices like smartphones or tablets. Nonetheless, the different approaches proposed by the most important vendors are still underdeveloped to support multiscreen and interaction capabilities for modern public displays, because most of them are intended for domestic environments. This research proposes multiscreen interactive middleware for public displays, which was developed from the principles of a loosely coupled interaction model, simplicity, stability, concurrency, low latency, and the usage of open standards and technologies. Moreover, a validation prototype is proposed in one of the most interesting public display scenarios: the advertising.

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.

Determinar si existe una relación entre las Enfermedades de transmisión sexual (ETS) y Abuso Sexual en los nifíos se investigaron 70 nifíos de ambos sexos cuyas edades oscilarun entre 2 y 12 afíos, que asistieron a la Consulta Externa dei Centro de Diagnóstico de ETS, sin presentar antecedentes de abuso sexual. Se hicieron exámenes en fresco, tinción de Gram, Tayer Martin, cultivas para la identificación de bacterias, VDRL y test de embarazo. Dei grupo, 43 tuvieron diagnóstico de Gonorrea, 21 de Candidiasis, 8 Trichomoniasis, 2 Sífilis, 2 Verruga venérea y I Embarazo. Se encontrá que 50 nifíos habían tenido alguna forma de relación sexual, ya sea esta por manoseo con personas dei mismo sexo, con sexo opuesto, o por franco contacto sexual; los 20 restantes no reportaron experiencia sexual alguna. Cuarenta de los 50 ninõs que habían tenido experiencia sexual, la realizaron con personas conocidas. De ahi que siempre que se encuentre una ETS en un nifío debemos de pensar como antecedente el abuso sexual.

Background: To begin research with cross-thematic topics, an analysis is necessary that collects data and historical accounts about these topics. In the field of health and technology, this principle is not unknown. In this case, it is the interest of this investigation to inquire about the topics 'wearable' and 'health' in order to gather enough information to establish how it has been developed, what the current topics are and what areas it is planned to study in a near future. The purpose of this study is to analyse the growth of the wearable technology and health subject matter. This will create an account of the most relevant themes over time in this subject and their evolution. This study will have an emphasis on recent years, in order to see which themes may be important for future research and publications. Methods: This paper aims at making a bibliometric exploration using information from the Scopus database using the search chain “weareable AND health” within the years of 2000 to 2017. The bibliometric software SciMAT was used for data visualization and analysis. Results: The results obtained are an analysis of the growth rate that is presented in Scopus publications between 2000 and 2017. In addition, a bibliometric analysis is obtained through the SciMat software, which shows the relevant issues that arise through time and that may be relevant for future research. Conclusions: Publications associated with the search chain “Wearable AND Health” from 2000 to 2017 were generally in constant growth over the years. With the SciMAT software, the main theme that was found was that of monitoring systems. In addition, it shows how new themes that are highly relevant emerge, such as mobile technologies and the study of algorithms, which emerge as an evolution from signal processing.

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.

Background: Machine learning is becoming increasingly important for companies and the scientific community. In this study, we perform a bibliometric analysis on machine learning research, in order to provide an overview of the scientific work during the period 2007-2017 in this area and to show trends that could be the basis for future developments in the field. Methods: This study is carried out using the SciMAT tool based on results extracted from Scopus. This analysis shows the strategic diagrams of evolution and a set of thematic networks. The results provide information on broad tendencies of machine learning. Results: The results show that SciMAT is a useful tool to carry out a science mapping analysis, and emphasizes the premise that machine learning has boundless applications and will continue to be an interesting research field in the future. Conclusions: Some of the conclusions exposed in this study show that classification algorithms have been widely studied and represent a relevant tool for generating different machine learning applications. Nonetheless, regression algorithms are becoming increasingly important in the scientific community, allowing the generation of solutions to predict diseases, sales, and yields, for example.

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.

Background: Sericulture, since its discovery in China, has spread to become a valued activity in a range of other countries. China remains the leading producer of silk, followed by India and other Asian countries, Europe, Brazil and Colombia. This article examines the evolution of sericultural research between 1892 and 2016, identifying the main themes and applications. Methods: The SciMat software tool and the Bibliometrix R package were used as tools for data analysis in this study, based on records from the Clarivate Analytics Web of Science and SCOPUS databases. Results: The results show that research has been growing, both in number of publications from the 1990s onwards, and in the emergence of topics closely related to the sericulture research field, a field that proves to be multi-disciplinary, exhibiting expansion and vitality. Conclusions: The information gathered will contribute greatly to the definition of relevant research strands, bearing in mind a number of significant gaps in information in this field. It will furthermore provide a better insight into the development of sericulture research over time.