NOTA: a novel online teaching and assessment scheme using Blockchain for emergency cases.

Technology advancements promote a redefinition of traditional instructional methodologies, as well as the roles of teachers and learners towards an efficient e-learning ecosystem. To date, all existing solutions are combined with the conventional face-to-face learning process. However, the latter can be unexpectedly hindered in some emergency cases, like the Coronavirus (COVID-19) pandemic. To handle such unexpected scenarios, this paper presents NOTA, a novel online teaching and assessment scheme that takes advantage of Blockchain technology to maintain the expected teaching quality and assessment fairness while respecting the courses' and examinations' schedule. Besides, NOTA also motivates both learners and teachers to persist in their endeavours, even from home, through Blockchain's incentive strategies. The preliminary results taken during the CoronaVirus period showed a very high satisfaction ratio, exceeding the 90%. This made us feel very optimistic about the potential of our proposal when deployed at a larger scale.

Towards a Sustainable City for Cyclists: Promoting Safety through a Mobile Sensing Application.

Riding a bicycle is a great manner to contribute to the preservation of our ecosystem. Cycling helps to reduce air pollution and traffic congestion, and so, it is one of the simplest ways to lower the environmental footprint of people. However, the cohabitation of cars and vulnerable road users, such as bikes, scooters, or pedestrians, is prone to cause accidents with serious consequences. In this context, technological solutions are sought that enable the generation of alerts to prevent these accidents, thereby promoting a safer city for these road users, and a cleaner environment. Alert systems based on smartphones can alleviate these situations since nearly all people carry such a device while traveling. In this work, we test the suitability of a smartphone based alert system, determining the most adequate communications architecture. Two protocols have been designed to send position and alert messages to/from a centralized server over 4G cellular networks. One of the protocols is implemented using a REST architecture on top of the HTTP protocol, and the other one is implemented over the UDP protocol. We show that the proposed alarm system is feasible regarding communication response time, and we conclude that the application should be implemented over the UDP protocol, as response times are about three times better than for the REST implementation. We tested the applications in real deployments, finding that drivers are warned of the presence of bicycles when closer than 150 m, having enough time to pay attention to the situation and drive more carefully to avoid a collision.

Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms.

Internet of Things (IoT) is becoming a new socioeconomic revolution in which data and immediacy are the main ingredients. IoT generates large datasets on a daily basis but it is currently considered as "dark data", i.e., data generated but never analyzed. The efficient analysis of this data is mandatory to create intelligent applications for the next generation of IoT applications that benefits society. Artificial Intelligence (AI) techniques are very well suited to identifying hidden patterns and correlations in this data deluge. In particular, clustering algorithms are of the utmost importance for performing exploratory data analysis to identify a set (a.k.a., cluster) of similar objects. Clustering algorithms are computationally heavy workloads and require to be executed on high-performance computing clusters, especially to deal with large datasets. This execution on HPC infrastructures is an energy hungry procedure with additional issues, such as high-latency communications or privacy. Edge computing is a paradigm to enable light-weight computations at the edge of the network that has been proposed recently to solve these issues. In this paper, we provide an in-depth analysis of emergent edge computing architectures that include low-power Graphics Processing Units (GPUs) to speed-up these workloads. Our analysis includes performance and power consumption figures of the latest Nvidia's AGX Xavier to compare the energy-performance ratio of these low-cost platforms with a high-performance cloud-based counterpart version. Three different clustering algorithms (i.e., k-means, Fuzzy Minimals (FM), and Fuzzy C-Means (FCM)) are designed to be optimally executed on edge and cloud platforms, showing a speed-up factor of up to 11× for the GPU code compared to sequential counterpart versions in the edge platforms and energy savings of up to 150% between the edge computing and HPC platforms.

Three Dimensional UAV Positioning for Dynamic UAV-to-Car Communications.

In areas with limited infrastructure, Unmanned Aerial Vehicles (UAVs) can come in handy as relays for car-to-car communications. Since UAVs are able to fully explore a three-dimensional environment while flying, communications that involve them can be affected by the irregularity of the terrains, that in turn can cause path loss by acting as obstacles. Accounting for this phenomenon, we propose a UAV positioning technique that relies on optimization algorithms to improve the support for vehicular communications. Simulation results show that the best position of the UAV can be timely determined considering the dynamic movement of the cars. Our technique takes into account the current flight altitude, the position of the cars on the ground, and the existing flight restrictions.

Leveraging a Publish/Subscribe Fog System to Provide Collision Warnings in Vehicular Networks.

Fog computing, an extension of the Cloud Computing paradigm where routers themselves may provide the virtualisation infrastructure, aims at achieving fluidity when distributing in-network functions, in addition to allowing fast and scalable processing, and exchange of information. In this paper we present a fog computing architecture based on a "content island" which interconnects sets of "things" to exchange and process data among themselves or with other content islands. We then present a use case that focuses on a smartphone-based forward collision warning application for a connected vehicle scenario. This application makes use of the optical sensor of smartphones to estimate the distance between the device itself and other vehicles in its field of view. The vehicle travelling directly ahead is identified relying on the information from the GPS, camera, and inter-island communication. Warnings are generated at both content islands, if the driver does not maintain a predefined safe distance towards the vehicle ahead. Experiments performed with the application show that with the developed method, we are able to estimate the distance between vehicles, and the inter-island communication has a very low overhead, resulting in improved performance. On comparing our proposed solution based on edge/fog computing with a cloud-based api, it was observed that our solution outperformed the cloud-based api, thus making us optimistic of the utility of the proposed architecture.

A Distributed Approach for Collision Avoidance between Multirotor UAVs Following Planned Missions.

As the number of potential applications for Unmanned Aerial Vehicles (UAVs) keeps rising steadily, the chances that these devices get close to each other during their flights also increases, causing concerns regarding potential collisions. This paper proposed the Mission Based Collision Avoidance Protocol (MBCAP), a novel UAV collision avoidance protocol applicable to all types of multicopters flying autonomously. It relies on wireless communications in order to detect nearby UAVs, and to negotiate the procedure to avoid any potential collision. Experimental and simulation results demonstrated the validity and effectiveness of the proposed solution, which typically introduces a small overhead in the range of 15 to 42 s for each risky situation successfully handled.

FSF: Applying Machine Learning Techniques to Data Forwarding in Socially Selfish Opportunistic Networks.

Opportunistic networks are becoming a solution to provide communication support in areas with overloaded cellular networks, and in scenarios where a fixed infrastructure is not available, as in remote and developing regions. A critical issue, which still requires a satisfactory solution, is the design of an efficient data delivery solution trading off delivery efficiency, delay, and cost. To tackle this problem, most researchers have used either the network state or node mobility as a forwarding criterion. Solutions based on social behaviour have recently been considered as a promising alternative. Following the philosophy from this new category of protocols, in this work, we present our "FriendShip and Acquaintanceship Forwarding" (FSF) protocol, a routing protocol that makes its routing decisions considering the social ties between the nodes and both the selfishness and the device resources levels of the candidate node for message relaying. When a contact opportunity arises, FSF first classifies the social ties between the message destination and the candidate to relay. Then, by using logistic functions, FSF assesses the relay node selfishness to consider those cases in which the relay node is socially selfish. To consider those cases in which the relay node does not accept receipt of the message because its device has resource constraints at that moment, FSF looks at the resource levels of the relay node. By using the ONE simulator to carry out trace-driven simulation experiments, we find that, when accounting for selfishness on routing decisions, our FSF algorithm outperforms previously proposed schemes, by increasing the delivery ratio up to 20%, with the additional advantage of introducing a lower number of forwarding events. We also find that the chosen buffer management algorithm can become a critical element to improve network performance in scenarios with selfish nodes.

Supporting Beacon and Event-Driven Messages in Vehicular Platoons through Token-Based Strategies.

Timely and reliable inter-vehicle communications is a critical requirement to support traffic safety applications, such as vehicle platooning. Furthermore, low-delay communications allow the platoon to react quickly to unexpected events. In this scope, having a predictable and highly effective medium access control (MAC) method is of utmost importance. However, the currently available IEEE 802.11p technology is unable to adequately address these challenges. In this paper, we propose a MAC method especially adapted to platoons, able to transmit beacons within the required time constraints, but with a higher reliability level than IEEE 802.11p, while concurrently enabling efficient dissemination of event-driven messages. The protocol circulates the token within the platoon not in a round-robin fashion, but based on beacon data age, i.e., the time that has passed since the previous collection of status information, thereby automatically offering repeated beacon transmission opportunities for increased reliability. In addition, we propose three different methods for supporting event-driven messages co-existing with beacons. Analysis and simulation results in single and multi-hop scenarios showed that, by providing non-competitive channel access and frequent retransmission opportunities, our protocol can offer beacon delivery within one beacon generation interval while fulfilling the requirements on low-delay dissemination of event-driven messages for traffic safety applications.

Crowdsensing in Smart Cities: Overview, Platforms, and Environment Sensing Issues.

Evidence shows that Smart Cities are starting to materialise in our lives through the gradual introduction of the Internet of Things (IoT) paradigm. In this scope, crowdsensing emerges as a powerful solution to address environmental monitoring, allowing to control air pollution levels in crowded urban areas in a distributed, collaborative, inexpensive and accurate manner. However, even though technology is already available, such environmental sensing devices have not yet reached consumers. In this paper, we present an analysis of candidate technologies for crowdsensing architectures, along with the requirements for empowering users with air monitoring capabilities. Specifically, we start by providing an overview of the most relevant IoT architectures and protocols. Then, we present the general design of an off-the-shelf mobile environmental sensor able to cope with air quality monitoring requirements; we explore different hardware options to develop the desired sensing unit using readily available devices, discussing the main technical issues associated with each option, thereby opening new opportunities in terms of environmental monitoring programs.

GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing.

Noise pollution is an emerging and challenging problem of all large metropolitan areas, affecting the health of citizens in multiple ways. Therefore, obtaining a detailed and real-time map of noise in cities becomes of the utmost importance for authorities to take preventive measures. Until now, these measurements were limited to occasional sampling made by specialized companies, that mainly focus on major roads. In this paper, we propose an alternative approach to this problem based on crowdsensing. Our proposed architecture empowers participating citizens by allowing them to seamlessly, and based on their context, sample the noise in their surrounding environment. This allows us to provide a global and detailed view of noise levels around the city, including places traditionally not monitored due to poor accessibility, even while using their vehicles. In the paper, we detail how the different relevant issues in our architecture, i.e., smartphone calibration, measurement adequacy, server design, and client⁻server interaction, were solved, and we have validated them in real scenarios to illustrate the potential of the solution achieved.

Modeling and Characterization of Traffic Flows in Urban Environments.

Currently, one of the main challenges faced in large metropolitan areas is traffic congestion. To address this problem, adequate traffic control could produce many benefits, including reduced pollutant emissions and reduced travel times. If it were possible to characterize the state of traffic by predicting future traffic conditions for optimizing the route of automated vehicles, and if these measures could be taken to preventively mitigate the effects of congestion with its related problems, the overall traffic flow could be improved. This paper performs an experimental study of the traffic distribution in the city of Valencia, Spain, characterizing the different streets of the city in terms of vehicle load with respect to the travel time during rush hour traffic conditions. Experimental results based on realistic vehicular traffic traces from the city of Valencia show that only some street segments fall under the general theory of vehicular flow, offering a good fit using quadratic regression, while a great number of street segments fall under other categories. Although in some cases such discrepancies are related to lack of traffic, injecting additional vehicles shows that significant mismatches still persist. Thus, in this paper we propose an equation to characterize travel times over a segment belonging to the sigmoid family; specifically, we apply logistic regression, being able to significantly improve the curve fitting results for most of the street segments under analysis. Based on our regression results, we performed a clustering analysis of the different street segments, showing that they can be classified into three well-defined categories, which evidences a predictable traffic distribution using the logistic regression throughout the city during rush hours, and allows optimizing the traffic for automated vehicles.

A Location-Aware Waypoint-Based Routing Protocol for Airborne DTNs in Search and Rescue Scenarios.

In this paper, we propose GeoSaW, a delay-tolerant routing protocol for Airborne Networks in Search and Rescue scenarios. The protocol exploits the geographical information of UAVs to make appropriate message forwarding decisions. More precisely, the information about the future UAV's motion path is exploited to select the best UAV carrying the message towards the destination. Simulation results show that the proposed solution outperforms the classic DTN routing protocols in terms of several performance metrics.

A Forward Collision Warning System for Smartphones Using Image Processing and V2V Communication.

In this paper, we present a forward collision warning application for smartphones that uses license plate recognition and vehicular communication to generate warnings for notifying the drivers of the vehicle behind and the one ahead, of a probable collision when the vehicle behind does not maintain an established safe distance between itself and the vehicle ahead. The application was tested in both static and mobile scenarios, from which we confirmed the working of our application, even though its performance is affected by the hardware limitations of the smartphones.

Accurate Ambient Noise Assessment Using Smartphones.

Nowadays, smartphones have become ubiquitous and one of the main communication resources for human beings. Their widespread adoption was due to the huge technological progress and to the development of multiple useful applications. Their characteristics have also experienced a substantial improvement as they now integrate multiple sensors able to convert the smartphone into a flexible and multi-purpose sensing unit. The combined use of multiple smartphones endowed with several types of sensors gives the possibility to monitor a certain area with fine spatial and temporal granularity, a procedure typically known as crowdsensing. In this paper, we propose using smartphones as environmental noise-sensing units. For this purpose, we focus our study on the sound capture and processing procedure, analyzing the impact of different noise calculation algorithms, as well as in determining their accuracy when compared to a professional noise measurement unit. We analyze different candidate algorithms using different types of smartphones, and we study the most adequate time period and sampling strategy to optimize the data-gathering process. In addition, we perform an experimental study comparing our approach with the results obtained using a professional device. Experimental results show that, if the smartphone application is well tuned, it is possible to measure noise levels with a accuracy degree comparable to professional devices for the entire dynamic range typically supported by microphones embedded in smartphones, i.e., 35-95 dB.

Towards Realistic Urban Traffic Experiments Using DFROUTER: Heuristic, Validation and Extensions.

Traffic congestion is an important problem faced by Intelligent Transportation Systems (ITS), requiring models that allow predicting the impact of different solutions on urban traffic flow. Such an approach typically requires the use of simulations, which should be as realistic as possible. However, achieving high degrees of realism can be complex when the actual traffic patterns, defined through an Origin/Destination (O-D) matrix for the vehicles in a city, remain unknown. Thus, the main contribution of this paper is a heuristic for improving traffic congestion modeling. In particular, we propose a procedure that, starting from real induction loop measurements made available by traffic authorities, iteratively refines the output of DFROUTER, which is a module provided by the SUMO (Simulation of Urban MObility) tool. This way, it is able to generate an O-D matrix for traffic that resembles the real traffic distribution and that can be directly imported by SUMO. We apply our technique to the city of Valencia, and we then compare the obtained results against other existing traffic mobility data for the cities of Cologne (Germany) and Bologna (Italy), thereby validating our approach. We also use our technique to determine what degree of congestion is expectable if certain conditions cause additional traffic to circulate in the city, adopting both a uniform pattern and a hotspot-based pattern for traffic injection to demonstrate how to regulate the overall number of vehicles in the city. This study allows evaluating the impact of vehicle flow changes on the overall traffic congestion levels.

Defects in subventricular zone pigmented epithelium-derived factor niche signaling in the senescence-accelerated mouse prone-8.

We studied potential changes in the subventricular zone (SVZ) stem cell niche of the senescence-accelerated mouse prone-8 (SAM-P8) aging model. Bromodeoxyuridine (BrdU) assays with longtime survival revealed a lower number of label-retaining stem cells in the SAM-P8 SVZ compared with the SAM-Resistant 1 (SAM-R1) control strain. We also found that in SAM-P8 niche signaling is attenuated and the stem cell pool is less responsive to the self-renewal niche factor pigmented epithelium-derived factor (PEDF). Protein analysis demonstrated stable amounts of the PEDF ligand in the SAM-P8 SVZ niche; however, SAM-P8 stem cells present a significant expression decrease of patatin-like phospholipase domain containing 2, a receptor for PEDF (PNPLA2-PEDF) receptor, but not of laminin receptor (LR), a receptor for PEDF (LR-PEDF) receptor. We observed changes in self-renewal related genes (hairy and enhancer of split 1 (Hes1), hairy and enhancer of split 1 (Hes5), Sox2] and report that although these genes are down-regulated in SAM-P8, differentiation genes (Pax6) are up-regulated and neurogenesis is increased. Finally, sheltering mammalian telomere complexes might be also involved given a down-regulation of telomeric repeat binding factor 1 (Terf1) expression was observed in SAM-P8 at young age periods. Differences between these 2 models, SAM-P8 and SAM-R1 controls, have been previously detected at more advanced ages. We now describe alterations in the PEDF signaling pathway and stem cell self-renewal at a very young age, which could be involved in the premature senescence observed in the SAM-P8 model.

Friendly-Sharing: Improving the Performance of City Sensoring through Contact-Based Messaging Applications.

Regular citizens equipped with smart devices are being increasingly used as "sensors" by Smart Cities applications. Using contacts among users, data in the form of messages is obtained and shared. Contact-based messaging applications are based on establishing a short-range communication directly between mobile devices, and on storing the messages in these devices for subsequent delivery to cloud-based services. An effective way to increase the number of messages that can be shared is to increase the contact duration. We thus introduce the Friendly-Sharing diffusion approach, where, during a contact, the users are aware of the time needed to interchange the messages stored in their buffers, and they can thus decide to wait more time in order to increase the message sharing probability. The performance of this approach is anyway closely related to the size of the buffer in the device. We therefore compare various policies either for the message selection at forwarding times and for message dropping when the buffer is full. We evaluate our proposal with a modified version of the Opportunistic Networking Environment (ONE) simulator and using real human mobility traces.

[Models of care and classification of "Children with special health care needs-CSHCN": Recommendations from the CSHCN Committee, Chilean Paediatric Society]. / Modelo de atención y clasificación de «Niños y adolescentes con necesidades especiales de atención en salud-NANEAS¼: recomendaciones del Comité NANEAS de la Sociedad Chilena de Pediatría.

"Children with special health care needs" (CSHCN) is an emerging and heterogeneous group of paediatric patients, with a wide variety of medical conditions and with different uses of health care services. There is consensus on how to classify and assess these patients according to their needs, but not for their specific diagnosis. Needs are classified into 6 areas: a) specialised medical care; b) use or need of prescription medication; c) special nutrition; d) dependence on technology; e) rehabilitation therapy for functional limitation; and f) special education services. From the evaluation of each area, a classification for CSHCN is proposed according to low, medium, or high complexity health needs, to guide and distribute their care at an appropriate level of the health care system. Low complexity CSHCN should be incorporated into Primary Care services, to improve benefits for patients and families at this level. It is critical to train health care professionals in taking care of CSHCN, promoting a coordinated, dynamic and communicated work between different levels of the health care system. Compliance with these guidelines will achieve a high quality and integrated care for this vulnerable group of children.

Sensing Traffic Density Combining V2V and V2I Wireless Communications.

Wireless technologies are making the development of new applications and services in vehicular environments possible since they enable mobile communication between vehicles (V2V), as well as communication between vehicles and infrastructure nodes (V2I). Usually, V2V communications are dedicated to the transmission of small messages mainly focused on improving traffic safety. Instead, V2I communications allow users to access the Internet and benefit from higher level applications. The combination of both V2V and V2I, known as V2X communications, can increase the benefits even further, thereby making intelligent transportation systems (ITS) a reality. In this paper, we introduce V2X-d, a novel architecture specially designed to estimate traffic density on the road. In particular, V2X-d exploits the combination of V2V and V2I communications. Our approach is based on the information gathered by sensors (i.e., vehicles and road side units (RSUs)) and the characteristics of the roadmap topology to accurately make an estimation of the instant vehicle density. The combination of both mechanisms improves the accuracy and coverage area of the data gathered, while increasing the robustness and fault tolerance of the overall approach, e.g., using the information offered by V2V communications to provide additional density information in areas where RSUs are scarce or malfunctioning. By using our collaborative sensing scheme, future ITS solutions will be able to establish adequate dissemination protocols or to apply more efficient traffic congestion reduction policies, since they will be aware of the instantaneous density of vehicles.

Perioperative Health Interventions in Children With Chronic Neuromuscular Conditions Undergoing Major Musculoskeletal Surgery: A Scoping Review.

BACKGROUND AND OBJECTIVES: Children with chronic neuromuscular conditions (CCNMC) have many coexisting conditions and often require musculoskeletal surgery for progressive neuromuscular scoliosis or hip dysplasia. Adequate perioperative optimization may decrease adverse perioperative outcomes. The purpose of this scoping review was to allow us to assess associations of perioperative health interventions (POHI) with perioperative outcomes in CCNMC. METHODS: Eligible articles included those published from January 1, 2000 through March 1, 2022 in which the authors evaluated the impact of POHI on perioperative outcomes in CCNMC undergoing major musculoskeletal surgery. Multiple databases, including PubMed, Embase, Cumulative Index of Nursing and Allied Health Literature, Web of Science, the Cochrane Library, Google Scholar, and ClinicalTrials.gov, were searched by using controlled vocabulary terms and relevant natural language keywords. Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines were used to perform the review. A risk of bias assessment for included studies was performed by using the Risk of Bias in Non-randomized Studies of Interventions tool. RESULTS: A total of 7013 unique articles were initially identified, of which 6286 (89.6%) were excluded after abstract review. The remaining 727 articles' full texts were then reviewed for eligibility, resulting in the exclusion of 709 (97.5%) articles. Ultimately, 18 articles were retained for final analysis. The authors of these studies reported various impacts of POHI on perioperative outcomes, including postoperative complications, hospital length of stay, and hospitalization costs. Because of the heterogeneity of interventions and outcome measures, meta-analyses with pooled data were not feasible. CONCLUSIONS: The findings reveal various impacts of POHI in CCNMC undergoing major musculoskeletal surgery. Multicenter prospective studies are needed to better address the overall impact of specific interventions on perioperative outcomes in CCNMC.