Fog computing for sustainable smart cities: a survey

The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, specially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g. network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build an sustainable IoT infrastructure for smart cities. In this paper, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges towards implementing them, so as to shed light on future research directions on realizing Fog computing for building sustainable smart cities

Deep sampling of the Palomero maize transcriptome by a high throughput strategy of pyrosequencing

<p>Abstract</p> <p>Background</p> <p>In-depth sequencing analysis has not been able to determine the overall complexity of transcriptional activity of a plant organ or tissue sample. In some cases, deep parallel sequencing of Expressed Sequence Tags (ESTs), although not yet optimized for the sequencing of cDNAs, has represented an efficient procedure for validating gene prediction and estimating overall gene coverage. This approach could be very valuable for complex plant genomes. In addition, little emphasis has been given to efforts aiming at an estimation of the overall transcriptional universe found in a multicellular organism at a specific developmental stage.</p> <p>Results</p> <p>To explore, in depth, the transcriptional diversity in an ancient maize landrace, we developed a protocol to optimize the sequencing of cDNAs and performed 4 consecutive GS20–454 pyrosequencing runs of a cDNA library obtained from 2 week-old <it>Palomero Toluqueño </it>maize plants. The protocol reported here allowed obtaining over 90% of informative sequences. These GS20–454 runs generated over 1.5 Million reads, representing the largest amount of sequences reported from a single plant cDNA library. A collection of 367,391 quality-filtered reads (30.09 Mb) from a single run was sufficient to identify transcripts corresponding to 34% of public maize ESTs databases; total sequences generated after 4 filtered runs increased this coverage to 50%. Comparisons of all 1.5 Million reads to the Maize Assembled Genomic Islands (MAGIs) provided evidence for the transcriptional activity of 11% of MAGIs. We estimate that 5.67% (86,069 sequences) do not align with public ESTs or annotated genes, potentially representing new maize transcripts. Following the assembly of 74.4% of the reads in 65,493 contigs, real-time PCR of selected genes confirmed a predicted correlation between the abundance of GS20–454 sequences and corresponding levels of gene expression.</p> <p>Conclusion</p> <p>A protocol was developed that significantly increases the number, length and quality of cDNA reads using massive 454 parallel sequencing. We show that recurrent 454 pyrosequencing of a single cDNA sample is necessary to attain a thorough representation of the transcriptional universe present in maize, that can also be used to estimate transcript abundance of specific genes. This data suggests that the molecular and functional diversity contained in the vast native landraces remains to be explored, and that large-scale transcriptional sequencing of a presumed ancestor of the modern maize varieties represents a valuable approach to characterize the functional diversity of maize for future agricultural and evolutionary studies.</p

Load Index Metrics for an Optimized Management of Web Services: A Systematic Evaluation

The lack of precision to predict service performance through load indices may lead to wrong decisions regarding the use of web services, compromising service performance and raising platform cost unnecessarily. This paper presents experimental studies to qualify the behaviour of load indices in the web service context. The experiments consider three services that generate controlled and significant server demands, four levels of workload for each service and six distinct execution scenarios. The evaluation considers three relevant perspectives: the capability for representing recent workloads, the capability for predicting near-future performance and finally stability. Eight different load indices were analysed, including the JMX Average Time index (proposed in this paper) specifically designed to address the limitations of the other indices. A systematic approach is applied to evaluate the different load indices, considering a multiple linear regression model based on the stepwise-AIC method. The results show that the load indices studied represent the workload to some extent; however, in contrast to expectations, most of them do not exhibit a coherent correlation with service performance and this can result in stability problems. The JMX Average Time index is an exception, showing a stable behaviour which is tightly-coupled to the service runtime for all executions. Load indices are used to predict the service runtime and therefore their inappropriate use can lead to decisions that will impact negatively on both service performance and execution cost

A multi-objective optimized service level agreement approach applied on a cloud computing ecosystem

The cloud ecosystem provides transformative advantages that allow elastically offering ondemand services. However, it is not always possible to provide adequate services to all customers and thus to fulfill service level agreements (SLA). To enable compliance with these agreements, service providers leave the customer responsible for determining the service settings and expect that the client knows what to do. Some studies address SLA compliance, but the existing works do not adequately address the problem of resource allocation according to clients’ needs since they consider a limited set of objectives to be analyzed and fulfilled. In previous work, we have already addressed the problem considering a single-objective approach. In that work, we identified that the problem has a multi-objective characteristic since several attributes simultaneously influence the SLA agreement, which can lead to conflicts. This paper proposes a multi-objective combinatorial optimization approach for computational resources provisioning, seeking to optimize the efficient use of the infrastructure and provide the client with greater flexibility in contract closure.Toledo, Azevedo and Estrella had supported in part by CNPq, CAPES, and FAPESP (processes IDs: 15/11623-4 and 16/14219-2) and use of the computational resources of the Center for Mathematical Sciences Applied to Industry (CeMEAI) funded by FAPESP (grant 2013/07375-0

ENLACE: A Combination of Layer-Based Architecture and Wireless Communication for Emotion Monitoring in Healthcare

Owing to the increase in the number of people with disabilities, as a result of either accidents or old age, there has been an increase in research studies in the area of ubiquitous computing and the Internet of Things. They are aimed at monitoring health, in an efficient and easily accessible way, as a means of managing and improving the quality of life of this section of the public. It also involves adopting a Health Homes policy based on the Internet of Things and applied in smart home environments. This is aimed at providing connectivity between the patients and their surroundings and includes mechanisms for helping the diagnosis and prevention of accidents and/or diseases. Monitoring gives rise to an opportunity to exploit the way computational systems can help to determine the real-time emotional state of patients. This is necessary because there are some limitations to traditional methods of health monitoring, for example, establishing the behavior of the user’s routine and issuing alerts and warnings to family members and/or medical staff about any abnormal event or signs of the onset of depression. This article discusses how a layer-based architecture can be used to detect emotional factors to assist in healthcare and the prevention of accidents within the context of Smart Home Health. The results show that this process-based architecture allows a load distribution with a better service that takes into account the complexity of each algorithm and the processing power of each layer of the architecture to provide a prompt response when there is a need for some intervention in the emotional state of the user

Lipoproteomics: Methodologies and Analysis of Lipoprotein-Associated Proteins along with the Drug Intervention

Lipoproteins are specialized particles involved in the transport and distribution of hydrophobic lipids, as cholesterol and triglycerides, throughout the body. The lipoproteins exhibit a basic spherical shape as complexes of lipids and proteins, and these latter are known as apolipoproteins. Initially, the proteins associated with lipoproteins were recognized as integral or peripheral proteins that only maintain the dynamics and metabolism of lipoproteins. However, there exist many studies on different lipoproteins evidencing that the quantity and type of apolipoproteins and lipoprotein-associated proteins are diverse and could be associated with different lipoprotein function outcomes. Here, we summarized recent processes in the determination of apolipoproteins and lipoprotein-associated proteins profiles through a proteomic approach, analyzing the major methods available and are used to achieve this. We also discuss the relevance of these lipoproteomic analyses on the human disease outcomes

Volatile organic compounds from Pachyrhizus ferrugineus and Pachyrhizus erosus (Fabaceae) leaves

In México, Pachyrhizus erosus (Fabaceae) commonly called "jícama", is widely known for its edible tubers. It is cultivated since the pre-Columbian period, and the powdered seeds have been used for the treatment of mange, lice, and fleas, due to their content of rotenone, a well-known insecticidal compound. On the other hand, P. ferrugineus, a wild species can only be found in the Tropical Forests, and has no commercial value. It is known that plants release volatile organic compounds (VOCs) showing qualitative and quantitative differences if are wild or cultivated. VOCs are also involved as repelling or attracting chemical signals to insect herbivores, and their natural enemies. Until now, the VOCs of the leaves of P. erosus and P. ferrugineus have not been investigated. In the present contribution the VOCs of both species were characterized by headspace solid-phase (HS-SPME) extraction and gas chromatography-mass spectrometry (GC-MS-TOF). In P. erosus 21 VOCs were found, being the most abundant: cyclohexanone (32.8%), 3-hexen-1-ol (Z) (32.7%), 3-hexenal (Z) (10.5%). The majoritarian compounds were C6 or C5 derivatives In P. ferrugineus, the most abundant VOCs were: 5-hexene-1-ol acetate (51.5%), undecanal (22.4%), 2-hepten-1-al (14.5%). The majoritarian compounds were C6, C7 or C11 derivatives

The BioMart community portal: an innovative alternative to large, centralized data repositories.

The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations

Hyperoxemia and excess oxygen use in early acute respiratory distress syndrome : Insights from the LUNG SAFE study

Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Concerns exist regarding the prevalence and impact of unnecessary oxygen use in patients with acute respiratory distress syndrome (ARDS). We examined this issue in patients with ARDS enrolled in the Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE (LUNG SAFE) study. Methods: In this secondary analysis of the LUNG SAFE study, we wished to determine the prevalence and the outcomes associated with hyperoxemia on day 1, sustained hyperoxemia, and excessive oxygen use in patients with early ARDS. Patients who fulfilled criteria of ARDS on day 1 and day 2 of acute hypoxemic respiratory failure were categorized based on the presence of hyperoxemia (PaO2 > 100 mmHg) on day 1, sustained (i.e., present on day 1 and day 2) hyperoxemia, or excessive oxygen use (FIO2 ≥ 0.60 during hyperoxemia). Results: Of 2005 patients that met the inclusion criteria, 131 (6.5%) were hypoxemic (PaO2 < 55 mmHg), 607 (30%) had hyperoxemia on day 1, and 250 (12%) had sustained hyperoxemia. Excess FIO2 use occurred in 400 (66%) out of 607 patients with hyperoxemia. Excess FIO2 use decreased from day 1 to day 2 of ARDS, with most hyperoxemic patients on day 2 receiving relatively low FIO2. Multivariate analyses found no independent relationship between day 1 hyperoxemia, sustained hyperoxemia, or excess FIO2 use and adverse clinical outcomes. Mortality was 42% in patients with excess FIO2 use, compared to 39% in a propensity-matched sample of normoxemic (PaO2 55-100 mmHg) patients (P = 0.47). Conclusions: Hyperoxemia and excess oxygen use are both prevalent in early ARDS but are most often non-sustained. No relationship was found between hyperoxemia or excessive oxygen use and patient outcome in this cohort. Trial registration: LUNG-SAFE is registered with ClinicalTrials.gov, NCT02010073publishersversionPeer reviewe