Viriot: A cloud of things that offers iot infrastructures as a service

Many cloud providers offer IoT services that simplify the collection and processing of IoT information. However, the IoT infrastructure composed of sensors and actuators that produces this information remains outside the cloud; therefore, application developers must install, connect and manage the cloud. This requirement can be a market barrier, especially for small/medium software companies that cannot afford the infrastructural costs associated with it and would only prefer to focus on IoT application developments. Motivated by the wish to eliminate this barrier, this paper proposes a Cloud of Things platform, called VirIoT, which fully brings the Infrastructure as a service model typical of cloud computing to the world of Internet of Things. VirIoT provides users with virtual IoT infrastructures (Virtual Silos) composed of virtual things, with which users can interact through dedicated and standardized broker servers in which the technology can be chosen among those offered by the platform, such as oneM2M, NGSI and NGSI-LD. VirIoT allows developers to focus their efforts exclusively on IoT applications without worrying about infrastructure management and allows cloud providers to expand their IoT services portfolio. VirIoT uses external things and cloud/edge computing resources to deliver the IoT virtualization services. Its open-source architecture is microservice-based and runs on top of a distributed Kubernetes platform with nodes in central and edge data centers. The architecture is scalable, efficient and able to support the continuous integration of heterogeneous things and IoT standards, taking care of interoperability issues. Using a VirIoT deployment spanning data centers in Europe and Japan, we conducted a performance evaluation with a two-fold objective: showing the efficiency and scalability of the architecture; and leveraging VirIoT’s ability to integrate different IoT standards in order to make a fair comparison of some open-source IoT Broker implementations, namely Mobius for oneM2M, Orion for NGSIv2, Orion-LD and Scorpio for NGSI-LD

Bromatological and mineral compositions of collected pollen for africanized honeybees (Apis mellifera L.) in Botucatu, São Paulo State

The bromatological and mineral compositions of pollen collected by Africanized honeybees (Apis mellifera L.) in the Botucatu region, São Paulo State, Brazil, from August to November, 1966, were investigated. The pollen collections from six beehives, at weekly intervals, lasted for seven consecutive days. Mean fresh pollen composition was: 75.9% dry matter, 26.2% crude protein (CP), 5.1% ether extract (EE), 2.6% ash, 1.1% crude fiber, 3.58% nitrogen, 0.43% phosphorus, 0.67% potassium, 0.26% calcium, 0.08% magnesium, 0.21% sulfur, 114.2 ppm iron, 88.4 ppm zinc, 15.0 ppm copper, 32.4 ppm manganese, and 9.9 ppm boron. Statistical analysis showed that pollen collected in October had the highest contents of CP, and of the elements P, S and Mn compared to the other months. According to cluster and principal components analyses for bromatological composition, the variables with most and least discriminatory power were the percentages of CP and EE, respectively. As to mineral composition, the most discriminatory variables were S, Mn, and N, while the least discriminatory were Mg, B, and K

Maroon Archaeology Beyond the Americas: A View From Kenya

Archaeological research on Maroons—that is, runaway slaves—has been largely confined to the Americas. This essay advocates a more global approach. It specifically uses two runaway slave communities in 19th-century coastal Kenya to rethink prominent interpretive themes in the field, including “Africanisms,” Maroons’ connections to indigenous groups, and Maroon group cohesion and identity. This article’s analysis demonstrates that the comparisons enabled by a more globalized perspective benefit the field. Instead of eliding historical and cultural context, these comparisons support the development of more localized and historically specific understandings of individual runaway slave communities both in Kenya and throughout the New World

Dinkelbach Approach to Solving a Class of Fractional Optimal Control Problems

We consider optimal control problems with functional given by the ratio of two integrals (fractional optimal control problems). In particular, we focus on a special case with affine integrands and linear dynamics with respect to state and control. Since the standard optimal control theory cannot be used directly to solve a problem of this kind, we apply Dinkelbach’s approach to linearize it. Indeed, the fractional optimal control problem can be transformed into an equivalent monoparametric family { P q } of linear optimal control problems. The special structure of the class of problems considered allows solving the fractional problem either explicitly or requiring straightforward classical numerical techniques to solve a single equation. An application to advertising efficiency maximization is presented

Contamination of rural surface and ground water by endosulfan in farming areas of the Western Cape, South Africa

BACKGROUND: In South Africa there is little data on environmental pollution of rural water sources by agrochemicals. METHODS: This study investigated pesticide contamination of ground and surface water in three intensive agricultural areas in the Western Cape: the Hex River Valley, Grabouw and Piketberg. Monitoring for endosulfan and chlorpyrifos at low levels was conducted as well as screening for other pesticides. RESULTS: The quantification limit for endosulfan was 0.1 μg/L. Endosulfan was found to be widespread in ground water, surface water and drinking water. The contamination was mostly at low levels, but regularly exceeded the European Drinking Water Standard of 0.1 μg/L. The two most contaminated sites were a sub-surface drain in the Hex River Valley and a dam in Grabouw, with 0.83 ± 1.0 μg/L (n = 21) and 3.16 ± 3.5 μg/L (n = 13) average endosulfan levels respectively. Other pesticides including chlorpyrifos, azinphos-methyl, fenarimol, iprodione, deltamethrin, penconazole and prothiofos were detected. Endosulfan was most frequently detected in Grabouw (69%) followed by Hex River (46%) and Piketberg (39%). Detections were more frequent in surface water (47%) than in groundwater (32%) and coincided with irrigation, and to a lesser extent, to spraying and trigger rains. Total dietary endosulfan intake calculated from levels found in drinking water did not exceed the Joint WHO/FAO Meeting on Pesticide Residues (JMPR) criteria. CONCLUSION: The study has shown the need for monitoring of pesticide contamination in surface and groundwater, and the development of drinking water quality standards for specific pesticides in South Africa

Disease Gene Characterization through Large-Scale Co-Expression Analysis

In the post genome era, a major goal of biology is the identification of specific roles for individual genes. We report a new genomic tool for gene characterization, the UCLA Gene Expression Tool (UGET).Celsius, the largest co-normalized microarray dataset of Affymetrix based gene expression, was used to calculate the correlation between all possible gene pairs on all platforms, and generate stored indexes in a web searchable format. The size of Celsius makes UGET a powerful gene characterization tool. Using a small seed list of known cartilage-selective genes, UGET extended the list of known genes by identifying 32 new highly cartilage-selective genes. Of these, 7 of 10 tested were validated by qPCR including the novel cartilage-specific genes SDK2 and FLJ41170. In addition, we retrospectively tested UGET and other gene expression based prioritization tools to identify disease-causing genes within known linkage intervals. We first demonstrated this utility with UGET using genetically heterogeneous disorders such as Joubert syndrome, microcephaly, neuropsychiatric disorders and type 2 limb girdle muscular dystrophy (LGMD2) and then compared UGET to other gene expression based prioritization programs which use small but discrete and well annotated datasets. Finally, we observed a significantly higher gene correlation shared between genes in disease networks associated with similar complex or Mendelian disorders.UGET is an invaluable resource for a geneticist that permits the rapid inclusion of expression criteria from one to hundreds of genes in genomic intervals linked to disease. By using thousands of arrays UGET annotates and prioritizes genes better than other tools especially with rare tissue disorders or complex multi-tissue biological processes. This information can be critical in prioritization of candidate genes for sequence analysis