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On the basis of the notion of approximating family of cones and a generalized type of Gerstewitz’s/Tammer’s nonlinear scalarization functional, we establish variants of the Ekeland variational principle (for short, EVP) involving set perturbations for a type of approximate proper solutions in the sense of Henig of a vector equilibrium problem. Initially, these results are obtained for both an unconstrained and a constrained vector equilibrium problem, where the objective function takes values in a real locally convex Hausdorff topological linear space. After that, we consider special cases when the objective function takes values in a normed space and in a finite-dimensional vector space. For the finite-dimensional objective space with a polyhedral ordering cone, we give the explicit representation of variants of EVP depending on matrices, and in such a way, some selected applications for multiobjective optimization problems and vector variational inequality problems are also derived.

In this paper, a new type of heterogeneous catalytic system was devised for the synthesis of 4,4′-bis-(2,6-di-t-butylphenol), (hereinafter referred to as bisphenol-VN). The properties of the catalytic system were studied using SEM–EDS and transmission electron microscope methods. The results have demonstrated the presence of hydrophobic phenolate layer on the surface of the catalytic system, which provides high catalytic activity in the synthesis of bisphenol-VN. The bisphenol-VN obtained was taken for structural analysis by SEM, IR and MS-HPLC methods to prove the purity of the product. Simultaneously, the bisphenol-VN was also tested in natural rubber to enhance the physical–mechanical properties. The use of bisphenol-VN extends the oxidation induction time of natural rubber by three times in the comparison with the commercial antioxidant 6PPD.

In this paper, we provide variants of the Ekeland variational principle for a type of approximate proper solutions of a vector equilibrium problem, whose final space is finite dimensional and partially ordered by a polyhedral cone. Depending on the choice of an approximation set that defines these solutions, we prove that they approximate suitably exact weak efficient/proper efficient/efficient solutions of the problem. The variants of the Ekeland variational principle are obtained for an unconstrained and also for a cone-constrained vector equilibrium problem, through a nonlinear scalarization, and expressed by means of the matrix that defines the ordering cone, which makes them easier to handle. At the end, the results are applied to multiobjective optimization problems, for which a related vector variational inequality problem is defined.

We prove an induction theorem and versions of the Ekeland variational principle for functions on partial metric spaces. As the distance is a partial metric, these versions have additional terms in ...

Parabens are a class of compounds primarily used as antimicrobial preservatives in pharmaceutical products, cosmetics, and foodstuff. Their widely used field leads to increasing concentrations detected in various environmental matrices like water, soil, and sludges, even detected in human tissue, blood, and milk. Treatment techniques, including chemical advanced oxidation, biological degradation, and physical adsorption processes, have been widely used to complete mineralization or to degrade parabens into less complicated byproducts. All kinds of processes were reviewed to give a completed picture of parabens removal. In light of these treatment techniques, advanced photocatalysis, which is emerging rapidly and widely as an economical, efficient, and environmentally-friendly technique, has received considerable attention. TiO2-based and non-TiO2-based photocatalysts play an essential role in parabens degradation. The effect of experimental parameters, such as the concentration of targeted parabens, concentration of photocatalyst, reaction time, and initial solution pH, even the presence of radical scavengers, are surveyed and compared from the literature. Some representative parabens such as methylparaben, propylparaben, and benzylparaben have been successfully studied the reaction pathways and their intermediates in their degradation process. As reported in the literature, the degradation of parabens involves the production of highly reactive species, mainly hydroxyl radicals. These reactive radicals would attack the paraben preservatives, break, and finally mineralize them into simpler inorganic and nontoxic molecules. Concluding perspectives on the challenges and opportunities for photocatalysis toward parabens remediation are also intensively highlighted.

International audience; Data owners are creating an ever richer set of information resources online, and these are being used for more and more applications. Spatial data on the Web is becoming ubiquitous and voluminous with the rapid growth of location-based services, spatial technologies, dynamic location-based data and services published by different organizations. However, the heterogeneity and the peculiarities of spatial data, such as the use of different coordinate reference systems, make it difficult for data users, Web applications, and services to discover, interpret and use the information in the large and distributed system that is the Web. To make spatial data more effectively available, this paper summarizes the work of the joint W3C/OGC Working Group on Spatial Data on the Web that identifies 14 best practices for publishing spatial data on the Web. The paper extends that work by presenting the identified challenges and rationale for selection of the recommended best practices, framed by the set of principles that guided the selection. It describes best practices that are employed to enable publishing, discovery and retrieving (querying) spatial data on the Web, and identifies some areas where a best practice has not yet emerged.

The wide adoption of increasingly cheap and computationally powerful single-board computers, has triggered the emergence of new paradigms for collaborative data processing among IoT devices. Motivated by the billions of ARM chips having been shipped as IoT gateways so far, our paper proposes a novel continuous federation approach that uses RDF Stream Processing (RSP) engines as autonomous processing agents. These agents can coordinate their resources to distribute processing pipelines by delegating partial workloads to their peers via subscribing continuous queries. Our empirical study in “cooperative sensing” scenarios with resourceful experiments on a cluster of Raspberry Pi nodes shows that the scalability can be significantly improved by adding more autonomous agents to a network of edge devices on demand. The findings open several new interesting follow-up research challenges in enabling semantic interoperability for the edge computing paradigm.

Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio&ndash, temporal correlations. Most semantic approaches do not have spatio&ndash, temporal support. Some of them have attempted to provide full spatio&ndash, temporal support, but have poor performance for complex spatio&ndash, temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio&ndash, temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio&ndash, temporal computing in the linked sensor data context.

To enable efficiency in stream processing, the evaluation of a query is usually performed over bounded parts of (potentially) unbounded streams, i.e., processing windows “slide” over the streams. To avoid inefficient re-evaluations of already evaluated parts of a stream in respect to a query, incremental evaluation strategies are applied, i.e., the query results are obtained incrementally from the result set of the preceding processing state without having to re-evaluate all input buffers. This method is highly efficient but it comes at the cost of having to maintain processing state, which is not trivial, and may defeat performance advantages of the incremental evaluation strategy. In the context of RDF streams the problem is further aggravated by the hard-to-predict evolution of the structure of RDF graphs over time and the application of sub-optimal implementation approaches, e.g., using relational technologies for storing data and processing states which incur significant performance drawbacks for graph-based query patterns. To address these performance problems, this paper proposes a set of novel operator-aware data structures coupled with incremental evaluation algorithms which outperform the counterparts of relational stream processing systems. This claim is demonstrated through extensive experimental results on both simulated and real datasets.

Today, the Internet of Things (IoT) comprises vertically oriented platforms for things. Developers who want to use them need to negotiate access individually and adapt to the platform-specific API and information models. Having to perform these actions for each platform often outweighs the possible gains from adapting applications to multiple platforms. This fragmentation of the IoT and the missing interoperability result in high entry barriers for developers and prevent the emergence of broadly accepted IoT ecosystems. The BIG IoT (Bridging the Interoperability Gap of the IoT) project aims to ignite an IoT ecosystem as part of the European Platforms Initiative. As part of the project, researchers have devised an IoT ecosystem architecture. It employs five interoperability patterns that enable cross-platform interoperability and can help establish successful IoT ecosystems.

The Water Framework Directive aims at reaching the good ecological status of the surface and ground water bodies (László et al. Microchem J 85(1):65-71, 2007). The paper deals with quality evaluation of waters with special focus on the water chemistry parameters as defined in the Water Framework Directive and pertaining legal regulations. The purpose of this paper is to devise a quantitative type of water quality assessment method which could provide rapid, accurate, and reliable information on the quality of the surface waters by using water chemistry parameters. Quality classes have been defined for every water chemistry parameter in light of the legal limit values of the water parameters. In addition to this, weight indices were calculated on the basis of the outcome of the paired comparison of water chemistry parameters and normalized matrix. This was followed by the parametric level analysis of the water chemistry parameters, and finally, the aquatic environment index (AEI) was calculated, which provided general information on the quality of water regarding the water chemistry parameters. The method was illustrated on Lake Balaton, Hungary in which case water samples taken from Balatonfüred City lake area were analyzed and evaluated with the method devised.

The aim of this study was to prepare novel supramolecular hybrid nanoparticles (HNPs) that can selectively separate and recover scandium metal ions, Sc(III), from an aqueous phase based on molecular recognition technology (MRT). Moreover, this approach is fully compatible with green chemistry principles. In this work, natural amorphous silica (SiO2) nanoparticles were prepared by a precipitation method from Iraqi rice husk (RH) followed by surface modification with 3-amino-propyl triethoxysilane (APTES) as coupling agent and Kryptofix 2.2.2 (K2.2.2) as polycyclic ligand. To evaluate the potential of the hybrid nanoparticles, the prepared HNPs were used for the solid&ndash, liquid extraction of scandium, Sc(III), ions from model solutions due to the fact that K2.2.2 are polycyclic molecules. These polycyclic molecules are able to encapsulate cations according to the corresponding cavity size with the ionic radius of metal by providing a higher protection due their cage-like structures. Moreover, the authors set the objectives to design a high-technology process using these HNPs and to develop a Sc recovery method from the aqueous model solution prior to employing it in industrial applications, e.g., for Sc recovery from red mud leachate. The concentrations of Sc model solutions were investigated using the UV-Vis spectrophotometer technique. Different characterization techniques were used including scanning electron microscope (SEM), atomic force microscopy (AFM), Brunauer&ndash, Emmett&ndash, Teller (BET), X-ray diffraction (XRD), X-ray fluorescence (XRF), and Fourier transform infrared (FTIR). The extraction efficiency of Sc varied from 81.3% to 96.7%. Moreover, the complexed Sc ions were efficiently recovered by HCl with 0.1 mol/L concentration. The stripping ratios of Sc obtained ranged from 93.1% to 97.8%.

Semantic interoperability for the Internet of Things (IoT) is enabled by standards and technologies from the Semantic Web. As recent research suggests a move towards decentralised IoT architectures, we have investigated the scalability and robustness of RDF (Resource Description Framework)engines that can be embedded throughout the architecture, in particular at edge nodes. RDF processing at the edge facilitates the deployment of semantic integration gateways closer to low-level devices. Our focus is on how to enable scalable and robust RDF engines that can operate on lightweight devices. In this paper, we have first carried out an empirical study of the scalability and behaviour of solutions for RDF data management on standard computing hardware that have been ported to run on lightweight devices at the network edge. The findings of our study shows that these RDF store solutions have several shortcomings on commodity ARM (Advanced RISC Machine) boards that are representative of IoT edge node hardware. Consequently, this has inspired us to introduce a lightweight RDF engine, which comprises an RDF storage and a SPARQL processor for lightweight edge devices, called RDF4Led. RDF4Led follows the RISC-style (Reduce Instruction Set Computer) design philosophy. The design constitutes a flash-aware storage structure, an indexing scheme, an alternative buffer management technique and a low-memory-footprint join algorithm that demonstrates improved scalability and robustness over competing solutions. With a significantly smaller memory footprint, we show that RDF4Led can handle 2 to 5 times more data than popular RDF engines such as Jena TDB (Tuple Database) and RDF4J, while consuming the same amount of memory. In particular, RDF4Led requires 10%&ndash, 30% memory of its competitors to operate on datasets of up to 50 million triples. On memory-constrained ARM boards, it can perform faster updates and can scale better than Jena TDB and Virtuoso. Furthermore, we demonstrate considerably faster query operations than Jena TDB and RDF4J.

This study aims to develop advanced sorbents for the removal of hydrocarbons from surface waters. The microemulsion method was implemented to modify the surface structure of multiwalled carbon nanotube (MWCNTs) by attaching a hydrocarbon tail on its surface. The structural and surface chemistry properties of the prepared adsorbents were studied by different surface analytical techniques such as Brunauer-Emmett-Teller (BET) method, X-Ray diffraction (XRD), Raman-spectroscopy, Fourier-transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TG). Scanning electron microscopy (SEM) was used to study the microstructure and morphology of the samples. Different model hydrocarbon compounds were used for this investigation. The hydrocarbon removal efficiencies of the unmodified and functionalized sorbents were studied by using total organic carbon analyzer (TOC), gas chromatography (GC), and UV–vis spectroscopy (UV–vis) techniques. Surface chemistry studies over raw and microemulsified multiwalled carbon nanotubes (μEMWCNT) revealed that that microemulsion functionalization resulted in changes in the CH bending vibrations of the functionalized MWCNTs as compared to the raw MWCNTs, demonstrating the existence of intermolecular CH-π interactions between the carbon nanotubes and lauric/miristic acid. The experimental results revealed that microemulsion technique as a type of surface functionalization solved one main issue regarding MWCNTs modification as it proved to have a beneficial effect on MWCNTs’ hydrophobic properties without the need for additional functionalization and substitution steps to attach hydrocarbon side chains. Using kerosene as a hydrocarbon model mixture, compared with raw MWCNTs, the maximum adsorption capacity of μMWCNTs increased by 63.5 %. Additionally, the outcomes indicated that the n-octane adsorption capacities over μEMWCNTs reached 6.07 g/g. The kinetic studies demonstrated that the adsorption process over μMWCNTs could be well described by the pseudo-second-order model with a high correlation coefficient.

Semantic interoperability for the Internet of Things(IoT) is being enabled by standards and technologies from the Semantic Web. As recent research suggests a move towards decentralised IoT architectures, our focus is on how to enable scalable and robust RDF engines that can be embedded throughout the architecture, in particular at edge nodes. RDF processing at edge enables the creation of semantic integration gateways for locally connected low-level devices. We introduce a lightweight RDF engine, which comprises of RDF storage and SPARQL processor, for the lightweight edge devices, called RDF4Led. RDF4Led follows the RISCstyle (Reduce Instruction Set Computer) design philosophy. The design comprises a flash-aware storage structure, an indexing scheme and a low-memory-footprint join algorithm which improves scalability as well as robustness over competing solutions. With a significantly smaller memory footprint, we show that RDF4Led can handle 2 to 5 times more data than RDF engines such as Jena TDB and Virtuoso. On three types of ARM boards, RDF4Led requires 10--30% memory of its competitors to operate up to 30 million triples dataset; it can perform faster updates and can scale better than Jena TDB and Virtuoso. Furthermore, we demonstrate considerably faster query operations than Jena TDB.

International audience; The joint W3C (World Wide Web Consortium) and OGC (Open Geospatial Consortium) Spatial Data on the Web (SDW) Working Group developed a set of ontologies to describe sensors, actuators, samplers as well as their observations, actuation, and sampling activities. The ontologies have been published both as a W3C recommendation and as an OGC implementation standard. The set includes a lightweight core module called SOSA (Sensor, Observation, Sampler, and Actuator) available at: http://www.w3.org/ns/sosa/, and a more expressive extension module called SSN (Semantic Sensor Network) available at: http://www.w3.org/ns/ssn/. Together they describe systems of sensors and actuators, observations, the used procedures, the subjects and their properties being observed or acted upon, samples and the process of sampling, and so forth. The set of ontologies adopts a modular architecture with SOSA as a self-contained core that is extended by SSN and other modules to add expressivity and breadth. The SOSA/SSN ontologies are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Internet of Things. In this paper we give an overview of the ontologies and discuss the rationale behind key design decisions, reporting on the differences between the new SSN ontology presented here and its predecessor [9] developed by the W3C Semantic Sensor Network Incubator group (the SSN-XG). We present usage examples and describe alignment modules that foster interoperability with other ontologies.

The wide adoption of the RDF data model has called for efficient and scalable RDF query processing schemes. As a response to this call, a number of centralized RDF query processing systems have been designed to tackle this challenge. In these systems, the storage and query processing of RDF datasets are managed on a single node. In this chapter, we give an overview of various techniques and systems for centrally querying RDF datasets.

The nature of Web data is changing. The popularity of news feeds and social media, the rise of the Web of Things, and the adoption of sensor technologies are examples of streaming data that reached the Web scale. The different nature of streaming data calls for specific solutions to problems like data integration and analytics. There is a need for streaming-specific Web resources: new vocabularies to describe, find and select streaming data sources, and systems that can cooperate dynamically to solve stream processing tasks. To foster interoperability between these streaming services on the Web, we propose the Vocabulary & Catalog of Linked Streams (VoCaLS). VoCaLS is a three-module ontology to (i) publish streaming data following Linked Data principles, (ii) describe streaming services and (iii) track the provenance of stream processing.

The ever-increasing growth of the Internet of Things (IoT) has attracted a considerable amount of research attention from the Semantic Web community in order to address the challenge of poor interoperability. However, our survey of research work has shown that the goal of providing an intelligent pro- cessing and analysis engine for IoT has still not been fully achieved. Central to this problem is the requirement for a semantic spatio-temporal query processing engine that is able to not only analyze spatio-temporal correlations in a massive amount of IoT data, but that can also generate an effective query plan for a given query to execute in a timely manner. Needless to say, query planning for the multidimensional data like IoT is a costly operation. The most known techniques are either based on the cost model or by using spatio-temporal data statistics and heuristics. In this paper, we propose an alternative solution that uses query similarity identification in conjunction with machine learning techniques to recommend a previously generated query plan to the optimizer for a given query. Our approach also aims to predict the query execution time for the purposes of workload management and capacity planning. Our extensive experiments indicate the efficiency of our learning approach with an impressive prediction accuracy on test queries.

The join operator is a core component of an RDF Stream Processing engine. The join operations usually dominate the processing load of a query execution plan. Due to the constantly updating nature of continuous queries, the query optimiser has to frequently change the optimal execution plan for a query. However, optimising the join executing plan for every execution step might be prohibitively expensive, hence, dynamic optimisation of continuous join operations is still a challenging problem so far. Therefore, this paper proposes the first adaptive optimisation approach towards this problem in the context of RDF Stream Processing. The approach comes with two dynamic cost-based optimisation algorithms which use a light-weight process to search for the best execution plan for every execution step. The experiments show the encouraging results towards this direction.

International audience; The Sensor, Observation, Sample, and Actuator (SOSA) ontology provides a formal but lightweight general-purpose specification for modellingthe interaction between the entities involved in the acts of observation, actuation, and sampling. SOSA is the result of rethinking the W3C-XG Semantic Sensor Network (SSN) ontology based on changes in scope and target audience, technical developments, and lessons learned over the past years. SOSA also acts as a replacement of SSN’s Stimulus Sensor Observation (SSO) core. It has been developed by the first joint working group of the Open Geospatial Consortium (OGC) and the World Wide Web Consortium (W3C) on Spatial Data on the Web. In this work, we motivate the need for SOSA, provide an overview of the main classes and properties, and briefly discuss its integration with the new release of the SSN ontology as well as various other alignments to specifications such as OGC’s Observations and Measurements (O&M), Dolce-Ultralite (DUL), and other prominent ontologies. We will also touch upon common modelling problems and application areas related to publishing and searching observation, sampling, and actuation data on the Web. The SOSA ontology and standard can be accessed at https://www.w3.org/TR/vocab-ssn/.

The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations — the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.

The Internet of Things (IoT) with billions of connected devices has been generating an enormous amount of data every hour. Connecting every data item generated by IoT to the rest of the digital world to turn this data into meaningful actions will create new capabilities, richer experiences, and unprecedented economic opportunities for businesses, individuals, and countries. However, providing an integrated view for exploring and querying such data at real-time is extremely challenging due to its Big Data natures: big volume, fast real-time update and messy data sources. To address this challenge, we provide a unified integrated and live view for heterogeneous IoT data sources using Linked Data, called the Graph of Things (GoT). GoT is backed by a scalable and elastic software stack to deal with billions of records of historical and static datasets in conjunction with millions of triples being fetched and enriched to connect to GoT per hour in real time. GoT makes approximately a half of million stream data sources queryable via a SPARQL endpoint and a continuous query channel that enable us to create a live explorer of GoT (http://graphofthings.org/) with just HTML and Javascript.

The current state of the art in RDF Stream Processing (RSP) proposes several models and implementations to combine Semantic Web technologies with Data Stream Management System (DSMS) operators like windows. Meanwhile, only a few solutions combine Semantic Web and Complex Event Processing (CEP), which includes relevant features, such as identifying sequences of events in streams. Current RSP query languages that support CEP features have several limitations: EP-SPARQL can identify sequences, but its selection and consumption policies are not all formally defined, while C-SPARQL offers only a naive support to pattern detection through a timestamp function. In this work, we introduce an RSP query language, called RSEP-QL, which supports both DSMS and CEP operators, with a special interest in formalizing CEP selection and consumption policies. We show that RSEP-QL captures EP-SPARQL and C-SPARQL, and offers features going beyond the ones provided by current RSP query languages.

Linked Stream Data extends the Linked Data paradigm to dynamic data sources. It enables the integration and joint processing of heterogeneous stream data with quasi-static data from the Linked Data Cloud in near-real-time. Several Linked Stream Data processing engines exist but their scalability still needs to be in improved in terms of (static and dynamic) data sizes, number of concurrent queries, stream update frequencies, etc. So far, none of them supports parallel processing in the Cloud, i.e., elastic load profiles in a hosted environment. To remedy these limitations, this paper presents an approach for elastically parallelizing the continuous execution of queries over Linked Stream Data. For this, we have developed novel, highly efficient, and scalable parallel algorithms for continuous query operators. Our approach and algorithms are implemented in our CQELS Cloud system and we present extensive evaluations of their superior performance on Amazon EC2 demonstrating their high scalability and excellent elasticity in a real deployment.

The Internet and the World Wide Web have transformed modern life by connecting the real world. With the massive connectivity extension of the Net to a much wider world wide web of the manifold physical objects, we are on the verge of the next evolution of the Internet, so called the Internet of Everything (IoE). However, enabling seamless interoperability is still a biggest challenge towards building the next generation of IoE applications. Believing in Linked Data as a promising solution to address this challenge, we propose the idea of “Linking Everything” by extending Linked Data Principles to interlink “everything” into a hypergraph. Via this book chapter, we present how to make this hypergraph programmable via the emerging Semantic Web technologies.

In industry, dashboards are often used to monitor fleets of assets, such as trains, machines or buildings. In such industrial fleets, the vast amount of sensors evolves continuously, new sensor data exchange protocols and data formats are introduced, new visualization types may need to be introduced and existing dashboard visualizations may need to be updated in terms of displayed sensors. These requirements motivate the development of dynamic dashboarding applications. These, as opposed to fixed-structure dashboard applications, allow users to create visualizations at will and do not have hard-coded sensor bindings. The state-of-the-art in dynamic dashboarding does not cope well with the frequent additions and removals of sensors that must be monitored&mdash, these changes must still be configured in the implementation or at runtime by a user. Also, the user is presented with an overload of sensors, aggregations and visualizations to select from, which may sometimes even lead to the creation of dashboard widgets that do not make sense. In this paper, we present a dynamic dashboard that overcomes these problems. Sensors, visualizations and aggregations can be discovered automatically, since they are provided as RESTful Web Things on a Web Thing Model compliant gateway. The gateway also provides semantic annotations of the Web Things, describing what their abilities are. A semantic reasoner can derive visualization suggestions, given the Thing annotations, logic rules and a custom dashboard ontology. The resulting dashboarding application automatically presents the available sensors, visualizations and aggregations that can be used, without requiring sensor configuration, and assists the user in building dashboards that make sense. This way, the user can concentrate on interpreting the sensor data and detecting and solving operational problems early.