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Summary A nine-week feeding trial was conducted to test the hypothesis that an adequate methionine diet might improve growth, feed utilization, body composition and physiology, and biochemical parameters in pre-adult blunt snout bream Megalobrama amblycephala, whereas a methionine deficiency might have adverse effects on these parameters. Six isonitrogenous and isoenergetics semi-purified diets (33.0% crude protein, 7.0% crude lipid) were formulated to contain graded methionine levels (0.39–1.54% of dry weight) at 0.25% increments replaced by equal proportions of glycine. Results show that the survival rate (SR) was not significantly affected by the dietary methionine level. Final weight (FW), feed efficiency ratios (FER), weight gain (WG), and specific growth rate (SGR) increased with increasing dietary methionine levels up to 1.00% and then showed a declining trend. Using quadratic regression analysis of FER and SGR, the dietary methionine requirement was estimated to be 0.74% (2.24% of dietary protein) and 0.76% of the diet (2.30% of dietary protein), respectively. Fish fed the 0.39% methionine diet showed significantly lower whole body protein content compared to those fed with 0.85, 1.00 and 1.24% methionine diets (P 0.05). No significant differences were found in other indexes such as the hepatosomatic index (HSI), viscerosomatic index (VSI), condition factor (CF), albumin (ALB), total protein (TP), alanine aminotransferase (ALT), and spartate transaminase (AST) (P > 0.05). Most important, the optimal dietary methionine level of pre-adult blunt snout bream should be 0.74–0.76% of the diet (2.24–2.30% of dietary protein).

Context-aware systems are intelligent systems that recommend users with adaptive service choices which are appropriate to their profile preferences and contextual situations. However, the key challenges in these systems are extracting dynamic context information and updating user's profile information. Recently by the popularity of IoT devices as well as the availability of the wireless network, a massive volume of data is available to gain useful insights into user profile which contains dynamic contextual information. IoT sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data, we need to understand them and use them in a context-aware computing platform. In this article, an adaptive context-aware service composition system is proposed using an IoT based social network dataset to find users' preferences, improve quality of experience, and boosts the personalization level of services. This paper proposes an 10T-based context-aware service composition to seamlessly find adaptive services offered by a service provider at anytime and anywhere. Firstly, the proposed system considers the dynamic social profile of the user and clusters their preferences while other systems take only static user interests into account. Secondly, this system increases the level of personalization by extracting context information from a user's IoT devices. Finally, the adaptive service composition provides users with various context-aware recommendation including movies, music, venues, radio stations, and events simultaneously. To evaluate the efficiency of the proposed system, we target some of the smart objects such as smart watches/bands, smartphones/tablets, Google Home, and personal computers for detecting several contexts (e.g location, time, user activity, interest and e.t.c.). Experimental evaluations show that the proposed algorithm can improve accuracy and personalization of filtered items with compare to other widely-applied conventional filtering algorithms.

Spatial Data Infrastructures (SDI) established during the past two decades &ldquo, unlocked&rdquo, heterogeneous geospatial datasets. The European Union INSPIRE Directive laid down the foundation of a pan-European SDI where thousands of public sector data providers make their data, including sensor observations, available for cross-border and cross-domain reuse. At the same time, SDIs should inevitably adopt new technology and standards to remain fit for purpose and address in the best possible way the needs of different stakeholders (government, businesses and citizens). Some of the recurring technical requirements raised by SDI stakeholders include: (i) the need for adoption of RESTful architectures, together with (ii) alternative (to GML) data encodings, such as JavaScript Object Notation (JSON) and binary exchange formats, and (iii) adoption of asynchronous publish&ndash, subscribe-based messaging protocols. The newly established OGC standard SensorThings API is particularly interesting to investigate for INSPIRE, as it addresses together all three topics. In this manuscript, we provide our synthesised perspective on the necessary steps for the OGC SensorThings API standard to be considered as a solution that meets the legal obligations stemming out of the INSPIRE Directive. We share our perspective on what should be done concerning: (i) data encoding, and (ii) the use of SensorThings API as a download service.

Summary An 8-week feeding trial was carried out to test the hypothesis that adequate dietary valine might improve growth, feed utilization and protein content in blunt snout bream, whereas a valine deficiency might have adverse effects on these parameters. Six isonitrogenous (34% crude protein) and isoenergetic (14.2 MJ kg−1 digestible energy) experimental diets were formulated to contain graded valine levels (0.66, 0.95, 1.26, 1.55, 1.87 and 2.16% of dry weight) at about 0.30% increments replaced by equal proportions of glycine. At the end of the experiment the survival rate was not significantly affected by the dietary valine level. Final weight, weight gain (WG), specific growth rate (SGR), feed efficiency ratio (FER) and protein efficiency ratio (PER) increased with an increasing dietary valine level up to the 1.26% diet, and thereafter remained relatively constant. Dietary valine levels significantly affected the viscerosomatic index, but not the hepatosomatic index or condition factor. The dietary valine levels significantly affected the protein contents of whole body and plasma. Dietary valine supplementation significantly increased the plasma valine concentration, but not the levels of other branched-chain amino acids (isoleucine and leucine). Based on SGR and FER, the optimal dietary valine requirements of juvenile blunt snout bream were determined to be 1.32% of the diet (3.88% of dietary protein) and 1.26% of the diet (3.71% of dietary protein), respectively, using broken-line regression analysis.

Water level monitoring provides essential information about the condition of aquifers and their responses to water extraction, land-use change, and climatic variability. It is important to have a spatially distributed, long-term monitoring well network for sustainable groundwater resource management. Community-based monitoring involving citizen scientists provides an approach to complement existing government-run monitoring programs. This article demonstrates the feasibility of establishing a large-scale water level monitoring network of private water supply wells using an example from Rocky View County (3900 km(2) ) in Alberta, Canada. In this network, community volunteers measure the water level in their wells, and enter these data through a web-based data portal, which allows the public to view and download these data. The close collaboration among the university researchers, county staff members, and community volunteers enabled the successful implementation and operation of the network for a 5-year pilot period, which generated valuable data sets. The monitoring program was accompanied by education and outreach programs, in which the educational materials on groundwater were developed in collaboration with science teachers from local schools. The methodology used in this study can be easily adopted by other municipalities and watershed stewardship groups interested in groundwater monitoring. As governments are starting to rely increasingly on local municipalities and conservation authorities for watershed management and planning, community-based groundwater monitoring provides an effective and affordable tool for sustainable water resources management.

The aim of this research was to study the influence of a high-carbohydrate diet on the growth and lipid metabolism of Megalobrama amblycephala. Wheat starch (carbohydrate source) and soybean oil (lipid source) were used to prepare a semipurified diet (control) and a high-carbohydrate diet (experimental). A series of measurements were conducted, which included growth performance, blood biochemical indices, muscle components, fatty acid composition, liver lipid deposition, and tissue structure. Results indicated that high levels of carbohydrate significantly reduce weight gain, specific growth rate, protein efficiency, and muscle protein content and increase lipids in fish. Composition of the muscle fatty acids was also altered and the activity of the hepatopancreatic lipid-metabolism enzymes decreased. A large quantity of lipid droplets, and vacuole degeneration, were observed in fish liver sections of the experimental group, indirectly suggesting a negative effect of a high-carbohydrate diet on the liver of M. amblycephala.

Recent advances in sensor and platform technologies such as satellite systems, unmanned aerial vehicles (UAV), manned aerial platforms, and ground-based sensor networks have resulted in massive volumes of data is produced and collected about the earth. Processing, managing, and analyzing these data is one of the main challenges in 3D synthetic representation used in modeling and simulation (M&S) of the natural environment. M&S devices, such as flight simulators, traditionally require a variety of different databases to provide a synthetic representation of the world. M&S often requires integration of data from a variety of sources stored in different formats. Thus, for simulation of a complex synthetic environment, such as a 3D terrain model, tackling interoperability among its components (geospatial data, natural and man-made objects, dynamic and static models) is a critical challenge. Conventional approaches used local proprietary data models and formats. These approaches often lacked interoperability and created silos of content within the simulation community. Therefore, open geospatial standards are increasingly perceived as a means to promote interoperability and reusability for 3D M&S. In this paper, the Open Geospatial Consortium (OGC) CDB Standard is introduced. “CDB” originally refers to Common DataBase which is currently considered as a name with no abbreviation in the OGC community. The OGC CDB is an international standard for structuring, modeling, and storing geospatial information required in high performance modeling and simulation applications. CDB defines the core conceptual models, use cases, requirements, and specifications for employing geospatial data in 3D M&S. The main features of the OGC CDB Standard are described as run-time performance, full plug-and-play interoperable geospatial data store, usefulness in 3D and dynamic simulation environment, ability to integrate proprietary and open-source data formats. Furthermore, compatibility with the OGC standards baseline reduces the complexity of discovering, transforming, and streaming geospatial data into the synthetic environment and makes them more widely acceptable to major geospatial data/software producers. This paper includes an overview of OGC CDB version 1.0 which defines a conceptual model and file structure for the storage, access, and modification of a multi-resolution 3D synthetic environment data store. Finally, this paper presents a perspective of future versions of the OGC CDB and what the steps are for humanizing the OGC CDB standard with the other OGC/ISO standards baseline.

The ubiquity of mobile sensors (such as GPS, accelerometer and gyroscope) together with increasing computational power have enabled an easier access to contextual information, which proved its value in next generation of the recommender applications. The importance of contextual information has been recognized by researchers in many disciplines, such as ubiquitous and mobile computing, to filter the query results and provide recommendations based on different user status. A context-aware recommendation system (CoARS) provides a personalized service to each individual user, driven by his or her particular needs and interests at any location and anytime. Therefore, a contextual recommendation system changes in real time as a user’s circumstances changes. CoARS is one of the major applications that has been refined over the years due to the evolving geospatial techniques and big data management practices. In this paper, a CoARS is designed and implemented to combine the context information from smartphones’ sensors and user preferences to improve efficiency and usability of the recommendation. The proposed approach combines user’s context information (such as location, time, and transportation mode), personalized preferences (using individuals past behavior), and item-based recommendations (such as item’s ranking and type) to personally filter the item list. The context-aware methodology is based on preprocessing and filtering of raw data, context extraction and context reasoning. This study examined the application of such a system in recommending a suitable restaurant using both web-based and android platforms. The implemented system uses CoARS techniques to provide beneficial and accurate recommendations to the users. The capabilities of the system is evaluated successfully with recommendation experiment and usability test.