Automated zebrafish chorion removal and single embryo placement: optimizing throughput of zebrafish developmental toxicity screens

The potential of the developing zebrafish model for toxicology and drug discovery is limited by inefficient approaches to manipulating and chemically exposing zebrafish embryos-namely, manual placement of embryos into 96- or 384-well plates and exposure of embryos while still in the chorion, a barrier of poorly characterized permeability enclosing the developing embryo. We report the automated dechorionation of 1600 embryos at once at 4 h postfertilization (hpf) and placement of the dechorionated embryos into 96-well plates for exposure by 6 hpf. The process removed ≥95% of the embryos from their chorions with 2% embryo mortality by 24 hpf, and 2% of the embryos malformed at 120 hpf. The robotic embryo placement allocated 6-hpf embryos to 94.7% ± 4.2% of the wells in multiple 96-well trials. The rate of embryo mortality was 2.8% (43 of 1536) from robotic handling, the rate of missed wells was 1.2% (18 of 1536), and the frequency of multipicks was <0.1%. Embryo malformations observed at 24 hpf occurred nearly twice as frequently from robotic handling (16 of 864; 1.9%) as from manual pipetting (9 of 864; 1%). There was no statistical difference between the success of performing the embryo placement robotically or manually.     

On an evolutionary approach for constrained optimization problem solving

Over the last few decades, many different evolutionary algorithms have been introduced for solving constrained optimization problems. However, due to the variability of problem characteristics, no single algorithm performs consistently over a range of problems. In this paper, instead of introducing another such algorithm, we propose an evolutionary framework that utilizes existing knowledge to make logical changes for better performance. The algorithmic aspects considered here are: the way of using search operators, dealing with feasibility, setting parameters, and refining solutions. The combined impact of such modifications is significant as has been shown by solving two sets of test problems: (i) a set of 24 test problems that were used for the CEC2006 constrained optimization competition and (ii) a second set of 36 test instances introduced for the CEC2010 constrained optimization competition. The results demonstrate that the proposed algorithm shows better performance in comparison to the state-of-the-art algorithms.     

Phase characterization and mechanical and flame‐retarding properties of nano‐CaSO4/polypropylene and nano‐Ca3(PO4)2/polypropylene composites

An in situ deposition approach was used for the synthesis of nano‐CaSO₄ and nano‐Ca₃(PO₄)₂. The nanosize particles were confirmed with an X‐ray diffraction technique. Composites of polypropylene (PP) with 0.1–0.5 wt % nano‐ or commercial CaSO₄ or nano‐Ca₃(PO₄)₂ were prepared. The transition from the α phase to the β phase was observed for 0.1–0.3 wt % nano‐CaSO₄/PP and nano‐Ca₃(PO₄)₂/PP composites. This was confirmed by Fourier transform infrared. A differential scanning calorimetry analysis was carried out to determine the thermal behavior of the nanocomposites with increasing amounts of the nano‐CaSO₄ and nano‐Ca₃(PO₄)₂ fillers. Increases in the tensile strength and Young's modulus were observed up to certain loading and were followed by a decrease in the tensile strength. A continuous decrease in the elongation at break (%) was also observed for commercial CaSO₄ and larger nano‐Ca₃(PO₄)₂. A decrease in the mechanical properties after a certain loading might have been due to the agglomeration and phase transition of PP in the composites. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 670–680, 2007     

Generating vegetation leaf area index Earth system data record from multiple sensors. Part 2: Implementation,analysis and validation

The evaluation of a new global monthly leaf area index (LAI) data set for the period July 1981 to December 2006 derived from AVHRR Normalized Difference Vegetation Index (NDVI) data is described. The physically based algorithm is detailed in the first of the two part series. Here, the implementation, production and evaluation of the data set are described. The data set is evaluated both by direct comparisons to ground data and indirectly through inter-comparisons with similar data sets. This indirect validation showed satisfactory agreement with existing LAI products, importantly MODIS, at a range of spatial scales, and significant correlations with key climate variables in areas where temperature and precipitation limit plant growth. The data set successfully reproduced well-documented spatio-temporal trends and inter-annual variations in vegetation activity in the northern latitudes and semi-arid tropics. Comparison with plot scale field measurements over homogeneous vegetation patches indicated a 7% underestimation when all major vegetation types are taken into account. The error in mean values obtained from distributions of AVHRR LAI and high-resolution field LAI maps for different biomes is within 0.5 LAI for six out of the ten selected sites. These validation exercises though limited by the amount of field data, and thus less than comprehensive, indicated satisfactory agreement between the LAI product and field measurements. Overall, the inter-comparison with short-term LAI data sets, evaluation of long term trends with known variations in climate variables, and validation with field measurements together build confidence in the utility of this new 26 year LAI record for long term vegetation monitoring and modeling studies.     

Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review

Meteorological changes urge engineering communities to look for sustainable and clean energy technologies to keep the environment safe by reducing CO2 emissions. The structure of these technologies relies on the deep integration of advanced data-driven techniques which can ensure efcient energy generation, transmission, and distribution. After conducting thorough research for more than a decade, the concept of the smart grid (SG) has emerged, and its practice around the world paves the ways for efcient use of reliable energy technology. However, many developing features evoke keen interest and their improvements can be regarded as the next-generation smart grid (NGSG). Also, to deal with the non-linearity and uncertainty, the emergence of data-driven NGSG technology can become a great initiative to reduce the diverse impact of non-linearity. This paper exhibits the conceptual framework of NGSG by enabling some intelligent technical features to ensure its reliable operation, including intelligent control, agent-based energy conversion, edge computing for energy management, internet of things (IoT) enabled inverter, agent-oriented demand side management, etc. Also, a study on the development of data-driven NGSG is discussed to facilitate the use of emerging data-driven techniques (DDTs) for the sustainable operation of the SG. The prospects of DDTs in the NGSG and their adaptation challenges in real-time are also explored in this paper from various points of view including engineering, technology, et al. Finally, the trends of DDTs towards securing sustainable and clean energy evolution from the NGSG technology in order to keep the environment safe is also studied, while some major future issues are highlighted. This paper can ofer extended support for engineers and researchers in the context of data-driven technology and the SG.     

Multicast Network Security with Asymmetric Cooperative Relaying

International Journal of Wireless Information Networks - This paper exhibits the confidentiality performance study of a cooperative multicast network consisting of $${\mathcal {K}}$$ asymmetric...     

Query-oriented topical influential users detection for top-k trending topics in twitter

Applied Intelligence - Online Social Networks (OSNs) have become inevitable for any new methodology both for viral promoting applications and instructing the creation of inciting information and...     

A multi-agent system model of a diesel engine using rule induction

The proliferation of a multi-agent system (MAS) and ideas from Artificial Intelligence (AI)/distributed AI have changed the way systems, in general are controlled, and operation of a system (diesel engine) in particular is automated. In this paper a distributed multi-agent architecture for a diesel engine and the knowledge sources that handle electricity generation is developed. Electronic devices and components used for data handling are described. The sensed data are presented in fuzzy logic and calculated in entropy values and depicted in a decision hierarchy. A comparative performance assessment of the proposed multi-agent based system with an existing system is presented and discussed.     

Unpacking the effect of IT capability on the performance of export-focused SMEs: a report from China

Abstract.  Export-focused small and medium-sized enterprises (SMEs) in China face a number of barriers to success, two primary ones being the liability of foreignness and resource scarcity. In order to transcend these challenges and be able to survive/prosper in the hypercompetitive international market, where players include large resourceful multinational organizations with experience in varied national contexts, these firms need to develop different organizational capabilities. In this paper, we specifically examine the role of a key organizational capability – information technology (IT) capability – and its different dimensions, in determining performance of export-focused SMEs in China. Our study reveals that IT capability has a positive impact on such firms' performance. This finding indicates the need for their owners/managers to invest in IT capability. Further, the study also highlights specific sub-dimensions of IT capability that export-focused Chinese SMEs should (or should not) develop, so as to derive maximum performance-related gains for the minimum amount spent on IT.     

A similarity coefficient measure and machine-parts grouping in cellular manufacturing systems

For more than three decades, similarity coefficient measures one of the important tools for solving group technology problems have gained the attention of the research community in cellular manufacturing systems. A new similarity coefficient measure that uses a set of important characteristic properties for grouping is developed here for use as an intermediate tool to form cohesive cells. A mathematical model that uses this similarity coefficient for optimally solving the cell-formation problems in cellular manufacturing is developed. A heuristic procedure that improves the optimal methodology in term of solution capability of the large instances is devised for an efficient solution. Both the optimal methodology and the heuristic are applied to some well-known problems from literature to compare the grouping efficiencies. The similarity coefficient and the solution methodologies developed are able to solve the cell formation problems efficiently.