62 research outputs found
The vibrational temperature of organic molecules and the relationship between absorption and fluorescence spectra in solutions
A tanulĂłk önállĂł Ă©s differenciált munkájának segĂtĂ©se a feladatmegoldás lĂ©pĂ©seinek programozásával
The effect of time-dependent coherence of excitation on the primary processes of photosynthesis
Efficient classification of billions of points into complex geographic regions using hierarchical triangular mesh
We present a case study about the spatial indexing and regional
classification of billions of geographic coordinates from geo-tagged social
network data using Hierarchical Triangular Mesh (HTM) implemented for Microsoft
SQL Server. Due to the lack of certain features of the HTM library, we use it
in conjunction with the GIS functions of SQL Server to significantly increase
the efficiency of pre-filtering of spatial filter and join queries. For
example, we implemented a new algorithm to compute the HTM tessellation of
complex geographic regions and precomputed the intersections of HTM triangles
and geographic regions for faster false-positive filtering. With full control
over the index structure, HTM-based pre-filtering of simple containment
searches outperforms SQL Server spatial indices by a factor of ten and
HTM-based spatial joins run about a hundred times faster.Comment: appears in Proceedings of the 26th International Conference on
Scientific and Statistical Database Management (2014
The effect of arsenic (As) contamination on domestic vegetables
Elemental arsenic and arsenic compounds are classified as "toxic" and "dangerous for the environment" in
the European Union under directive 67/548/EEC. The International Agency for Research on Cancer (IARC)
recognizes arsenic and arsenic compounds as group 1 carcinogens, and the EU lists arsenic trioxide, arsenic
pentoxide and arsenate salts as category 1 carcinogens. Arsenic is easily absorbed by vegetables from
irrigation water. The accumulation of arsenic in vegetables could pose a serious risk on the quality of
vegetables and human health. The two forms of inorganic arsenic, arsenate/As(V) and arsenite/As(III), are
easily taken up by plant root cells (e.g. carrot, parsley, kohlrabi). Once in the cell, As(V) can be readily
converted to As(III), the more toxic form of arsenic. In the present research we have determined the level of
arsenic contamination in two of the economically most important vegetables grown in Hungary (sweet
pepper; tomato) and the irrigation water in an arsenic contaminated area. In order to eliminate arsenic,
decontamination was achieved by use of a Japanese-developed special cerium filter
The effect of arsenic (As) contamination on domestic vegetables
Elemental arsenic and arsenic compounds are classified as "toxic" and "dangerous for the environment" in
the European Union under directive 67/548/EEC. The International Agency for Research on Cancer (IARC)
recognizes arsenic and arsenic compounds as group 1 carcinogens, and the EU lists arsenic trioxide, arsenic
pentoxide and arsenate salts as category 1 carcinogens. Arsenic is easily absorbed by vegetables from
irrigation water. The accumulation of arsenic in vegetables could pose a serious risk on the quality of
vegetables and human health. The two forms of inorganic arsenic, arsenate/As(V) and arsenite/As(III), are
easily taken up by plant root cells (e.g. carrot, parsley, kohlrabi). Once in the cell, As(V) can be readily
converted to As(III), the more toxic form of arsenic. In the present research we have determined the level of
arsenic contamination in two of the economically most important vegetables grown in Hungary (sweet
pepper; tomato) and the irrigation water in an arsenic contaminated area. In order to eliminate arsenic,
decontamination was achieved by use of a Japanese-developed special cerium filter
Towards Reliable Multisensory Perception and Its Automotive Applications
Autonomous driving poses numerous challenging problems, one of which is perceiving and understanding the environment. Since self-driving is safety critical and many actions taken during driving rely on the outcome of various perception algorithms (for instance all traffic participants and infrastructural objects in the vehicle's surroundings must reliably be recognized and localized), thus the perception might be considered as one of the most critical subsystems in an autonomous vehicle. Although the perception itself might further be decomposed into various sub-problems, such as object detection, lane detection, traffic sign detection, environment modeling, etc. In this paper the focus is on fusion models in general (giving support for multisensory data processing) and some related automotive applications such as object detection, traffic sign recognition, end-to-end driving models and an example of taking decisions in multi-criterial traffic situations that are complex for both human drivers and for the self-driving vehicles as well
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