Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: hybrid approaches

Sortuj według:

Ogranicz wyniki do:

Potential of electrocoagulation technology for the treatment of tannery industrial effluents: A brief review

Kumar Verma Rishi, Gautam Kajal, Agrahari Sakshi, Kumar Sushil

Chemical and Process Engineering

2022

Vol. 43, nr 2

217–-222

In this new era, we are facing a major problem regarding wastewater in the environment, which has an adverse effect on human life. Wastewater from tanning industries is one of the major contributors to the pollution in aquatic systems. Tannery industries have always contributed to the world’s economy and trade despite facing criticism due to environmental pollution. Tanning effluent consists of organic, inorganic (chromium, nitrogenous compounds), and a large amount of solid content like TDS, TSS, TVS. To overcome these significant challenges, there have been few advancements related to tannery wastewater treatment. This article aims to provide a brief review on electrocaogulation based treatment technologies for eliminating the impurities from tannery wastewater. This review consists of the background with characteristics of tannery wastewater, the alternatives for treating the tannery effluent over the years along. A detailed description of the advanced technologies based on electrocoagulations is implemented to overcome the drawbacks of the existing methods.

Full-fledged temporal processing : bridging the gap between deep linguistic processing and temporal extraction

Costa F., Branco A.

Journal of Language Modelling

2013

Vol. 1, No. 1

97--154

The full-fledged processing of temporal information presents specific challenges. These difficulties largely stem from the fact that the temporal meaning conveyed by grammatical means interacts with many extra-linguistic factors (world knowledge, causality, calendar systems, reasoning). This article proposes a novel approach to this problem, based on a hybrid strategy that explores the complementarity of the symbolic and probabilistic methods. A specialized temporal extraction system is combined with a deep linguistic processing grammar. The temporal extraction system extracts eventualities, times and dates mentioned in text, and also temporal relations between them, in line with the tasks of the recent TempEval challenges; and uses machine learning techniques to draw from different sources of information (grammatical and extra-grammatical) even if it is not explicitly known how these combine to produce the final temporal meaning being expressed. In turn, the deep computational grammar delivers richer truth-conditional meaning representations of input sentences, which include a principled representation of temporal information, on which higher level tasks, including reasoning, can be based. These deep semantic representations are extended and improved according to the output of the aforementioned temporal extraction module. The prototype implemented shows performance results that increase the quality of the temporal meaning representations and are better than the performance of each of the two components in isolation.