Ricin poisoning: A review on contamination source, diagnosis, treatment, prevention and reporting of ricin poisoning

Toxicon ◽  
2021 ◽  
Vol 195 ◽  
pp. 86-92
Author(s):  
Melissa Abbes ◽  
Marc Montana ◽  
Christophe Curti ◽  
Patrice Vanelle
Keyword(s):  
Contamination Source ◽  
Source Diagnosis

Contamination Troubleshooting Approach from Problem to Solution

2019 ◽  
Author(s):  
Victor K. F. Chia ◽  
Hugh E. Gotts ◽  
Fuhe Li ◽  
Mark Camenzind
Keyword(s):  
Case Studies ◽  
Semiconductor Devices ◽  
Analytical Techniques ◽  
General Knowledge ◽  
Contamination Source ◽  
Root Cause ◽  
Sources Of Contamination

Abstract Semiconductor devices are sensitive to contamination that can cause product defects and product rejects. There are many possible types and sources of contamination. Root cause resolution of the contamination source can improve yield. The purpose of contamination troubleshooting is to identify and eliminate major yield limiters. This requires the use of a variety of analytical techniques[1]. Most important, it requires an understanding of the principle of contamination troubleshooting and general knowledge of analytical tests. This paper describes a contamination troubleshooting approach with case studies as examples of its application.

scholarly journals A Head Formulation for the Steady-State Analysis of Water Distribution Systems Using an Explicit and Exact Expression of the Colebrook–White Equation

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1163
Author(s):  
Mengning Qiu ◽  
Avi Ostfeld
Keyword(s):  
Steady State ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Solution Method ◽  
Water Distribution System ◽  
Academic Research ◽  
Exact Expression ◽  
Contamination Source

Steady-state demand-driven water distribution system (WDS) solution is the bedrock for much research conducted in the field related to WDSs. WDSs are modeled using the Darcy–Weisbach equation with the Swamee–Jain equation. However, the Swamee–Jain equation approximates the Colebrook–White equation, errors of which are within 1% for ϵ/D∈[10−6,10−2] and Re∈[5000,108]. A formulation is presented for the solution of WDSs using the Colebrook–White equation. The correctness and efficacy of the head formulation have been demonstrated by applying it to six WDSs with the number of pipes ranges from 454 to 157,044 and the number of nodes ranges from 443 to 150,630. The addition of a physically and fundamentally more accurate WDS solution method can improve the quality of the results achieved in both academic research and industrial application, such as contamination source identification, water hammer analysis, WDS network calibration, sensor placement, and least-cost design and operation of WDSs.

scholarly journals Strategies for Improving Optimal Positioning of Quality Sensors in Urban Drainage Systems for Non-Conservative Contaminants

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 934
Author(s):  
Mariacrocetta Sambito ◽  
Gabriele Freni
Keyword(s):  
Treatment Plant ◽  
Optimal Solution ◽  
Monitoring Network ◽  
Computational Effort ◽  
Bayesian Optimization ◽  
Urban Drainage ◽  
Optimization Approach ◽  
Sensor Location ◽  
Contamination Source ◽  
Monitoring Point

In the urban drainage sector, the problem of polluting discharges in sewers may act on the proper functioning of the sewer system, on the wastewater treatment plant reliability and on the receiving water body preservation. Therefore, the implementation of a chemical monitoring network is necessary to promptly detect and contain the event of contamination. Sensor location is usually an optimization exercise that is based on probabilistic or black-box methods and their efficiency is usually dependent on the initial assumption made on possible eligibility of nodes to become a monitoring point. It is a common practice to establish an initial non-informative assumption by considering all network nodes to have equal possibilities to allocate a sensor. In the present study, such a common approach is compared with different initial strategies to pre-screen eligible nodes as a function of topological and hydraulic information, and non-formal 'grey' information on the most probable locations of the contamination source. Such strategies were previously compared for conservative xenobiotic contaminations and now they are compared for a more difficult identification exercise: the detection of nonconservative immanent contaminants. The strategies are applied to a Bayesian optimization approach that demonstrated to be efficient in contamination source location. The case study is the literature network of the Storm Water Management Model (SWMM) manual, Example 8. The results show that the pre-screening and ‘grey’ information are able to reduce the computational effort needed to obtain the optimal solution or, with equal computational effort, to improve location efficiency. The nature of the contamination is highly relevant, affecting monitoring efficiency, sensor location and computational efforts to reach optimality.

scholarly journals Identification of a strong contamination source for graphene in vacuum systems

2013 ◽  
Vol 24 (40) ◽  
pp. 405201 ◽  
Author(s):  
Christophe Caillier ◽  
Dong-Keun Ki ◽  
Yuliya Lisunova ◽  
Iaroslav Gaponenko ◽  
Patrycja Paruch ◽  
...  
Keyword(s):  
Contamination Source ◽  
Vacuum Systems

scholarly journals Towards Development of an Optimization Model to Identify Contamination Source in a Water Distribution Network

Water ◽  
2018 ◽  
Vol 10 (5) ◽  
pp. 579 ◽  
Author(s):  
Oluwaseye Adedoja ◽  
Yskandar Hamam ◽  
Baset Khalaf ◽  
Rotimi Sadiku
Keyword(s):  
Optimization Model ◽  
Water Distribution ◽  
Distribution Network ◽  
Water Distribution Network ◽  
Contamination Source

scholarly journals Emerging contaminants : a tutorial mini-review

2013 ◽  
Vol 14 (1) ◽  
pp. 72-79 ◽  
Keyword(s):  
Emerging Contaminants ◽  
Residue Analysis ◽  
Sewage Treatment ◽  
Emerging Pollutants ◽  
Environmental Issue ◽  
Published Data ◽  
Aquatic Environments ◽  
Contamination Source ◽  
Toxicological Effects ◽  
Reliable Methods

Nowadays, the scientific community has focused and prioritised research on “emerging pollutants”. The term “emerging pollutants” stands for the substances that are released in the environment for which currently no regulations are established for their environmental monitoring. Their occurrence is reported worldwide in a range of aquatic environments, such as lakes, rivers, freshwater catchments, estuaries, reservoirs and marine waters. Nevertheless, due to their large number (ranging in an order of thousands), only few of these compounds are toxicologically evaluated. Published data concerning occurrence and potential toxicological effects is limited. The contamination source of the aquatic environment is mainly the effluents from the sewage treatment plants (STPs). Reliable methods are available for residue analysis of these pollutants down to low ng L-1 levels. However, an urgent need is highlighted for the investigation (primarily in environmental media and following in biological ones) of the toxicity and transformation pathways of all emerging pollutants. The aims of this mini-review are to briefly present: (a) the major classes of emerging pollutants; (b) the reasons why these substances constitute an environmental issue; and (c) developments and applications of environmental analysis in this field.

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