Reduction of Pertechnetate from Off-gas Condensate using Aged Zero-valent Iron as a Reducing Agent

The Hanford site located in Washington State was one of the major nuclear processing facilities in the U.S. where plutonium was produced between the 1940s and the 1980’s. As a result of this forty yearlong production, approximately 53 million gallons of radioactive waste was generated and currently stored in 177 single and double shelled underground tanks. There were around 60 major and minor leaking incidents in the tanks through the years. These leaking incidents were wakeup calls to a search for an effective long term disposal solution. This paper will concentrate on the low activity waste (LAW), specifically the radioactive element technetium (Tc). Tc-99 is radioactive with a half-life of 211,000 years, formed during nuclear fission of Uranium-235. Tc-99 can be found in different oxidation states. However, Tc(VII), as pertechnetate anion (TcO 4 - ), is the most abundant in Hanford low activity aqueous waste due to its high solubility. Currently there is no operational treatment plant at the site, but, once the treatment plant starts operation, the planned technique for the LAW is vitrification. Because of the higher volatility of technetium, this method can be challenging to accomplish. Here, we investigate another way of dealing with this highly volatile Tc(VII) via zero valent iron (ZVI). This material could be used to reduce Tc(VII) into a less volatile isotope Tc(IV) followed by possible precipitation

Nonhuman humanitarianism: when ‘AI for good’ can be harmful

Artificial intelligence (AI) applications have been introduced in humanitarian operations in order to help with the significant challenges the sector is facing. This article focuses on chatbots which have been proposed as an efficient method to improve communication with, and accountability to affected communities. Chatbots, together with other humanitarian AI applications such as biometrics, satellite imaging, predictive modelling and data visualisations, are often understood as part of the wider phenomenon of ‘AI for social good’. The article develops a decolonial critique of humanitarianism and critical algorithm studies which focuses on the power asymmetries underpinning both humanitarianism and AI. The article asks whether chatbots, as exemplars of ‘AI for good’, reproduce inequalities in the global context. Drawing on a mixed methods study that includes interviews with seven groups of stakeholders, the analysis observes that humanitarian chatbots do not fulfil claims such as ‘intelligence’. Yet AI applications still have powerful consequences. Apart from the risks associated with misinformation and data safeguarding, chatbots reduce communication to its barest instrumental forms which creates disconnects between affected communities and aid agencies. This disconnect is compounded by the extraction of value from data and experimentation with untested technologies. By reflecting the values of their designers and by asserting Eurocentric values in their programmed interactions, chatbots reproduce the coloniality of power. The article concludes that ‘AI for good’ is an ‘enchantment of technology’ that reworks the colonial legacies of humanitarianism whilst also occluding the power dynamics at play

Saving Social Media Data: Understanding Data Management Practices Among Social Media Researchers and their Implications for Archives

Social media data offer researchers new opportunities to leverage those data for their work in broad areas such as public opinion, digital culture, labor trends, and public health. Success of efforts to save social media data for reuse by researchers will depend on aligning data management and archiving practices with evolving norms around capture, use, sharing, and security of datasets containing this new type of data. This paper presents an initial foray into understanding how established practices for managing and preserving data should adapt to new demands from social media data, researchers who use and reuse social media data, and people who are subjects in social media data. We examine the data management practices of researchers who use social media data through a survey and an analysis of published articles. We discuss the data management practices described, how they differ from management of more conventional data types, and the implications for creating and maintaining stable archives for these important research resources. We discuss the similarities and differences between social media data and other types of social science research data, including other types of “found” data, and discuss the implications for data archives including social media data in their collections.National Science FoundationInstitute of Museum and Library Serviceshttps://deepblue.lib.umich.edu/bitstream/2027.42/154750/1/Hemphill Leonard Hedstrom JASIST pre-peer review.pdf1550Description of Hemphill Leonard Hedstrom JASIST pre-peer review.pdf : Pre-peer review articl

Detecting natural disasters, damage, and incidents in the wild

Responding to natural disasters, such as earthquakes, floods, and wildfires, is a laborious task performed by on-the-ground emergency responders and analysts. Social media has emerged as a low-latency data source to quickly understand disaster situations. While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes. However, no large-scale image datasets for incident detection exists. In this work, we present the Incidents Dataset, which contains 446,684 images annotated by humans that cover 43 incidents across a variety of scenes. We employ a baseline classification model that mitigates false-positive errors and we perform image filtering experiments on millions of social media images from Flickr and Twitter. Through these experiments, we show how the Incidents Dataset can be used to detect images with incidents in the wild. Code, data, and models are available online at http://incidentsdataset.csail.mit.edu.Comment: ECCV 202

High Resistance of Plasmodium falciparum to Sulphadoxine/Pyrimethamine in Northern Tanzania and the Emergence of dhps Resistance Mutation at Codon 581

BACKGROUND: Sulphadoxine-pyrimethamine (SP) a widely used treatment for uncomplicated malaria and recommended for intermittent preventive treatment of malaria in pregnancy, is being investigated for intermittent preventive treatment of malaria in infants (IPTi). High levels of drug resistance to SP have been reported from north-eastern Tanzania associated with mutations in parasite genes. This study compared the in vivo efficacy of SP in symptomatic 6-59 month children with uncomplicated malaria and in asymptomatic 2-10 month old infants. METHODOLOGY AND PRINCIPAL FINDINGS: An open label single arm (SP) standard 28 day in vivo WHO antimalarial efficacy protocol was used in 6 to 59 months old symptomatic children and a modified protocol used in 2 to 10 months old asymptomatic infants. Enrolment was stopped early (87 in the symptomatic and 25 in the asymptomatic studies) due to the high failure rate. Molecular markers were examined for recrudescence, re-infection and markers of drug resistance and a review of literature of studies looking for the 581G dhps mutation was carried out. In symptomatic children PCR-corrected early treatment failure was 38.8% (95% CI 26.8-50.8) and total failures by day 28 were 82.2% (95% CI 72.5-92.0). There was no significant difference in treatment failures between asymptomatic and symptomatic children. 96% of samples carried parasites with mutations at codons 51, 59 and 108 in the dhfr gene and 63% carried a double mutation at codons 437 and 540. 55% carried a third mutation with the addition of a mutation at codon 581 in the dhps gene. This triple: triple haplotype maybe associated with earlier treatment failure. CONCLUSION: In northern Tanzania SP is a failed drug for treatment and its utility for prophylaxis is doubtful. The study found a new combination of parasite mutations that maybe associated with increased and earlier failure. TRIAL REGISTRATION: ClinicalTrials.gov NCT00361114