Rock Melon Activated Carbon (RMAC) for Removal of Cd(II), Ni(II) and Cu(II) from Wastewater: Kinetics and Adsorption Equilibrium

The use of effective adsorbents has been investigating as a replacement of current costly methods for heavy metals removal. The present work evaluates the potential of rock melon shell waste as alternative adsorbent for cadmium, nickel and copper ions in aqueous solution. The rock melon shells were dried, ground and separated based on the sizes through sieve shaker. Then, the rock melon shell powder was activated at temperature range of 400 ˚C - 650 ˚C. FESEM and BET were used for adsorbent morphology and surface area analysis. The prepared adsorbent and adsorbate were applied for testing and manipulating the process parameter effects. The results were analyzed by using the Atomic Absorption Spectroscopy (AAS). The optimal process conditions were used for adsorption equilibrium and kinetics justification. The removal of the heavy metals improved as the pH, contact time and adsorbent dosage increased. However, it tended to achieve equilibrium state once the active sites of the adsorbent were fully occupied. The highest removal of Cd(II), Ni(II) and Cu(II) ions equilibrated within 120 min, at pH of 8 and adsorbent dosage was 0.3 g which was exceed 99%. The second order kinetics model best fits the obtained data while the mecahanism indicates surface adsorption and intraparticle diffusion. The adsorption equilibrium accompanies the Freundlich isotherm for cadmium and nickel, but the Langmuir for copper ion

SMArtCast: predicting soil moisture interpolations into the future using Earth observation data in a deep learning framework

Soil moisture is critical component of crop health and monitoring it can enable further actions for increasing yield or preventing catastrophic die off. As climate change increases the likelihood of extreme weather events and reduces the predictability of weather, and non-optimal soil moistures for crops may become more likely. In this work, we a series of LSTM architectures to analyze measurements of soil moisture and vegetation indiced derived from satellite imagery. The system learns to predict the future values of these measurements. These spatially sparse values and indices are used as input features to an interpolation method that infer spatially dense moisture map for a future time point. This has the potential to provide advance warning for soil moistures that may be inhospitable to crops across an area with limited monitoring capacity

Optimal use of multi-spectral satellite data with convolutional neural networks

The analysis of satellite imagery will prove a crucial tool in the pursuit of sustainable development. While Convolutional Neural Networks (CNNs) have made large gains in natural image analysis, their application to multi-spectral satellite images (wherein input images have a large number of channels) remains relatively unexplored. In this paper, we compare different methods of leveraging multi-band information with CNNs, demonstrating the performance of all compared methods on the task of semantic segmentation of agricultural vegetation (vineyards). We show that standard industry practice of using bands selected by a domain expert leads to a significantly worse test accuracy than the other methods compared. Specifically, we compare: using bands specified by an expert; using all available bands; learning attention maps over the input bands; and leveraging Bayesian optimisation to dictate band choice. We show that simply using all available band information already increases test time performance, and show that the Bayesian optimisation, first applied to band selection in this work, can be used to further boost accuracy

Addressing tuberculosis control in fragile states: Urban DOTS experience in Kabul, Afghanistan, 2009-2015

<div><p>Tuberculosis (TB) is a major public health problem in Afghanistan, but experience in implementing effective strategies to prevent and control TB in urban areas and conflict zones is limited. This study shares programmatic experience in implementing DOTS in the large city of Kabul. We analyzed data from the 2009–2015 reports of the National TB Program (NTP) for Kabul City and calculated treatment outcomes and progress in case notification using rates, ratios, and confidence interval. Urban DOTS was implemented by the NTP in partnership with United States Agency for International Development (USAID)-funded TB projects, the World Health Organization (WHO), and the private sector. Between 2009 and 2015, the number of DOTS-providing centers in Kabul increased from 22 to 85. In total, 24,619 TB patients were enrolled in TB treatment during this period. The case notification rate for all forms of TB increased from 59 per 100,000 population to 125 per 100,000. The case notification rate per 100,000 population for sputum-smear-positive TB increased from 25 to 33. The treatment success rate for all forms of TB increased from 31% to 67% and from 47% to 77% for sputum-smear-positive TB cases. The treatment success rate for private health facilities increased from 52% in 2010 to 80% in 2015. In 2013, contact screening was introduced, and the TB yield was 723 per 100,000—more than two times higher than the estimated national prevalence of 340 per 100,000. Contact screening contributed to identifying 2,509 child contacts of people with TB, and 76% of those children received isoniazid preventive therapy. The comprehensive urban DOTS program significantly improved service accessibility, TB case finding, and treatment outcomes in Kabul. Public- and private-sector involvement also improved treatment outcomes; however, the treatment success rate remains higher in private health facilities. While the treatment success rate increased significantly, it remains lower than the national average, and more efforts are needed to improve treatment outcomes in Kabul. We recommend that the urban DOTS approach be replicated in other countries and cities in Afghanistan with settings similar to Kabul.</p></div

Flow chart of TB case notification and treatment outcomes in Kabul, 2009–2015.

<p>Flow chart of TB case notification and treatment outcomes in Kabul, 2009–2015.</p

Contribution of urban DOTS to TB service delivery in Kabul city, 2009–2015.

<p>Contribution of urban DOTS to TB service delivery in Kabul city, 2009–2015.</p

Algorithm for screening children in contact with an index case.

<p>Algorithm for screening children in contact with an index case.</p

Treatment outcomes for SS+ TB, Kabul, 2009–2015.

<p>Treatment outcomes for SS+ TB, Kabul, 2009–2015.</p

Case notification rate per 100,000 population in Kabul, 2009–2015.

<p>Case notification rate per 100,000 population in Kabul, 2009–2015.</p