Detecting hate speech on twitter using a convolution-GRU based deep neural network

In recent years, the increasing propagation of hate speech on social media and the urgent need for effective counter-measures have drawn significant investment from governments, companies, as well as empirical research. Despite a large number of emerging scientific studies to address the problem, existing methods are limited in several ways, such as the lack of comparative evaluations which makes it difficult to assess the contribution of individual works. This paper introduces a new method based on a deep neural network combining convolutional and long short term memory networks, and conducts an extensive evaluation of the method against several baselines and state of the art on the largest collection of publicly available datasets to date. We show that our proposed method outperforms state of the art on 6 out of 7 datasets by between 0.2 and 13.8 points in F1. We also carry out further analysis using automatic feature selection to understand the impact of the conventional manual feature engineering process that distinguishes most methods in this field. Our findings challenge the existing perception of the importance of feature engineering, as we show that: the automatic feature selection algorithm drastically reduces the original feature space by over 90% and selects predominantly generic features from datasets; nevertheless, machine learning algorithms perform better using automatically selected features than the original features

Passive remediation of acid mine drainage using cryptocrystalline magnesite: A batch experimental and geochemical modelling approach

Acid mine drainage is generated when mining activities expose sulphidic rock to water and oxygen leading to generation of sulphuric acid effluents rich in Fe, Al, SO4 and Mn with minor concentrations of Zn, Cu, Mg, Ca, Pb depending on the geology of the rock hosting the minerals. These effluents must be collected and treated before release into surface water bodies. Mining companies are in constant search for cheaper, effective and efficient mine water treatment technologies. This study assessed the potential of applying magnesite as an initial remediation step in an integrated acid mine drainage (AMD) management system. Neutralization and metal attenuation was evaluated using batch laboratory experiments and simulations using geochemical modelling. Contact of AMD with cryptocrystalline magnesite for 60 min at 1 g: 100 mℓ S/L ratio led to an increase in pH, and a significant increase in metals attenuation. Sulphate concentration was reduced to ≈1 910 mg/ℓ. PH redox equilibrium (in C language) (PHREEQC) geochemical modelling results showed that metals precipitated out of solution to form complex mineral phases of oxy-hydroxysulphates, hydroxides, gypsum and dolomite. The results of this study showed that magnesite has potential to neutralize AMD, leading to the reduction of sulphate and precipitation of metals.SP201

Neutralization and Attenuation of Metal Species in Acid Mine Drainage and Mine Leachates Using Magnesite: a Batch Experimental Approach

Abstract This paper evaluates the potential application of amorphous magnesite for remediation of Acid Mine Drainage (AMD). Magnesite was mixed with simulated AMD at specific S/L ratios and agitated in an orbital shaker and its capacity to remove metals and neutralize the acidity assessed over time. XRF analysis showed that magnesite contains MgO (88.54 %) as the major element. XRD revealed that magnesite is amorphous and contains periclase as major mineral phase. Results indicate that contact of AMD with magnesite leads to an increase in pH (pH≥10), and a reduction in EC, TDS and metal concentration to below DWAF guidelines. PHREEQC geochemical modeling predicted precipitation of Al, Fe, Mn, Mg bearing mineral phases could be responsible for attenuation of most metal species. However a high proportion of alkali and alkaline earth metals remained in the treated water which might require post treatment polishing

Data set on Revisiting intercropping indices with respect to potato-legume intercropping systems

This data set contain the data used in computing 16 intercropping indices with classical examples of the graphs for easy reference and guidance to all user

Data set on Revisiting intercropping indices with respect to potato-legume intercropping systems

This data set contain the data used in computing 16 intercropping indices with classical examples of the graphs for easy reference and guidance to all user

Defluoridation of groundwater using mixed Mukondeni clay soils

Excess fluoride in drinking water is harmful to human health and therefore it needs to be removed from water before consumption. The potential of locally available mixed Mukondeni clay soils (MMCS) as a cheap adsorbent for the removal of fluoride from aqueous solution was investigated. Characterization of MMCS was done by X-ray fluorescence, X-ray diffraction, scanning electron microscopy, Fourier transform infrared and Brunauer Emmett Teller. Cation exchange capacity and point of zero charge of the clays were determined using standard methods. Parameters optimized included: contact time, adsorbent dosage, initial fluoride concentration, pH and temperature. Optimization experiments were done in batch procedures. The results showed that the optimum conditions for the defluoridation of water using MMCS are 60 min, 1.5 g, 9 mg/L, 1.5/100 S/L ratios, pH of 2 and a temperature of 25 °C. The equilibrium isotherm regression parameter (R2 = 0.95) showed that the Freundlich isotherm gave a better fit than the Langmuir isotherm (R2 = 0.52) which indicates multilayer adsorption. Kinetic studies revealed that the adsorption followed pseudo second order kinetics. This study indicated that locally available MMCS are good in the defluoridation of groundwater but modification through blending with metal oxide modified clays can enhance their adsorption capacity.</jats:p

Suicide clusters among young Kenyan men

Suicide is a leading cause of global mortality. Suicide clusters have recently been identified among peer networks in high-income countries. This study investigates dynamics of suicide clustering within social networks of young Kenya men ( n = 532; 18–34 years). We found a strong, statistically significant association between reported number of friends who previously attempted suicide and present suicide ideation (odds ratio = 1.9; 95% confidence interval (1.42, 2.54); p &lt; 0.001). This association was mediated by lower collective self-esteem (23% of total effect). Meaning in life further mediated the association between collective self-esteem and suicide ideation. Survivors of peer suicide should be evaluated for suicide risk. </jats:p