Numerical investigation of ibuprofen removal from pharmaceutical wastewater using adsorption process

In the present study, a mathematical modelling was developed to investigate ibuprofen adsorption from pharmaceutical wastewater into activated carbon and sonicated activated carbon. The developed model was dissolved based on the finite element method. Effect of different operating parameters including particle porosity and diameter as well as ibuprofen diffusion coefficient in solution on the amount of ibuprofen adsorption at different time point and position in the particle were evaluated. It was found good agreement between experimental values and modelling results in terms of ibuprofen adsorption as a function time. The 84.5% and 92.5% of maximum adsorption was achieved for the AC and SAC at the centre of particle after 150 min. Increasing the particle porosity and ibuprofen diffusion coefficient was improved the ibuprofen adsorption into the adsorbent. However, the particle diameter had negative impact on the system performance. There was a decrease in solute adsorption from 84.10 to 7.30 mg/g and from 106 to 15.73 mg/g for the AC and SAC respectively with increasing the particle radius from 173 to 500 µm. Finally, it was concluded that the particle specifications play important role in the adsorption process as it was observed considerable change in the amount of adsorption at different positions in the particle with changing the particle specifications.

Modeling the Pressure Transverse for Foam Drilling Operation in Vertical Well

Pressure transverse in foam drilling operation is sensitive and difficult to predict particular at the start of flow that follows the unavoidable shut in due to inevitable procedure of stop and proceed arising from re-connection of additional drilling pipe to further drill depth. The practice in drilling may not enable the flow to attain steadiness flow region before running in the length of drill pipe. Most existing models in the literature for predicting pressure transverse in foam drilling operation only captured the steadiness flow region of the foam drilling operation by keeping out restriction terms induced by accumulation and kinetic for simplicity sake, hence unsteadiness flow region experienced during foam drilling operation was rarely modelled. It is highly expedient to derive a model that evident the unsteadiness region in order to accurately predict pressure transverse, hence sufficiently analyses the well stability during foam drilling operation. In this study, a model for forecasting pressure transverse in foam drilling operation was established considering restriction term caused by accumulation and kinetic that constitute for accurate formulation of hydraulic model that govern flow of foam during underbalanced drilling. By applying the proposed model to a case study reported in literature, pressure transverse at unsteadiness flow region for foam drilling operation can be quantitatively estimated and analyzed. The result obtained in a case study carried out indicates high variance in pressure as function time at the beginning of flow in foam drilling where unsteadiness is promoted before matching up closely with the results obtained from the existing Guo et al 2003 model at the steadiness flow region. The new model has a better accuracy with a percentage error of 0.74% and 6.4% as compared to previous models by Guo et al 2003. The proposed model make possible for drilling engineer to take decision with larger precision during hydraulic design of foam drilling operation and guaranteeing well stability in complex drilling system.

Analysis of the endangered monuments in the Masovian Voivodeship, excluding Warsaw

This paper is devoted to analysing the endangered heritage buildings under law protection in Masovian Voivodeship (Poland) that have not lost heritage values. The methodology based on source research. The study presents the division to the most numerous categories: the original function, time of creation, location, and others. The paper presents the results showing categories under increased threat and identifies the need to perform such analysis for other voivodships that can help to channel the resources to prevent further destruction of most endangered categories. That includes performing detailed surveys to secure spatial information about buildings under discussion.

Thermal Stability of Mg/TeO2 Priming Compositions

The insensitive ignitors with magnesium/tellurium dioxide (Mg/TeO2) priming composition installed on the spacecraft will experience a harsh thermal environment. Therefore, it is necessary to verify their availability at extreme temperatures. Differential Scanning Calorimetry and Thermogravimetric Analysis (DSC-TGA) were used to analyze the reaction process of the Mg/TeO2 priming composition. The morphology change of the Mg/TeO2 priming composition after -70 ℃6d, room temperature, 165 ℃6d were analyze by digital cameras, scanning electron microscope (SEM), as well as the output pressure, ignition time, function time and firing sensitivity of the Mg/TeO2 priming composition under three temperature conditions were tested. The results show that in a temperature range of -70 ℃ ~ 165 ℃, the temperature conditions have an effect on the output pressure less than 5%, the influence of high temperature conditions on ignition/ function time and sensitivity is greater than low temperature. It can be used as a priming composition for insensitive ignitors at -70 ℃ ~ 165 ℃.

Post-Operative Delirium and Cognitive Decline in Kidney Transplant Recipients

Post-operative delirium may be a marker for cognitive reserve, or a greater cognitive vulnerability to stressors. As a result, those with post-operative delirium may experience steeper decline in cognitive performance following stressors of surgery post-KT. We leveraged a single-center cohort study of 912 adult KT recipients with delirium assessments abstracted from medical records. Global cognitive function (3MS) and executive function (time to complete TMT-B minus TMT-A) were measured at time of KT, 1-month, 3-months, 6-months, 1-year, and annually thereafter post-KT. We used mixed effects models with fixed and random effects for person and time to describe repeated measures of cognitive performance and compared trajectories by post-operative delirium. Among 912 KT recipients, 44 (4.8%) had post-operative delirium. Post-operative delirium was associated with higher levels of cognitive impairment at KT (18.2% vs 8.0%), and was associated with lower 3MS component scores including in memory, identification/association, and orientation. After adjustment, those with delirium had 3MS scores that were on average 3.6 points lower than those without delirium (95%CI: -6.9, 0.3) at time of KT; delirium was not associated with differing global cognitive trajectories post-KT (difference=0.04 points/month, 95%CI:-0.1, 0.2). However, delirium was associated with lower executive function at KT (difference=44.0s, 95%CI: 17.4, 70.6) and steeper decline in executive function post-KT (difference=-1.1s/month, 95%CI:-2.1,-0.05). KT recipients with delirium experience greater decline in executive function, indicating greater cognitive vulnerability with potential vascular etiologies. Transplant centers should be aware of the cognitive risks associated with post-KT delirium and implement available preventative interventions to reduce risk of delirium.

Investigating ultrafast two-pulse experiments on single DNQDI fluorophores: a stochastic quantum approach

The vibrational and electronic structure of the fluorophore calculated with DFT methods coupled with wave function time propagation by SSE.

Text Recognition and Machine Learning: For Impaired Robots and Humans

As robots and machines become more reliable, developing tools that utilize their potential in manufacturing and beyond is an important step being addressed by many, including the LIMDA team at the University of Alberta. A common and effective means to improve artificial performance is through optical character recognition methods. Within the category of artificial intelligence under classification machine learning, research has focussed on the benefits of convolutional neural networks (CNN) and the improvement provided compared to its parent method, neural networks. Neural networks serious flaw comes from memorization and the lack of learning about what the images contain, while CNN's combat those issues. CNN’s are designed to connect information received by the network and begins to closely mimic how humans experience learns. Using the programming language Python and machine learning libraries such as Tensorflow and Keras, different versions of CNN’s were tested against datasets containing low-resolution images with handwritten characters. The first two CNN’s were trained against the MNIST database against digits 0 through 9. The results from these tests illustrated the benefits of elements like max-pooling and the addition of convolutional layers. Taking that knowledge a final CNN was written to prove the accuracy of the algorithm against alphabet characters. After training and testings were complete the network showed an average 99.34% accuracy and 2.23% to the loss function. Time-consuming training epochs that don’t wield higher or more impressive results could also be eliminated. These and similar CNN’s have proven to yield positive results and in future research could be implemented into the laboratory to improve safety. Continuing to develop this work will lead to better translators for language, solid text to digital text transformation, and aides for the visual and speech impaired.

Holographic function time

Holographiv function