Treatment of textile industry wastewater by electrocoagulation technology

Experiments were carried out by treating the waste samples with electrocoagulation technology. This is done to determine the effectiveness of the removal of the electrocoagulation device against textile waste. The sample used is a synthetic sample with a concentration of 1091 mg/L Pt-Co units. The research was conducted twice with the first experiment being conducted to determine the most effective electrical voltage to remove the existing COD and color pollutants while the second experiment was conducted to determine the type of anode and cathode that was most effective in removing COD, Color, and heavy metal pollutants. In the first experiment, it was found that the electric voltage that could produce the best removal was 4 amperes and in the second experiment, the anode-cathode type with the highest % removal was Fe-Fe with % COD removal of 64.09639% and % color removal of 60.00619%. It was concluded that electrocoagulation method could effectively remove color and COD in waste water.

Analysis of Risk Factors of Neurobiological Pipeline Care and Investigation of Preventive Measures

During clinical care, most neurosurgical patients are critically ill. They have sudden onset of illness that should be treated on time with proper care. The patients require continuous hospitalization for proper treatment. The recovery of patients may be relatively slow and takes some time. Patients and Methods. To explore where the risks of pipeline care lie and the preventive measures. (1) In this paper, 100 neurosurgical patients were treated in our hospital from September 2018 to March 2020. They were firstly selected and divided into two groups. Group A was implemented with routine pipeline care and group B was implemented with the intervention developed by the pipeline team. (2) The design and SMOTE assume that, during the generation of a new synthetic sample of minority classes, the immediate neighbors of the minority class instances were also all minority classes, regardless of their true distribution characteristics, to analyze risk factors during care and summarize preventive measures. Results. The experimental results showed that the total efficiency of nursing care was higher in group B as compared to group A, P < 0.05 ; also, the number of pipeline accidents was lower in group B. Conclusion It is important to be meticulous and thoughtful in pipeline care and to comprehensively analyze the possible risk events and then propose preventive measures so that risk events can be reduced.

Clinically-Driven Virtual Patient Cohorts Generation: An Application to Aorta

The combination of machine learning methods together with computational modeling and simulation of the cardiovascular system brings the possibility of obtaining very valuable information about new therapies or clinical devices through in-silico experiments. However, the application of machine learning methods demands access to large cohorts of patients. As an alternative to medical data acquisition and processing, which often requires some degree of manual intervention, the generation of virtual cohorts made of synthetic patients can be automated. However, the generation of a synthetic sample can still be computationally demanding to guarantee that it is clinically meaningful and that it reflects enough inter-patient variability. This paper addresses the problem of generating virtual patient cohorts of thoracic aorta geometries that can be used for in-silico trials. In particular, we focus on the problem of generating a cohort of patients that meet a particular clinical criterion, regardless the access to a reference sample of that phenotype. We formalize the problem of clinically-driven sampling and assess several sampling strategies with two goals, sampling efficiency, i.e., that the generated individuals actually belong to the target population, and that the statistical properties of the cohort can be controlled. Our results show that generative adversarial networks can produce reliable, clinically-driven cohorts of thoracic aortas with good efficiency. Moreover, non-linear predictors can serve as an efficient alternative to the sometimes expensive evaluation of anatomical or functional parameters of the organ of interest.

Phosphorus removal in domestic wastewater treatment plant by calcined eggshell

Recovery of phosphorus (P) from wastewater is a topic of great interest. Besides being a non-renewable resource, P discharge in receiving waters can trigger algae blooms. The present study aimed to quantify the removal of P from two sites at a wastewater treatment plant using calcined eggshell (CES) as adsorbent. CES was prepared from raw shells calcined at 600 °C (CES600) and 800 °C (CES800). Calcined eggshells at 800 °C proved to be an efficient material for P removal. Efficiencies greater than 70% were achieved using CES800 concentrations of 0.1 g L−1 for synthetic sample, 0.3 g L−1 for preliminary treated wastewater and 20 g L−1 for supernatant from sludge anaerobic digester. The adsorption process was fast, occurring mostly in the first 30 minutes. Both Langmuir and Freundlich isotherms fitted the experimental data on adsorption. In kinetic experiments, a pseudo-second order model fitted P adsorption from synthetic, preliminary effluent and digester supernatant. Thermogravimetric analysis showed a 54% eggshell mass loss at 800 °C. Calcination increased calcium and reduced carbon fractions in the eggshells, while increasing the surface area.

Technical note: Uncovering the influence of methodological variations on the extractability of iron-bound organic carbon

Association of organic carbon (OC) with reactive iron (FeR) represents an important mechanism by which OC is protected against remineralisation in soils and marine sediments. Recent studies indicate that the molecular structure of organic compounds and/or the identity of associated FeR phases exert a control on the ability of an OC–FeR complex to be extracted by the citrate–bicarbonate–dithionite (CBD) method. However, many variations of the CBD extraction are used, and these are often uncalibrated to each other, rendering comparisons of OC–FeR values extracted via the different methods impossible. Here, we created synthetic ferrihydrite samples coprecipitated with simple organic structures and subjected these to modifications of the most common CBD method. We altered some of the method parameters (reagent concentration, time of the extraction and sample preparation methods) and measured FeR recovery to determine which (if any) modifications affected the release of FeR from the synthetic sample. We provide an assessment of the reducing capacity of Na dithionite in the CBD method (the amount of Fe reduced by a fixed amount of dithionite) and find that the concentration of dithionite deployed can limit OC–FeR extractability for sediments with a high FeR content. Additionally, we show that extending the length of any CBD extraction offers no benefit in removing FeR. Moreover, we demonstrate that for synthetic OC–FeR samples dominated by ferrihydrite, freeze-drying samples can significantly reduce OC–FeR extractability; this appears to be less of an issue for natural marine sediments where natural ageing mechanisms may mimic the freeze-drying process for more stable Fe phases. While our study is not an all-inclusive method comparison and is not aimed at delivering the “perfect” extraction setup, our findings provide a collected summary of critical factors which influence the efficiency of the CBD extraction for OC–FeR. As such, we provide a platform from which OC–FeR values obtained under different methods can be interpreted and future studies of sediment carbon cycling can build upon.

Genetic Algorithm Based Oversampling Approach to Prune the Class Imbalance issue in Software Defect Prediction

Class Imbalance is the potential problem that has been existent in machine learning, which hinders the performance of the classification algorithm when applied in real world applications such as electricity pilferage, fraudulent transactions, anomaly detection, prediction of rare diseases, etc. Class Imbalance refers to the problem where the distribution of the sample is skewed or biased towards one particular class. Due to its intrinsic nature the software fault prediction dataset falls into the same category where the software modules contain fewer defective modules compared to the non-defective modules. Majority of the over sampling techniques that has been proposed is to address the issue by generating synthetic samples of minority class to balance the dataset. But the synthetic samples generated are near duplicates that also results in over-generalization issue. We thus propose a novel oversampling approach to introduce synthetic samples using Genetic algorithm (GA). GA is a form of evolutionary algorithm that employs biologically inspired techniques such as inheritance, mutation, selection, and crossover. The proposed algorithm generates synthetic sample of minority class based on the distribution measure and ensures that the samples are diverse within the class and are efficient. The proposed over sampling algorithm has been compared with SMOTE,B-SMOTE, ADASYN, Random Oversampling, MAHAKIL and no sampling approach with a 20 defect prediction dataset from the Promise repository and five prediction models. The results indicate that the Genetic algorithm over sampling approach improves the fault prediction performance and reduced false alarm rate.