scholarly journals Mathematical Modeling of the growth of Acinetobacter baumannii YNWH 226 on Azo dye Congo red

2021 ◽  
Vol 4 (2) ◽  
pp. 7-10
Author(s):  
Ibrahim Alhaji Sabo ◽  
Salihu Yahuza ◽  
Mohd Yunus Shukor
Keyword(s):  
Confidence Interval ◽  
Azo Dyes ◽  
Statistical Tests ◽  
Industrial Effluents ◽  
Information Criteria ◽  
Bacterial Growth Rate ◽  
Aquatic Life ◽  
Bias Factor ◽  
Three Phase ◽  
Schnute Model

Industrial effluents (Azo dyes) are brightly coloured, making their disposal into receiving waters undesirable not only because many Azo dyes and their breakdown products are toxic to aquatic life and mutagenic to humans, but also because many Azo dyes and their breakdown products are harmful to aquatic life due to the presence of aromatics and metals, chlorides, and other chemicals. Various kinetic models, including modified Gompertz, Baranyi-Roberts, modified Richards, Von Bertalanffy, modified Logistics, modified Schnute, Buchanan three-phase, and the most recently presented Huang, were used in this study. Based on statistical tests, the modified Schnute model provided the best fit, with the lowest values for RMSE and corrected Akaike Information Criteria (AICc), the greatest value for adjusted R2, and the closest to unity for both Accuracy and Bias Factor. The Modified Schnute parameters such as λ (lag time), µmax (maximum specific bacterial growth rate) and curve fitting parameters α and β (Constant), were found to be -4.39 (95% confidence interval of -77.58 to 68.79), 57.00 (95% confidence interval of -2854.30 to 2968.30), 0.78 (95% confidence interval of -0.34 to 1.89) and 0.96 (95% confidence interval of -0.85 to 2.78, respectively.

scholarly journals Mathematical Modelling of the Growth of Bacillus cereus Strain wwcp1on Malachite Green Dye

2021 ◽  
Vol 9 (2) ◽  
pp. 25-29
Author(s):  
Salihu Yahuza ◽  
Ibrahim Alhaji Sabo
Keyword(s):  
Confidence Interval ◽  
Bacillus Cereus ◽  
Malachite Green ◽  
Bacterial Growth ◽  
Growth Models ◽  
Von Bertalanffy ◽  
Bacterial Growth Rate ◽  
Von Bertalanffy Model ◽  
Malachite Green Dye ◽  
Bias Factor

In this paper, various growth models such as Von Bertalanffy, Huang, Baranyi-Roberts, Modified Gompertz, Buchnam-3-phase, Modified-Richards and Modified-Logistics, were presented in fitting and evaluating the growth of Bacillus cereus wwcp1 on Malachite green dye. The Von Bertalanffy model was found to be the best model with the lowest RMSE and highest R2 values. The Accuracy and Bias factor values were near unity (1.0). The von Bertalanffy parameters such as A (lower asymptote bacterial growth), μ (bacterial growth rate) and k (curve fitting parameter) were found to be 2.757 (95% confidence interval from 2.131 to 3.382 ), 0.287 (95% confidence interval from 0.244 to 0.329) and 4.323 (95% confidence interval from 4.285 to 4.361) respectively.

scholarly journals Association of interleukin-17A rs2275913 polymorphism with rheumatoid arthritis susceptibility in Sudanese population

2021 ◽  
Vol 9 ◽  
pp. 205031212110202
Author(s):  
Rgda Mohamed Osman ◽  
Mounkaila Noma ◽  
Abdallah Elssir Ahmed ◽  
Hanadi Abdelbagi ◽  
Rihab Ali Omer ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Confidence Interval ◽  
Statistical Tests ◽  
Demographic Data ◽  
Control Group ◽  
P Value ◽  
Case Group ◽  
Molecular Technique ◽  
Interleukin 17A ◽  
Significant Difference

Objectives: Rheumatoid arthritis is a chronic inflammatory autoimmune disease. This study aimed to determine the association of interleukin-17A-197G/A polymorphism with rheumatoid arthritis in Sudanese patients. Methods: A case–control study was conducted between March and December 2018. Clinical and demographic data of the study participants were collected and analyzed. Polymerase chain reaction restriction fragment length polymorphism molecular technique was done to investigate interleukin-17A-197G/A polymorphisms. All statistical tests were considered statistically significant when p < 0.05. Results: The study population included 266 participants aged between 1 and 85 years, with an average of 40 years, classified into 85 (31.2%) cases (mean age 48.5 ± 11.3 years), and 181 (68.8%) controls (mean age 35.3 ± 15.9 years). The interleukin-17A homozygote AA genotype was more frequent among the control group compared to the case group; 95 (52.5%) and 7 (8.2%), respectively. The homozygote GG and the heterozygote AG genotypes were proportionally not different among the cases and control groups; 13 (54.2%) and 11 (45.8%), and 65 (46.4%) and 75 (53.6%), respectively. According to the distribution of interleukin-17A genotypes, a statistically significant difference was observed among cases with the interleukin-17A AA and AG genotypes, p values 0.001 and 0.004, respectively. For the association interleukin-17A genotypes and family history a negatively significant association was reported (95% confidence interval, –0.219, p value = 0.001). There was also a negatively significant association of interleukin-17A genotypes and anti-cyclic citrullinated peptide (95% confidence interval, −0.141, p value = 0.002). Conclusion: This study is the first study in Sudan established the association between interleukin-17A-197G/A (rs2275913) polymorphisms and susceptibly to rheumatoid arthritis. These findings appeal for further research in Sudan to investigate the exact role of IL-17A in immunopathology and disease severity among Sudanese rheumatoid arthritis

scholarly journals Confidence interval approach for evaluating bias in laboratory methods

1988 ◽  
Vol 10 (3) ◽  
pp. 144-146 ◽  
Author(s):  
K. F. Yee
Keyword(s):  
Confidence Interval ◽  
Statistical Tests ◽  
Laboratory Methods ◽  
Mean Values ◽  
Practical Concern ◽  
Alternative Approach ◽  
Interval Approach ◽  
Significant Difference ◽  
The Mean

A statistically significant difference in mean values between two laboratory quantitation methods is interpreted as a bias. Sometimes such a difference is so minute that it does not constitute any practical concern. An alternative approach is to test statistically whether the two methods are close enough, not for equality. This is to look at the confidence interval of the mean method difference and does not entail any additional statistical tests.

scholarly journals PENGARUH PADAT TEBAR TERHADAP KANDUNGAN ZAT GIZI IKAN LELE YANG DIPELIHARA DENGAN TEKNOLOGI BIOFLOK

2019 ◽  
Vol 20 (2) ◽  
pp. 120-131
Author(s):  
Settings Anang Suhardianto ◽  
Ariyanti Hartari
Keyword(s):  
Fatty Acids ◽  
Confidence Interval ◽  
Water Content ◽  
Unsaturated Fatty Acids ◽  
Statistical Tests ◽  
Stocking Density ◽  
Nutrient Content ◽  
Omega 3 ◽  
Omega 6 ◽  
Total Fat

This study aims to determine the effect of stocking density on the nutrient content of catfish that is maintained with biofloc technology. Nutrients observed: 1) water content, 2) protein, 3) carbohydrates, 4) total fat, 5) saturated fatty acids / SFA, 6) monounsaturated fatty acids/ MUFA, 7) plural unsaturated fatty acids / PUFA , 8) omega-3, 9) omega-6, and 10) omega 9. Statistical tests on the 10 variables showed that stocking density did not have a significant effect on the 10 variables at a 5% confidence interval. Stocking density of treatment is 1000 heads/pond (T1), 2000 heads/pond (T2), 3000 heads/pond (T3), with a pond size of 2.0 m x height 1.0 m. Research results: 1. The average water content is 69.40–71.47% and the highest T3. 2. The protein content is 14.70-15.90%, the highest T2. 3. Carbohydrate content of 5.16-5.50%, the highest T2. 4. The average total fat content of 6.73-7.78%, the highest T1. 5. SFA content is around 43%, PUFA around 23%, and MUFA around 32%. 6. The highest omega-3 content is T3, then T1, and T2. Omega-6 and 9 sequence contents are T1, T2, and T3. It was concluded, the treatment of biofloc catfish stocking densities at a 5% confidence interval did not have a significant effect on the specified nutrient content. Penelitian ini bertujuan untuk menentukan pengaruh padat tebar terhadap kandungan zat gizi ikan lele yang dipelihara dengan teknologi bioflok. Zat gizi yang diamati: 1) kandungan air, 2) protein, 3) karbohidrat, 4) lemak total, 5) asam lemak jenuh/SFA, 6) asam lemak tak jenuh tunggal/MUFA, 7) asam lemak tak jenuh jamak/PUFA, 8) omega-3, 9) omega 6, dan 10) omega 9. Uji statistik terhadap ke-10 variabel menunjukkan padat tebar tidak memberikan pengaruh nyata terhadap ke-10 variabel pada selang kepercayaan 5%.  Padat tebar perlakuan adalah 1000 ekor/kolam (T1),  2000 ekor/kolam (T2), 3000 ekor/kolam (T3), dengan ukuran kolam diameter 2,0 m x tinggi 1,0 m. Hasil penelitian: 1. Rata-rata kandungan air 69,40–71,47% dan T3 tertinggi. 2. Kandungan protein 14,70–15,90%, T2 tertinggi. 3. Kandungan karbohidrat 5,16–5,50%, T2 tertinggi. 4. Rata-rata kandungan lemak total 6,73–7,98%, T1 tertinggi. 5. Kandungan SFA sekitar 43%, PUFA sekitar 23%, dan MUFA sekitar 32%. 6. Kandungan omega-3 tertinggi T3, kemudian T1, dan T2. Omega-6 dan 9 urutan kandungannya T1, T2, dan T3.  Disimpulkan, perlakuan padat tebar lele bioflok pada selang kepercayaan 5% tidak memberikan pengaruh yang nyata terhadap kandungan zat gizi yang ditentukan.

scholarly journals Simplified large African carnivore density estimators from track indices

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2662 ◽  
Author(s):  
Christiaan W. Winterbach ◽  
Sam M. Ferreira ◽  
Paul J. Funston ◽  
Michael J. Somers
Keyword(s):  
Linear Regression ◽  
Confidence Interval ◽  
Population Size ◽  
Linear Model ◽  
Standard Error ◽  
Linear Models ◽  
Information Criteria ◽  
Large Carnivores ◽  
Simple Linear Regression ◽  
Akaike Information Criteria

BackgroundThe range, population size and trend of large carnivores are important parameters to assess their status globally and to plan conservation strategies. One can use linear models to assess population size and trends of large carnivores from track-based surveys on suitable substrates. The conventional approach of a linear model with intercept may not intercept at zero, but may fit the data better than linear model through the origin. We assess whether a linear regression through the origin is more appropriate than a linear regression with intercept to model large African carnivore densities and track indices.MethodsWe did simple linear regression with intercept analysis and simple linear regression through the origin and used the confidence interval for ß in the linear modely = αx + ß, Standard Error of Estimate, Mean Squares Residual and Akaike Information Criteria to evaluate the models.ResultsThe Lion on Clay and Low Density on Sand models with intercept were not significant (P > 0.05). The other four models with intercept and the six models thorough origin were all significant (P < 0.05). The models using linear regression with intercept all included zero in the confidence interval for ß and the null hypothesis that ß = 0 could not be rejected. All models showed that the linear model through the origin provided a better fit than the linear model with intercept, as indicated by the Standard Error of Estimate and Mean Square Residuals. Akaike Information Criteria showed that linear models through the origin were better and that none of the linear models with intercept had substantial support.DiscussionOur results showed that linear regression through the origin is justified over the more typical linear regression with intercept for all models we tested. A general model can be used to estimate large carnivore densities from track densities across species and study areas. The formulaobserved track density = 3.26 × carnivore densitycan be used to estimate densities of large African carnivores using track counts on sandy substrates in areas where carnivore densities are 0.27 carnivores/100 km2or higher. To improve the current models, we need independent data to validate the models and data to test for non-linear relationship between track indices and true density at low densities.

scholarly journals The Association Between Medicare Low-Income Subsidy and Anticancer Treatment Uptake in Advanced Lung Cancer

2019 ◽  
Vol 112 (6) ◽  
pp. 637-646 ◽  
Author(s):  
Yi-Ting Chou ◽  
Joel F Farley ◽  
Thomas E Stinchcombe ◽  
Amber E Proctor ◽  
Jennifer Elston Lafata ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Confidence Interval ◽  
Low Income ◽  
Advanced Nsclc ◽  
Statistical Tests ◽  
Advanced Lung Cancer ◽  
Part D ◽  
Treatment Uptake ◽  
Anticancer Treatment ◽  
Out Of Pocket Costs

Abstract Background High out-of-pocket costs may impact anticancer treatment uptake. The Low-Income Subsidy (LIS) program can reduce patient out-of-pocket cost for Medicare Part D–covered treatments. We examined whether the LIS increased uptake and reduced time to initiate orally administered anticancer drugs in patients with advanced non–small cell lung cancer (NSCLC). Methods Using Surveillance, Epidemiology and End Results (SEER)-Medicare data, we identified older adults (aged 65 years and older) diagnosed with advanced NSCLC from 2007 through 2013 and categorized them as full LIS, partial LIS, or non-LIS. We used propensity-score weighted (IPTW) Cox proportional hazards regression to assess the likelihood of and time to initiate Part D treatments. Part B medication uptake was our negative control because supplemental insurance reduces out-of-pocket costs for those drugs. All statistical tests were two-sided. Results Among 19 746 advanced NSCLC patients, approximately 10% initiated Part D treatments. Patients with partial or no LIS were less likely to initiate Part D treatments than were those with full subsidies (partial LIS vs full LIS HRIPTW = 0.77, 95% confidence interval = 0.62 to 0.97; non-LIS vs full LIS HRIPTW = 0.87, 95% confidence interval  = 0.79 to 0.95). Time to initiate Part D treatments was also slightly shorter among full-LIS patients (full LIS mean [SD] = 10.8 [0.04] months; partial LIS mean [SD] = 11.3 [0.08] months; and non-LIS mean [SD] = 11.1 [0.03] months, P &lt; .001). Conversely, patients with partial or no LIS had shorter time to initiation of Part B drugs. Conclusions Patients receiving the full LIS had higher orally administered anticancer treatment uptake than patients without LIS. Notably, patients with partial LIS had the lowest treatment uptake, likely because of their low incomes combined with high expected out-of-pocket spending. High out-of-pocket costs for Part D medications may be a barrier to treatment use for patients without full LIS.

Model Predictive Current Control of a Three-Phase T-Type NPC Inverter to Reduce Common Mode Voltage

2017 ◽  
Vol 27 (02) ◽  
pp. 1850028 ◽  
Author(s):  
Eedara Aswani Kumar ◽  
Koritala Chandra Sekhar ◽  
Rayapudi Srinivasa Rao
Keyword(s):  
Control Method ◽  
Current Control ◽  
Computationally Efficient ◽  
Common Mode ◽  
Common Mode Voltage ◽  
Bias Factor ◽  
Three Phase ◽  
Objective Model ◽  
Control Set ◽  
Peak Value

This paper presents a reduced control set model predictive control (RCSMPC) method for three-phase T-type neutral-point-clamped (NPC) inverter. The whole control set (WCS) consists of all the 27 switching states of T-type NPC inverter. The reduced control set (RCS) with 19 switching states is formed from WCS by excluding the switching states with common mode voltage (CMV) value higher than one-sixth of input DC voltage [Formula: see text]. With RCS, single-objective model predictive current control method can restrict the CMV peak value to [Formula: see text]. To further reduce the CMV below this threshold, a cost function with the weighted sum of two control targets is formulated in the RCSMPC method. The two control targets of RCSMPC method are CMV mitigation and load current control. The weight for CMV is called bias factor. The RCSMPC method is computationally efficient, as the number of switching states is less than that of WCSMPC. To further reduce the computational burden, CMV values corresponding to all the switching states are calculated offline and stored in memory. Robustness of both the methods is investigated with parameter deviations at different bias factors and reference currents. The proposed method is validated using simulation and experimental results and compared with the existing methods.

scholarly journals Remediation of Oil and Gas Sewage Effluents Using Physical Treatments

2020 ◽  
Vol 6 (1) ◽  
pp. 12-19
Author(s):  
Mohd Najib Razali ◽  
Amira Ermafiqka Anuar ◽  
Musfafikri Musa ◽  
Najmuddin Mohd Ramli
Keyword(s):  
Oil And Gas ◽  
Industrial Effluent ◽  
Oxygen Demand ◽  
Industrial Effluents ◽  
Oil And Gas Industry ◽  
Aluminium Chloride ◽  
Wastewater Sample ◽  
Aquatic Life ◽  
Gas Industry ◽  
Mechanical Assistance

The industrial effluents from the oil and gas industry contain harmful contaminants that bring detrimental effects to the aquatic life and human population. The primary concerns are the high value of Chemical Oxygen Demand (COD), Total Suspended Solids (TSS), turbidity and heavy metal content such as ferum and copper in the effluents, which did not comply with the Environmental Quality Act (1974) Industrial Effluent (Regulations) 2009 of Malaysian Department of Environment (DOE). This research aims to study the efficiency of natural bio-coagulants in treating the industrial effluent from the oil and gas industry. The industrial effluent sample was treated by using two natural biocoagulants F.A and F.B and three commercial treatment agents (bio-solvent, alum, and poly aluminium chloride (PAC)). Different beakers consisting of 7.5 wt% of each agent were added into 1.5 L of wastewater sample and left for a week without mechanical assistance. For the second stage, only F.A and alum were used during the experiment. By using five different weight percentages: 2.5%, 5.0%, 7.5%, 10.0%, and 12.5%, the treatment agents were added into 100 ml of wastewater and left for a week without any mechanical assistance. Then, the samples were analyzed for each of the five parameters. The results showed F.A is the best agent in COD treatment, with 41% reduction; followed by alum with 36%, PAC with 26% and bio-solvent with 22% reduction, respectively. The obtained results also showed that F.A and alum are at optimum performances at 7.5 wt%. The F.A and alum efficiency are deteriorating when the dosage is below and above 7.5 wt%.

Nano-Photocatalysts in the Treatment of Colored Wastewater - A Review

2012 ◽  
Vol 734 ◽  
pp. 349-363 ◽  
Author(s):  
Neetu Divya ◽  
Ajay Bansal ◽  
Asim K. Jana
Keyword(s):  
Organic Compounds ◽  
Health Hazard ◽  
Water System ◽  
Ultra Violet ◽  
Industrial Effluents ◽  
Agricultural Runoff ◽  
Aquatic Life ◽  
Chemical Spills ◽  
Colored Wastewater ◽  
Complex Organic

In the present scenario, the problem of water pollution is remarkable. The need to maintain clean water for both flora and fauna has become a major, even a critical concern. A large number of organic substances are introduced into the natural water system from various sources such as industrial effluents, agricultural runoff and chemical spills. Textiles industries specifically pollute the water sources due to the random use and discharge of various types of dyes. It may significantly affect photosynthetic activity in aquatic life and their presence in drinking water constitutes a potential human health hazard. It is therefore essential either to remove the dyes from water or to treat them in such a way so as to minimize their effects on the environment and also to decolorize the water. Various research works on different processes are reviewed and discussed in the present article. It has been observed that the advanced oxidation processes are used widely to degrade the organic compounds in water. Photocatalytic systems are effective for the degradation of many unwanted complex organic compounds through the use of efficient nanophotocatalysts activated under ultra-violet (UV) irradiation.

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