Two key ways to identify beer or ale is by color and turbidity. The paper shows how to measure each using a Visible Spectrometer (the i-LAB from MicrOptix), although others may be used.
In this experiment, you will learn about Beer’s law and how it correlates absorbance with concentration. You will be introduced to the concept of spectroscopy and colorimetry. You will prepare a series of samples and use a colorimeter to create a Beer’s law curve. From that curve, you will determine the concentration of
two unknown samples. Source: http://www.expertsmind.com
lab manual on biophysics, bioinformatics and biostaistics for under graduates...MSCW Mysore
covers topics like
light quantification ,beer lamberts law, molar extinction coefficient, absorption spectra,
databases,pubmed,entrez,retriving sequences and structural data .BLAST FASTA
Simple problems on central tendency and dispersion.
Final submission –Pay attention to APA formatting, spelling, andChereCheek752
Final submission –
Pay attention to APA formatting, spelling, and grammar. Your similarity index/plagiarism score must be below 10%. Higher scores may impact your grade.
The final submission is the combination of the other four phases into one paper. You will combine Phase I, Phase II, Phase III, and Phase IV to make Phase V. You are responsible for editing and formatting your paper so that your paper will flow for the reader. This paper will need to be corrected with all the feedback provided from previous papers. Include conclusion and learning experiences from the essentials and from the class. Do not forget to document limitations and implications for future research/practice. Please review the PowerPoint prior to submitting your assignment, thank you.
Amino Acids and Proteins
Structure of -amino acids
The 20 Amino Acids Found in Proteins
Formation of a Peptide
Polypeptide backbone
9.bin
10.bin
Proteins are made of 20 amino acids linked by peptide bonds
Polypeptide backbone is the repeating sequence of the N-C-C-N-C-C… in the peptide bond
The side chain or R group is not part of the backbone or the peptide bond
ProteinsMake up about 15% of the cellHave many functions in the cellEnzymesStructuralTransportMotorStorageSignalingReceptorsGene regulationSpecial functions
Motor- myosin
Storage- ferritin, transport- hemoglobyn
*
Importance of ProteinsMain catalysts in biochemistry: enzymes (involved in virtually every biochemical reaction)Structural components of cells (both inside and outside of cells in tissues)Regulatory functions (if/when a cell divides, which genes are expressed, etc.)Carrier and transport functions (ions, small molecules)
Levels of Protein StructurePrimary Structure - amino acid sequence in a polypeptide
Secondary Structure - local spatial arrangement of a polypeptide’s backbone atoms (without regard to
side chain conformation)
Tertiary Structure - three-dimensional structure of entire polypeptide
Quaternary Structure - spatial arrangement of subunits of proteins composed of multiple polypeptides (protein complexes)
3-D Structure of Myoglobin
People with proteinuria have urine containing an abnormal amount of protein. The condition is often a sign of kidney disease.
Healthy kidneys do not allow a significant amount of protein to pass through their filters. Kidney disease often has no early symptoms. One of its first signs may be proteinuria that's discovered by a urine test done during a routine physical exam. Blood tests will then be done to see how well the kidneys are working.
Both diabetes and high blood pressure can cause damage to the kidneys, which leads to proteinuria.
Proteinuria (Protein in Urine)
Proteinuria (Protein in Urine)
Methods of Protein Estimation
Quantitative
Biruet methodBradford methodFolin-Lowry methodKjeldahl methodBicinchoninic acid method (BCA method)UV methodFlourimetric methodMass spectrometry
Protein Determination assay
Bicinch ...
In this experiment, you will learn about Beer’s law and how it correlates absorbance with concentration. You will be introduced to the concept of spectroscopy and colorimetry. You will prepare a series of samples and use a colorimeter to create a Beer’s law curve. From that curve, you will determine the concentration of
two unknown samples. Source: http://www.expertsmind.com
lab manual on biophysics, bioinformatics and biostaistics for under graduates...MSCW Mysore
covers topics like
light quantification ,beer lamberts law, molar extinction coefficient, absorption spectra,
databases,pubmed,entrez,retriving sequences and structural data .BLAST FASTA
Simple problems on central tendency and dispersion.
Final submission –Pay attention to APA formatting, spelling, andChereCheek752
Final submission –
Pay attention to APA formatting, spelling, and grammar. Your similarity index/plagiarism score must be below 10%. Higher scores may impact your grade.
The final submission is the combination of the other four phases into one paper. You will combine Phase I, Phase II, Phase III, and Phase IV to make Phase V. You are responsible for editing and formatting your paper so that your paper will flow for the reader. This paper will need to be corrected with all the feedback provided from previous papers. Include conclusion and learning experiences from the essentials and from the class. Do not forget to document limitations and implications for future research/practice. Please review the PowerPoint prior to submitting your assignment, thank you.
Amino Acids and Proteins
Structure of -amino acids
The 20 Amino Acids Found in Proteins
Formation of a Peptide
Polypeptide backbone
9.bin
10.bin
Proteins are made of 20 amino acids linked by peptide bonds
Polypeptide backbone is the repeating sequence of the N-C-C-N-C-C… in the peptide bond
The side chain or R group is not part of the backbone or the peptide bond
ProteinsMake up about 15% of the cellHave many functions in the cellEnzymesStructuralTransportMotorStorageSignalingReceptorsGene regulationSpecial functions
Motor- myosin
Storage- ferritin, transport- hemoglobyn
*
Importance of ProteinsMain catalysts in biochemistry: enzymes (involved in virtually every biochemical reaction)Structural components of cells (both inside and outside of cells in tissues)Regulatory functions (if/when a cell divides, which genes are expressed, etc.)Carrier and transport functions (ions, small molecules)
Levels of Protein StructurePrimary Structure - amino acid sequence in a polypeptide
Secondary Structure - local spatial arrangement of a polypeptide’s backbone atoms (without regard to
side chain conformation)
Tertiary Structure - three-dimensional structure of entire polypeptide
Quaternary Structure - spatial arrangement of subunits of proteins composed of multiple polypeptides (protein complexes)
3-D Structure of Myoglobin
People with proteinuria have urine containing an abnormal amount of protein. The condition is often a sign of kidney disease.
Healthy kidneys do not allow a significant amount of protein to pass through their filters. Kidney disease often has no early symptoms. One of its first signs may be proteinuria that's discovered by a urine test done during a routine physical exam. Blood tests will then be done to see how well the kidneys are working.
Both diabetes and high blood pressure can cause damage to the kidneys, which leads to proteinuria.
Proteinuria (Protein in Urine)
Proteinuria (Protein in Urine)
Methods of Protein Estimation
Quantitative
Biruet methodBradford methodFolin-Lowry methodKjeldahl methodBicinchoninic acid method (BCA method)UV methodFlourimetric methodMass spectrometry
Protein Determination assay
Bicinch ...
Assignment - 03
Model Building, Selection, & Prediction
Question 1:
1. Predicting the Output Variable Y – Energy Production Prediction
a) Importing the data from CSV data and splitting into test and training data:
Using the read.csv() function we can import the data into R
INPUT:
OUTPUT:
INPUT:
OUTPUT:
b) Fitting a Linear Regression Model:
Running the Linear Regression Model with all the Variables
INPUT:
OUTPUT:
The Adjusted R-Squared value is found to be 0.2366.
From the data It can seen that Pressure and Wind are only significant.
So, we run the model only with wind and pressure variables.
Reduced Regression Model (Wind and Pressures Variable only)
INPUT:
OUTPUT:
Removing the Wind Variable since the Adjusted R Squared Value is only 0.0229. Now we run the regression using only the Pressure Variable.
Running the Regression model with only Wind Variable:
INPUT:
OUTPUT:
The Adjusted R-Squared value is found to be 0.219, which is less than the previous regression models.
ANOVA test is to be conducted to find the significance of the all variable included model and the reduced pressure variable model.
INPUT:
OUTPUT:
Between the All variable and Reduced model, the P value is found to be 0.2578, so we should not reject the Null hypothesis and use the Reduced Model.
Between the Pressure variable and Reduced model, the P value is found to be 0.0768, so we should not reject the Null hypothesis and use the Pressure Model.
Running Best Subset to find the model:
Best Subset find the value of statistics for all variables involved and print the statistics for comparison, using which we can select the appropriate variable
INPUT:
OUTPUT:
RSS Value decrease as the variable increase.
Model with 5 variable has the highest Adjusted R Square.
Model with 3 variable has the smallest AIC (or Cp).
Model with 8 variable has the smallest BIC.
Since the Bestsubset approach provides a broad result we check the predicted R square and use the model with highest R square and lower RMSE
R square and RMSE Prediction:
For all variable considered Model:
INPUT:
OUTPUT:
For the Reduced Model with Pressure and Wind Variables:
INPUT:
OUTPUT:
Single Model with Pressure as the dependent variable:
INPUT:
OUTPUT:
Summary:
From the Analysis we can conclude that model with the pressure as the dependent variable is better than the other models. The Adjusted R square value of 0.31 is the best and the RMSE value is also the least in case of the pressur model.
From the Adjusted R Squared value we conclude that the pressure model is the best and can predict the energy produced rate accurately for 31% of the data.
c) Backward Selection Approach:
Regression Model using all the variables:
INPUT:
OUTPUT:
Conclusion:
The backward step AIC function tells a slightly different result then the models generated above. However, when we create the regression model we see a low R2 value then our single mod.
Assignment - 03
Model Building, Selection, & Prediction
Question 1:
1. Predicting the Output Variable Y – Energy Production Prediction
a) Importing the data from CSV data and splitting into test and training data:
Using the read.csv() function we can import the data into R
INPUT:
OUTPUT:
INPUT:
OUTPUT:
b) Fitting a Linear Regression Model:
Running the Linear Regression Model with all the Variables
INPUT:
OUTPUT:
The Adjusted R-Squared value is found to be 0.2366.
From the data It can seen that Pressure and Wind are only significant.
So, we run the model only with wind and pressure variables.
Reduced Regression Model (Wind and Pressures Variable only)
INPUT:
OUTPUT:
Removing the Wind Variable since the Adjusted R Squared Value is only 0.0229. Now we run the regression using only the Pressure Variable.
Running the Regression model with only Wind Variable:
INPUT:
OUTPUT:
The Adjusted R-Squared value is found to be 0.219, which is less than the previous regression models.
ANOVA test is to be conducted to find the significance of the all variable included model and the reduced pressure variable model.
INPUT:
OUTPUT:
Between the All variable and Reduced model, the P value is found to be 0.2578, so we should not reject the Null hypothesis and use the Reduced Model.
Between the Pressure variable and Reduced model, the P value is found to be 0.0768, so we should not reject the Null hypothesis and use the Pressure Model.
Running Best Subset to find the model:
Best Subset find the value of statistics for all variables involved and print the statistics for comparison, using which we can select the appropriate variable
INPUT:
OUTPUT:
RSS Value decrease as the variable increase.
Model with 5 variable has the highest Adjusted R Square.
Model with 3 variable has the smallest AIC (or Cp).
Model with 8 variable has the smallest BIC.
Since the Bestsubset approach provides a broad result we check the predicted R square and use the model with highest R square and lower RMSE
R square and RMSE Prediction:
For all variable considered Model:
INPUT:
OUTPUT:
For the Reduced Model with Pressure and Wind Variables:
INPUT:
OUTPUT:
Single Model with Pressure as the dependent variable:
INPUT:
OUTPUT:
Summary:
From the Analysis we can conclude that model with the pressure as the dependent variable is better than the other models. The Adjusted R square value of 0.31 is the best and the RMSE value is also the least in case of the pressur model.
From the Adjusted R Squared value we conclude that the pressure model is the best and can predict the energy produced rate accurately for 31% of the data.
c) Backward Selection Approach:
Regression Model using all the variables:
INPUT:
OUTPUT:
Conclusion:
The backward step AIC function tells a slightly different result then the models generated above. However, when we create the regression model we see a low R2 value then our single mod ...
In this study, a new Shimadzu electrolytic suppressor was used as part of a Shimadzu modular IC system to determine inorganic anions according to methods EPA 300.
A catalog including carefully selected products, from the most prestigious brands in the Water Treatment market, operative website, personalized services, precise and efficient logistics, flexible organization, but also the basic importance that is attributed to human factors and relationships with partners, make of Sinergroup Srl a reference for many companies of the market.
Counting of wb cs and rbcs from blood images using gray thresholdingeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A catalog including carefully selected products, from the most prestigious brands in the Water Treatment market, operative website, personalized services, precise and efficient logistics, flexible organization, but also the basic importance that is attributed to human factors and relationships with partners, make of Sinergroup Srl a reference for many companies of the market.
This application note describes the methodology and use of the Shimadzu ICPMS-2030 ICP mass spectrometer for the analysis of trace elements in drinking and fresh waters following the EPA 200.8 method. This method is also used for analysis of wastewater. Here, we demonstrate the stability and sensitivity of the ICPMS-2030 for EPA 200.8 analyses.
Assignment - 03
Model Building, Selection, & Prediction
Question 1:
1. Predicting the Output Variable Y – Energy Production Prediction
a) Importing the data from CSV data and splitting into test and training data:
Using the read.csv() function we can import the data into R
INPUT:
OUTPUT:
INPUT:
OUTPUT:
b) Fitting a Linear Regression Model:
Running the Linear Regression Model with all the Variables
INPUT:
OUTPUT:
The Adjusted R-Squared value is found to be 0.2366.
From the data It can seen that Pressure and Wind are only significant.
So, we run the model only with wind and pressure variables.
Reduced Regression Model (Wind and Pressures Variable only)
INPUT:
OUTPUT:
Removing the Wind Variable since the Adjusted R Squared Value is only 0.0229. Now we run the regression using only the Pressure Variable.
Running the Regression model with only Wind Variable:
INPUT:
OUTPUT:
The Adjusted R-Squared value is found to be 0.219, which is less than the previous regression models.
ANOVA test is to be conducted to find the significance of the all variable included model and the reduced pressure variable model.
INPUT:
OUTPUT:
Between the All variable and Reduced model, the P value is found to be 0.2578, so we should not reject the Null hypothesis and use the Reduced Model.
Between the Pressure variable and Reduced model, the P value is found to be 0.0768, so we should not reject the Null hypothesis and use the Pressure Model.
Running Best Subset to find the model:
Best Subset find the value of statistics for all variables involved and print the statistics for comparison, using which we can select the appropriate variable
INPUT:
OUTPUT:
RSS Value decrease as the variable increase.
Model with 5 variable has the highest Adjusted R Square.
Model with 3 variable has the smallest AIC (or Cp).
Model with 8 variable has the smallest BIC.
Since the Bestsubset approach provides a broad result we check the predicted R square and use the model with highest R square and lower RMSE
R square and RMSE Prediction:
For all variable considered Model:
INPUT:
OUTPUT:
For the Reduced Model with Pressure and Wind Variables:
INPUT:
OUTPUT:
Single Model with Pressure as the dependent variable:
INPUT:
OUTPUT:
Summary:
From the Analysis we can conclude that model with the pressure as the dependent variable is better than the other models. The Adjusted R square value of 0.31 is the best and the RMSE value is also the least in case of the pressur model.
From the Adjusted R Squared value we conclude that the pressure model is the best and can predict the energy produced rate accurately for 31% of the data.
c) Backward Selection Approach:
Regression Model using all the variables:
INPUT:
OUTPUT:
Conclusion:
The backward step AIC function tells a slightly different result then the models generated above. However, when we create the regression model we see a low R2 value then our single mod.
Assignment - 03
Model Building, Selection, & Prediction
Question 1:
1. Predicting the Output Variable Y – Energy Production Prediction
a) Importing the data from CSV data and splitting into test and training data:
Using the read.csv() function we can import the data into R
INPUT:
OUTPUT:
INPUT:
OUTPUT:
b) Fitting a Linear Regression Model:
Running the Linear Regression Model with all the Variables
INPUT:
OUTPUT:
The Adjusted R-Squared value is found to be 0.2366.
From the data It can seen that Pressure and Wind are only significant.
So, we run the model only with wind and pressure variables.
Reduced Regression Model (Wind and Pressures Variable only)
INPUT:
OUTPUT:
Removing the Wind Variable since the Adjusted R Squared Value is only 0.0229. Now we run the regression using only the Pressure Variable.
Running the Regression model with only Wind Variable:
INPUT:
OUTPUT:
The Adjusted R-Squared value is found to be 0.219, which is less than the previous regression models.
ANOVA test is to be conducted to find the significance of the all variable included model and the reduced pressure variable model.
INPUT:
OUTPUT:
Between the All variable and Reduced model, the P value is found to be 0.2578, so we should not reject the Null hypothesis and use the Reduced Model.
Between the Pressure variable and Reduced model, the P value is found to be 0.0768, so we should not reject the Null hypothesis and use the Pressure Model.
Running Best Subset to find the model:
Best Subset find the value of statistics for all variables involved and print the statistics for comparison, using which we can select the appropriate variable
INPUT:
OUTPUT:
RSS Value decrease as the variable increase.
Model with 5 variable has the highest Adjusted R Square.
Model with 3 variable has the smallest AIC (or Cp).
Model with 8 variable has the smallest BIC.
Since the Bestsubset approach provides a broad result we check the predicted R square and use the model with highest R square and lower RMSE
R square and RMSE Prediction:
For all variable considered Model:
INPUT:
OUTPUT:
For the Reduced Model with Pressure and Wind Variables:
INPUT:
OUTPUT:
Single Model with Pressure as the dependent variable:
INPUT:
OUTPUT:
Summary:
From the Analysis we can conclude that model with the pressure as the dependent variable is better than the other models. The Adjusted R square value of 0.31 is the best and the RMSE value is also the least in case of the pressur model.
From the Adjusted R Squared value we conclude that the pressure model is the best and can predict the energy produced rate accurately for 31% of the data.
c) Backward Selection Approach:
Regression Model using all the variables:
INPUT:
OUTPUT:
Conclusion:
The backward step AIC function tells a slightly different result then the models generated above. However, when we create the regression model we see a low R2 value then our single mod ...
In this study, a new Shimadzu electrolytic suppressor was used as part of a Shimadzu modular IC system to determine inorganic anions according to methods EPA 300.
A catalog including carefully selected products, from the most prestigious brands in the Water Treatment market, operative website, personalized services, precise and efficient logistics, flexible organization, but also the basic importance that is attributed to human factors and relationships with partners, make of Sinergroup Srl a reference for many companies of the market.
Counting of wb cs and rbcs from blood images using gray thresholdingeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A catalog including carefully selected products, from the most prestigious brands in the Water Treatment market, operative website, personalized services, precise and efficient logistics, flexible organization, but also the basic importance that is attributed to human factors and relationships with partners, make of Sinergroup Srl a reference for many companies of the market.
This application note describes the methodology and use of the Shimadzu ICPMS-2030 ICP mass spectrometer for the analysis of trace elements in drinking and fresh waters following the EPA 200.8 method. This method is also used for analysis of wastewater. Here, we demonstrate the stability and sensitivity of the ICPMS-2030 for EPA 200.8 analyses.
BREEDING METHODS FOR DISEASE RESISTANCE.pptxRASHMI M G
Plant breeding for disease resistance is a strategy to reduce crop losses caused by disease. Plants have an innate immune system that allows them to recognize pathogens and provide resistance. However, breeding for long-lasting resistance often involves combining multiple resistance genes
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptxRASHMI M G
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
2. 2
“Quantifying” Beer Color
The history of quantifying beer color goes back
to the 1880s with Joseph Lovibond, a brewery
owner, creating a colorimeter for beer quality.
Lovibond’s “tintometer”3
(Figure 1) was a crude,
but relatively accurate instrument that relied on
the user “viewing the colour to be measured,
and the (calibrated) glasses used as measures”.
The instrument relied heavily on the user’s
vision, the sunlight (a northern exposure was
preferred), and multiple calibration standards.
Fig. 1 Lovibond’s Tintometer
With the advent of electricity and advanced
optics, a spectrophotometer was first
developed in 1940 at the National Technical
Laboratories Company. Arnold Beckman gets
the credit as the inventor, with the project
leader being Howard Cary4
. Both gentlemen
went on to form instrument companies that
produced high quality spectrophotometers,
including the Beckman DU spectrophotometer
and the Cary spectrophotometers.
Fig. 2 An early Advertisement for the Beckman
Spectrophotometer
The spectrophotometer removed a great deal
of subjectivity (especially the user’s depth and
color perception, along with the need for glass
standards), environmental factors (sun light
intensity changes) and other complications
(mirror angle and variable sample positioning).
However, the spectrophotometer was, and still
is, a bulky, bench top unit built for a laboratory
environment.
In 1950 the ASBC (the American Society of
Brewing Chemists) created the Standard
Reference Method (SRM) color system.
Independently, the EBC (European Brewing
Convention) developed another color system
that used visual comparisons, somewhat styled
after Lovibond’s scales. In the 1970s, the EBC
changed to a spectrophotometer system, like
the ASBC, but with slightly different
calculations5
.
3. 3
In the latter part of the 20th
century and into
the 21st
century, further advances in electronics,
materials, and computers resulted in newer and
faster spectrophotometers. Many bench top
units actually became larger in size, with the
premise of the bigger the unit, the more
expensive. Still, the bench top
spectrophotometer had its place in the lab, but
not on a production floor.
Over the last few years, the i-LAB® visible
spectrophotometer was developed to provide
high quality measurements (wavelength
resolution of ~1 nm), in addition to portability
(about the size of an electric razor at half the
weight) for various markets- including the beer
industry. The i-LAB instrument uses LEDs (light
emitting diodes) as a light source and a linear
variable filter with multidiode array as the
sensor. The instrument was designed to be
compact due to this technology and have no
moving parts that are subject to mechanical
breakdown. Three “AA” batteries are all that is
needed to energize the measurement.
Software methods were recently added to the i-
LAB unit for measuring and calculating beer
properties, such as several ASBC (and/or EBC)
tests including color and polyphenols. The
focus of this paper is color with potential uses
that include end-research of new brews,
monitoring processes, and quality control.
Materials and Equipment
The materials in the study consist of thirteen
beers (Tables 1 and 2), three visible i-LAB®
spectrophotometers with cuvette adaptors and
10 mm polystyrene cuvettes (PLASTIBRAND®,
Aldrich Cat. No. 75070D, optically matched).
Table 1 shows the beer name, type and brewer.
Table 2 shows the container or source of the
beer (can or bottle), the concentration used for
determining color, and the reference color6
. In
theory, the color of beer is uniform, although
age, environmental conditions (temperature
and light), and preservatives (or lack of) to alter
the initial hue.
Procedure
Beer Sample Preparation:
All beers were decarbonated by vigorously
shaking and allowed to sit for five minutes, and
then the procedure was repeated. No bubbles
were observed in the beer after the procedure.
i-LAB® Calibration and Background:
The i-LAB unit was initially calibrated, which
accounts for the adaptor optics, using a 10 mm
cuvette using distilled water and a black-ink
solution. The background Transmission was
measured using a cuvette of distilled water.
Once the unit was calibrated, and the
background measured, no further
measurements of standards were needed.
Beer Measurements:
Turbidity
Decarbonated beers were then poured into
cuvettes and the Transmission was measured
from 400 nm to 700 nm (Fig. 3) and converted
to Absorbance value (Fig. 4). The programming
within the unit then calculated the Absorbance
values (ABS) at 430 nm and 700 nm for a light
path of ½ inch. To convert a path length of 10
mm to that of ½ inch, a factor of 1.27 was used.
ABS = -log (Transmission)
ABS 1/2IN = 1.27 x ABS
ABS 1/2IN (430 nm) = 1.27 x ABS (430 nm)
4. 4
ABS 1/2IN (700 nm) = 1.27 x ABS (700 nm)
If ABS 1/2IN (700 nm) ≤ 0.039 ABS 1/2IN (430 nm),
then beer sample is “free of turbidity”.
The i-LAB color program compares the two
values and if the Absorbance at 700 nm was less
than or equal to 0.0039 that of the Absorbance
at 430 nm it was “free of turbidity” and the i-
LAB reported “No Turbidity”. Conversely, if the
the unit reported “Turbidity” if the value was
more than 0.0039, as per the ASBC Color test.
“Turbid” beers should be clarified, to get a true
color, by centrifugation or filtration. In that
case, filtration or clarification value along with
free of turbidity value should be reported.
Color
Beer color is defined as the Absorbance at 430
nm for a (calculated) half-inch path length times
10.
Beer Color = 10 ABS 1/2IN (430 nm)
Beer color is reported on the i-LAB instrument
in units of “degrees” and to one decimal point,
in accordance with the ASBC test.
Results and Discussion
Figure 3 and 4 show the transmission and
absorbance spectrum of beers, respectively, in
the visible range. Even though the ASBC color
test only uses a couple of wavelengths, the 400
nm to 700 nm wavelength range is inherently
measured, and many calculations (e.g., ASBC
color, EBC color, L*,a*, b*, etc.,) may be made.
Table 3 has the determined color values with
the standard deviation for five measurements.
In general, the standard deviations are close to
each other, indicating that each unit has good
reproducibility. Unit to unit measurements
vary, but are still in reasonable agreement with
each other. Standard deviation generally
increases with beer color, as will be explained
later. Figure 5 is a histogram of the beer color
results for the three i-LAB visible units and
literature values. The values shown are an
average of five readings for each unit. The color
values from the i-LAB instruments are also
similar to the referenced values, especially
those without dilution. As the dilution
increases in order to obtain a meaningful
reading (e.g., Sam Adams Boston Lager and
Abita Jockamo Pale Ale both at 1:2, and Abita
Turbo Dog at 1:10) the calculated error
increases. The cause of the error is mainly
attributed to the variance in calculations that
are multiplied by the dilution factor and the
errors in dilution. For example, if a stout beer is
diluted 1:10, and the diluted color reading is 5.8
+/-0.2, then the “calculated” color ranges from
56 to 60 degrees. Thus, the darker the beer,
the potential for greater error due to dilution
factors.
ASBC and EBC
The ASBC SRM (Standard Reference Method)
was used in this study. The method used by the
European Brewing Convention (EBC) is also
5. 5
measured at 430 nm, but in a smaller cuvette.
The EBC color value is approximately 1.97 times
that of the ASBC value. The i-LAB instrument
can easily measure and calculate the EBC color
value, as well as the L*, a*, and b* values for
the beer samples (ASBC Color Test C.)
Summary
Thirteen various beers were tested using three
i-LAB visual spectrophotometers for ASBC color
and turbidity. The color values obtained
matched the reference values and were similar
for each unit. The units also had low standard
deviations in their measurements. The i-LAB
instrument inherently measures the full visible
spectrum of 400 nm to 700 nm, and may use
that data to calculate the ASBC color and
turbidity, EBC color, and tristimulus values (L*,
a*, b*). The units reported if the beers were
turbid or not, as well as the SRM color value.
6. 6
Beer Type Brewer
Old Milwaukee American Lager Jos. Schlitz Brewing Co. Milwaukee, WI
Corona Light Light Lager Grupo Modelo S.A. de C.V., Mexico City, Mexico
Molson Canadian American Lager Molson Brewers of Canada, Ltd., toronto, ONT. Canada
Allagash White Wheat Beer Allagash Brewing Co., Portland, ME
Sam Adams Light Light Lager Boston Beer Co., Boston, MA
Baxter "X"* Extra Pale Ale Baxter Brewing Co., Lewiston, ME
Sam Adams Boston Ale Vienna Lager Boston Beer Co., Boston, MA
Abita Jockoma IPA American IndianPale Ale Abita Brewing Co., Abita Springs, LA
Baxter "I"* Indian Pale Ale Baxter Brewing Co., Lewiston, ME
Allagash Dubbel Ale Dubbel Allagash Brewing Co., Portland, ME
Geary's Hampshire Special Ale English Strong Ale Geary's Brewing Co., Portland, ME
Allagash Black Belgian Strong Dark Ale Allagash Brewing Co., Portland, ME
Abita Turbodog English Brown Ale Abita Brewing Co., Abita Springs, LA
* Not commercial at this time
Table 1: Beers Used in the Study- Name, Type, and Brewer
Beer Name
Beer Source
(Bottle or Can)
Concentration
Measured
SRMReported
Value (6)
Old Milwaukee C 100% 2.7
Corona Light B 100% 3.6
Molson Canadian B 100% ---
Allagash White B 100% ---
Sam Adams Light B 100% 11.0
Baxter "X" B 100% ---
Sam Adams Boston Ale B 50% 11.0
Abita Jockoma IPA B 50% 16.0
Baxter "I" B 50% ---
Allagash Dubbel Ale B 25% ---
Geary's Hampshire Special Ale B 25% ---
Allagash Black B 10% ---
Abita Turbodog B 10% 60.0
Table 2: Beers Used in Study with Source, Measured Concentration, and Literature Values
(Note: Baxter Beers will be commercial in only cans, but bottles used in this study)
7. 7
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
400 450 500 550 600 650 700
Transmission
Wavelength (nm)
Old Milwaukee
Molson Canadian
Allagash Black
Baxter "I"
Fig. 3: Visible Transmission Spectrum of Selected Beers from 400 nm to 700nm
(Note: Baxter “I” was diluted 1:2 with distilled water, Allagash Black was diluted 1:10)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
400 450 500 550 600 650 700
Absorpbance
Wavelength (nm)
Old Milwaukee
Molson Canadian
Allagash Black
Baxter "I"
Fig. 4: Visible Absorbance Spectrum of Selected Beers from 400 nm to 700nm
(Note: Baxter “I” was diluted 1:2 with distilled water, Allagash Black was diluted 1:10)
8. 8
Three
Unit
Average
Average Std Dev Average Std Dev Average Std Dev
Old Milwaukee 100% 2.9 0.01 2.8 0.00 2.9 0.01 2.88
Corona Light 100% 3.3 0.01 3.6 0.00 3.2 0.01 3.33
Molson Canadian 100% 3.7 0.03 3.6 0.01 3.7 0.00 3.68
Allagash White 100% 5.5 0.02 5.7 0.01 5.5 0.03 5.56
Sam Adams Light 100% 10.0 0.06 11.4 0.06 9.4 0.06 10.23
Baxter "X" 100% 9.6 0.07 11.0 0.10 10.9 0.08 10.49
Sam Adams Boston Ale 50% 13.6 0.04 14.0 0.13 14.4 0.12 13.97
Abita Jockoma IPA 50% 17.0 0.03 17.7 0.13 17.3 0.15 17.33
Baxter "I" 50% 18.6 0.11 21.5 0.09 19.2 0.18 19.75
Allagash Dubbel Ale 25% 23.4 0.02 24.4 0.11 23.4 0.09 23.72
Geary's Hampshire Special Ale 25% 23.2 0.06 23.7 0.06 23.1 0.18 23.30
Allagash Black 10% 53.6 0.05 55.1 0.11 54.2 0.19 54.30
Abita Turbodog 10% 54.4 0.08 54.9 0.06 54.5 0.20 54.60
Unit 637 Unit 670 Unit 671
Concen-
tration
Measured
Beer
Table 3: Color Values for the i-LAB® units with standard deviation for five measurements
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
Old
Milwaukee
Corona
Light
Molson
Canadian
Allagash
White
Sam Adams
Light
Baxter
"X"
Sam Adams
Boston
Ale
Abita
Jockoma
IPA
Baxter
"I"
Allagash
Dubbel
Ale
Geary's
Hampshire
Special Ale
Allagash
Black
Abita
Turbodog
SRMColor(degrees)
Beer Color
i-LAB(R)Measurements and Literature Values
Unit 637
Unit 671
Unit 670
Literature
Fig. 5: A Histogram of Beer Color measured by i-LAB® units and Literature Values
9. 9
REFERENCES:
1. Beer 10-A Spectrophotometric Color Method", ASBC Methods of Analysis
2. “The Dark Prince: Master of the "Unholy" Art of Contract Brewing, Jim Koch is the Man his
Competitors Love to Hate - Boston Beer Company Ltd”, Modern Brewery Age, Sept 10, 1990, Peter V.K.
Reid
3. An Introduction to the Phenomena of Colour Phenomena by Joseph L. Lovibond, Spon and Charbelain
Publishers, London & NY 1905
4. A Classic Instrument: The Beckman DU Spectrophotometer and Its Inventor, Arnold O. Beckman ,
Robert D. Simoni, Robert L. Hill, Martha Vaughan and Herbert Tabor; December 5, 2003 The Journal of
Biological Chemistry, 278,6.
5. Analytica EBC; http://www.europeanbreweryconvention.org/EBCmain/organisation/publication.php
6. SRM Color Values as reported by Destination Beer; http://destinationbeer.com/