Trending December 2023 # Desert Critters Avoid Noisy Wind Farm Turbines # Suggested January 2024 # Top 18 Popular

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In preparation for war in 1940s, U.S. Army tanks crunched across the wild deserts of Southern California, leaving tracks that will remain for thousands of years. Jeffrey Lovich, a U.S. Geologic Survey research ecologist, has trekked through these harsh desert environs for decades, and sometimes comes across the tank- imprinted past. “It’s a consequence of history,” he says.

Over 70 years later, the Mojave and Sonoran deserts are experiencing a new round of history: rapidly expanding wind and solar farms. A decade ago, Lovich didn’t see the renewable energy revolution coming. But then a massive report landed on his desk in Flagstaff, Arizona, detailing the impacts of solar farms in six U.S. states. “That was an ‘aha’ moment for me,” he says, immediately realizing how little was known about how these farms might effect the desert environment, and the creatures teeming therein.

“I was stunned by the dearth of information,” says Lovich.

Still today, our understanding of renewable energy impacts remains woefully deficient, but a new study, published last month in The Journal of Wildlife Management, suggests that windfarms affect the hunting and scavenging behaviors of the desert’s foxes, coyotes, and bobcats. Scientists visited a wind farm near Palm Springs, California, home to 460 lofty wind turbines, and set up motion-activated cameras in front of 46 desert tortoise burrows. They found that mesocarnivores (animals that mostly munch meat, but also occasionally eat some fungi and plant material), like foxes and bobcats, more often visited tortoise burrows that were farther away from the noisy, spinning machines.

Lovich, a co-author of the study, explains that this sort of research aims to improve our understanding of how wind turbines affect wildlife, so future farms can be designed in less impactful ways. “My research is all about trying to find ways to minimize the negative effects of renewable energy, while maximizing the positive effects for society,” he says.

Driving along the Interstate 15, between Los Angeles and Las Vegas, it might appear that the dull, brown desert is dead, and the scant wildlife therein have little concern for the presence of wind turbines and solar panels. But next time you find yourself at an I-15 rest stop, try walking 10 feet into the Mohave. The ground swarms with insects, lizards play dead on rocks, and birds zip around shrubs and cacti. Come night, the place becomes infested with bats, as they chase down the desert’s fat, juicy moths.

In a five and a half month span, the team’s 46 cameras identified nearly 5,000 wildlife events outside of the tortoise burrows. Many animals—such as lizards, rodents, and snakes—make use of the tortoise’s finely dug subterranean homes, whose conspicuous holes invite the interest of hungry foxes and coyotes. After reviewing thousands of images, Lovich and his team determined that these predators are more likely to visit a burrow farther from a turbine than one closer to a turbine, suggesting that noise and vibrations generated by the machines encouraged animals to seek the more tranquil areas of the wind farm.

This study, while important, still leaves researchers with an insufficient grasp on how wind farms affect wildlife. “There’s a general lack of scientific information on the effects of wind and solar development, particularly for things that don’t fly,” says Lovich. So as more wind farms spread over vast swathes of the windswept Mohave Desert, or inhabit farmland in Texas and Tennessee, scientists want to know which sort of designs work best to reduce effects on wildlife. “Now is a perfect time for scientists to get involved,” says Lovich. “Renewable energy is exploding.”

As for wind farm design improvement, the tortoise burrow study provided a not-too-subtle hint. If wildlife are avoiding the turbines, explains Lovich, it might behoove windfarm engineers to space the turbines farther apart. This would mean placing fewer turbines on the land, but the lost energy might be recovered by using higher-capacity turbines. “Minimizing the footprint is always better,” he says.

In the U.S., renewable energy capacity and generation has been steadily rising for over a decade. U.S. Department of Energy

What’s more, wind energy generates jobs. In 2023 the industry employed 101,000 workers, according to the 2023 U.S. Energy and Employment Report. By 2050, the U.S. Department of Energy reports that wind has the potential to support over half a million jobs.

Wind energy will likely become increasingly cheaper, and in a capitalist society, money wins the race. As more wind turbines are planted in American deserts, scientists like Lovich want companies to produce the most successful, least impactful farms for all parties involved—including the ones you can’t see from the highway.

“Renewable energy is here, whether anyone likes it or not,” he says.

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Predicting The Wind Speed Using K

This article was published as a part of the Data Science Blogathon

Hi, all Analytics Vidhya readers. Hope you all are doing well in this hard time of the Covid era. 

In this article, we are going to predict the wind speed of the current date and time for any given latitude and longitude coordinates. We’ll be using a K-neighbors classifier to build our predicting model. The dataset we are using is available on GitHub here.

Starting with our task

The first step which I always suggest is to check the python version which you are using. I am using an anaconda package and a jupyter notebook. So whichever editor you are using, type in the following

from platform import python_version print(python_version())

The output will be. I am using Python version 3.8.8 as shown here.

3.8.8 Importing important libraries- 

The first step is to import libraries. We are using pandas, numpy, scikit-learn and datetime modules to analyse our dataset.

import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn import metrics from sklearn.neighbours import KNeighborsClassifier from datetime import datetime Reading the dataset- 

The second step is to read the data file which we are going to analyse. The file is in .csv format.

data = pd.read_csv('sat.csv')

The output of the above code is:

RangeIndex: 72890 entries, 0 to 72889 Data columns (total 4 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 X 72890 non-null float64 1 Y 72890 non-null float64 2 time 72890 non-null float64 3 wind_speed 72501 non-null float64 dtypes: float64(4) memory usage: 2.2 MB

If you see the output following things can be analysed:

There are 72890 entries in the dataset.

There are 4 columns: X, Y, time and wind_speed.

The data type is float.

Some values are missing in the wind_speed column. We’ll analyse this in a while.

Let’s print the first 5 values of our dataset.


The output is:

OK. So, X and Y correspond to latitudes and longitudes coordinates respectively. If we closely see the time column the entries are written in epoch timestamp. To predict the wind speed of the current duration, we need to determine what time this epoch timestamp is showing. For this, the datetime module is quite handy.

Let’s take the first value from the time column and see what time and date it is pointing.

timestamp = 42368.149155 #convert timestamp to datetime object dt_object = datetime.fromtimestamp(timestamp) print("dt_object:", dt_object)

The output of the above code is:

dt_object: 1970-01-01 17:16:08.149155

If we see the output, the timestamp is pointing at the year 1970, January 01, and the timing is 17:16:08.149155. Now we can also convert the time column data into date-time value like this.

for i in data['time']: dt_object = datetime.fromtimestamp(i) print(dt_object)

We can create a new column and it in the existing dataset also. This will help to analyse timestamp and time relation properly.

data['Time_Analysis'] = pd.to_datetime(data['time'], unit='s') data.head()

Executing this, we have,

We can see that the data is of January only and the difference is in seconds only between two data points.

Data Cleaning

Now moving on, we aim to determine the wind_speed. And previously we saw that the wind_speed column has some missing data in it. So, let’s analyse it. If we check for the presence of null values and sum it, we can see that there are 389 rows with no entries in them.


The output is:


What I’ll do, I’ll drop these 389 values. One can also use interpolation techniques and fill these empty cells with methods like forward-fill, backwards-fill, etc. But for the time being, let’s drop these rows.

data = data.dropna(how = 'any', axis = 0)

OK. Now it’s time to train our data. Starting with the first step of feature selection.

Feature and Target selection-

Our target is to find the wind speed so that will be the target or our dependent variable. To determine the wind speed we need X, Y and time, so they will be our features.

If we check the shape of the data, we see that there are 72501 rows and 5 columns.

data. shape

And the shape of the data is

(72501, 5)

Remember that, we have drop 389 empty cells. That’s why 72501 rows are present. Let’s assign the feature and target samples now.

feature_col = ['X', 'Y', 'time'] target = ['wind_speed']

Also, we can see the shapes of our feature dataset and target dataset. Let X be the variable assigned for the feature dataset and y be the target dataset.

X = data[feature_col] print(X.shape)

y = data[target]


The output shapes are,

(72501, 3) # feature dataset shape (72501, 1) # target dataset shape

We can also visualize our dataset. For this, I am using the seaborn data visualization library.

import seaborn as sns %matplotlib inline sns.pairplot(data, x_vars = ['X', 'Y', 'time'], y_vars = 'wind_speed')

If we observe, the data is approximately uniform.

You can see some variation in wind_speed vs. time graph, but don’t forget that this graph has values changing over fractions of seconds. Over a long duration of time, we can consider it uniform.

Splitting the data into training and testing dataset

Using the train_test_split function we can now split our dataset into training and testing datasets. I’ll be using the default values only. but one can split the dataset according to their need.

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 1) print(X_train.shape) print(X_test.shape) print(y_train.shape) print(y_test.shape)

Just, in brief, let me explain what these terms mean:

X_train- it has all the feature variables, a fraction of which are used to train the model.

X_test- it also has all the feature variables which were left from training.

y_train- it is the target data that will be predicted by the ML model. At the time of training itself, the target variable is set.

y_test- it is used to test the accuracy between actual and predicted values.

And printing the shapes will give the dimensions of the training and testing dataset.

(54375, 3) # X_train shape (18126, 3) # X_test shape (54375, 1) # y_train shape (18126, 1) # y_test shape

We can observe that by default, 75% of data is used for training and 25% of data is used for testing. Now it’s time to implement our machine learning model. We are using K-neighbors classifier for this. Let’s get a very brief introduction to K-Neighbors classifier.

According to Wikipedia, “In statistics, the k-nearest neighbour’s algorithm (k-NN) is a non-parametric classification method first developed by Evelyn Fix and Joseph Hodges in 1951 and later expanded by Thomas Cover. It is used for classification and regression. In both cases, the input consists of the k closest training examples in the data set. The output depends on whether k-NN is used for classification or regression. In k-NN regression, the output is the property value for the object. This value is the average of the values of k nearest neighbours.“

In our case, we are dealing with a regression problem and the number of neighbours we are choosing is 1.

knn = KNeighborsClassifier(n_neighbors = 1),y)

On running we are not getting any errors. That’s good, but we are getting some warning.

We can remove the warning. This can happen sometimes if you have float data. The solution is also provided in the warning itself.

knn = KNeighborsClassifier(n_neighbors = 1),y.values.ravel())

OK, now the warning is also removed. Let’s check if our model is working correctly or not. For that first, I am using the first row data.

So, we’ll use these values of X, Y and time and see if we get this wind_speed or not. We can directly use any values straight away, but this will help us to see if our model is giving correct predictions or not.

check_coord = [[11.79, 33.169998, 42368.149155]] print(knn.predict(check_coord))

The output we are getting is:


So our model is predicting correctly. We can measure the accuracy also.

y_pred = knn.predict(X) print(metrics.accuracy_score(y,y_pred))

And the output is:


An accuracy of 1.0 means 100%. So, our model is predicting with 100% accuracy. What if we have neighbours = 5?

knn = KNeighborsClassifier(n_neighbors = 5),y.values.ravel()) print(knn.predict(check_coord))

The output is:


Again checking the accuracy,

y_pred = knn.predict(X) print(metrics.accuracy_score(y,y_pred))

And the accuracy is:


The accuracy, in this case, is very poor, around 34%. So, we’ll use neighbours value equal to 1.

So now we can check it on any coordinates. Let’s take some random coordinates which are not present in our dataset. Let X = 23.1 and Y = 79.9. First, check whether these coordinates are present in our dataset or not.

data.loc[(data['X'] == 23.1) & (data['Y'] == 79.9)]

As we can see, there are no such coordinates and hence nothing is displayed.

Now we have some random X Y coordinates and to determine wind_speed for these coordinates, we need time. For this model, we are interested in the current timestamp. using any online epoch converter, we can check what is the timestamp value for the current time and date. If we check the current timestamp value is 1627112345. Let’s verify it with the datetime module.

from datetime import datetime timestamp = 1627112345 #convert timestamp to datetime object curr_time = datetime.fromtimestamp(timestamp) print("Current Time:", curr_time)

OK. The output is:

Current Time: 2023-07-24 13:09:05

Now we have X, Y and time features and we can straight away put these into our model to determine the wind speed. Remember to take neighbours value equal to 1.

new_coord = [[23.1, 79.9, 1627112345]] print(knn.predict(new_coord))

[26.] # output

Again, let’s check the accuracy

y_pred = knn.predict(X) print(metrics.accuracy_score(y,y_pred))

The accuracy obtained is:


So, our model is predicting the wind speed with 100% accuracy. This is how we can use latitude, longitude and time to predict wind_speed.


In this article, we have learned how to read data using. CSV files, how to clean data eliminating unwanted data, how to split data into training and testing datasets and then training the model to predict the output. We also learn to determine the accuracy of the predicted output.

We can use other interpolation techniques to check for other possibilities. I used Naive Bayes Classifier and Logistic Regression methods also. You can try with other learning models. I hope this article explains all the steps required to learn prediction using the K-neighbors classifier. If you liked this article then please share it with your friends. Learning is sharing more knowledge.

Thanks for reading this post. Have a great time ahead !!!

About the Author-

Hi all Geeks. I am Abhishek Singh, Assistant Professor, Electronics and Communication Engineering Department, Gyan Ganga Institute of Technology and Sciences, Jabalpur, MP. I am Data Scientist, YouTuber, Content Writer, Blogger, Python Editor and Author. I love to teach Electronics, Python and VLSI. Please feel free to contact me through-




Thank You!!!

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Nasa Bets The Farm On The Long

If you give an astronaut a packet of food, she’ll eat for a day. If you teach an astronaut how to farm in space, she’ll eat for a lifetime—or at least for a 6-month-long expedition on the International Space Station.

Since its earliest missions, NASA has been focused on food, something astronauts need whether they’re at home on Earth or orbiting 250-odd miles above it. Over the years, the administration has tried a series of solutions: John Glenn had pureed beef and veggie paste, other flight crews used new-age freeze drying technology. More recently, NASA’s been trying to enable its astronauts to grow their own food in orbit.


Bryan Onate, an engineer stationed at the Kennedy Space Center, is on the forefront of this technology. He helped lead the team that built Veggie, NASA’s first plant growth system, and next month he’s sending up Veggie’s new and improved brother, the Advanced Plant Habitat.

The habitat is the size of a mini-fridge. But instead of storing soda, it will carefully record every step in the growth of plants aboard the space station. This will allow researchers on the ground unprecedented insight into how plants are shaped by microgravity and other forces at work in outer space. And, Onate says, “astronauts may get to enjoy the fruit of our labor.”

Though it’s small, the new habitat is equipped with over 180 sensors and three cameras. The sensors will record data about temperature, moisture and oxygen in the unit. The cameras—one of which is infrared—will provide further insight into what’s happening in the chamber. All of the data is processed by a computer named, with NASA’s characteristic acronymic humor, “PHARMER”—Plant Habitat Avionics Real-Time Manager in Express Rack. “It’s really, truly a scientific toolbox,” Onate says.

Except for installation, the system should run with very little input and cut down on the astronomical (wink wink) cost of shipping food to the station. Currently, it costs more than $10,000 a pound to send food and other supplies hurtling upwards. That means your typical 14 ounce loaf of bread—just $3.35 here on Earth—would cost somewhere in the ballpark of $8,750 to send to space. Plus, the freshest stuff doesn’t last long. “If I pack a bag of cherry tomatoes…My tomatoes are going to only be good for a week or two maybe,” Onate says. “But if I take seeds with me, I can grow food.”

Advanced Plant Habitat NASA/Bill White

One of the habitat’s real innovations is its light. The sun emits about 2,000 micromoles to Earth. NASA’s new habitat will put out 1,000, or half the light of the sun.

That’s a crucial source of luminescence for the plants, which need a glow to grow, but don’t receive it on the space station, which, like the rest of space, is always cloaked in darkness. NASA researchers hope to test the red, green, blue and white spectrum lights at different intensities to find out what best stimulates plant growth. “We can really target a light treatment,” Onate says, “just so we can start learning the differences.”

Ultimately, the habitat is more of a research project than a bonafide space farm. But Onate sees it as the first step in a larger mission to make human life sustainable off-world. “In the future, on Mars, if we colonize out there, resources are a premium,” he says. The key will be finding a way to manage plant growth long-term, in settings we’ve only just begun to understand.

“These [plant projects] are all the little footsteps we’re taking to understand” how to keep humans in space more permanently, he says. And one day soon, NASA may take the giant leap to self-sustainable space farming.

How To Make A Cow Farm In Minecraft

Cows are, without a doubt, the most amazing domesticated mob in Minecraft. The cows are friendly, easy to breed, and yield some amazing items. You can get food, leather, and milk, which acts as an anti-potion element, from the cows. But it’s usually the most crucial time in the game when you can’t find any cows near your base. Luckily, that issue has a simple solution. You just need to learn how to make a cow farm in Minecraft. Once it’s ready and populated, you will never have to search for another cow again. With that said, let’s get started!

Make a Cow Farm in Minecraft (2023)

We will first cover the mechanics and requirements to build a cow farm in-game. But if you want to skip ahead to the functionality, you can do so using the table below.

Mechanics of a Minecraft Cow Farm

Unlike other useful Minecraft farms, the cow farm can’t be automated – at least not without the use of Minecraft commands. So, you must put in some effort now and then to keep your automatic cow farm functional. But, at the same time, it also implies that cow farms are the easiest to make. 

To make a cow farm in Minecraft, you simply have to trap some cows and breed them. Once your farm is overpopulated with cows, you can kill them to collect your rewards. Moreover, if you know how to use an Allay in Minecraft, it can take care of the collection process, making the farm semi-automatic right away.

Items Required to Make a Cow Farm

Two cows

Stacks of wheat (as many as possible)

A stack of fences (or less, depending on the area)

A fence gate

You can easily find two cows within the plains biome and its variants in Minecraft. You can read about all the Minecraft biomes via the link right here. They are much more common around villages. The same villages can also help you get wheat. Though, you can grow your wheat in Minecraft to get enough of it for your farm.

How to Make a Fence Gate & Fence

Crafting recipe of Fence (up) and Fence Gates (down)

The crafting recipe of fences and fence gates only requires sticks and wooden planks. You can use any wood to create them. Moreover, their recipe is, in a way, opposite of each other. And thanks to the abundance of wood in Minecraft, you can quickly build many fences. 

How to Make a Cow Farm in Minecraft

Follow the steps below to quickly and easily build a cow farm and get various resources in Minecraft:

1. First, find an open area that is at least 4 x 4 blocks. It’s best not to choose a cold biome to avoid snow being collected on your farm.

3. Next, use wheat to make the cows follow you and have them enter the farm area. You only need two cows to get started but having more of them is only better.

4. Next, to activate the farm, hold a piece of wheat and stand outside the farm. The cows will move towards you as soon as they notice the wheat.

5. When the cows approach, you have to feed them wheat to make the cows breed. As soon as both cows are fed, you will see a baby cow spawn. It will take 20 minutes for the baby cow to grow into an adult. And it only takes 5 minutes for cows to be ready to breed again. So, if you time it right, you can get plenty of cows in no time.

Automatically Kill Cows to Collect XP and Items

In cow farms, most Minecraft players usually grab a sword and kill as many cows as they want. It is usually a need-based action. Though, unless you save some of them, you will have to start from scratch. In any case, you can follow these steps below if you wish to improve the killing process:

2. Then, place water in one corner of the farm. This will create a flow of water that pushes cows in one corner of the farm.

3. In the corner where water takes the cows, replace the corner block with a hopper and its surrounding blocks with magma blocks. 

4. Now, every cow that reaches this corner will die because of damage from magma blocks. Their dropped items will get collected into the chest via the hopper.

The above mentioned method is the most efficient for killing a bunch of cows at once. Whenever you want to stop, you only have to remove the placed water to stop the flow of cows.

Frequently Asked Questions 

Unfortunately, there is no way to feed, and thus, breed the cows automatically in vanilla Minecraft. But you can use Minecraft mods to get this feature.

How big should a cow farm be Minecraft?

The size of the cow farm depends upon your requirements. You can start small and slowly expand it as the time goes by.

Can dispensers feed mobs?

How to get seeds to grow wheat in Minecraft?

Wheat seeds are the most common ones in the game. You can get them by breaking not only wheat but even regular grass. 

Create Your Own Cow Farm in Minecraft 1.19

How Seo Professionals Can Avoid Becoming Obsolete

Year after year, Google is constantly changing their search algorithm and the SERPs as a whole.

With constant change – including the rise of machine learning – comes a natural fear that, someday, the roles of SEO professionals will become obsolete.

I hope to remedy some of that fear today and walk some of you through what I feel will allow you to stay relevant within the SEO industry.

The main talking points of this article will focus on the following:

How to stay relevant in the field of SEO

The value of technical SEO expertise

When to learn new skills

Being able to communicate the value of SEO

1. Stay Up to Date

The most important habit every SEO professional needs to develop is reading. Make sure you set aside some time to find resources that will allow you to get timely information when you need it.

An out of date SEO professional is an obsolete SEO professional.

The number one way to avoid becoming obsolete in SEO is being able to notice changes within the search industry.

The two main things you should always watch for:

Major algorithm changes.

SERP appearance changes.

These changes drastically alter how you approach and alter your SEO campaigns.

To stay current with the latest SEO trends you will want to periodically check the following:

The Webmaster Central Blog comes straight from Google. It’s a helpful resource to stay abreast of big industry changes. They usually provide helpful tips, references, and guides on proper implementation to meet those changes.

2. The Value of Technical SEO Expertise

Technical SEO to me revolves around the main concepts of:




When we have a conversation with people completely new to our industry space we must be able to clearly answer a few common questions.

How do I get my website indexed?

How does my content get crawled?

SEO professionals have no control over the organic search rankings. Thus, we can’t guarantee that our clients will rank for any given query.

But, what SEOs should always be able to offer is the ability to get webpages into Google’s index.

Before webpages are indexed, SEO pros need to understand what needs to be in place for all the contents of those pages to be crawled. Google should have everything it needs to understand the contents of each page.

The most important tool any SEO needs to learn how to use is Google Search Console. All SEO professionals use a variety of tools, but the common tool among all of them should be the Search Console. It’s the bridge between a website and Google.

This is where you will handle areas pertaining to indexing, which include:

Submitting sitemaps

Testing robots.txt

Fetching your webpage content as Google

Analyzing crawl errors and crawl stats

Requesting to remove URLs from the index

Addressing manual actions

Correcting structured data

All of the above helps you manage and audit how Google indexes and crawls a website.

Can you explain the fundamentals of how Google crawls a website? This stretches into a lengthier discussion about the importance of site architecture, sitemap optimization, and using HTML elements to name a few.

We talk about evergreen content a lot in SEO, but I think an evergreen skill in SEO is the strong understanding of how Google crawls and indexes your site.

If you know these core principles then you are well on your way to never being obsolete.

3. Learning New Skills When Needed

SEO professionals need to learn new skills and new concepts in order to have success moving forward.

You also need a solid understanding of how the web works in general.

For example, not too long ago we saw boosts in rankings for more secure, faster, and mobile-friendly websites. At each of these moments, we had to ask ourselves:

How do I make a website more secure?

How do I make my website faster?

How do I optimize my website for mobile?

These questions require technical knowledge outside the normal realm of traditional SEO knowledge.

In order to communicate your needs for these initiatives to potential engineering or development teams, you’ll also need to have a solid understanding of what they are asking for.

You don’t need to know how to implement it, but you need to know enough to give solid direction on where to look and where to begin to fix foundational problems like security, speed, or mobile responsiveness, as examples.

4. How to Communicate the Value of SEO

As long as a search engine results pages exist, SEO will never be obsolete.

As long as the algorithm continues to crawl our websites there will always be a need for SEO professionals.

Organic search will always be a channel that can provide immense value to any business.

The only time SEO could become obsolete is if the stakeholders of an organization don’t fully understand the value SEO brings to the table.

In order for you to communicate the value of SEO, you must understand everything discussed in this article so far.

You must understand exactly how search engines rank content now – and how to get a website in the index.

If you know how content is ranked and how to get pages indexed, then you can then have a conversation about utilizing the channel to hit specific KPIs and building a strategy based on those core concepts.

Traditionally, we know that the main KPIs we are most commonly tasked to hit are organic conversions and organic traffic.

Here are a few ways SEO touches and adds value to other areas of the business outside of just organic search:

Providing insights to content and copy teams with real-world search trends.

Generating initial traffic to later be used for retargeting campaigns.

Enhancing site performance ultimately benefits those in user experience.

Getting more traffic provides a larger sample size to perform tests.

Getting initial exposure to a brand to later becoming social media followers.

Nothing that provides value is ever obsolete.

Closing Thoughts

Having a solid foundation of how Google crawls and indexes your website will be all the building blocks you will need to develop comprehensive SEO strategies.

Staying up to date with the latest search changes will prevent you from marketing last year’s SEO tactics this year. Don’t be the person who promises to rank in the local 7-pack, today.

Always focus on providing value. Have a solid plan on how to provide that value and you will never be obsolete.

More SEO & Marketing Career Resources Here:

7 Blood Sugar Testing Mistakes To Avoid

One of the most common blood sugar tests is to measure the sugar level by pricking the finger and testing the blood for glucose. There are many ways you can check your blood sugar.

1. Fingertip Testing of Blood Sugar

Fingertip testing is prevalent for diabetes and other health issues. Many nerves are ending at our fingertips. The pad on the fingertip is a sensitive spot. Testing the blood by pricking your fingertip may hurt.

A better way to test the blood is to put your hands together by keeping the palms flat and pressing the fingertips together.

Test the sugar in the blood from the visible edges of the hand. It is less painful as you will not touch things with the edge areas. You can use other areas (thighs, forearms) to test blood sugar levels.

2. Not Shaking your Fingers Before Testing

Avoid drawing blood from stale hand fingertips. Before pricking the finger, put your hands and fingers downward and keep shaking your hands and finger for several seconds. It induces blood to flow to the fingertips. Place the lancet tip on the side of the fingertip to avoid using the spot you used for finger pricking last time.

3. Using the Same Finger Every Time for the Test

People unknowingly use the same fingertip spot because they are used to it. Try using different fingertips for every new blood test.

Use different fingertips each time you go for a blood test, which allows pricked fingers the time to heal and recover. It also helps you avoid the painful experience of pricking the same finger spot for repeat jabs.

You can try alternate site testing like the palm of your hand if your blood test sugar reading remains consistent. You do not have to use only the fingertips for the blood. Using the same spot on your finger can sore the finger.

4. Not Cleaning the Finger Correctly

Blood from an uncleaned finger will have more than a 10% difference in blood sugar level in the first and the second drop tests. The result may be even higher if the person has touched a fruit before the test.

Ensure you have washed your hands thoroughly before the test with a chemical-free soap and warm water, and pat it dry before the test. Avoid touching anything before the blood test is over.

5. Not Using a Fresh Lancet for Testing

Multiple usages of the same needle may get blunt by penetrating your skin and may be painful. You may feel an electric shock on the fingertip. A sharp needle in the lancet will give you a smoother prick and a less painful experience.

6. Waste of Lancet and Tests Strips

Authorized medical body of your country should accredit your testing equipment for the accuracy of the glucose meter, dependability of the lancets, and manufacturer-verified test strips.

Skimping will affect your blood sugar reading and the eventuality of your health condition. The most common fault in the lancet is it starts very sharp but soon becomes dull and blunt, which hurts in the next prick reusing them.

Check the expiry date of the strips. Preserve the test strips according to the instructions mentioned in the box to give you accurate numbers.

Always use a fresh lancet. Ensure you keep the test strips in a closed airtight box. You can get the medicated boxes in medical supply stores and ensure to check the strips’ date of expiry before using them.

7. Not Understanding the Glucose Meter

We can easily find accurate and sophisticated glucose meters in the market. They are easy to use, but periodically checking the accuracy of the readings is imperative. Follow the manufacturer rulebook, user manual, and guidelines to the tee and correctly. Understand the fine points of using the meter and all the error messages. Seek help from a health specialist for the first few tests if you do not know how to read the meter.


Avoid mindless testing or testing too frequently and testing soon after a meal. Know when to test. Incorrect testing will generate misleading results and misguide you. Wait for two hours after a meal to get an accurate sugar level. Make a calendar or a schedule for your tests.

Understand test results. You will waste the strips and time if you can decode the meter readings. Test once in the morning after an 8-10 hours gap from the meal at night and two hours after breakfast. Then test once in the night before bed, after two hours of the night meal.

It will help you understand the level of insulin resistance. The results, if understood well, can help you change your lifestyle to manage your diabetes and the sugar levels under control.

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