text:
Simplilearn’s Machine Learning course in Pune will make you proficient in machine learning. Machine learning is a form of artificial intelligence that automates data analysis to enable computers to learn and adapt from experience. With machine learning, computers can perform specific tasks without precise programming. You will master the concepts and techniques including mathematical and heuristic aspects, supervised and unsupervised learning, and hands-on modeling to develop algorithms and learn introduction to machine learning which will prepare you for the role of Machine Learning Engineer.
With Machine learning taking over the world, the companies greatly need professionals to be aware of the ins and outs of machine learning.
Machine learning, a form of artificial intelligence, is revolutionizing the world of computing as well as people’s digital interactions. Machine learning is critical to countless new and future applications by making it possible to quickly, automatically, and cheaply process and analyze huge volumes of complex data. Machine learning empowers innovative automated technologies such as fraud protection, recommendation engines, facial recognition, and even self-driving cars.
This Machine Learning course in Pune gives data scientists, engineers, and other professionals the hands-on skills and the knowledge required for job competency and a certification in machine learning. The demand for machine learning skills and knowledge is growing fast. According to payscale.com, the average salary of a Machine Learning Engineer is $134,293 (USD).
What skills you learn in this Machine Learning Certification Course?
Acquire a thorough knowledge of the mathematical and heuristic aspects of machine learning.
Master the practicalities of the algorithms, principles, and applications of machine learning by the hands-on learning approach which includes 28 projects to work on and one capstone project.
Be able to understand the theoretical concepts and how they relate to the practical aspects of machine learning.
Gain Mastery in the concepts of unsupervised, supervised, and reinforcement modeling and learning concepts.
Understand the operation and concepts of naive Bayes, decision tree classifier, logistic regression, support vector machines, kernel SVM, K-nearest neighbors, random forest classifier, K-means clustering, and more.
Who should take this Machine Learning Training in Pune?
Across industries, there is an increasing demand for skilled machine learning engineers. Skilled machine learning engineers are in demand throughout the globe and across all industries.
How Much does Machine Learning Course Costs in pune?
INR Rs.20,999/- for Online Classroom Flexi-Pass.
What is the Duration of Machine Learning Certification Training in Pune?
The first is the Self-paced e-Learning methodology with a validity of 180 days (6 months). This enables the students to work at their own pace through high-quality e-learning video modules.
The second methodology is the Online Classroom Flexi-Pass with a validity of 180 days (6 months). This consists of high-quality e-learning videos along with access to 8+ instructor-led online training classes for 90 days.
Simplilearn's Machine Learning Training course is code-driven and provides hands-on experience. Mathematical problem formulation and theoretical motivation must be provided only while introducing the concepts.
In this Machine Learning course, one primary capstone project and 25+ ancillary exercises based on 17 machine learning algorithms have been included.
Description: The project involves building a model that predicts average house values in the Californian districts. The candidates will be given metrics such as average income, population, average housing price, and so on for each block group in California. Block groups are the smallest geographical units for which the US Census Bureau publishes sample data (a lock group typically has a population of 600 to 3,000 people). The model built by the candidates must learn from this data and be able to predict the average housing price in any district.
Project 1: What are the problems that you find in the plot produced by the code, on the basis of the above problem statement?
Project 2: What is the estimated price of the houses with areas 1700 and 1900?
Project 3: Review the training dataset (Excel file). Note that the weights are not available for the fifth and eighth rows. What are the values computed by the imputer for these two missing rows?
Project 4: In the tutorial code, find the call to the Imputer class. Replace the strategy parameter from “mean” to “median” and execute it again. What is the new value assigned to the blank fields Weight and Height for the two rows?
Project 5: In the code snippet given below in the tutorial, why does the array X have 5 columns instead of 3 columns as earlier?
Project 6: What does the hyperplane shadow represent in the PCA output chart on random data?
Project 7: What is the reconstruction error after PCA transformation? Give explanation.
Project 8: Modify the degree of the polynomial from Polynomial Features (degree = 1) to 1, 2, 3, and depict the resulting regression plot. Mention if it is right-fitted, under fitted, or overfitted.
Project 9: Determine the insurance claims for age 70 with polynomial regression n with degree 2 and linear regression.
Project 10: From the code snippet given in the tutorial, why does the array X have 5 columns instead of 3 columns as before?
Project 11: Alter the code to determine insurance claim values for anyone above the age of 55 in the given dataset.
Project 13: Discern the output after modifying the max_depth to 20.
Project 14: What is the class prediction for petal_width = 1 cm and petal_length = 3 cm for max_depth = 2?
Project 15: Explain the Decision Tree regression graphs produced when max_depths are 2 and 3. How many leaf nodes are present in the two cases? What does the average value represent in both the situations? Use the information given.
Project 16: Adjust the regularization parameter min_sample_leaf from 10 to 6, and examine the output of Decision Tree regression. What is the result and why?
Case Study 7: By making use of the Random Forests, predict the insurance per year based on the person’s age.
Project 17: What is the output insurance value for individuals aged 60 and with n_estimators = 10?
Project 18: The program depicts a learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Give your explanation for these charts.
Project 19: The program represents the learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Try to alter the estimations to 0.001, 0.25, and 0.9 and examine the results. Provide an explanation.
Project 20: Typically, the value of nearest_neighbors for testing class in KNN is 5. Alter the code to transform the value of nearest_neighbours to 2 and 20, and record the observations.
Case Study 10: Classify IRIS dataset using SVM, and illustrate how Kernel SVMs can help classify non-linear data.
Project 21: Alter the kernel trick from RBF to linear to observe the type of classifier produced for the XOR data in this program. Interpret the data.
Project 22: For the Iris dataset, add a new code at the end of this program to produce classification for RBF kernel trick with gamma = 1.0. Explain the output.
Project 23: Run decision tree on the IRIS dataset with max depths of 3 and 4, and display the tree output.
Project 24: Predict and print class probability for Iris flower instance with petal_len 1 cm and petal_width 0.5 cm.
Project 25: Add Logistic Regression classification to the program and compare classification output to previous algorithms.
Case Study 13: Demonstrate Clustering algorithm and the Elbow method on a random dataset.
Project 26: Modify the number of clusters k to 2, and record the observations.
Project 27: Modify the n_samples from 150 to 15000 and the number of centers to 4 with n_clusters as 3. Check the output, and record your observations.
Project 28: Modify the code to change the number of centers to 4 and the n_samples from 150 to 15000, keeping n_clusters at 4. Examine the output.
Project 29: Modify the number of clusters k to 6, and record the observations.
How will you get Simplilearn's Machine Learning Course Completion Certificate?
Completing 85% of the course.
What are the prerequisites for learning machine learning?
This course covers the concepts of statistics and mathematics that are necessary for machine learning and Simplilearn will provide you with a free Python course on purchasing our Machine Learning Certification course.
Simplilearn will award you the machine learning certification after successfully completing this course.
No. This Machine Learning Certification course is not certified officially by any standard organization.
How long does it to take to complete the Machine Learning certification course?
The successful completion time for this Machine Learning certification course is about 40-50 hours.
You can become a machine learning engineer by learning to code in R language or Python, implementation of machine learning using the Tensorflow framework, real-time machine learning applications, etc. By making use of this online machine learning course you can consider getting a Programming language certification and the degree to become a more valuable candidate. Once you have a basic skill set, you can get experienced by completing personal engineering projects, taking up the machine learning internship and participating in Kaggle competitions etc.
How will you get Simplilearn's Machine Course Completion Certificate?
Completion of one project and one simulation test with a minimum score of 80%.
No, this course is not officially accredited by any standard or organization.
How do I pass Simplilearn's Machine Learning Course Certification exam?
How long is the Machine Learning course certificate from Simplilearn valid for?
Machine Learning Course Certificate by Simplilearn has a lifelong validity.
What Average Salary companies pay for a Machine Learning Professionals in Pune?
Machine Learning engineers have a median salary of 8 lakhs per year in Pune, according to the analysis by Payscale and other top research firms like Glassdoor. However, an experienced Machine Learning professional can earn up to 18 lakhs per year. It varies depending on the total experience, city, and more. Simplilearn’s Machine Learning Engineer Training enables ML professionals to request for salary hike beyond the average when they shift in the Market or can increase their earning potential if they are already employed.
What roles companies offer for machine learning professionals in Pune?
Which companies are hiring Machine Learning Engineers in Pune?
Yes, cancellation of your enrolment is possible if necessary. Simplilearn will refund your course fee after deducting the administrative fee. Read our Refund Policy for more details.
Yes, group discount options are available for our training programs. You can contact Simplilearn by selecting the Live Chat link or using the form on the right of any page on our website. You can also get additional details from our customer service representatives.

lowercased:
simplilearn’s machine learning course in pune will make you proficient in machine learning. machine learning is a form of artificial intelligence that automates data analysis to enable computers to learn and adapt from experience. with machine learning, computers can perform specific tasks without precise programming. you will master the concepts and techniques including mathematical and heuristic aspects, supervised and unsupervised learning, and hands-on modeling to develop algorithms and learn introduction to machine learning which will prepare you for the role of machine learning engineer.
with machine learning taking over the world, the companies greatly need professionals to be aware of the ins and outs of machine learning.
machine learning, a form of artificial intelligence, is revolutionizing the world of computing as well as people’s digital interactions. machine learning is critical to countless new and future applications by making it possible to quickly, automatically, and cheaply process and analyze huge volumes of complex data. machine learning empowers innovative automated technologies such as fraud protection, recommendation engines, facial recognition, and even self-driving cars.
this machine learning course in pune gives data scientists, engineers, and other professionals the hands-on skills and the knowledge required for job competency and a certification in machine learning. the demand for machine learning skills and knowledge is growing fast. according to payscale.com, the average salary of a machine learning engineer is $134,293 (usd).
what skills you learn in this machine learning certification course?
acquire a thorough knowledge of the mathematical and heuristic aspects of machine learning.
master the practicalities of the algorithms, principles, and applications of machine learning by the hands-on learning approach which includes 28 projects to work on and one capstone project.
be able to understand the theoretical concepts and how they relate to the practical aspects of machine learning.
gain mastery in the concepts of unsupervised, supervised, and reinforcement modeling and learning concepts.
understand the operation and concepts of naive bayes, decision tree classifier, logistic regression, support vector machines, kernel svm, k-nearest neighbors, random forest classifier, k-means clustering, and more.
who should take this machine learning training in pune?
across industries, there is an increasing demand for skilled machine learning engineers. skilled machine learning engineers are in demand throughout the globe and across all industries.
how much does machine learning course costs in pune?
inr rs.20,999/- for online classroom flexi-pass.
what is the duration of machine learning certification training in pune?
the first is the self-paced e-learning methodology with a validity of 180 days (6 months). this enables the students to work at their own pace through high-quality e-learning video modules.
the second methodology is the online classroom flexi-pass with a validity of 180 days (6 months). this consists of high-quality e-learning videos along with access to 8+ instructor-led online training classes for 90 days.
simplilearn's machine learning training course is code-driven and provides hands-on experience. mathematical problem formulation and theoretical motivation must be provided only while introducing the concepts.
in this machine learning course, one primary capstone project and 25+ ancillary exercises based on 17 machine learning algorithms have been included.
description: the project involves building a model that predicts average house values in the californian districts. the candidates will be given metrics such as average income, population, average housing price, and so on for each block group in california. block groups are the smallest geographical units for which the us census bureau publishes sample data (a lock group typically has a population of 600 to 3,000 people). the model built by the candidates must learn from this data and be able to predict the average housing price in any district.
project 1: what are the problems that you find in the plot produced by the code, on the basis of the above problem statement?
project 2: what is the estimated price of the houses with areas 1700 and 1900?
project 3: review the training dataset (excel file). note that the weights are not available for the fifth and eighth rows. what are the values computed by the imputer for these two missing rows?
project 4: in the tutorial code, find the call to the imputer class. replace the strategy parameter from “mean” to “median” and execute it again. what is the new value assigned to the blank fields weight and height for the two rows?
project 5: in the code snippet given below in the tutorial, why does the array x have 5 columns instead of 3 columns as earlier?
project 6: what does the hyperplane shadow represent in the pca output chart on random data?
project 7: what is the reconstruction error after pca transformation? give explanation.
project 8: modify the degree of the polynomial from polynomial features (degree = 1) to 1, 2, 3, and depict the resulting regression plot. mention if it is right-fitted, under fitted, or overfitted.
project 9: determine the insurance claims for age 70 with polynomial regression n with degree 2 and linear regression.
project 10: from the code snippet given in the tutorial, why does the array x have 5 columns instead of 3 columns as before?
project 11: alter the code to determine insurance claim values for anyone above the age of 55 in the given dataset.
project 13: discern the output after modifying the max_depth to 20.
project 14: what is the class prediction for petal_width = 1 cm and petal_length = 3 cm for max_depth = 2?
project 15: explain the decision tree regression graphs produced when max_depths are 2 and 3. how many leaf nodes are present in the two cases? what does the average value represent in both the situations? use the information given.
project 16: adjust the regularization parameter min_sample_leaf from 10 to 6, and examine the output of decision tree regression. what is the result and why?
case study 7: by making use of the random forests, predict the insurance per year based on the person’s age.
project 17: what is the output insurance value for individuals aged 60 and with n_estimators = 10?
project 18: the program depicts a learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. give your explanation for these charts.
project 19: the program represents the learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. try to alter the estimations to 0.001, 0.25, and 0.9 and examine the results. provide an explanation.
project 20: typically, the value of nearest_neighbors for testing class in knn is 5. alter the code to transform the value of nearest_neighbours to 2 and 20, and record the observations.
case study 10: classify iris dataset using svm, and illustrate how kernel svms can help classify non-linear data.
project 21: alter the kernel trick from rbf to linear to observe the type of classifier produced for the xor data in this program. interpret the data.
project 22: for the iris dataset, add a new code at the end of this program to produce classification for rbf kernel trick with gamma = 1.0. explain the output.
project 23: run decision tree on the iris dataset with max depths of 3 and 4, and display the tree output.
project 24: predict and print class probability for iris flower instance with petal_len 1 cm and petal_width 0.5 cm.
project 25: add logistic regression classification to the program and compare classification output to previous algorithms.
case study 13: demonstrate clustering algorithm and the elbow method on a random dataset.
project 26: modify the number of clusters k to 2, and record the observations.
project 27: modify the n_samples from 150 to 15000 and the number of centers to 4 with n_clusters as 3. check the output, and record your observations.
project 28: modify the code to change the number of centers to 4 and the n_samples from 150 to 15000, keeping n_clusters at 4. examine the output.
project 29: modify the number of clusters k to 6, and record the observations.
how will you get simplilearn's machine learning course completion certificate?
completing 85% of the course.
what are the prerequisites for learning machine learning?
this course covers the concepts of statistics and mathematics that are necessary for machine learning and simplilearn will provide you with a free python course on purchasing our machine learning certification course.
simplilearn will award you the machine learning certification after successfully completing this course.
no. this machine learning certification course is not certified officially by any standard organization.
how long does it to take to complete the machine learning certification course?
the successful completion time for this machine learning certification course is about 40-50 hours.
you can become a machine learning engineer by learning to code in r language or python, implementation of machine learning using the tensorflow framework, real-time machine learning applications, etc. by making use of this online machine learning course you can consider getting a programming language certification and the degree to become a more valuable candidate. once you have a basic skill set, you can get experienced by completing personal engineering projects, taking up the machine learning internship and participating in kaggle competitions etc.
how will you get simplilearn's machine course completion certificate?
completion of one project and one simulation test with a minimum score of 80%.
no, this course is not officially accredited by any standard or organization.
how do i pass simplilearn's machine learning course certification exam?
how long is the machine learning course certificate from simplilearn valid for?
machine learning course certificate by simplilearn has a lifelong validity.
what average salary companies pay for a machine learning professionals in pune?
machine learning engineers have a median salary of 8 lakhs per year in pune, according to the analysis by payscale and other top research firms like glassdoor. however, an experienced machine learning professional can earn up to 18 lakhs per year. it varies depending on the total experience, city, and more. simplilearn’s machine learning engineer training enables ml professionals to request for salary hike beyond the average when they shift in the market or can increase their earning potential if they are already employed.
what roles companies offer for machine learning professionals in pune?
which companies are hiring machine learning engineers in pune?
yes, cancellation of your enrolment is possible if necessary. simplilearn will refund your course fee after deducting the administrative fee. read our refund policy for more details.
yes, group discount options are available for our training programs. you can contact simplilearn by selecting the live chat link or using the form on the right of any page on our website. you can also get additional details from our customer service representatives.


text:
THE COURTHOUSE CLOCK CHIMED SEVEN TIMES. The echoes of the chimes faded.
Warm summer twilight here in upper Illinois country in this little town deep far away from everything, kept to itself by a river and a forest and a meadow and a lake. The sidewalks still scorched. The stores closing and the streets shadowed. And there were two moons; the clock moon with four faces in four night directions above the solemn black courthouse, and the real moon rising in vanilla whiteness from the dark east.
In the drugstore fans whispered in the high ceiling. In the rococo shade of porches, a few invisible people sat. Cigars glowed pink, on occasion. Screen doors whined their springs and slammed. On the purple bricks of the summer-night streets, Douglas Spaulding ran; dogs and boys followed after.
The boys loped away. Waving after them quietly, Lavinia Nebbs sat all alone with a tall cool lemonade in her white fingers, tapping it to her lips, sipping, waiting.
She turned and there was Francine, all in snow white, at the bottom steps of the porch, in the smell of zinnias and hibiscus.
They walked down the street.
“Where you going, girls?” cried Miss Fern and Miss Roberta from their porch over the way.
“Oh, bosh!” Lavinia heard the old women’s door bang and lock, and she drifted on, feeling the warm breath of summer night shimmering off the oven-baked sidewalks. It was like walking on a hard crust of freshly warmed bread. The heat pulsed under your dress, along your legs, with a stealthy and not unpleasant sense of invasion.
They stood upon the edge of the ravine that cut the town half in two. Behind them were the lit houses and music, ahead was deepness, moistness, fireflies and dark.
Lavinia looked and the ravine was a dynamo that never stopped running, night or day; there was a great moving hum, a bumbling and murmuring of creature, insect, or plant life. It smelled like a greenhouse, of secret vapors and ancient, washed shales and quicksands. And always the black dynamo humming, with sparkles like great electricity where fireflies moved on the air.
“It’s early. Lonely One won’t be out till late.” Lavinia took the other’s arm and led her down and down the crooked path into the cricket warmth and frog sound and mosquito-delicate silence. They brushed through summer-scorched grass, burs prickling at their bare ankles.
They turned a curve in the path—and there it was.
In the singing deep night, in the shade of warm trees, as if she had laid herself out to enjoy the soft stars and the easy wind, her hands at either side of her like the oars of a delicate craft, lay Elizabeth Ramsell!
The woman lay as if she had floated there, her face moonlit, her eyes wide and like flint, her tongue sticking from her mouth.
Lavinia stood in the middle of a thousand warm shadows with the crickets screaming and the frogs loud.
“We’d better get the police,” she said at last.
Lavinia held Francine and the policemen were brushing through the crackling grass, flashlights ducked about, voices mingled, and the night grew toward eight-thirty.
“It’s like December. I need a sweater,” said Francine, eyes shut, against Lavinia.
Lavinia and Francine walked away from the police and the sheet over the delicate thing upon the ravine grass.
Lavinia felt her heart going loudly in her and she was cold, too, with a February cold; there were bits of sudden snow all over her flesh, and the moon washed her brittle fingers whiter, and she remembered doing all the talking while Francine just sobbed against her.
“No, we’ll make it,” said Lavinia to nobody, and they walked on. They walked through the nuzzling, whispering ravine, the ravine of whispers and clicks, the little world of investigation growing small behind them with its lights and voices.
“I’ve never seen a dead person before,” said Francine.
They started up the ravine side, on the stony path, in the dark. And suddenly there, barring their way, standing very still in one spot, not seeing them, but looking on down at the moving lights and the body and listening to the official voices, was Douglas Spaulding.
He stood there, white as a mushroom, with his hands at his sides, staring down into the ravine.
“Get home!” cried Francine. He did not hear.
Douglas jerked his head, stared at them as if they were not there. His mouth moved. He gave a bleating sound. Then, silently, he whirled about and ran. He ran silently up the distant hills into the warm darkness.
Francine sobbed and cried again and, doing this, walked on with Lavinia Nebbs.
The three young women stood in the summer night looking at each other. “I’ve got a notion to go in the house and lock the doors,” said Helen at last.
The three women moved along the street under the black trees, past suddenly locked houses. How soon the news had spread outward from the ravine, from house to house, porch to porch, telephone to telephone. Now, passing, the three women felt eyes looking out at them from curtained windows as locks rattled into place. How strange the popsicle, the vanilla night, the night of close-packed ice cream, of mosquitolotioned wrists, the night of running children suddenly veered from their games and put away behind glass, behind wood, the popsicles in melting puddles of lime and strawberry where they fell when the children were scooped indoors. Strange the hot rooms with the sweating people pressed tightly back into them behind the bronze knobs and knockers. Baseball bats and balls lay upon the unfootprinted lawns. A half-drawn, white-chalk game of hopscotch lay on the broiled, steamed sidewalk. It was as if someone had predicted freezing weather a moment ago.
“We’re crazy being out on a night like this,” said Helen.
A shadow fell across their terrified faces. A figure loomed behind a tree. As if someone had struck an organ a terrible blow with his fist, the three women gave off a scream, in three different shrill notes.
“Got you!” roared a voice. The man plunged at them. He came into the light, laughing. He leaned against a tree, pointing at the ladies weakly, laughing again.
“Hey! I’m the Lonely One!” said Frank Dillon.
Francine began to cry hysterically.
They moved. He moved to follow.
“Stay right there, Mr. Lonely One, and scare yourself. Go take a look at Elizabeth Ramsell’s face and see if it’s funny. Good night!” Lavinia took the other two on along the street of trees and stars, Francine holding a kerchief to her face.
The drugstore was a small pool of sluggish air which the great wooden fans stirred in tides of arnica and tonic and soda-smell out onto the brick streets.
“I need a nickel’s worth of green peppermint chews,” said Lavinia to the druggist. His face was set and pale, like all the faces they had seen on the half-empty streets. “For eating in the show,” said Lavinia as the druggist weighed out a nickel’s worth of the green candy with a silver shovel.
“Man sitting at the counter—watched you walk out. Said to me, ‘Say, who’s that?’ Why, that’s Lavinia Nebbs, prettiest maiden lady in town, I said. ‘She’s beautiful,’ he said. ‘Where does she live?’ ” Here the druggist paused uncomfortably.
“I guess I didn’t think. I said, ‘Oh, over on Park Street, you know, near the ravine.” A casual remark. But now, tonight, them finding the body, I heard a minute ago, I thought, My God, what’ve I done!” He handed over the package, much too full.
“You fool!” cried Francine, and tears were in her eyes.
Lavinia stood with the three people looking at her, staring at her. She felt nothing. Except, perhaps, the slightest prickle of excitement in her throat. She held out her money automatically.
“There’s no charge on those peppermints,” said the druggist, turning to shuffle some papers.
“It was just a man,” said Lavinia, turning in a slow circle to look at the town.
he first showing was over, intermission was on, and the dim auditorium was sparsely populated. The three ladies sat halfway down front, in the smell of ancient brass polish, and watched the manager step through the worn red velvet curtains to make an announcement.
“That means us, Lavinia!” whispered Francine.
The lights went out. The screen leaped to life.
One by one the three women turned to look.
They saw a white face there, flickering with unholy light from the silver screen. It seemed to be all men’s faces hovering there in the dark.
“Helen, come back!” cried Lavinia, rising.
They tapped their empty soda glasses down, each with a vanilla mustache on their upper lip, which they found with their tongues, laughing.
“I’m sorry,” said Helen faintly.
The clock said eleven-thirty now. They had come out of the dark theater, away from the fluttering rush of men and women hurrying everywhere, nowhere, on the street while laughing at Helen. Helen was trying to laugh at herself.
“I apologized,” said Helen, looking up at the great fan still whirling, whirling the warm late night air, stirring, restirring the smells of vanilla, raspberry, peppermint and Lysol.
“Closing up, ladies.” The druggist switched off the lights in the cool white-tiled silence.
Outside, the streets were swept clean and empty of cars or trucks or people. Bright lights still burned in the small store windows where the warm wax dummies lifted pink wax hands fired with blue-white diamond rings, or flourished orange wax legs to reveal hosiery. The hot blueglass eyes of the mannequins watched as the ladies drifted down the empty river bottom street, their images shimmering in the windows like blossoms seen under darkly moving waters.
There were a thousand people in the windows, stiff and silent, and three people on the street, the echoes following like gunshots from store fronts across the way when they tapped their heels on the baked pavement.
A red neon sign flickered dimly, buzzed like a dying insect, as they passed.
Baked and white, the long avenues lay ahead. Blowing and tall in a wind that touched only their leafy summits, the trees stood on either side of the three small women. Seen from the courthouse peak, they appeared like three thistles far away.
And so they walked, they drifted like three prim clothes forms over a moonlit sea of lawn and concrete, Lavinia watching the black trees flit by each side of her, listening to the voices of her friends murmuring, trying to laugh; and the night seemed to quicken, they seemed to run while walking slowly, everything seemed fast and the color of hot snow.
They sang sweetly and quietly, arm in arm, not looking back. They felt the hot sidewalk cooling underfoot, moving, moving.
They listened to the summer night. The summer-night crickets and the far-off tone of the courthouse clock making it eleven forty-five.
Lavinia listened. A porch swing creaked in the dark and there was Mr. Terle, not saying anything to anybody, alone on his swing, having a last cigar. They saw the pink ash swinging gently to and fro.
Now the lights were going, going, gone. The little house lights and big house lights and yellow lights and green hurricane lights, the candles and oil lamps and porch lights, and everything felt locked up in brass and iron and steel, everything, thought Lavinia, is boxed and locked and wrapped and shaded. She imagined the people in their moonlit beds. And their breathing in the summernight rooms, safe and together. And here we are, thought Lavinia, our footsteps on along the baked summer evening sidewalk. And above us the lonely street lights shining down, making a drunken shadow.
“Lavinia, Helen, stay here tonight. It’s late, almost midnight now. You can sleep in the parlor. I’ll make hot chocolate—it’ll be such fun!” Francine was holding them both now, close to her.
And Francine began to cry.
“Oh, not again, Francine,” said Lavinia.
“Good night, good night!” Rushing upstairs, Francine whisked behind a door, which slammed to be snap-bolted tight on the instant.
The courthouse clock struck the hour. The sounds blew across a town that was empty, emptier than it had ever been. Over empty streets and empty lots and empty lawns the sound faded.
“Nine, ten, eleven, twelve,” counted Lavinia, with Helen on her arm.
“Don’t you feel funny?” asked Helen.
The sound of the deep warm dark ravine came near.
She heard the man’s voice.
A man’s voice singing far away among the trees.
She walked a little faster.
Down the street in the dim moonlight a man walked slowly and casually along.
And that’s who it was, of course.
She went on, leaving him under a light, humming to himself, alone.
Here I am, she thought.
She stood on the edge of the one hundred and thirteen steps that went down the steep hill and then across the bridge seventy yards and up the hills leading to Park Street. And only one lantern to see by. Three minutes from now, she thought, I’ll be putting my key in my house door. Nothing can happen in just one hundred eighty seconds.
She started down the long dark-green steps into the deep ravine.
“One, two, three, four, five, six, seven, eight, nine, ten steps,” she counted in a whisper.
She felt she was running, but she was not running.
“Fifteen, sixteen, seventeen, eighteen, nineteen, twenty steps,” she breathed.
“One fifth of the way!” she announced to herself.
The ravine was deep, black and black, black! And the world was gone behind, the world of safe people in bed, the locked doors, the town, the drugstore, the theater, the lights, everything was gone. Only the ravine existed and lived, black and huge, about her.
She listened to her shoes on the steps.
She screamed. It was like nothing she’d ever heard, that scream. She had never screamed that loud in her life. She stopped, she froze, she clung to the wooden banister. Her heart exploded in her. The sound of the terrified beating filled the universe.
Nothing, she thought, holding her heart. Nothing. Fool! That story I told myself. How silly. What shall I do?
Shall I call the officer—did he hear me scream?
I’ll go the rest of the way. That silly story.
She began again, counting the steps.
She took a step. There was an echo.
Another echo. Another step, just a fraction of a moment later.
A step and an echo.
The crickets were listening. The night was listening to her. For a change, all of the far summer-night meadows and close summer-night trees were suspending motion; leaf, shrub, star, and meadow grass ceased their particular tremors and were listening to Lavinia Nebbs’s heart. And perhaps a thousand miles away, across locomotive-lonely country, in an empty way station, a single traveler reading a dim newspaper under a solitary naked bulb, might raise up his head, listen, and think, What’s that? and decide, Only a woodchuck, surely, beating on a hollow log. But it was Lavinia Nebbs, it was most surely the heart of Lavinia Nebbs.
Silence. A summer-night silence which lay for a thousand miles, which covered the earth like a white and shadowy sea. Faster, faster! She went down the steps.
She heard music. In a mad way, in a silly way, she heard the great surge of music that pounded at her, and she realized as she ran, as she ran in panic and terror, that some part of her mind was dramatizing, borrowing from the turbulent musical score of some private drama, and the music was rushing and pushing her now, higher and higher, faster, faster, plummeting and scurrying, down, and down into the pit of the ravine.
Only a little way, she prayed. One hundred eight, nine, one hundred ten steps! The bottom! Now, run! Across the bridge!
She told her legs what to do, her arms, her body, her terror; she advised all parts of herself in this white and terrible moment, over the roaring creek waters, on the hollow, thudding, swaying almost alive, resilient bridge planks she ran, followed by the wild footsteps behind, behind, with the music following, too, the music shrieking and babbling.
He’s following, don’t turn, don’t look, if you see him, you’ll not be able to move, you’ll be so frightened. Just run, run!
She ran across the bridge.
Oh, God, God, please, please let me get up the hill! Now up the path, now between the hills, oh God, it’s dark, and everything so far away. If I screamed now it wouldn’t help; I can’t scream anyway. Here’s the top of the path, here’s the street, oh, God, please let me be safe, if I get home safe I’ll never go out alone; I was a fool, let me admit it, I was a fool, I didn’t know what terror was, but if you let me get home from this I’ll never go without Helen or Francine again! Here’s the street. Across the street!
She crossed the street and rushed up the sidewalk.
Oh God, the porch! My house! Oh God, please give me time to get inside and lock the door and I’ll be safe!
And there—silly thing to notice—why did she notice, instantly, no time, no time—but there it was anyway, flashing by—there on the porch rail, the half-filled glass of lemonade she had abandoned a long time, a year, half an evening ago! The lemonade glass sitting calmly, imperturbably there on the rail,. . . and . . .
She heard her clumsy feet on the porch and listened and felt her hands scrabbling and ripping at the lock with the key. She heard her heart. She heard her inner voice screaming.
Unlock the door, quick, quick!
Now, inside. Slam it! She slammed the door.
“Now lock it, bar it, lock it!” she gasped wretchedly.
The door was locked and bolted tight.
The music stopped. She listened to her heart again and the sound of it diminishing into silence.
Home! Oh God, safe at home! Safe, safe and safe at home! She slumped against the door. Safe, safe. Listen. Not a sound. Safe, safe, oh thank God, safe at home. I’ll never go out at night again. I’ll stay home. I won’t go over that ravine again ever. Safe, oh safe, safe home, so good, so good, safe! Safe inside, the door locked. Wait.
Why, there’s no one there at all! Nobody. There was nobody following me at all. Nobody running after me. She got her breath and almost laughed at herself. It stands to reason If a man had been following me, he’d have caught me! I’m not a fast runner. . . . There’s no one on the porch or in the yard. How silly of me. I wasn’t running from anything. That ravine’s as safe as anyplace. Just the same, it’s nice to be home. Home’s the really good warm place, the only place to be.
She put her hand out to the light switch and stopped.
Behind her in the living room, someone cleared his throat.

lowercased:
the courthouse clock chimed seven times. the echoes of the chimes faded.
warm summer twilight here in upper illinois country in this little town deep far away from everything, kept to itself by a river and a forest and a meadow and a lake. the sidewalks still scorched. the stores closing and the streets shadowed. and there were two moons; the clock moon with four faces in four night directions above the solemn black courthouse, and the real moon rising in vanilla whiteness from the dark east.
in the drugstore fans whispered in the high ceiling. in the rococo shade of porches, a few invisible people sat. cigars glowed pink, on occasion. screen doors whined their springs and slammed. on the purple bricks of the summer-night streets, douglas spaulding ran; dogs and boys followed after.
the boys loped away. waving after them quietly, lavinia nebbs sat all alone with a tall cool lemonade in her white fingers, tapping it to her lips, sipping, waiting.
she turned and there was francine, all in snow white, at the bottom steps of the porch, in the smell of zinnias and hibiscus.
they walked down the street.
“where you going, girls?” cried miss fern and miss roberta from their porch over the way.
“oh, bosh!” lavinia heard the old women’s door bang and lock, and she drifted on, feeling the warm breath of summer night shimmering off the oven-baked sidewalks. it was like walking on a hard crust of freshly warmed bread. the heat pulsed under your dress, along your legs, with a stealthy and not unpleasant sense of invasion.
they stood upon the edge of the ravine that cut the town half in two. behind them were the lit houses and music, ahead was deepness, moistness, fireflies and dark.
lavinia looked and the ravine was a dynamo that never stopped running, night or day; there was a great moving hum, a bumbling and murmuring of creature, insect, or plant life. it smelled like a greenhouse, of secret vapors and ancient, washed shales and quicksands. and always the black dynamo humming, with sparkles like great electricity where fireflies moved on the air.
“it’s early. lonely one won’t be out till late.” lavinia took the other’s arm and led her down and down the crooked path into the cricket warmth and frog sound and mosquito-delicate silence. they brushed through summer-scorched grass, burs prickling at their bare ankles.
they turned a curve in the path—and there it was.
in the singing deep night, in the shade of warm trees, as if she had laid herself out to enjoy the soft stars and the easy wind, her hands at either side of her like the oars of a delicate craft, lay elizabeth ramsell!
the woman lay as if she had floated there, her face moonlit, her eyes wide and like flint, her tongue sticking from her mouth.
lavinia stood in the middle of a thousand warm shadows with the crickets screaming and the frogs loud.
“we’d better get the police,” she said at last.
lavinia held francine and the policemen were brushing through the crackling grass, flashlights ducked about, voices mingled, and the night grew toward eight-thirty.
“it’s like december. i need a sweater,” said francine, eyes shut, against lavinia.
lavinia and francine walked away from the police and the sheet over the delicate thing upon the ravine grass.
lavinia felt her heart going loudly in her and she was cold, too, with a february cold; there were bits of sudden snow all over her flesh, and the moon washed her brittle fingers whiter, and she remembered doing all the talking while francine just sobbed against her.
“no, we’ll make it,” said lavinia to nobody, and they walked on. they walked through the nuzzling, whispering ravine, the ravine of whispers and clicks, the little world of investigation growing small behind them with its lights and voices.
“i’ve never seen a dead person before,” said francine.
they started up the ravine side, on the stony path, in the dark. and suddenly there, barring their way, standing very still in one spot, not seeing them, but looking on down at the moving lights and the body and listening to the official voices, was douglas spaulding.
he stood there, white as a mushroom, with his hands at his sides, staring down into the ravine.
“get home!” cried francine. he did not hear.
douglas jerked his head, stared at them as if they were not there. his mouth moved. he gave a bleating sound. then, silently, he whirled about and ran. he ran silently up the distant hills into the warm darkness.
francine sobbed and cried again and, doing this, walked on with lavinia nebbs.
the three young women stood in the summer night looking at each other. “i’ve got a notion to go in the house and lock the doors,” said helen at last.
the three women moved along the street under the black trees, past suddenly locked houses. how soon the news had spread outward from the ravine, from house to house, porch to porch, telephone to telephone. now, passing, the three women felt eyes looking out at them from curtained windows as locks rattled into place. how strange the popsicle, the vanilla night, the night of close-packed ice cream, of mosquitolotioned wrists, the night of running children suddenly veered from their games and put away behind glass, behind wood, the popsicles in melting puddles of lime and strawberry where they fell when the children were scooped indoors. strange the hot rooms with the sweating people pressed tightly back into them behind the bronze knobs and knockers. baseball bats and balls lay upon the unfootprinted lawns. a half-drawn, white-chalk game of hopscotch lay on the broiled, steamed sidewalk. it was as if someone had predicted freezing weather a moment ago.
“we’re crazy being out on a night like this,” said helen.
a shadow fell across their terrified faces. a figure loomed behind a tree. as if someone had struck an organ a terrible blow with his fist, the three women gave off a scream, in three different shrill notes.
“got you!” roared a voice. the man plunged at them. he came into the light, laughing. he leaned against a tree, pointing at the ladies weakly, laughing again.
“hey! i’m the lonely one!” said frank dillon.
francine began to cry hysterically.
they moved. he moved to follow.
“stay right there, mr. lonely one, and scare yourself. go take a look at elizabeth ramsell’s face and see if it’s funny. good night!” lavinia took the other two on along the street of trees and stars, francine holding a kerchief to her face.
the drugstore was a small pool of sluggish air which the great wooden fans stirred in tides of arnica and tonic and soda-smell out onto the brick streets.
“i need a nickel’s worth of green peppermint chews,” said lavinia to the druggist. his face was set and pale, like all the faces they had seen on the half-empty streets. “for eating in the show,” said lavinia as the druggist weighed out a nickel’s worth of the green candy with a silver shovel.
“man sitting at the counter—watched you walk out. said to me, ‘say, who’s that?’ why, that’s lavinia nebbs, prettiest maiden lady in town, i said. ‘she’s beautiful,’ he said. ‘where does she live?’ ” here the druggist paused uncomfortably.
“i guess i didn’t think. i said, ‘oh, over on park street, you know, near the ravine.” a casual remark. but now, tonight, them finding the body, i heard a minute ago, i thought, my god, what’ve i done!” he handed over the package, much too full.
“you fool!” cried francine, and tears were in her eyes.
lavinia stood with the three people looking at her, staring at her. she felt nothing. except, perhaps, the slightest prickle of excitement in her throat. she held out her money automatically.
“there’s no charge on those peppermints,” said the druggist, turning to shuffle some papers.
“it was just a man,” said lavinia, turning in a slow circle to look at the town.
he first showing was over, intermission was on, and the dim auditorium was sparsely populated. the three ladies sat halfway down front, in the smell of ancient brass polish, and watched the manager step through the worn red velvet curtains to make an announcement.
“that means us, lavinia!” whispered francine.
the lights went out. the screen leaped to life.
one by one the three women turned to look.
they saw a white face there, flickering with unholy light from the silver screen. it seemed to be all men’s faces hovering there in the dark.
“helen, come back!” cried lavinia, rising.
they tapped their empty soda glasses down, each with a vanilla mustache on their upper lip, which they found with their tongues, laughing.
“i’m sorry,” said helen faintly.
the clock said eleven-thirty now. they had come out of the dark theater, away from the fluttering rush of men and women hurrying everywhere, nowhere, on the street while laughing at helen. helen was trying to laugh at herself.
“i apologized,” said helen, looking up at the great fan still whirling, whirling the warm late night air, stirring, restirring the smells of vanilla, raspberry, peppermint and lysol.
“closing up, ladies.” the druggist switched off the lights in the cool white-tiled silence.
outside, the streets were swept clean and empty of cars or trucks or people. bright lights still burned in the small store windows where the warm wax dummies lifted pink wax hands fired with blue-white diamond rings, or flourished orange wax legs to reveal hosiery. the hot blueglass eyes of the mannequins watched as the ladies drifted down the empty river bottom street, their images shimmering in the windows like blossoms seen under darkly moving waters.
there were a thousand people in the windows, stiff and silent, and three people on the street, the echoes following like gunshots from store fronts across the way when they tapped their heels on the baked pavement.
a red neon sign flickered dimly, buzzed like a dying insect, as they passed.
baked and white, the long avenues lay ahead. blowing and tall in a wind that touched only their leafy summits, the trees stood on either side of the three small women. seen from the courthouse peak, they appeared like three thistles far away.
and so they walked, they drifted like three prim clothes forms over a moonlit sea of lawn and concrete, lavinia watching the black trees flit by each side of her, listening to the voices of her friends murmuring, trying to laugh; and the night seemed to quicken, they seemed to run while walking slowly, everything seemed fast and the color of hot snow.
they sang sweetly and quietly, arm in arm, not looking back. they felt the hot sidewalk cooling underfoot, moving, moving.
they listened to the summer night. the summer-night crickets and the far-off tone of the courthouse clock making it eleven forty-five.
lavinia listened. a porch swing creaked in the dark and there was mr. terle, not saying anything to anybody, alone on his swing, having a last cigar. they saw the pink ash swinging gently to and fro.
now the lights were going, going, gone. the little house lights and big house lights and yellow lights and green hurricane lights, the candles and oil lamps and porch lights, and everything felt locked up in brass and iron and steel, everything, thought lavinia, is boxed and locked and wrapped and shaded. she imagined the people in their moonlit beds. and their breathing in the summernight rooms, safe and together. and here we are, thought lavinia, our footsteps on along the baked summer evening sidewalk. and above us the lonely street lights shining down, making a drunken shadow.
“lavinia, helen, stay here tonight. it’s late, almost midnight now. you can sleep in the parlor. i’ll make hot chocolate—it’ll be such fun!” francine was holding them both now, close to her.
and francine began to cry.
“oh, not again, francine,” said lavinia.
“good night, good night!” rushing upstairs, francine whisked behind a door, which slammed to be snap-bolted tight on the instant.
the courthouse clock struck the hour. the sounds blew across a town that was empty, emptier than it had ever been. over empty streets and empty lots and empty lawns the sound faded.
“nine, ten, eleven, twelve,” counted lavinia, with helen on her arm.
“don’t you feel funny?” asked helen.
the sound of the deep warm dark ravine came near.
she heard the man’s voice.
a man’s voice singing far away among the trees.
she walked a little faster.
down the street in the dim moonlight a man walked slowly and casually along.
and that’s who it was, of course.
she went on, leaving him under a light, humming to himself, alone.
here i am, she thought.
she stood on the edge of the one hundred and thirteen steps that went down the steep hill and then across the bridge seventy yards and up the hills leading to park street. and only one lantern to see by. three minutes from now, she thought, i’ll be putting my key in my house door. nothing can happen in just one hundred eighty seconds.
she started down the long dark-green steps into the deep ravine.
“one, two, three, four, five, six, seven, eight, nine, ten steps,” she counted in a whisper.
she felt she was running, but she was not running.
“fifteen, sixteen, seventeen, eighteen, nineteen, twenty steps,” she breathed.
“one fifth of the way!” she announced to herself.
the ravine was deep, black and black, black! and the world was gone behind, the world of safe people in bed, the locked doors, the town, the drugstore, the theater, the lights, everything was gone. only the ravine existed and lived, black and huge, about her.
she listened to her shoes on the steps.
she screamed. it was like nothing she’d ever heard, that scream. she had never screamed that loud in her life. she stopped, she froze, she clung to the wooden banister. her heart exploded in her. the sound of the terrified beating filled the universe.
nothing, she thought, holding her heart. nothing. fool! that story i told myself. how silly. what shall i do?
shall i call the officer—did he hear me scream?
i’ll go the rest of the way. that silly story.
she began again, counting the steps.
she took a step. there was an echo.
another echo. another step, just a fraction of a moment later.
a step and an echo.
the crickets were listening. the night was listening to her. for a change, all of the far summer-night meadows and close summer-night trees were suspending motion; leaf, shrub, star, and meadow grass ceased their particular tremors and were listening to lavinia nebbs’s heart. and perhaps a thousand miles away, across locomotive-lonely country, in an empty way station, a single traveler reading a dim newspaper under a solitary naked bulb, might raise up his head, listen, and think, what’s that? and decide, only a woodchuck, surely, beating on a hollow log. but it was lavinia nebbs, it was most surely the heart of lavinia nebbs.
silence. a summer-night silence which lay for a thousand miles, which covered the earth like a white and shadowy sea. faster, faster! she went down the steps.
she heard music. in a mad way, in a silly way, she heard the great surge of music that pounded at her, and she realized as she ran, as she ran in panic and terror, that some part of her mind was dramatizing, borrowing from the turbulent musical score of some private drama, and the music was rushing and pushing her now, higher and higher, faster, faster, plummeting and scurrying, down, and down into the pit of the ravine.
only a little way, she prayed. one hundred eight, nine, one hundred ten steps! the bottom! now, run! across the bridge!
she told her legs what to do, her arms, her body, her terror; she advised all parts of herself in this white and terrible moment, over the roaring creek waters, on the hollow, thudding, swaying almost alive, resilient bridge planks she ran, followed by the wild footsteps behind, behind, with the music following, too, the music shrieking and babbling.
he’s following, don’t turn, don’t look, if you see him, you’ll not be able to move, you’ll be so frightened. just run, run!
she ran across the bridge.
oh, god, god, please, please let me get up the hill! now up the path, now between the hills, oh god, it’s dark, and everything so far away. if i screamed now it wouldn’t help; i can’t scream anyway. here’s the top of the path, here’s the street, oh, god, please let me be safe, if i get home safe i’ll never go out alone; i was a fool, let me admit it, i was a fool, i didn’t know what terror was, but if you let me get home from this i’ll never go without helen or francine again! here’s the street. across the street!
she crossed the street and rushed up the sidewalk.
oh god, the porch! my house! oh god, please give me time to get inside and lock the door and i’ll be safe!
and there—silly thing to notice—why did she notice, instantly, no time, no time—but there it was anyway, flashing by—there on the porch rail, the half-filled glass of lemonade she had abandoned a long time, a year, half an evening ago! the lemonade glass sitting calmly, imperturbably there on the rail,. . . and . . .
she heard her clumsy feet on the porch and listened and felt her hands scrabbling and ripping at the lock with the key. she heard her heart. she heard her inner voice screaming.
unlock the door, quick, quick!
now, inside. slam it! she slammed the door.
“now lock it, bar it, lock it!” she gasped wretchedly.
the door was locked and bolted tight.
the music stopped. she listened to her heart again and the sound of it diminishing into silence.
home! oh god, safe at home! safe, safe and safe at home! she slumped against the door. safe, safe. listen. not a sound. safe, safe, oh thank god, safe at home. i’ll never go out at night again. i’ll stay home. i won’t go over that ravine again ever. safe, oh safe, safe home, so good, so good, safe! safe inside, the door locked. wait.
why, there’s no one there at all! nobody. there was nobody following me at all. nobody running after me. she got her breath and almost laughed at herself. it stands to reason if a man had been following me, he’d have caught me! i’m not a fast runner. . . . there’s no one on the porch or in the yard. how silly of me. i wasn’t running from anything. that ravine’s as safe as anyplace. just the same, it’s nice to be home. home’s the really good warm place, the only place to be.
she put her hand out to the light switch and stopped.
behind her in the living room, someone cleared his throat.


text:
Bitte die Fotos anklicken - Please enter above!
Dear foreign visitor, you have to understand German language for most of the parts here. But of course you will find also information in English language. Have a look to the English flag and also sometimes you find a special note at the end of a page.
Optimiert für den Einsatz des MS Windows Explorer ab Version 8.0 aufwärts. Beste Auflösung ab 1024x768 pixel.
Java, Applets, ActiveX und Scripting sollte für alle Funktion aktiviert sein.
or higher - Java, Applets, ActiveX and Scripting should be also allowed.
für, über und zu meiner hiesigen website. - Thanks a lot for your awards!

lowercased:
bitte die fotos anklicken - please enter above!
dear foreign visitor, you have to understand german language for most of the parts here. but of course you will find also information in english language. have a look to the english flag and also sometimes you find a special note at the end of a page.
optimiert für den einsatz des ms windows explorer ab version 8.0 aufwärts. beste auflösung ab 1024x768 pixel.
java, applets, activex und scripting sollte für alle funktion aktiviert sein.
or higher - java, applets, activex and scripting should be also allowed.
für, über und zu meiner hiesigen website. - thanks a lot for your awards!


text:
Telstra is having major issues across Australia when it comes to people using both iMessage and FaceTime.
Listed on both their website and social media pages, Telstra urged customers to turn off their iMessage if they were having trouble sending messages, instead opting to send a regular text message. It’s not really clear what’s causing the issue, nor has a timeframe been provided for when it’ll be fixed, but this has been an ongoing issue for hours and definitely isn’t the first time.
Our team are working to fix an issue with Apple iMessage and Facetime for some customers. You can continue to send and receive SMS messages. We’ll provide an update ASAP.

lowercased:
telstra is having major issues across australia when it comes to people using both imessage and facetime.
listed on both their website and social media pages, telstra urged customers to turn off their imessage if they were having trouble sending messages, instead opting to send a regular text message. it’s not really clear what’s causing the issue, nor has a timeframe been provided for when it’ll be fixed, but this has been an ongoing issue for hours and definitely isn’t the first time.
our team are working to fix an issue with apple imessage and facetime for some customers. you can continue to send and receive sms messages. we’ll provide an update asap.


text:
One of the best markets for landscape photography are annual calendars. Recently two of Jay’s images were chosen by a local company for publication in their large art calendar. This photo, titled Reflections of Beauty, was selected for the cover.
This one – titled Stranded – was selected for the October page.
Images for calendars like these are chosen based upon location, theme, or terrain (seascapes, forests, waterfalls, mountains, etc.) – and shots from National Parks are some of the most popular. Most of the time, the calendar company is looking for landscape images that are appropriate for a particular season – so we try to make sure that we shoot in a variety of seasons. For example, if you have photos of Yosemite in Summer, but not in Winter, the publisher will have to go with someone else for those winter images. We visit locations more than once to fill in gaps in our portfolios.
Several months ago, we got a call from National Geographic asking about images for their 2010 Calendars. In the end, they selected two images from National Parks which were published in their 2010 National Park’s engagement calender.
The market for landscape photography for calender companies is pretty broad. You can maximize your chances of getting published by expanding your portfolio to include a diverse set of images from a variety of locations – during a variety of seasons.
What happens at a workshop stays at a workshop Welcome!

lowercased:
one of the best markets for landscape photography are annual calendars. recently two of jay’s images were chosen by a local company for publication in their large art calendar. this photo, titled reflections of beauty, was selected for the cover.
this one – titled stranded – was selected for the october page.
images for calendars like these are chosen based upon location, theme, or terrain (seascapes, forests, waterfalls, mountains, etc.) – and shots from national parks are some of the most popular. most of the time, the calendar company is looking for landscape images that are appropriate for a particular season – so we try to make sure that we shoot in a variety of seasons. for example, if you have photos of yosemite in summer, but not in winter, the publisher will have to go with someone else for those winter images. we visit locations more than once to fill in gaps in our portfolios.
several months ago, we got a call from national geographic asking about images for their 2010 calendars. in the end, they selected two images from national parks which were published in their 2010 national park’s engagement calender.
the market for landscape photography for calender companies is pretty broad. you can maximize your chances of getting published by expanding your portfolio to include a diverse set of images from a variety of locations – during a variety of seasons.
what happens at a workshop stays at a workshop welcome!


text:
Veneers work by covering the front of the teeth to improve their overall appearance. They can alter shape, size, length, and color. Porcelain veneers are highly resistant to staining and thus remain a beautiful looking solution for many years.
The veneers process begins with a consultation where patients can discuss their concerns and desired results. Once the decision has been made to move forward with veneers, they can be placed in two appointments in as little as two weeks.
Veneers are a conservative approach to dramatically enhancing the appearance of the smile. They are a stronger and more aesthetically appeasing than crowns and do not require extensive alteration of the natural tooth. With proper care porcelain veneers can be long lasting.
Home » How do Veneers Work?

lowercased:
veneers work by covering the front of the teeth to improve their overall appearance. they can alter shape, size, length, and color. porcelain veneers are highly resistant to staining and thus remain a beautiful looking solution for many years.
the veneers process begins with a consultation where patients can discuss their concerns and desired results. once the decision has been made to move forward with veneers, they can be placed in two appointments in as little as two weeks.
veneers are a conservative approach to dramatically enhancing the appearance of the smile. they are a stronger and more aesthetically appeasing than crowns and do not require extensive alteration of the natural tooth. with proper care porcelain veneers can be long lasting.
home » how do veneers work?


text:
There are 5 Auto insurance companies in Coweta, OK. Below is a list of the 10 most popular Auto insurance companies on Auto Insurance Directory. Are you looking for a auto insurance company specialized in a specific insurance? Refine your search by selecting a insurance.
NEED A COWETA AUTO INSURANCE COMPANY?
Auto Insurance Directory is one of the worlds largest auto insurance company directories listing a total of 5 Auto insurance companies in Coweta, Oklahoma.
Each Coweta auto insurance company profile covers contact details, reviews, specialization, prices, quotes, fees, auto insurance company overview and more.
Our goal is to make your search for the right car and auto insurance companies in Coweta, Oklahoma quick and easy.

lowercased:
there are 5 auto insurance companies in coweta, ok. below is a list of the 10 most popular auto insurance companies on auto insurance directory. are you looking for a auto insurance company specialized in a specific insurance? refine your search by selecting a insurance.
need a coweta auto insurance company?
auto insurance directory is one of the worlds largest auto insurance company directories listing a total of 5 auto insurance companies in coweta, oklahoma.
each coweta auto insurance company profile covers contact details, reviews, specialization, prices, quotes, fees, auto insurance company overview and more.
our goal is to make your search for the right car and auto insurance companies in coweta, oklahoma quick and easy.


text:
Rats were exposed to continuous inhalation of ethanol vapors in an inhalation chamber plus daily injections of an alcohol dehydrogenase inhibitor pyrazole (68 mg/kg, i.p.). Ethanol vapors entered the inhalation chamber at flow rates of from o.45 liters/min. to 0.95 liters/min. which when mixed with a constant stream of air (5.0 liters/min) produced chamber concentrations of from approximately 8.8 mg/liter to 26.5 mg/liter. Rats exposed to these conditions for 5 to 7 days developed blood ethanol levels of 0.83± 0.09 mg/ml/blood to 2.19 ± 0.14 mg/ml/blood. Administration of pyrazole with or without ethanol caused weight loss in these rats. Rats developed tolerance to ethanol after continuous inhalation of ethanol vapors for 5 days demonstrated by increased onset and decreased duration of ethanol- induced narcosis. After 5 days of continuous ethanol inhalation rats became physically dependent on ethanol and developed withdrawal signs of piloerection, abnormal posture, tremors, convulsions, headshakes, tail-lifts and mortality upon cessation of ethanol administration. The four withdrawal signs of piloerection, abnormal posture, tremors and convulsions were combined into an ethanol withdrawal syndrome measurement. Pretreatment of ethanol dependent rats 30 minutes prior to 36 hours of ethanol withdrawal, the period of maximum intensity of the withdrawal syndrome, with ethanol (2.0 g/kg, orally), chlordiazepoxide (40 mg/kg, i.p.) or morphine (10 mg/kg, i.p.) significantly reduced the intensity of the ethanol withdrawal syndrome as measured during the thirty-sixth hour of ethanol withdrawal. Rats withdrawn after continuous inhalation of ethanol vapors showed intense aggression (attacks and bites, rearing and vocalizations) after small doses of apomorphine (2 . 5 mg/kg, i.p.) or ct-amphetamine (2.0 mg/kg, i.p.), although little spontaneous aggression was seen. Other ethanol withdrawal signs returned to near control levels by 72 hours after ethanol withdrawal, however drug-induced aggression was present for at least 7 days after ethanol withdrawal. Administration of ethanol (4.0 g/kg, orally), chlordiazepoxide (80 mg/kg, i.p.) or morphine (10 mg/kg, i.p.) 30 minutes prior to apomorphine administration (2.5 mg/kg, i.p.) significantly reduced apomorphine-induced aggression in ethanol withdrawn rats. During withdrawal from ethanol, mortality was 20% among rats exposed for 5 days to ethanol inhalation and not treated with any drugs during' withdrawal. None of the drugs used in this study significantly reduced the mortality rate in ethanol withdrawn rats . However, administration of chlordiazepoxide (160 mg/kg, i.p.) prior to the thirty-sixth hour of ethanol withdrawal resulted in a significant increase in the mortality to 75%. This same dose of chlordiazepoxide administered intraperitoneally to untreated rats resulted in a mortality of 66.7% of the rats treated within 36 hours after injection.
Mann, Steven W., "Effect of Continuous Inhalation of Ethanol Vapors in the Rat" (1976). Open Access Master's Theses. Paper 204.

lowercased:
rats were exposed to continuous inhalation of ethanol vapors in an inhalation chamber plus daily injections of an alcohol dehydrogenase inhibitor pyrazole (68 mg/kg, i.p.). ethanol vapors entered the inhalation chamber at flow rates of from o.45 liters/min. to 0.95 liters/min. which when mixed with a constant stream of air (5.0 liters/min) produced chamber concentrations of from approximately 8.8 mg/liter to 26.5 mg/liter. rats exposed to these conditions for 5 to 7 days developed blood ethanol levels of 0.83± 0.09 mg/ml/blood to 2.19 ± 0.14 mg/ml/blood. administration of pyrazole with or without ethanol caused weight loss in these rats. rats developed tolerance to ethanol after continuous inhalation of ethanol vapors for 5 days demonstrated by increased onset and decreased duration of ethanol- induced narcosis. after 5 days of continuous ethanol inhalation rats became physically dependent on ethanol and developed withdrawal signs of piloerection, abnormal posture, tremors, convulsions, headshakes, tail-lifts and mortality upon cessation of ethanol administration. the four withdrawal signs of piloerection, abnormal posture, tremors and convulsions were combined into an ethanol withdrawal syndrome measurement. pretreatment of ethanol dependent rats 30 minutes prior to 36 hours of ethanol withdrawal, the period of maximum intensity of the withdrawal syndrome, with ethanol (2.0 g/kg, orally), chlordiazepoxide (40 mg/kg, i.p.) or morphine (10 mg/kg, i.p.) significantly reduced the intensity of the ethanol withdrawal syndrome as measured during the thirty-sixth hour of ethanol withdrawal. rats withdrawn after continuous inhalation of ethanol vapors showed intense aggression (attacks and bites, rearing and vocalizations) after small doses of apomorphine (2 . 5 mg/kg, i.p.) or ct-amphetamine (2.0 mg/kg, i.p.), although little spontaneous aggression was seen. other ethanol withdrawal signs returned to near control levels by 72 hours after ethanol withdrawal, however drug-induced aggression was present for at least 7 days after ethanol withdrawal. administration of ethanol (4.0 g/kg, orally), chlordiazepoxide (80 mg/kg, i.p.) or morphine (10 mg/kg, i.p.) 30 minutes prior to apomorphine administration (2.5 mg/kg, i.p.) significantly reduced apomorphine-induced aggression in ethanol withdrawn rats. during withdrawal from ethanol, mortality was 20% among rats exposed for 5 days to ethanol inhalation and not treated with any drugs during' withdrawal. none of the drugs used in this study significantly reduced the mortality rate in ethanol withdrawn rats . however, administration of chlordiazepoxide (160 mg/kg, i.p.) prior to the thirty-sixth hour of ethanol withdrawal resulted in a significant increase in the mortality to 75%. this same dose of chlordiazepoxide administered intraperitoneally to untreated rats resulted in a mortality of 66.7% of the rats treated within 36 hours after injection.
mann, steven w., "effect of continuous inhalation of ethanol vapors in the rat" (1976). open access master's theses. paper 204.


text:
A wild catgirl has appeared!
So some of you may have played my cg420 maps. If you have, you already know about AA MiGs.
You may noticed them balanced for cg420 rules. If you're lucky you got to see my cg420 MiGs balanced for default game before my maps became the cancer that they are.
So, I managed to balance them before, and am now trying to do it again for the current release.
I provided a pol to vote if it seems balanced. If it does or does not, please provide some feedback with your thoughts on the matter.

lowercased:
a wild catgirl has appeared!
so some of you may have played my cg420 maps. if you have, you already know about aa migs.
you may noticed them balanced for cg420 rules. if you're lucky you got to see my cg420 migs balanced for default game before my maps became the cancer that they are.
so, i managed to balance them before, and am now trying to do it again for the current release.
i provided a pol to vote if it seems balanced. if it does or does not, please provide some feedback with your thoughts on the matter.


text:
Hempirical Evidence is the research journal of Nick Johnson, historian, editor, and author of the book Grass Roots: A History of Cannabis in the American West (Oregon State University Press, 2017).
On this blog you'll find short essays based on my ongoing research, book reviews, and commentary on current cannabis-related issues and events. The purpose of this blog is simply to present research and thoughtful opinions with the hope of encouraging intelligent, honest, and productive dialogue about cannabis and its place in world history and society.
In the fall of 2012, I came to Fort Collins, Colorado to get my MA in history at Colorado State University. That November, I witnessed the passage of Amendment 64, the ballot initiative that legalized the adult use of cannabis in Colorado. I was looking for something to write about for my master's thesis, and soon found that behind this momentous change in drug policy was a millennia-old relationship between a useful plant and its human propagators.
I brought my training in environmental history to bear on the cannabis story and completed my master's thesis, "Rocky Mountain High," in the spring of 2014. I argued that studying cannabis as a plant as well as a drug can allow us to better understand its various roles in American culture and society.
Grass Roots is a further exploration of the plant's historical and current impact on American society. The book examines cannabis's past through an agricultural lens in order to explain, critically engage, and offer possible solutions to the environmental and social challenges associated with the rise of America's newest legitimate cash crop.

lowercased:
hempirical evidence is the research journal of nick johnson, historian, editor, and author of the book grass roots: a history of cannabis in the american west (oregon state university press, 2017).
on this blog you'll find short essays based on my ongoing research, book reviews, and commentary on current cannabis-related issues and events. the purpose of this blog is simply to present research and thoughtful opinions with the hope of encouraging intelligent, honest, and productive dialogue about cannabis and its place in world history and society.
in the fall of 2012, i came to fort collins, colorado to get my ma in history at colorado state university. that november, i witnessed the passage of amendment 64, the ballot initiative that legalized the adult use of cannabis in colorado. i was looking for something to write about for my master's thesis, and soon found that behind this momentous change in drug policy was a millennia-old relationship between a useful plant and its human propagators.
i brought my training in environmental history to bear on the cannabis story and completed my master's thesis, "rocky mountain high," in the spring of 2014. i argued that studying cannabis as a plant as well as a drug can allow us to better understand its various roles in american culture and society.
grass roots is a further exploration of the plant's historical and current impact on american society. the book examines cannabis's past through an agricultural lens in order to explain, critically engage, and offer possible solutions to the environmental and social challenges associated with the rise of america's newest legitimate cash crop.


text:
The sky is falling, right on top of Snap Inc, and the sky’s name is CPM. Since they launched their self-service, auction-based buying environment, CPMs plunged 60%. 60%!?!?!? Cue the panic, right?
Not so fast. With companies like Facebook making up the competitive set, the pressure was always on Snap to not just post good results, but to crush it consistently. And a 60% decline in anything sounds pretty freakin’ bad. But this isn’t a 60% decline in just anything. It’s a 60% decline in ad CPMs as a result of just what the financial markets are supposed to track: market forces.
Snap, like nearly every publisher or platform that started out in traditional or otherwise non-programmatic advertising models, moved from a fairly opaque direct sales model where advertisers paid whatever Snap told them they should, to a model where advertisers got to serve themselves and set the maximum price they’re willing to pay for Snap’s inventory. Prior to going auction-based, publishers and platforms set their CPMs based on their own perceived value of the context they deliver. However, advertisers want the cheapest media they can get—no matter how much we all crow about quality and context– so they’re not bidding top-of-the-line CPMs right out of the gate. The market sets the price. Anybody who didn’t expect CPMs to come down precipitously when Snap moved to a programmatic model hasn’t been paying attention.
There are lots of good things that can and should happen from here. First, if advertisers see clear value from the performance of these programmatically bought ads, Snap should be able to attract tons of smaller advertisers who can now afford the lower CPMs. And as Facebook has certainly demonstrated, revenue can add up quickly when you’ve got millions of small advertisers. Then, Snap will have more advertisers vying for limited ad space and the laws of supply and demand will kick in, driving CPMs back up for the highest-quality, best-performing inventory.
So is the 60% drop in CPMs alone a sensible reason to freak out? No. However, that programmatic model relies on a large audience to support growing CPMs—targeting is one of the things that makes inventory high-quality and therefore more expensive, and you have to have a large audience to make targeting meaningful. And today, Snap’s user-base is not sufficiently on the rise. That’s why their decision to redesign the Snapchat app and solve the user experience issues makes sense: Snap believes it will make the app appeal to a wider audience.
But here is where advertising numbers and design may not turn everything around given the expectations Snap faces. Solving the how-to-use-Snapchat problem will reveal if there’s an even bigger barrier to entry for new users: why to use it. Snap created something that appeals to people who communicate in a distinct way—most, but not all, of whom are comparatively quite young– and it’s possible that there just aren’t billions of people on the planet who can be convinced to do so.
So don’t worry about the 60% drop in CPMs. That’s unfortunate but normal. Worry about possibly solving the wrong product problem. Worry about moving to a business model that might not support the best use case for the product. And worry about the impossible expectation that every user-focused tech company will become the next Facebook.

lowercased:
the sky is falling, right on top of snap inc, and the sky’s name is cpm. since they launched their self-service, auction-based buying environment, cpms plunged 60%. 60%!?!?!? cue the panic, right?
not so fast. with companies like facebook making up the competitive set, the pressure was always on snap to not just post good results, but to crush it consistently. and a 60% decline in anything sounds pretty freakin’ bad. but this isn’t a 60% decline in just anything. it’s a 60% decline in ad cpms as a result of just what the financial markets are supposed to track: market forces.
snap, like nearly every publisher or platform that started out in traditional or otherwise non-programmatic advertising models, moved from a fairly opaque direct sales model where advertisers paid whatever snap told them they should, to a model where advertisers got to serve themselves and set the maximum price they’re willing to pay for snap’s inventory. prior to going auction-based, publishers and platforms set their cpms based on their own perceived value of the context they deliver. however, advertisers want the cheapest media they can get—no matter how much we all crow about quality and context– so they’re not bidding top-of-the-line cpms right out of the gate. the market sets the price. anybody who didn’t expect cpms to come down precipitously when snap moved to a programmatic model hasn’t been paying attention.
there are lots of good things that can and should happen from here. first, if advertisers see clear value from the performance of these programmatically bought ads, snap should be able to attract tons of smaller advertisers who can now afford the lower cpms. and as facebook has certainly demonstrated, revenue can add up quickly when you’ve got millions of small advertisers. then, snap will have more advertisers vying for limited ad space and the laws of supply and demand will kick in, driving cpms back up for the highest-quality, best-performing inventory.
so is the 60% drop in cpms alone a sensible reason to freak out? no. however, that programmatic model relies on a large audience to support growing cpms—targeting is one of the things that makes inventory high-quality and therefore more expensive, and you have to have a large audience to make targeting meaningful. and today, snap’s user-base is not sufficiently on the rise. that’s why their decision to redesign the snapchat app and solve the user experience issues makes sense: snap believes it will make the app appeal to a wider audience.
but here is where advertising numbers and design may not turn everything around given the expectations snap faces. solving the how-to-use-snapchat problem will reveal if there’s an even bigger barrier to entry for new users: why to use it. snap created something that appeals to people who communicate in a distinct way—most, but not all, of whom are comparatively quite young– and it’s possible that there just aren’t billions of people on the planet who can be convinced to do so.
so don’t worry about the 60% drop in cpms. that’s unfortunate but normal. worry about possibly solving the wrong product problem. worry about moving to a business model that might not support the best use case for the product. and worry about the impossible expectation that every user-focused tech company will become the next facebook.


text:
Stop by the Barrymore downstairs for a drink that you can bring up with you. The food is a bit pricey however, so hit up the Peppermill or Denny's legit right across the street first.
Intimate theater on second floor. Arrive early for a seat up front.
A great break in the day...leave the gambling, drinking and shopping and have some laughs on Brad!!!
Nothing inside but can bring anything from outside.
$2 cans of soda or bottled water available.
Just dress like you're going out to a casual family gathering.
Bar and food from the restaurant just off the main lobby.
Nice quiet little time resort. no one request you attend presentation, thanks.

lowercased:
stop by the barrymore downstairs for a drink that you can bring up with you. the food is a bit pricey however, so hit up the peppermill or denny's legit right across the street first.
intimate theater on second floor. arrive early for a seat up front.
a great break in the day...leave the gambling, drinking and shopping and have some laughs on brad!!!
nothing inside but can bring anything from outside.
$2 cans of soda or bottled water available.
just dress like you're going out to a casual family gathering.
bar and food from the restaurant just off the main lobby.
nice quiet little time resort. no one request you attend presentation, thanks.


text:
Fit the mincer attachment using the largest diameter die. Place the mixing bowl underneath the mincer. Feed the eye fillet pieces through the mincer on a medium speed. Place the bowl into the fridge until needed.
Using the food processor attachment, add the onion, gherkins, capers & parsley. Attach the lid and pulse until a fine dice is achieved.
Into the bowl with the minced beef add the mixture from the attachment along with the brandy, Worcestershire sauce, Tabasco, ketchup, Dijon, egg yolks, salt & pepper, then attach the K-bar and stir at speed 1. Until the mixture is combined (do not over mix). Check and adjust seasoning if required.
Preheat the oven grill. Slice the ciabatta into 1cm thick slices. Brush each slice liberally with olive oil, then toast both sides under the medium–high oven grill until golden and crisp.
To serve, divide the mixture into 4 and make round balls with them. Press onto the plate to flatten a little. Make an indent into the top of the tartare and add an egg yolk.
Add small dots of the roasted garlic & horse radish aioli on the plate then place the toasted ciabatta beside the tartare and garnish with freshly grated parmesan and fresh truffle if you have it.

lowercased:
fit the mincer attachment using the largest diameter die. place the mixing bowl underneath the mincer. feed the eye fillet pieces through the mincer on a medium speed. place the bowl into the fridge until needed.
using the food processor attachment, add the onion, gherkins, capers & parsley. attach the lid and pulse until a fine dice is achieved.
into the bowl with the minced beef add the mixture from the attachment along with the brandy, worcestershire sauce, tabasco, ketchup, dijon, egg yolks, salt & pepper, then attach the k-bar and stir at speed 1. until the mixture is combined (do not over mix). check and adjust seasoning if required.
preheat the oven grill. slice the ciabatta into 1cm thick slices. brush each slice liberally with olive oil, then toast both sides under the medium–high oven grill until golden and crisp.
to serve, divide the mixture into 4 and make round balls with them. press onto the plate to flatten a little. make an indent into the top of the tartare and add an egg yolk.
add small dots of the roasted garlic & horse radish aioli on the plate then place the toasted ciabatta beside the tartare and garnish with freshly grated parmesan and fresh truffle if you have it.


text:
Port of Yokohama, one of the biggest ports in Japan, is conveniently located at the entrance of Tokyo Bay, geographically, it serves as the first port of W/B and last port of E/B on Asian side of trans-pacific services.
The port has 5 major complexes to accommodate various cargo including containers, break bulks, dry bulks, automobiles and LNG, etc. in addition, port of Yokohama is also one of the major ports for I/B and O/B passengers of cruise lines.
Port of Yokohama offers discount on port dues to those vessels which are considered as Eco-ship according to either IAPH Environmental Ship Index(ESI) or certification of Green Award Foundation.
In terms of LNG bunkering promotion, port of Yokohama has been leading the whole nation. Since August 2015, Truck to Ship LNG bunkering operation has been conducting for tug boat “Sakigake”, the first LNG-Fueled vessel in Japan.
In order to be ready for Ship to Ship operation in 2020, Yokohama Kawasaki International Port Corporation (YKIP), the managing entity of port of Yokohama, has been working on the feasibility study with 2 other partners, and will form a joint venture to build and run the first LNG bunkering barge in Tokyo bay by 2020.
YKIP is keen on international cooperation on further LNG bunkering promotion.

lowercased:
port of yokohama, one of the biggest ports in japan, is conveniently located at the entrance of tokyo bay, geographically, it serves as the first port of w/b and last port of e/b on asian side of trans-pacific services.
the port has 5 major complexes to accommodate various cargo including containers, break bulks, dry bulks, automobiles and lng, etc. in addition, port of yokohama is also one of the major ports for i/b and o/b passengers of cruise lines.
port of yokohama offers discount on port dues to those vessels which are considered as eco-ship according to either iaph environmental ship index(esi) or certification of green award foundation.
in terms of lng bunkering promotion, port of yokohama has been leading the whole nation. since august 2015, truck to ship lng bunkering operation has been conducting for tug boat “sakigake”, the first lng-fueled vessel in japan.
in order to be ready for ship to ship operation in 2020, yokohama kawasaki international port corporation (ykip), the managing entity of port of yokohama, has been working on the feasibility study with 2 other partners, and will form a joint venture to build and run the first lng bunkering barge in tokyo bay by 2020.
ykip is keen on international cooperation on further lng bunkering promotion.


text:
Do you have any tips for getting your belt to stay put? My belts tend to slip around when I wear them at this height and it disrupts the outfit. The worst is getting back to my office after a meeting to find my shirt half hanging out of my skirt and my whole outfit looking like a mess!
I have the same problem with the belts sliding around! I sometimes try to safety pin this belt to the skirt (it's woven so you can kind of wiggle a piece of the belt into the loop of the pin) but for other belts – I just have to remember to pay attention.
Wish I had a magic trick. If you find one – please let me know!
Thanks for sharing your pictures with us. That skirt is beautiful and with the shoes, your outfit is complete. I own a pretty flared miniskirt in those colors. It's a bright, colorful blend.
Your skirt is a good length and that makes it suitable for a wide range of situations. you could wear it to work or to lunch with someone. I haven't seen a fitted skirt like that recently.

lowercased:
do you have any tips for getting your belt to stay put? my belts tend to slip around when i wear them at this height and it disrupts the outfit. the worst is getting back to my office after a meeting to find my shirt half hanging out of my skirt and my whole outfit looking like a mess!
i have the same problem with the belts sliding around! i sometimes try to safety pin this belt to the skirt (it's woven so you can kind of wiggle a piece of the belt into the loop of the pin) but for other belts – i just have to remember to pay attention.
wish i had a magic trick. if you find one – please let me know!
thanks for sharing your pictures with us. that skirt is beautiful and with the shoes, your outfit is complete. i own a pretty flared miniskirt in those colors. it's a bright, colorful blend.
your skirt is a good length and that makes it suitable for a wide range of situations. you could wear it to work or to lunch with someone. i haven't seen a fitted skirt like that recently.


text:
You'll be able to trust in Air Duct Cleaning Guys to offer the most effective products and services regarding Air Duct Cleaning in Union, SC. You'll need the most advanced technology in the field, and our team of highly skilled contractors can provide exactly that. Our materials are always of the very best quality and we know how to save you money. We intend to help you to make decisions for the task, reply to your questions, and schedule an appointment with our workers when you call us at 800-376-4281.
At Air Duct Cleaning Guys, we are aware that you have to stay in your financial budget and lower your costs everywhere you're able to. Though, saving your money should never indicate that you compromise quality for Air Duct Cleaning in Union, SC. We provide the very best quality even while still saving you money. We utilize the finest practices and materials to guarantee that the job will withstand the test of time, and save you money with techniques that won't change the quality of any job. That is possible given that we appreciate how to save you time and cash on materials and work. Call up Air Duct Cleaning Guys when you need the very best services at the best cost. Call up 800-376-4281 to talk to our client service representatives, today.
You should be kept informed with regards to Air Duct Cleaning in Union, SC. You won't want to go in without consideration, and it's good to understand what to expect. We take the unexpected surprises out of the scenario by giving adequate and detailed advice. Begin by dialing 800-376-4281 to discuss your task. In this phone call, you will get your questions addressed, and we can set up a time to commence work. We will work closely with you all through the entire process, and our crew is going to arrive promptly and prepared.
You will find a lot of reasons to decide on Air Duct Cleaning Guys regarding Air Duct Cleaning in Union, SC. Our supplies are of the highest quality, our cash saving strategies are sensible and powerful, and our customer service ratings are unrivaled. We have got the skills you will want to fulfill your objectives. Contact 800-376-4281 when you want Air Duct Cleaning in Union, and we're going to work together with you to expertly complete your task.

lowercased:
you'll be able to trust in air duct cleaning guys to offer the most effective products and services regarding air duct cleaning in union, sc. you'll need the most advanced technology in the field, and our team of highly skilled contractors can provide exactly that. our materials are always of the very best quality and we know how to save you money. we intend to help you to make decisions for the task, reply to your questions, and schedule an appointment with our workers when you call us at 800-376-4281.
at air duct cleaning guys, we are aware that you have to stay in your financial budget and lower your costs everywhere you're able to. though, saving your money should never indicate that you compromise quality for air duct cleaning in union, sc. we provide the very best quality even while still saving you money. we utilize the finest practices and materials to guarantee that the job will withstand the test of time, and save you money with techniques that won't change the quality of any job. that is possible given that we appreciate how to save you time and cash on materials and work. call up air duct cleaning guys when you need the very best services at the best cost. call up 800-376-4281 to talk to our client service representatives, today.
you should be kept informed with regards to air duct cleaning in union, sc. you won't want to go in without consideration, and it's good to understand what to expect. we take the unexpected surprises out of the scenario by giving adequate and detailed advice. begin by dialing 800-376-4281 to discuss your task. in this phone call, you will get your questions addressed, and we can set up a time to commence work. we will work closely with you all through the entire process, and our crew is going to arrive promptly and prepared.
you will find a lot of reasons to decide on air duct cleaning guys regarding air duct cleaning in union, sc. our supplies are of the highest quality, our cash saving strategies are sensible and powerful, and our customer service ratings are unrivaled. we have got the skills you will want to fulfill your objectives. contact 800-376-4281 when you want air duct cleaning in union, and we're going to work together with you to expertly complete your task.