TTIMES WORLD: Today's News Report

Tuesday, July 23, 2024
Washington, DC, USA


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The Shopping Malls Debt Epidemic in America
E-Commerce to the Rescue?

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The Shopping Malls Debt Epidemic in America

E-Commerce Rescue?

by Leticia Miranda


Malls that buckled due to E-commerce or suffered during the pandemic are being given new life by the very entity that precipitated their decline — E-commerce

Over the last several months, the E-commerce giants has gone on a shopping spree of its own, buying up disused malls across the country and turning them into distribution centers.

In March,  a mall in Baton Rouge, Louisiana, was turn into a 3.4 million-square-foot distribution building, and a mall in Knoxville, Tennessee, into a 220,000-square-foot distribution center. In December, the local planning board in Worcester, Massachusetts, signed off on an e commerce company's  request to convert the city's Greendale Mall into a 121,000-square-foot distribution center.

Between 2016 and 2019, E- commerce comapnies converted around 25 shopping malls, according to an analysis by Coresight Research. 


“The reality is that the cash flow at these lower-quality malls is declining rapidly,” said Vince Tibone, lead retail and industrial analyst at the real estate analytics firm Green Street. “You have to decide, ‘Do I want to do something myself to invest a lot of money to transform this dead retail into thriving retail or put up offices?’ Selling a dead mall as land is a more attractive option.”

About 50 percent of mall-based department stores could permanently close by the end of 2021, according to Green Street. The majority of these mall closures are expected to be lower-tier shopping centers that make less than $320 per square foot of space, which makes it difficult to cover their mortgages, Tibone said.

Malls are already struggling to keep up with mortgage debt. Macerich, which runs about 50 shopping centers across the country, announced in February it is restructuring to rein in $1.5 billion in debt that comes due in July. CBL Properties, whose major tenants include Victoria's Secret and Foot Locker, reached an agreement in March with lenders to shave $1.6 billion from its balance sheet. There is no end in site soon. Will E commerce save this march of debt, time will tell.

How Businesses Select Best Credit Card Processing Companies
What works Best - Nig

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  • For small businesses that process less than $5,000 per month, the most affordable card processing option is usually to select a processor that charges a flat rate for each transaction and doesn't charge monthly or annual fees.
  • Businesses that process more than $5,000 per month should look for a processor that offers interchange-plus pricing and charges few monthly or annual fees.
  • Look for processors that are transparent with their pricing, clearly posting rates and fees on their websites.
  • This article is for startups and small business owners who are looking for a credit card processing service.

Nearly every business these days needs to accept credit cards and debit cards, but choosing a credit card processing company for your small business can be difficult – there are hundreds to choose from. We've spent hundreds of hours researching the credit card processing industry; we looked for credit card processors that offer transparent pricing, with reasonable rates and few credit card processing fees, reliable customer support, and no long-term contracts. Read our recommendations below and our credit card processing guide to find the best credit card processor for your business.

Credit Card Processing Review Summary

Processors researched35
Lowest processing rate1.7% + $0.07
Highest processing rate3.5% + $0.35
Pricing ModelFlat and interchange-plus

Importance Of Mental Health
In A Work Place

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Importance Of Mental Health In A Work Place By Daniel Ajiboye

A lot of persons just think mental health is about suffering from DEPRESSION or ANXIETY, but it goes a long way. Here is a short article about how one's mental health is as important as productivity in a work space.

Mental health is a condition of mental wellness that enables people to manage life's stressors, develop their potential, study and work effectively, and give back to their communities. It is a crucial element of health and well-being that supports both our individual and collective capacities for decision-making, relationship-building, and influencing the world in which we live. The absence of mental diseases is only one aspect of mental wellness. It has variable degrees of difficulty and suffering, is experienced differently by each individual, and may have very different social and therapeutic implications. It exists on a complex continuum. Mental health issues include psychosocial disabilities, mental disorders, and other mental states characterized by severe distress and functional impairment. Although this is not always or necessarily the case, people with mental health disorders are more likely to have lower levels of mental well-being.
Hence, it has been proven that good mental health should be maintained in the workplace to ensure productivity and also prevent avoidable workplace hazards. When a person’s absolute well-being is altered mentally, attention span dwindles thereby making the worker susceptible to mistakes, overlooking details and nagging.
12 billion working days are reportedly wasted annually due to poor mental health worldwide. 71% of people said they had trouble controlling stress and felt overburdened or "burned out." Their mental health influences how well they do their jobs, even when the problems that stress them out have nothing to do with their jobs.  Supporting workplace mental health shouldn't be seen as 'optional' any longer...but a genuine requirement for employment. Worker performance and mental wellness go hand in hand. It's what can help an employee succeed because they work as two pieces of the same puzzle. On the other hand, a negative outcome may occur in the absence of adequate growth or support for mental health.
IMPROVEMENT OF WORKERS MENTAL WELL-BEING.
 There are lots of ways a worker can improve his/her mental wellbeing, some of which are;
 Prioritize wellness.
 Ask for help.
 Keep in touch with workplace relationships.
 Promote work/life balance.
 Reduction of stigmatization in the workplace.
 Raising awareness about mental wellbeing, etcetera.
Improvement of workers’ well-being is the duty of employers, human resource departments and individual workers themselves. The workplace itself largely contributes to the state of its worker's mental health, in terms of workload, lack of incentives, level of employee motivation, total number of employees, infrastructure within the workplace, provision of basic workplace amenities, etcetera. The moment an employee starts rethinking, complaining or anticipating the stress they will encounter within a week, then it is evident that the employee is under mental stress from working and they might be in denial of such a fact.
In conclusion, some of the greatest remunerations that can be granted to an employee aside from monetary payment are rest days off and travel benefits. Returning from such short rest or vacation will aid productivity as workers' motivation will be increased.

10 Portfolio Projects you Can Try
As An Entry-Level Data Analyst/Scientist

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10 Portfolio Projects You Can Try As An Entry-Level Data Analyst/Scientist By Daniel Madu

Written by Qudirah on Medium 

I hate the word newbie. If you are in a hurry, skip to the third paragraph. I always do this “catching up” thing before going straight to the point.

It’s April. I missed out on writing twice last month so I will have to write four times this month. I do have an explanation though, I was so hooked by some personal projects and work of course and I participated in my first hackathon!! and Outreachy, again (the story isn’t so much different from the last.) March was so chaotic, but I am glad I participated in so many things. Hopefully, everything works out in the end.

If I am sure, my entire journey clocked a year by February. Looking at how I started and how my growth has been honestly exponential, I have God ofc to thank and my love for projects. Best believe if I learn how to spell ‘A’ I will enter a spelling bee competition. I constantly set myself up (I regret it sometimes) which has made me learn so much in so little time. Since I read medium posts about data a lot, I tend to have ideas about a lot of things so even if you ask me to jump on a project with you and I have just a little idea about what it is, I will still jump on it. I am always willing to learn and put to test what I am learning and another thing about me is I despise common projects. I just will not do it if it is common. So do not expect the covid analysis or titanic project on the list. Ofc you can try them for practice but honestly not for your portfolio.

In the course of my journey, here are 10 projects I had engaged in to build my portfolio/career.

Crop Recommendation System
Tools used: Python, HTML, CSS, Flask, Basic ML knowledge

Difficulty: Easy

This was the first project I ever did and even though I hate it so much now, I’m so proud of it. I built a decision tree model that recommends the best crop under certain weather and soil condition. I deployed it locally using Flask and I have a terrible version of the project on my github currently so I do not want to link it. When I push a better version, I will link it here.

2. Movie recommender system

Tools used: Python, Knowledge of NLTK and Cosine Similarity, Heroku, Streamlit

Difficulty: Medium

Now, this was my second project but it was nothing like the first project. It uses NLP and cosine similarity. I had just finished Andrew Ng’s Machine learning course on Coursera and watched a TMDB movie recommender tutorial on YouTube so I built one on the Netflix dataset. I also worked on streamlit to allow user access and even deployed using Heroku. For me, this is the hardest project I have ever done. I even cried. Currently, I have learned better ways to do things but I did learn a lot from it. This is a link to the github. It needs some tidying but it’s not that terrible.

3. Forbes 2022 EDA using Python

Tools used: Python (Pandas and Matplolib)

Difficulty: Easy

This was the first EDA project that I published. I had written about it too on this link. The project was easy, it made me realize you learn from small projects too. I revised my knowledge of Pandas and Matplolib. I also learned how to ask the right questions, and how analysis is targeted toward uncovering something. A whole lot of people got to know me through this project too. This is a GitHub link to the project.

4. Market Basket Analysis

Tools used: Python(pandas, matplotlib, association rules)

Difficulty: Medium

I haven't posted about this project yet but it’s one of the projects I think a data analyst should try. You get to understand association rules, how products in a company sell, and which products are best sold with each other. How a high-sales product can aid in selling a low-sales one and so on. I enjoyed learning and doing this one and might be pushing it on my GitHub soon but before then you should research and try it. It is easy.

5. Implementing Gayle-Shapley’s Stable Matching Algorithm

Tools Used: Python

Difficulty: Medium

Now, this isn’t a data-related project. I went for an academy program last year that is python oriented and I was opportune enough to implement this algorithm in python. This algorithm is so interesting. The Gayle-Sharply matching algorithm is aimed at ensuring stable matching. The end goal is meant to be that everyone gets married to a (man)/(woman) and they are all happy with their matches. They all get to be with their most available preference. I don’t think I am explaining it well enough. I might dedicate a whole post to it but before then, you can read/research about it on google.

6. The Bechdel test

Tools used: Tableau, Python (For analysis)

Difficulty: Easy

The Bechdel Test ascertains there exists at least a scene in a movie where a woman speaks to another woman and it isn’t about a man. I will definitely write a post about this project. It’s one of the ones that hooked me on the first read. The moment I heard of this test, I wanted to do something with it to tell people about it. I linked it with the evolution of feminism and researched if the impact of feminism has improved how society viewed women. As such, I grouped the years into different centuries and observed the number of movies that passed the test over the years. I even made a tableau visualization for it but I haven’t perfected it yet. I haven’t posted about it either.

7. Sentiment Analysis Project

Tools: Python, NLTK, Power BI

Difficulty: Easy

I had done a sentiment analysis project when black panther 2 came out and I did another recently with two different libraries. It’s quite easy to do and I think it’s something every data analyst should try. I even visualized it using Power BI and I dared to use a black background. Yes. I did that. Here is a link to the post: Black Panther.

8. Data science job salaries

Tools Used: PostgreSQL, Excel, Power BI

Difficulty: Medium

Again, one of the projects that made me out there. I got so many reviews and feedback on this project. I used SQL, Excel for cleaning, and Power BI for visualization. I had written about it and published it too on this link. The data was gotten from this link and I explored the salaries of data professionals by their professions, mobility, employment type, and many more. SQL was used for the data analysis. I had used window functions and subqueries and honestly, I was able to properly practice what I had learned.

9. Classification of a phishing mail

Tools used: Python

Difficulty: Hard

This is one of the toughest projects I have engaged in. I built models that classify phishing emails and non-phishing emails using email structure, stylometric features, and so on. It took quite a time. I worked on feature extraction, data cleaning, dimensionality reduction, cross-validation, and model building. explored different evaluation methods too. I haven't pushed this on my GitHub either but I will soon. I don’t think I can make a post about it though.

10. Open Source Contribution

There are still some more projects to talk about but the number 10 project will be to contribute to open source. I learned unit testing, git and so much more through open source. It is something I don't do often because I always have little jobs that keep me so occupied but once I have a full-time job, I will definitely become a regular contributor. There is so much to learn and open source is one of the fastest ways to learn them.

Now we have come to the end of today’s post. Hit like if you enjoy reading, let me know your thoughts and if you will be trying any out let me know too! If you want more project suggestions, Let me know. I will make another post about a few.

Facts About Skin Cancer
You Need to Know

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Fast Facts About Skin Cancer
•Unprotected skin can be damaged by the sun’s UV rays in as little as 15 minutes. Yet it can take as long as 12 hours for skin to show the full effect of sun exposure. Plan ahead so that when you’re having fun outdoors, you won’t forget to protect yourself from the sun.
•Even if it’s cool and cloudy, you still need protection. UV rays, not the temperature, do the damage.
•Tanned skin is damaged skin. Any change in the color of your skin after time outside indicates damage from UV rays.
•Anyone can get skin cancer, but some things put you at higher risk.
•Indoor tanning exposes users to UVA and UVB rays, which damage skin and can lead to cancer.
•A change somewhere on your skin is the most common sign of skin cancer. This could be a new growth, a sore that doesn’t heal, or a change in a mole.

Skin cancer is the most common cancer in the United States, and includes different types. Exposure to ultraviolet (UV) rays causes most cases of melanoma, the deadliest kind of skin cancer. To lower your skin cancer risk, protect your skin from the sun and avoid indoor tanning.

National Center For Health Statistics
Leading Causes of Death in the US Shows a drop in Cancer Rate

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The new data comes from the National Center for Health Statistics, which concludes that death rates rose across the board. (Though one bit of good news, cancer rates dropped.)

Last year Case and another researcher sounded the alarm about a surprising increase in mortality rates for white middle-aged Americans – thanks to a phenomenon poignantly referred to as the “diseases of despair” – overdoses, alcoholism and suicide. The new numbers point to the possibility that a wider group of Americans are becoming prone to major diseases.


Here are the top causes for 2015 according to the report, ranked high to low; numbers represent deaths per 100,000 of the standard population:

1. Heart disease: 168.5
2. Cancer: 158.5
3. Unintentional injuries: 43.2
4. Chronic lower respiratory diseases: 41.6
5. Stroke: 37.6
6. Alzheimer’s disease: 29.4
7. Diabetes: 21.3
8. Influenza and pneumonia: 15.2
9. Kidney disease: 13.4
10. Suicide: 13.3

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