Archive for Opinions – Page 37

Week Ahead: Will TSMC rejoin the trillion-dollar club?

By ForexTime 

  • US listed TSMC shares ↑ almost 80% YTD
  • Less than 4% away from all-time high created in July
  • Forward guidance for Q4 in focus
  • FXTM’s TWN index could see fresh volatility
  • Technical levels for TSMC – $192.65, $183.00 and $177.00

Another slew of key data releases and corporate earnings could rock markets in the week ahead:

Saturday, 12th October

  • CN50: China’s Ministry of Finance holds briefing

Sunday, 13th October

  • CN50: China PPI, CPI

Monday, 14th October

  • CN50: China trade
  • US500: Fed Governor Christopher Waller speech

Tuesday, 15th October

  • CAD: Canada CPI, existing home sales
  • EU50: Eurozone industrial production, Germany ZEW survey
  • JP225: Japan industrial production
  • UK100: UK jobless claims, unemployment
  • US500: Goldman Sachs, Bank of America, Citigroup earnings

Wednesday, 16th October

  • NZD: New Zealand CPI
  • ZAR: South Africa retail sales
  • UK100: UK CPI
  • NETH25: ASML earnings
  • US500: Morgan Stanley earnings

Thursday, 17th October

  • AU200: Australia unemployment
  • EU50: Eurozone CPI, ECB rate decision
  • JP225: Japan tertiary index, trade
  • SG20: Singapore trade
  • NAS100: US retail sales, jobless claims, industrial production, Netflix earnings
  • TWN: Taiwan Semiconductor Manufacturing Company (TSMC) earnings

Friday, 18th October  

  • CN50: China GDP, retail sales, industrial production, home prices
  • JP225: Japan CPI
  • UK100: UK retail sales

The spotlight shines on the world’s largest contract chipmaker with a market cap of almost $1 trillion.

US-listed shares of Taiwan Semiconductor Manufacturing Company (TSMC) are up almost 80% year-to-date, logging only one negative month in 2024.

Note: TSMC shares can be traded on the Taiwan Stock Exchange (TWSE) and New York Stock Exchange (NYSE).

Back in July, TSMC shares hit an all-time high at $192.65 after strong Q2 revenues – giving the company a trillion-dollar valuation momentarily before stocks later tumbled.

Still, prices have rebounded since August with a recent report revealing that TSMC’s September sales jumped 39.6% year-on-year.

bloomberg TSMC

This welcome development along with a positive earnings release could push the company’s stock higher.

  • When will earnings be published?

TSMC reports its third-quarter earnings on Thursday 17th October before US markets open.

  • Market expectations

The chipmaker is expected to post earnings of $1.78 per share with Q3 revenues seen rising to $23.28 billion from $17.28 in the prior year.

  • What to watch out for

Back in July, TSMC forecasted third quarter revenue in a range of between $22.4 billion to $23.2 billion.

But the chipmaker has already beaten these forecasts with consolidated sales in Q3 (July – September) hitting $23.6 billion, thanks to AI demand from major clients like Nvidia and Apple.

So much focus will be on the company’s earnings and forward guidance for Q4 which could serve as a key gauge for AI demand.

  • What does this mean for FXTM’s TWN index.

FXTM’s TWN index tracks the underlying FTSE Taiwan RIC Capped Index.

And TSMC makes up just under 20% of the index weighting, meaning that the upcoming earnings could result in heightened volatility.

The index is up almost 3% this month, bringing year-to-date gains to roughly 22%. Prices have been trending higher in recent weeks with the all-time high 7% away at 2046.8.

Key levels of interest can be found at 1930 and 1825.

TWN

  • Technical picture

TSMC shares are trending higher on the weekly charts with prices trading above the 21, 50 and 100-week SMA. However, the Relative Strength Index (RSI) is near 70 – signaling that prices may be overbought.

TSM

On the daily charts, the trend is bullish with prices are trading less than 4% away from its all-time high created in July at $192.65.

  • A decline below $183.00 may see prices test the 21-day SMA at $177.0 and $170.0.
  • Should $183.0 prove to be reliable support, this may open a path back to the all-time high at $192.65 and beyond.

TSMS1


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How a subfield of physics led to breakthroughs in AI – and from there to this year’s Nobel Prize

By Veera Sundararaghavan, University of Michigan 

John J. Hopfield and Geoffrey E. Hinton received the Nobel Prize in physics on Oct. 8, 2024, for their research on machine learning algorithms and neural networks that help computers learn. Their work has been fundamental in developing neural network theories that underpin generative artificial intelligence.

A neural network is a computational model consisting of layers of interconnected neurons. Like the neurons in your brain, these neurons process and send along a piece of information. Each neural layer receives a piece of data, processes it and passes the result to the next layer. By the end of the sequence, the network has processed and refined the data into something more useful.

While it might seem surprising that Hopfield and Hinton received the physics prize for their contributions to neural networks, used in computer science, their work is deeply rooted in the principles of physics, particularly a subfield called statistical mechanics.

As a computational materials scientist, I was excited to see this area of research recognized with the prize. Hopfield and Hinton’s work has allowed my colleagues and me to study a process called generative learning for materials sciences, a method that is behind many popular technologies like ChatGPT.

What is statistical mechanics?

Statistical mechanics is a branch of physics that uses statistical methods to explain the behavior of systems made up of a large number of particles.

Instead of focusing on individual particles, researchers using statistical mechanics look at the collective behavior of many particles. Seeing how they all act together helps researchers understand the system’s large-scale macroscopic properties like temperature, pressure and magnetization.

For example, physicist Ernst Ising developed a statistical mechanics model for magnetism in the 1920s. Ising imagined magnetism as the collective behavior of atomic spins interacting with their neighbors.

In Ising’s model, there are higher and lower energy states for the system, and the material is more likely to exist in the lowest energy state.

One key idea in statistical mechanics is the Boltzmann distribution, which quantifies how likely a given state is. This distribution describes the probability of a system being in a particular state – like solid, liquid or gas – based on its energy and temperature.

Ising exactly predicted the phase transition of a magnet using the Boltzmann distribution. He figured out the temperature at which the material changed from being magnetic to nonmagnetic.

Phase changes happen at predictable temperatures. Ice melts to water at a specific temperature because the Boltzmann distribution predicts that when it gets warm, the water molecules are more likely to take on a disordered – or liquid – state.

Statistical mechanics tells researchers about the properties of a larger system, and how individual objects in that system act collectively.

In materials, atoms arrange themselves into specific crystal structures that use the lowest amount of energy. When it’s cold, water molecules freeze into ice crystals with low energy states.

Similarly, in biology, proteins fold into low energy shapes, which allow them to function as specific antibodies – like a lock and key – targeting a virus.

Neural networks and statistical mechanics

Fundamentally, all neural networks work on a similar principle – to minimize energy. Neural networks use this principle to solve computing problems.

For example, imagine an image made up of pixels where you only can see a part of the picture. Some pixels are visible, while the rest are hidden. To determine what the image is, you consider all possible ways the hidden pixels could fit together with the visible pieces. From there, you would choose from among what statistical mechanics would say are the most likely states out of all the possible options.

A diagram showing statistical mechanics on the left, with a graph showing three atomic structures, with the one at the lowest energy labeled the most stable. On the right is labeled neural networks, showing two photos of trees, one only half visible.
In statistical mechanics, researchers try to find the most stable physical structure of a material. Neural networks use the same principle to solve complex computing problems.
Veera Sundararaghavan

Hopfield and Hinton developed a theory for neural networks based on the idea of statistical mechanics. Just like Ising before them, who modeled the collective interaction of atomic spins to solve the photo problem with a neural network, Hopfield and Hinton imagined collective interactions of pixels. They represented these pixels as neurons.

Just as in statistical physics, the energy of an image refers to how likely a particular configuration of pixels is. A Hopfield network would solve this problem by finding the lowest energy arrangements of hidden pixels.

However, unlike in statistical mechanics – where the energy is determined by known atomic interactions – neural networks learn these energies from data.

Hinton popularized the development of a technique called backpropagation. This technique helps the model figure out the interaction energies between these neurons, and this algorithm underpins much of modern AI learning.

The Boltzmann machine

Building upon Hopfield’s work, Hinton imagined another neural network, called the Boltzmann machine. It consists of visible neurons, which we can observe, and hidden neurons, which help the network learn complex patterns.

In a Boltzmann machine, you can determine the probability that the picture looks a certain way. To figure out this probability, you can sum up all the possible states the hidden pixels could be in. This gives you the total probability of the visible pixels being in a specific arrangement.

My group has worked on implementing Boltzmann machines in quantum computers for generative learning.

In generative learning, the network learns to generate new data samples that resemble the data the researchers fed the network to train it. For example, it might generate new images of handwritten numbers after being trained on similar images. The network can generate these by sampling from the learned probability distribution.

Generative learning underpins modern AI – it’s what allows the generation of AI art, videos and text.

Hopfield and Hinton have significantly influenced AI research by leveraging tools from statistical physics. Their work draws parallels between how nature determines the physical states of a material and how neural networks predict the likelihood of solutions to complex computer science problems.The Conversation

About the Author:

Veera Sundararaghavan, Professor of Aerospace Engineering, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Bitcoin: Wedged between 50 and 200-day SMA

By ForexTime 

  • Bitcoin ↓ 2.6% in October
  • HBO doc identifies Peter Todd as Bitcoin creator
  • Over past year Fed minutes triggered moves of ↑ 2.2% & ↓ 1%
  • Over past year US CPI triggered moves of ↑ 1.8% & ↓ 2.9%
  • Technical levels: $63,500 & $61,000

Bitcoin has found itself trapped within a range on the daily charts.

The world’s largest cryptocurrency could be waiting for a fresh fundamental spark to trigger significant price swings.

Bitcoin

Despite the growing anticipation, Bitcoin offered a muted response after HBO’s documentary pointed to Canadian Bitcoin developer Peter Todd as Satoshi Nakamoto. However, Todd immediately denied these claims on social media.

This was initially a big deal due to the mystery surrounding Satoshi Nakamoto who is estimated to hold 1.1 million Bitcoins worth $66 billion. If Satoshi’s identity was truly unmasked, it could have various implications for Bitcoin which has skyrocketed over the years and gained mainstream acceptance.

With our attention back to key data, here are 3 things to keep an eye on this week:

 

    1) Fed speeches + FOMC meeting minutes

Last Friday’s strong jobs report boosted confidence in the US economy and erased hopes around a 50bp Fed cut in November.

It will be interesting to see what Fed officials think about the latest developments and the potential impacts it could have on future rate cuts. Regarding the FOMC minutes, investors will be looking for fresh insight into the outlook for labour markets or future policy moves.

Given how cryptocurrencies have shown sensitivity to interest rates, the incoming event may spark price swings.

Golden nugget: Over the past year, the FOMC minutes have triggered upside moves of as much as 2.2% or declines of 1% in a 6-hour window post-release.

 

    2) US September CPI report

As highlighted in our week ahead report, the incoming inflation data may impact bets around how deep the Fed cuts rates in Q4.

Signs of cooling price pressures may boost expectations around lower interest rates, supporting Bitcoin as a result. The same is true vice versa.

Golden nugget: Over the past year, the US CPI report has triggered upside moves of as much as 1.8% or declines of 2.9% in a 6-hour window post-release.

  • A hotter-than-expected CPI report could drag Bitcoin prices lower as the dollar strengthens and rate cut bets cool.
  • A soft inflation report may support the argument around lower US interest rates, boosting Bitcoin prices

 

    3) Technical forces

Bitcoin remains trapped within a range on the daily charts with support around $61,000 and resistance at $63,500 where the 200-day SMA resides. 

  • A solid breakout and daily close above $63,500 could encourage a move toward $65,000 $66,000.
  • A break below the 100-day SMA at $61,000 could see prices test $60,000. Sustained weakness below here may encourage bears to attack $57,600.

Bitcoin23


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The Energy Bull Has Returned

Source: Michael Ballanger (10/7/24)

Michael Ballanger of GGM Advisory Inc. takes a look at the energy market, and shares his thoughts on junior miners. 

Last week, I decided to write about the fiscal bazooka engaged by Chinese President Xi Jinping that sent Chinese equities into a vertical moon rocket with relative strength for the major indices, hitting an all-time record at 91. From David Tepper to Louis Gave, the China bulls are now stampeding with the ferocity of the spooked herd while short sellers bleeding from the eye sockets and hair ablaze are covering with unfathomable urgency.

The move by the Chinese central bank to dive headlong into an easing cycle follows the past two years of pain as the real estate market has stagnated under the weight of oversupply and bubbly consumer attitudes. Overproduction in the EV sector has left inventories overflowing in both unsold units and the age of the fleet, as vast numbers of rotting vehicles are sitting in car lots around the country. Something had to be done, and it was as if Xi Jinping took aim and pulled the trigger.

Initially, the advance in Chinese equities was celebrated by only the bravado-laden diehard contrarians who had been buying large-cap Chinese companies at eight times cash flow with 75% of market cap in cash, similar to January 2023 when the Japanese equity markets suddenly caught a bid on the basis of their valuations relative to the over-owned, over-priced U.S. counterparts that have benefitted from a constant, never-ending combination of fiscal and monetary stimulus all designed to juice stock prices and maintain the asymmetrical wealth-effect so critical for sustained economic growth.

However, what few were talking about was the ancillary impact the Chinese stimulus move had on a number of other sectors. Copper, which I identified in early August as a “Buy” under $4.00/lb. (after exiting in May) was well on its way to test the 100-dma at around $4.40, but it received an enormous shot of adrenalin with the news that China had suddenly gone “full-Draghi,” deciding to do “whatever it takes” to get the economy back on track.

Copper is now ahead over 20% from those “carry trade crash” lows now cruising with a gale force Chinese tailwind behind it.

However, the sleeper in the China stimulus narrative is the one commodity that drives all economic growth — oil — and whether or nor it is politically correct, it is not going away anytime soon. Subscribers were sent an email alert last Tuesday before the opening suggesting that I was revisiting the “energy trade.”

In that email alert, I wrote:

The ETF that covers the big multinational oil & gas producers is the Energy Select Sector SPDR Fund (XLE:NYSEARC) that has traded as low as $78.98 last January and at USD $82.34 a couple of weeks ago. As can be seen from the chart, there have been three major “buy signals” since the lows of last month, with MACD, MFI, and now TRIX all kicking into gear. Accordingly, I want to take advantage of today’s pullback and take a starting position in the XLE. I have traded this ETF before, and when it moves, it moves fast with big gaps in price, and while it is not always easy to nail down the exact lows, sentiment numbers and trader positioning are about as dismal as one can get for any specific sector.

The chart shown below was from the Monday close at $87.80, and my instructions were that bids at $87.00 might be successful since oil was called lower for the Tuesday opening. The XLE opened at $87.03, traded down to $86.90 after which oil executed a massive reversal to the upside taking XLE to a closing price of $89.80.

There are a great many oil bears out there that want to see fossil fuels outlawed and ICE’s outright banned. I consider those attitudes as archaic and ill-sighted as the electrification transition will take decades to complete. Thinking that the world can survive and grow without the use of oil is delusional.

I am not a moralist; I am a financial opportunist. I pore over charts and essays and financial statements day after day to try to find what I believe are legitimate chances to profit, with not even the slightest consideration of what the company may or may not be doing to “save the planet.” I recall one afternoon driving home from hockey practice in 1962, listening to the CBC newscaster discussing relations between JFK and Soviet premier Nikita Khrushchev regarding nuclear disarmament.

Frightened by what I was hearing, I asked my dad if “mankind” was going to bring about the end of the world. My dad responded with an answer I can never forget. He said, “Son, “mankind” will never bring about the end of the world. It might bring about the end of “mankind but it will never cause the end of the world.”

The egotism of these moralists who preach about carbon credits, global pollution, and every imaginable ecological sin committed by Big Oil or Big Nuclear, or the military-industrial complex is beyond maddening. Watching the student body of a university lying in front of cars, trucks, and buses as a protest to the petroleum industry takes me to a place that I won’t even mention.

I was one of those poor slobs during the Arab oil embargo of 1973-1974, sitting behind the wheel of a 1965 Ford Custom while in a 30-car line-up waiting for a chance to fill the tank up, which was running on fumes. It was not a fun time.

Another name I now own is a fascinating little junior from the Alberta oil patch called HHemisphere Energy Corp. (HME:TSX.V), which I bought last Tuesday. Paying a 5% dividend, the company uses a Polymer-flooding technique to enhance oil recovery from pools in Alberta and Saskatchewan.

They have been growing production systematically since 2017 and had a record year in 2023 and expect even better for 2024.

It is a perfect addition to a mining-centric portfolio and delivers diversification with an enviable income stream.

Gold and Silver

Gold put in a decent week, and up until around 11:00 am this morning, after the traders had a couple of hours to mull over the jobs report, silver made a very brief sojourn into the new 52-week high ground before coming under the merciless wrath of the bullion bank behemoths that decided to crush the breakout with undeniable conviction and send it down from an $.80 advance to a $0.07 loss on the day.

The silver bugs were collectively disheartened and summarily vanquished as they always are whenever they start to trot out the champagne flutes, cymbals, and pompoms. I am positive about the inevitability of a silver breakout, but it will be led by gold and copper, the two primary drivers of the bull market in the metals. While gold is being driven higher by that constant and persistent central bank bid, copper is being driven by a rapidly approaching structural deficit that is going to disrupt the global flow of everything because copper is found in everything.

Housing, electronics, medicine, and a myriad of other products and industries that rely on copper for its universal application. Silver, while also used in a broad spectrum of industrial applications, is primarily driven by the retail crowd ever seeking a “poor man’s gold” and, as such, rarely winds up being owned but rather rented with an ownership horizon far shorter than either gold or copper.

That explains the volatility in the silver market and why it is that the bullion bank traders find it so much easier to bat silver around whenever they choose while rarely daring to try the same with gold and never trying it with a market as wide and expansive as copper. That said, there will be a point in time and soon when silver will overtake both gold and copper and assume a leadership role, which will make the silver bugs giddy with “I-told-you-so” excitement as it grabs the reins and vaults into the lead, grabbing headlines in every financial publication and two-bit tout sheet across the globe.

The silver bugs will all rejoice in their final and ultimate vindication of owning one of the worst-acting metals of the past four years, and while I will be an owner of silver when that occurs, I shall not be mired in self-adulation because, at the point in the metals cycle where silver grabs all of the headlines, it is also the terminus of the move in the metals.

Every metals bull market ends with the silver bugs shaking their fists at the world, and when that occurs, as happened in 1980 and 2011, I want to be in full liquidation mode of the more speculative pieces in my metals portfolio and moving to hedge the “never-sell” portions that are intractable items for the financial future.

This is why I have always wanted gold to lead the pack slowly, quietly, and methodically, as it has since 2020, correcting and advancing with higher highs and higher lows. I never want to see CNBC “Guest Commentators” voicing their opinions on a gold market that has been “LIMIT UP” for three straight days because once the prey comes out of the brush and into the broad daylight, it becomes an enviable target.

Near-term, gold prices are due for a correction. The bearish indicators all line up after RSI moved into overbought extremes in late September. Unlike last April when I tried to trade the correction, I will simply stand aside and let the market work off the overbought condition and try to time the pullback so I can be leveraged properly into new highs by year-end.

For now, no new buys in the bigger names, but the juniors are still ridiculously “cheap.”

Stocks

Friday’s NFP report showed a blow-out increase in the number of new jobs, sending the CNBC cheerleaders into a full-on feeding frenzy as the stocks took aim at all-time highs. The cheering centered around “good news” on the economy being “good news” for stocks, and despite the bond market taking it on the chin, when one lifted the hood and looked into the engine room, all one saw was a bunch of new government jobs and a “wages paid” number that set off the inflationary alarm bells with vigor.

However, the bulls are carrying the day but with the Middle East on fire and the REPO and SOFR markets starting to sweat bullets (i.e. liquidity drying up), I will remain fully-hedge until at least the end of the month.

Rising wages, rising oil, rising gold and rising 10-year yields are never earmarks of a risk-free equities environment. Caution is warranted.

 

Important Disclosures:

  1. Michael Ballanger: I, or members of my immediate household or family, own securities of: All. I determined which companies would be included in this article based on my research and understanding of the sector.
  2. Statements and opinions expressed are the opinions of the author and not of Streetwise Reports, Street Smart, or their officers. The author is wholly responsible for the accuracy of the statements. Streetwise Reports was not paid by the author to publish or syndicate this article. Streetwise Reports requires contributing authors to disclose any shareholdings in, or economic relationships with, companies that they write about. Any disclosures from the author can be found  below. Streetwise Reports relies upon the authors to accurately provide this information and Streetwise Reports has no means of verifying its accuracy.
  3.  This article does not constitute investment advice and is not a solicitation for any investment. Streetwise Reports does not render general or specific investment advice and the information on Streetwise Reports should not be considered a recommendation to buy or sell any security. Each reader is encouraged to consult with his or her personal financial adviser and perform their own comprehensive investment research. By opening this page, each reader accepts and agrees to Streetwise Reports’ terms of use and full legal disclaimer. Streetwise Reports does not endorse or recommend the business, products, services or securities of any company.

For additional disclosures, please click here.

Michael Ballanger Disclosures

This letter makes no guarantee or warranty on the accuracy or completeness of the data provided. Nothing contained herein is intended or shall be deemed to be investment advice, implied or otherwise. This letter represents my views and replicates trades that I am making but nothing more than that. Always consult your registered advisor to assist you with your investments. I accept no liability for any loss arising from the use of the data contained on this letter. Options and junior mining stocks contain a high level of risk that may result in the loss of part or all invested capital and therefore are suitable for experienced and professional investors and traders only. One should be familiar with the risks involved in junior mining and options trading and we recommend consulting a financial adviser if you feel you do not understand the risks involved.

Teachers feel most productive when they use AI for teaching strategies

By Samantha Keppler, University of Michigan and Clare Snyder, University of Michigan 

Teachers can use generative AI in a variety of ways. They may use it to develop lesson plans and quizzes. Or teachers may rely on a generative AI tool, such as ChatGPT, for insight on how to teach a concept more effectively.

In our new research, only the teachers doing both of those things reported feeling that they were getting more done. They also told us that their teaching was more effective with AI.

Over the course of the 2023-2024 school year, we followed 24 teachers at K-12 schools throughout the United States as they wrestled with whether and how to use generative AI for their work. We gave them a standard training session on generative AI in the fall of 2023. We then conducted multiple observations, interviews and surveys throughout the year.

We found that teachers felt more productive and effective with generative AI when they turned to it for advice. The standard methods to teach to state standards that work for one student, or in one school year, might not work as well in another. Teachers may get stuck and need to try a different approach. Generative AI, it turns out, can be a source of ideas for those alternative approaches.

While many focus on the productivity benefits of how generative AI can help teachers make quizzes or activities faster, our study points to something different. Teachers feel more productive and effective when their students are learning, and generative AI seems to help some teachers get new ideas about how to advance student learning.

Why it matters

K-12 teaching requires creativity, particularly when it comes to tasks such as lesson plans or how to integrate technology into the classroom. Teachers are under pressure to work quickly, however, because they have so many things to do, such as prepare teaching materials, meet with parents and grade students’ schoolwork. Teachers do not have enough time each day to do all of the work that they need to.

We know that such pressure often makes creativity difficult. This can make teachers feel stuck. Some people, in particular AI experts, view generative AI as a solution to this problem; generative AI is always on call, it works quickly, and it never tires.

However, this view assumes that teachers will know how to use generative AI effectively to get the solutions they are seeking. Our research reveals that for many teachers, the time it takes to get a satisfactory output from the technology – and revise it to fit their needs – is no shorter than the time it would take to create the materials from scratch on their own. This is why using generative AI to create materials is not enough to get more done.

By understanding how teachers can effectively use generative AI for advice, schools can make more informed decisions about how to invest in AI for their teachers and how to support teachers in using these new tools. Further, this feeds back to the scientists creating AI tools, who can make better decisions about how to design these systems.

What still isn’t known

Many teachers face roadblocks that prevent them from seeing the benefits of generative AI tools such as ChatGPT. These benefits include being able to create better materials faster. The teachers we talked to, however, were all new users of the technology. Teachers who are more familiar with ways to prompt generative AI – we call them “power users” – might have other ways of interacting with the technology that we did not see. We also do not yet know exactly why some teachers move from being new users to proficient users but others do not.

About the Authors:

The Research Brief is a short take on interesting academic work.The Conversation

Samantha Keppler, Assistant Professor of Technology and Operations, Stephen M. Ross School of Business, University of Michigan and Clare Snyder, PhD Candidate in Business Administration, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Want to solve a complex problem? Applied math can help

By Alan Veliz-Cuba, University of Dayton 

You can probably think of a time when you’ve used math to solve an everyday problem, such as calculating a tip at a restaurant or determining the square footage of a room. But what role does math play in solving complex problems such as curing a disease?

In my job as an applied mathematician, I use mathematical tools to study and solve complex problems in biology. I have worked on problems involving gene and neural networks such as interactions between cells and decision-making. To do this, I create descriptions of a real-world situation in mathematical language. The act of turning a situation into a mathematical representation is called modeling.

Translating real situations into mathematical terms

If you ever solved an arithmetic problem about the speed of trains or cost of groceries, that’s an example of mathematical modeling. But for more difficult questions, even just writing the real-world scenario as a math problem can be complicated. This process requires a lot of creativity and understanding of the problem at hand and is often the result of applied mathematicians working with scientists in other disciplines.

As an example, we could represent a game of Sudoku as a mathematical model. In Sudoku, the player fills empty boxes in a puzzle with numbers between 1 and 9 subject to some rules, such as no repeated numbers in any row or column.

The puzzle begins with some prefilled boxes, and the goal is to figure out which numbers go in the rest of the boxes.

Imagine that a variable, say x, represents the number that goes in one of those empty boxes. We can guarantee that x is between 1 and 9 by saying that x solves the equation (x-1)(x-2) … (x-9)=0. This equation is true only when one of the factors on the left side is zero. Each of the factors on the left side is zero only when x is a number between 1 and 9; for example, (x-1)=0 when x=1. This equation encodes a fact about our game of Sudoku, and we can encode the other features of the game similarly. The resulting model of Sudoku will be a set of equations with 81 variables, one for each box in the puzzle.

Another situation we might model is the concentration of a drug, say aspirin, in a person’s bloodstream. In this case, we would be interested in how the concentration changes as we ingest aspirin and the body metabolizes it. Just like with Sudoku, one can create a set of equations that describe how the concentration of aspirin evolves over time and how additional ingestion affects the dynamics of this medication. In contrast to Sudoku, however, the variables that represent concentrations are not static but rather change over time.

But the act of modeling is not always so straightforward. How would we model diseases such as cancer? Is it enough to model the size and shape of a tumor, or do we need to model every single blood vessel inside the tumor? Every single cell? Every single chemical in each cell? There is much that is unknown about cancer, so how can we model such unknown features? Is it even possible?

Applied mathematicians have to find a balance between models that are realistic enough to be useful and simple enough to be implemented. Building these models may take several years, but in collaboration with experimental scientists, the act of trying to find a model often provides novel insight into the real-world problem.

Mathematical models help find real solutions

After writing a mathematical problem to represent a situation, the second step in the modeling process is to solve the problem.

For Sudoku, we need to solve a collection of equations with 81 variables. For the aspirin example, we need to solve an equation that describes the rate of change of concentrations. This is where all the math that has been and is still being invented comes into play. Areas of pure math such as algebra, analysis, combinatorics and many others can be used – in some cases combined – to solve the complex math problems arising from applications of math to the real world.

The third step of the modeling process consists of translating the mathematical solution into the solution to the applied problem. In the case of Sudoku, the solution to the equations tells us which number should go in each box to solve the puzzle. In the case of aspirin, the solution would be a set of curves that tell us the aspirin concentration in the digestive system and bloodstream. This is how applied mathematics works.

When creating a model isn’t enough

Or is it? While this three-step process is the ideal process of applied math, reality is more complicated. Once I reach the second step where I want the solution of the math problem, very often, if not most of the time, it turns out that no one knows how to solve the math problem in the model. In some cases, the math to study the problem doesn’t even exist.

For example, it is difficult to analyze models of cancer because the interactions between genes, proteins and chemicals are not as straightforward as the relationships between boxes in a game of Sudoku. The main difficulty is that these interactions are “nonlinear,” meaning that the effect of two inputs is not simply the sum of the individual effects. To address this, I have been working on novel ways to study nonlinear systems, such as Boolean network theory and polynomial algebra. With this and traditional approaches, my colleagues and I have studied questions in areas such as
decision-making, gene networks, cellular differentiation and limb regeneration.

When approaching unsolved applied math problems, the distinction between applied and pure mathematics often vanishes. Areas that were considered at one time too abstract have been exactly what is needed for modern problems. This highlights the importance of math for all of us; current areas of pure mathematics can become the applied mathematics of tomorrow and be the tools needed for complex, real-world problems.The Conversation

About the Author:

Alan Veliz-Cuba, Associate Professor of Mathematics, University of Dayton

This article is republished from The Conversation under a Creative Commons license. Read the original article.

 

Companies keep selling harmful products – but history shows consumers can win in the end

By Jonathan D. Quick, Duke University and Eszter Rimanyi, Duke University 

In 2023, 42 state attorneys general sued Meta to remove Instagram features that Meta’s own studies had shown – and independent research had confirmed – are harmful to teenage girls.

The same year, a report from the nonprofit Sandy Hook Promise found gun manufacturers were targeting the youth market with eye-catching ads and product placements in video games.

And in the run-up to the Paris Olympics, a leading international health journal urged the International Olympic Committee to end its relationship with Coca-Cola because of the increased obesity, diabetes, heart disease and high blood pressure associated with sugary drinks.

Social media, guns, sugar: These are all examples of what we call “market-driven epidemics.”

When people think of epidemics, they might think they’re caused only by viruses or other germs. But as public health experts, we know that’s just part of the story. Commerce can cause epidemics, too. That’s why our team coined the phrase in a recent study because you can’t solve a problem without naming it.

Market-driven epidemics follow a familiar script. First, companies start selling an appealing, often addictive product. As more and more people start using it, the health harms become clearer. Yet even as evidence of damage grows and deaths pile up, sales continue to rise as companies resist efforts by health authorities, consumer groups and others to control them.

We see this pattern with many products today, including social media platforms, firearms, sugar-sweetened beverages, ultra-processed foods, opioids, nicotine products, infant formula and alcohol. Collectively, their harm contributes to more than 1 million deaths in the U.S. each year.

How to fight a commercial epidemic

In our study, we asked two critical questions: Is it possible to combat such epidemics by changing the consumption patterns of millions of people? And if so, what does it take?

We found the answers by looking at decades of efforts to reduce unhealthy consumption of three products: cigarettes, sugar and prescription opioids.

In each case, Americans kept consuming more and more of these products, even in the face of growing health concerns, until a tipping point was reached. That was followed by steady declines in consumption.

The immediate cause for each tipping point varied considerably. For cigarettes, it was the trusted, authoritative voice of the U.S. Surgeon General unequivocally declaring in 1964 that smoking causes cancer.

In the case of sugar, one of the key moments was a high-profile 1999 petition titled “America: Drowning In Sugar” submitted by the Center for Science in the Public Interest and supported by 72 leading public health organizations and experts. The petition urged the Food and Drug Administration to require food labels to disclose the number of added sugars and their percentage of the daily recommended intake.

Once enacted, this policy helped consumers make healthier food choices, while also highlighting just how full of sugar many items on the market were.

And for prescription opioids, in 2011, the U.S. Centers for Disease Control and Prevention declared an opioid epidemic, signaling to doctors that they were overprescribing, and to the drug industry that it was acting irresponsibly.

In each case, success came after years of persistent efforts by scientists, public health officials and advocates to sway public opinion, often against the deliberate efforts of corporations to undermine them.

The 1964 report on smoking came after a decade of confusion that the industry had sown to distract the public from the scientific consensus about the harms of tobacco. The report offered conclusive authority that changed the narrative. Smoking went from being viewed as a widely accepted social custom to a deadly habit almost overnight. Today, just 1 in 9 American adults smoke, down from nearly half of all adults in 1954.

The push in 1999 by public health leaders connected the dots between rising obesity rates and sugar-laden foods and drinks. People began scrutinizing their diets, especially their sugar intake. As result, annual sugar consumption has since dropped by more than 15 pounds per person, erasing half of the amount of sugar Americans added to their diets between 1950 and 2000.

And the CDC report on opioids effectively communicated to doctors that they couldn’t just rely on patients to avoid misuse of the highly addictive drugs, underscoring their responsibility to help control the epidemic by reducing prescriptions of opioids such as OxyContin. Since the report, opioid prescription has been reduced by 60% – more in line with actual medical need.

Learning from the past

While there are no easy solutions for today’s market-based epidemics, we can learn from history about steps that can be effective in reducing the consumption of harmful products.

Changing attitudes on smoking show that an authoritative governmental voice can still be immensely useful to combat corporate resistance and the spread of corporate mis- and disinformation.

It can be effective to provide clear guidance about products and alternatives, as public health leaders did in telling consumers to cut consumption of sugar-sweetened beverages.

And from opioids, we can learn that applying pressure to those who make decisions about consumption, who are not always the consumers themselves, can be immensely powerful in bending patterns of use.

Despite the progress made in these three cases, the U.S. continues to face ongoing and emerging epidemics of unhealthy products. For example, while smoking has dramatically declined, the shift to vaping and other nicotine delivery products is creating new challenges, especially among teenagers.

Meanwhile, gun deaths keep rising, and firearms are now the leading killer of children under 18, and the gun industry remains committed to opposing public health measures to reduce gun violence.

And ultra-processed foods now account for nearly 60% of the average American’s diet, yet as new evidence confirms their harms, the food industry defends them.

But our research shows that these problems can be solved – that it is in fact possible to change millions of Americans’ behavior. This is very good news. It means sound evidence and public health action can turn the tide on some of the world’s biggest health challenges, potentially saving millions of lives and billions of dollars in health-care costs.The Conversation

About the Author:

Jonathan D. Quick, Adjunct Professor of Global Health, Duke Global Health Institute, Duke University and Eszter Rimanyi, Chronic disease and addiction epidemiologist, Duke University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

CubeSats, the tiniest of satellites, are changing the way we explore the solar system

By Mustafa Aksoy, University at Albany, State University of New York 

Most CubeSats weigh less than a bowling ball, and some are small enough to hold in your hand. But the impact these instruments are having on space exploration is gigantic. CubeSats – miniature, agile and cheap satellites – are revolutionizing how scientists study the cosmos.

A standard-size CubeSat is tiny, about 4 pounds (roughly 2 kilograms). Some are larger, maybe four times the standard size, but others are no more than a pound.

As a professor of electrical and computer engineering who works with new space technologies, I can tell you that CubeSats are a simpler and far less costly way to reach other worlds.

Rather than carry many instruments with a vast array of purposes, these Lilliputian-size satellites typically focus on a single, specific scientific goal – whether discovering exoplanets or measuring the size of an asteroid. They are affordable throughout the space community, even to small startup, private companies and university laboratories.

Tiny satellites, big advantages

CubeSats’ advantages over larger satellites are significant. CubeSats are cheaper to develop and test. The savings of time and money means more frequent and diverse missions along with less risk. That alone increases the pace of discovery and space exploration.

CubeSats don’t travel under their own power. Instead, they hitch a ride; they become part of the payload of a larger spacecraft. Stuffed into containers, they’re ejected into space by a spring mechanism attached to their dispensers. Once in space, they power on. CubeSats usually conclude their missions by burning up as they enter the atmosphere after their orbits slowly decay.

Case in point: A team of students at Brown University built a CubeSat in under 18 months for less than US$10,000. The satellite, about the size of a loaf of bread and developed to study the growing problem of space debris, was deployed off a SpaceX rocket in May 2022.

A CubeSat can go from whiteboard to space in less than a year.

Smaller size, single purpose

Sending a satellite into space is nothing new, of course. The Soviet Union launched Sputnik 1 into Earth orbit back in 1957. Today, about 10,000 active satellites are out there, and nearly all are engaged in communications, navigation, military defense, tech development or Earth studies. Only a few – less than 3% – are exploring space.

That is now changing. Satellites large and small are rapidly becoming the backbone of space research. These spacecrafts can now travel long distances to study planets and stars, places where human explorations or robot landings are costly, risky or simply impossible with the current technology.

But the cost of building and launching traditional satellites is considerable. NASA’s lunar reconnaissance orbiter, launched in 2009, is roughly the size of a minivan and cost close to $600 million. The Mars reconnaissance orbiter, with a wingspan the length of a school bus, cost more than $700 million. The European Space Agency’s solar orbiter, a 4,000-pound (1,800-kilogram) probe designed to study the Sun, cost $1.5 billion. And the Europa Clipper – the length of a basketball court and scheduled to launch in October 2024 to the Jupiter moon Europa – will ultimately cost $5 billion.

These satellites, relatively large and stunningly complex, are vulnerable to potential failures, a not uncommon occurrence. In the blink of an eye, years of work and hundreds of millions of dollars could be lost in space.

Two scientists wearing masks, gloves, head coverings and white clean suits work on an instrument in a laboratory.
NASA scientists prep the ASTERIA spacecraft for its April 2017 launch.
NASA/JPL-Caltech

Exploring the Moon, Mars and the Milky Way

Because they are so small, CubeSats can be released in large numbers in a single launch, further reducing costs. Deploying them in batches – known as constellations – means multiple devices can make observations of the same phenomena.

For example, as part of the Artemis I mission in November 2022, NASA launched 10 CubeSats. The satellites are now trying to detect and map water on the Moon. These findings are crucial, not only for the upcoming Artemis missions but to the quest to sustain a permanent human presence on the lunar surface. The CubeSats cost $13 million.

The MarCO CubeSats – two of them – accompanied NASA’s Insight lander to Mars in 2018. They served as a real-time communications relay back to Earth during Insight’s entry, descent and landing on the Martian surface. As a bonus, they captured pictures of the planet with wide-angle cameras. They cost about $20 million.

CubeSats have also studied nearby stars and exoplanets, which are worlds outside the solar system. In 2017, NASA’s Jet Propulsion Laboratory deployed ASTERIA, a CubeSat that observed 55 Cancri e, also known as Janssen, an exoplanet eight times larger than Earth, orbiting a star 41 light years away from us. In reconfirming the existence of that faraway world, ASTERIA became the smallest space instrument ever to detect an exoplanet.

Two more notable CubeSat space missions are on the way: HERA, scheduled to launch in October 2024, will deploy the European Space Agency’s first deep-space CubeSats to visit the Didymos asteroid system, which orbits between Mars and Jupiter in the asteroid belt.

And the M-Argo satellite, with a launch planned for 2025, will study the shape, mass and surface minerals of a soon-to-be-named asteroid. The size of a suitcase, M-Argo will be the smallest CubeSat to perform its own independent mission in interplanetary space.

The swift progress and substantial investments already made in CubeSat missions could help make humans a multiplanetary species. But that journey will be a long one – and depends on the next generation of scientists to develop this dream.The Conversation

About the Author:

Mustafa Aksoy, Assistant Professor of Electrical & Computer Engineering, University at Albany, State University of New York

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Brown bananas, crowded ports, empty shelves: What to expect with the US dockworkers strike

By Anna Nagurney, UMass Amherst 

Getting any product to consumers, whether it’s a can of sardines or a screwdriver, requires that supply chains function well.

The availability of labor is essential in each link of the supply chain. That includes the workers who make sure that your tinned fish and handy tools smoothly journey from their point of origin to where they’ll wind up, whether it’s a supermarket, hardware store or your front door.

Amazingly, 90% of all internationally traded products are carried by ships at some point. At the height of the COVID-19 pandemic, it was hard not to notice the supply chain disruptions. For U.S. ports, there were many bouts of congestion. Demand for goods that were either more or less popular than they would normally be became volatile. Shortages of truckers and other freight service providers wreaked havoc on land-based and maritime transportation networks.

Consumers became exasperated when they saw all the empty shelves. They endured price spikes for items that were suddenly scarce, such as hand sanitizer, computer equipment and bleach.

I’m a scholar of supply chain management who belongs to a research group that studies ways to make supply chains better able to withstand disruptions. Based on that research, plus what I learned while writing a book about labor and supply chains, I’m concerned about the turmoil that could be in store for cargo arriving on ships.

Concerns over pay and technology

The International Longshoremen’s Association’s six-year contract with the East Coast and Gulf Coast ports expired on Sept. 30, 2024, at midnight without an agreement. About 45,000 dockworkers are now on a strike that has paralyzed ports from Maine to Texas. Only military cargo and cruise ships, as well as oil, gas and liquid chemicals, can go in and out.

It’s the first such work stoppage for the East Coast ports since 1977.

Labor and management disagree over how much to raise wages, and the union also wants to see strict limits on the use of automation for cranes, gates and trucks at the ports in the new contract. The union is seeking a 77% increase in pay over the next six years and is concerned that jobs may be lost because of automation. When management offered a nearly 50% raise, the union rejected it.

Dockworkers on the West Coast, who are not on strike, are paid much higher regular wages than their East Coast and Gulf Coast counterparts who are on strike. The West Coast workers earn at least an estimated US$116,000 per year, for a 40-hour work week, versus the roughly $81,000 dockworkers at the East Coast and Gulf Coast ports take home, not counting overtime pay.

Management is represented in the talks by the U.S. Maritime Association, which includes the major shippers, terminal operators and port authorities.

What to expect

Starting on Oct. 1, 36 ports, including those in Philadelphia and Houston, ceased operations due to the strike, blocking almost half of the cargo going in and out of the U.S. on ships.

If the strike lasts just a day, then it would not be noticeable to a typical consumer. However, businesses of all kinds would no doubt feel the pinch. J.P. Morgan estimates that a strike could cost the U.S. economy $5 billion every day.

Even if the strike were to last only a day, it could take about five days to straighten out the supply chain.

If a strike lasts a week, the results would quickly become apparent to most consumers.

Some shipping companies have already begun to reroute their cargo to the West Coast. Even had there been no strike at all, costs would have risen and the warehouses could have run out of room.

The effects on everything from bananas and cherries to chocolate, meat, fish and cheese could be severe, and the shipping disruption could also hamper trade in some prescription drugs if the strike lasts at least a week.

If the strike were to last a month or more, supplies needed by factories could be in short supply. Numerous consumer products would not be delivered. Workers would be laid off. U.S. exports, including agricultural ones, might get stuck rather than shipped to their destinations. Inflation might increase again. And there would be a new bout of heightened economic anxiety and uncertainty – along with immense financial losses.

All the while, West Coast ports would face unusually high demand for their services, wreaking havoc on shipping there too.

Yes, we’d have no bananas

My research group’s latest work on supply chain disruptions and the effects of various transportation disruptions, including delays, quantifies the impact on the quality of fresh produce. We did a case study on bananas.

This isn’t a niche problem.

Bananas are the most-consumed fresh fruit in the U.S.

Many of the bananas sold in the U.S. are grown in Ecuador, Guatemala and Costa Rica. About 75% of them arrive at ports on the East and Gulf coasts.

Although bananas are relatively easy to ship, they require appropriate temperatures and humidity. Even under the best conditions, their quality deteriorates. Long delays will mean shippers will be trying to foist mushy brown bananas on consumers who might reject them.

Alternatively, banana growers may opt to find other markets. It’s reasonable to expect to find fewer bananas and much higher prices – possibly of a lower quality. Flying bananas to the U.S. would be too expensive to sustain.

Fresh meat, seafood, cheese and other refrigerated foods could spoil before they can complete their journeys, and fresh berries, along with other fruits and vegetables, could perish before reaching their destinations.

Tons of fresh produce, including bananas arriving after Oct. 1, could end up having to be discarded. That is unfortunate, given the rising food insecurity rate in the U.S.

1947 Taft-Hartley Act

More than 170 trade groups had urged the Biden administration to intervene at the last minute to avoid a strike.

Even now, the government can invoke the 1947 Taft-Hartley Act, which allows the president to ask a court to order an 80-day cooling-off period when public health or safety is at risk.

However, President Joe Biden reportedly does not plan to invoke it. Instead, he has urged the two sides to settle their differences.

So if you’re planning to bake banana bread or were thinking you might get an early start on your holiday shopping, I’d advise you to make those shopping trips as soon as possible – just in case.

This is an updated version of an article first published on Sept. 28, 2024.The Conversation

About the Author:

Anna Nagurney, Professor and Eugene M. Isenberg Chair in Integrative Studies, UMass Amherst

This article is republished from The Conversation under a Creative Commons license. Read the original article.

 

Brent: Slips into Q4 on supply fears

By ForexTime 

  • Brent ↓17% in Q3
  • OPEC+ JMMC, EIA & NFP in focus
  • Over past year US NFP triggered ↑ 0.4% & ↓ 1.9%
  • Key level of interest – $70.80

The past few months have been rough and rocky for oil benchmarks.

Crude and Brent shed over 16% in Q3 due to expectations around OPEC+ bringing back production while a slowdown in China rubbed salt into the wound.

Brent monthly

Oil has already entered October on the back foot, falling 1% thanks to the bearish market sentiment.

Many forces are set to influence prices, ranging from China’s stimulus plans, a return of Libya’s oil production, ongoing geopolitical tensions, and bets around lower US interest rates.

This potent cocktail may translate to significant price swings in Q4.

Regarding Libya, the producer is preparing to restore output after a month-long shutdown. This is likely to fuel concerns over supply at a time when OPEC+ may move ahead with planned production increases in December.

The OPEC+ Joint Ministerial Monitoring Committee meeting on Wednesday 2nd October is expected to conclude with no policy changes. However, any hints of further delays to the planned production increase beyond December may support oil.

 

Also, watch out for the EIA data on Wednesday and US jobs report on Friday which could inject oil benchmarks with more volatility.

As covered in our week ahead report, the US jobs report has the potential to impact Fed cut cuts.

Note: Lower interest rates could stimulate economic growth, which fuels oil demand. Lower interest rates may also lead to a weaker dollar, which boosts oil which is priced in dollars.

Golden nugget: Over the past year, the US jobs report has triggered upside moves on Brent of as much as 0.4% or declines of 1.9% in a 6-hour window post-release.

 

Looking at the technicals…

Prices are under pressure on the daily charts with Brent respecting a bearish channel.

There have been consistently lower lows and lower highs while the MACD trades to the downside. However, daily support can be seen around the $70.80 level.

  • A solid breakdown and daily close below $70.80 could send prices back toward $68.80 and the levels not seen since December 2021at $67.00
  • Should $70.80 prove reliable support, this could trigger a rebound toward the 21-day SMA at $72.30 and $75.00.

brenttt98


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