Researchers at the Ningbo Institute of Materials Technology and Engineering (NIMTE) of the Chinese Academy of Sciences, in collaboration with research groups from the University of Electronic Science and Technology of China and Fudan University, have developed a fatigue-free ferroelectric material based on sliding ferroelectricity. The study is published in Science.

Ferroelectric materials have switchable spontaneous polarization that can be reversed by an external electric field, which has been widely applied to non-volatile memory, sensing, and energy conversion devices.

Due to the inherited ionic motion of ferroelectric switching, ferroelectric polarization fatigue inevitably occurs in conventional ferroelectric materials as the number of polarization reversal cycles increases. This can lead to performance degradation and device failure, thus limiting the practical applications of ferroelectric materials.

To solve this fatigue problem, the researchers developed a fatigue-free ferroelectric system based on sliding ferroelectricity. A bilayer 3R-MoS2 dual-gate device was fabricated using the chemical vapor transport method.

After 106 switching cycles with different pulse widths ranging from 1 ms to 100 ms, the ferroelectric polarization dipoles showed no loss, indicating that the device still retained its memory performance.

Compared with commercial ferroelectric devices, this device exhibits a superior total stress time of 105 s in an electric field, demonstrating its excellent endurance.

By means of a novel machine-learning potential model, theoretical calculations revealed that the fatigue-free property of sliding ferroelectricity can be attributed to its immobile charged defects.

This work provides an innovative solution to the problematic performance degradation of conventional ferroelectrics.

Source: phys.org, Author: Chinese Academy of Sciences

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Neuralink, the pioneering neurotechnology company, has received FDA approval to proceed with its second human trial. This marks a significant step forward in their ambitious quest to create brain-computer interfaces that could transform the treatment of neurological disorders.

Our sources reveal that the first patient, who has chosen to remain anonymous, has shared his emotional journey through the trial. After undergoing a procedure to implant Neuralink’s advanced device, he described feeling a mix of excitement and apprehension. The operation was carried out using Neuralink’s state-of-the-art surgical robot, designed for precision and safety.

Post-surgery, the patient experienced a range of emotions from hope to fear but was reassured by the diligent support from the Neuralink team. While it’s still early in the trial phase, initial results appear promising, bringing a glimmer of hope to many suffering from severe neurological conditions.

This development comes amidst ongoing debate. Critics have raised ethical and safety concerns about the long-term implications of such technology, while advocates argue the potential benefits, including restoring mobility and communication for those with paralysis, are too significant to ignore.

With the FDA’s green light for a second patient, Neuralink continues to push the boundaries of what’s possible in neurotechnology. This approval not only reflects the FDA’s confidence in the safety and potential efficacy of Neuralink’s approach but also paves the way for further advancements that could revolutionize medical treatments for neurological disorders.

As Neuralink moves forward, the world watches closely, hopeful that these trials could usher in a new era of medical innovation. We’ll continue to follow this story and bring you the latest updates.

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In the fast-evolving landscape of artificial intelligence, two concepts often spark vigorous debate among tech enthusiasts: Generative AI and Artificial General Intelligence (AGI). While both promise to revolutionize our interaction with machines, they serve fundamentally different functions and embody distinct potential futures. Let’s dive into these differences and explore what each form of AI means for tomorrow.

What Is Generative AI?

Think of Generative AI as a highly-skilled parrot. It’s capable of mimicking complex patterns, producing diverse content, and occasionally surprising us with outputs that seem creatively brilliant. However, like a parrot, Generative AI does not truly “understand” the content it creates. It operates by digesting large datasets and predicting what comes next, whether the next word in a sentence or the next stroke in a digital painting.

For example, when Generative AI writes a poem about love, it doesn’t draw on any deep, emotional reservoirs; instead, it relies on a vast database of words and phrases typically associated with love in human writing. This makes it excellent for tasks like drafting articles on global economics or generating marketing copy, as it can convincingly mimic human-like prose based on the information it’s trained on. However, it lacks the ability to grasp complex human experiences or perform tasks it hasn’t been specifically programmed to handle, such as managing your taxes or strategizing economic policies.

Artificial General Intelligence (AGI): The Next Frontier

AGI, or Artificial General Intelligence, represents a theoretical leap in the field of AI, aiming to create machines that do far more than perform tasks—they would understand, innovate, and adapt. The concept of AGI is to mimic human cognitive abilities comprehensively, enabling machines to learn and execute a vast array of tasks, from driving cars to making medical diagnoses. Unlike anything in current technology, AGI would not only replicate human actions but also grasp the intricacies and contexts of those actions.

However, it’s crucial to understand that AGI does not yet exist and remains a subject of considerable debate and speculation within the scientific community. Some experts believe the creation of AGI could be just around the corner, thanks to rapid advancements in technology, while others argue that true AGI might never be achieved due to insurmountable ethical, technical, and philosophical challenges.

Technical Challenges Facing AGI

The development of AGI faces numerous technical hurdles that are fundamentally different and more complex than those encountered in creating generative AI. One of the primary challenges is developing an understanding of context and generalization. Unlike generative AI, which operates within the confines of specific datasets, AGI would need to intuitively grasp how different pieces of information relate to each other across various domains. This requires not just processing power but a sophisticated model of artificial cognition that can mimic the human ability to connect disparate ideas and experiences.

Another significant challenge is sensory perception and interaction with the physical world. For AGI to truly function like a human, it would need to perceive its environment in a holistic manner—interpreting visual, auditory, and other sensory data to make informed decisions based on real-time inputs. This involves not only recognizing objects and sounds but understanding their significance in a broader context, a task that current AI systems struggle with.

Additionally, AGI must be able to learn from limited information and apply this learning adaptively across different situations. This concept, known as transfer learning, is something humans do naturally but is incredibly difficult to replicate in machines. Current AI models require vast amounts of data to learn effectively and are generally poor at applying what they’ve learned in one context to another without extensive retraining.

Key Distinctions Between Generative AI and AGI

To fully appreciate the transformative potential of AI, it’s essential to understand the fundamental distinctions between Generative AI and AGI. Here are the key differences:

1. Capability: Generative AI excels at replication and is adept at producing content based on learned patterns and datasets. It can generate impressive results within its specific scope but doesn’t venture beyond its programming. AGI, on the other hand, aims to be a powerhouse of innovation, capable of understanding and creatively solving problems across various fields, much like a human would.
2. Understanding: Generative AI operates without any real comprehension of its output; it uses statistical models and algorithms to predict and generate results based on previous data. AGI, by contrast, would need to develop a genuine understanding of the world around it, making connections and having insights that are currently beyond the reach of any AI system.
3. Application: Today, Generative AI is widely used across industries to enhance human productivity and foster creativity, performing tasks ranging from simple data processing to complex content creation. AGI, however, remains a conceptual goal. If realized, it could fundamentally transform society by autonomously performing any intellectual task that a human can, potentially redefining roles in every sector.

Ethical And Societal Implications

The distinction between these technologies isn’t just technical; it’s fundamentally ethical. Generative AI, while transformative, raises questions about authenticity and intellectual property. AGI, however, prompts deeper inquiries into the nature of consciousness, the rights of sentient machines, and the potential for unprecedented impacts on employment and societal structures.

Both forms of AI demand careful regulation and foresight. The ongoing development and potential realization of AGI must be approached with a balanced perspective, considering both the immense benefits and the significant risks.

The journey from Generative AI to AGI is not merely one of increasing complexity but a paradigm shift in how we interact with machines. As we advance, understanding these distinctions will be crucial for harnessing their potential responsibly. With Generative AI enhancing our capabilities and AGI potentially redefining them, our approach to technology’s future must be as adaptive and innovative as the intelligence we aspire to create.

Source: Forbes.com, Author: Bernard Marr

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Most of our cells have a genetic code that, when deciphered, tells our body how to make the proteins we need for survival. With the passing of time and experience of hardship, small modifications are added to act like ‘genetic switches’, affecting the way our cells interpret the instructions without changing the code itself.

Accumulations of these so-called epigenetic changes are often used to estimate the biological age of our cells and tissues. But researchers in Lithuania have now shown that the edits can fluctuate throughout the day, suggesting tests based on a single tissue sample aren’t as accurate as they could be.

The team studied multiple blood samples from a 52-year-old man taken every three hours over 72 hours, looking at 17 different epigenetic clocks within each specimen’s collection of white cells.

What they found was surprising. Thirteen of the 17 epigenetic clocks showed a substantial difference throughout the day, appearing ‘younger’ in the early hours of the morning and ‘older’ around midday, with relative differences equivalent to around 5.5 years’ worth of changes. This daily cycle is similar to what other scientists found in a 2020 study.

“The majority of the aging studies investigating epigenetic clocks use whole blood as the tissue of interest. However, experiments in our lab and from other groups have shown that white blood cell subtype counts and their proportions oscillate with a 24 hour periodicity,” statistician Karolis Koncevičius from Vilnius University and colleagues write in their published paper.

This means a single epigenetic test at one time of day might not give the whole picture.

Relying on just a single individual’s samples meant the team could focus on a single set of changes, at the cost of being able to generalize across a larger population. Further analysis of different blood samples taken over five hours from a small group also found age fluctuations, however.

Some of these cellular age changes might be because our blood contains different types of white blood cells at different times of the day. However, some measures still showed this age fluctuation even when the researchers focused on just one type of white blood cell.

The findings suggest that to get the most accurate picture of how old your cells are, scientists might need to take multiple samples at varied times of day in the future. A more complete measure of epigenetic age range might allow more precise predictions about risk of age-related diseases  in populations too.

“Our findings indicate that age predictions of epigenetic clocks oscillate throughout the day,” the authors write “Failure to account for daily oscillations may hamper estimates of epigenetic age.”

The research has been published in Aging Cell.

Source: Sciencealert.com, Author: Rebecca Dyer

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Chemistry, with its intricate processes and vast potential for innovation, has always been a challenge for automation. Traditional computational tools, despite their advanced capabilities, often remain underutilized due to their complexity and the specialized knowledge required to operate them.

AI Revolution in Chemistry

Now, researchers with the group of Philippe Schwaller at EPFL, have developed ChemCrow, an AI that integrates 18 expertly designed tools, enabling it to navigate and perform tasks within chemical research with unprecedented efficiency. “You might wonder why a crow?” asks Schwaller. “Because crows are known to use tools well.”

ChemCrow was developed by PhD students Andres Bran and Oliver Schilter (EPFL, NCCR Catalysis) in collaboration with Sam Cox and Professor Andrew White at (FutureHouse and University of Rochester).

ChemCrow is based on a large language model (LLMs), such as GPT-4, enhanced by LangChain for tool integration, to autonomously perform chemical synthesis tasks. The scientists augmented the language model with a suite of specialized software tools already used in chemistry, including WebSearch for internet-based information retrieval, LitSearch for scientific literature extraction, and various molecular and reaction tools for chemical analysis.

ChemCrow’s Capabilities

By integrating ChemCrow with these tools, the researchers enabled it to autonomously plan and execute chemical syntheses, such as creating an insect repellent and various organocatalysts, and even assist in discovering new chromophores, substances fundamental to dye and pigment industries.

What sets ChemCrow apart is its ability to adapt and apply a structured reasoning process to chemical tasks. “The system is analogous to a human expert with access to a calculator and databases that not only improve the expert’s efficiency, but also make them more factual – in the case of ChemCrow, reducing hallucinations,” explains Andres Camilo Marulanda Bran, the study’s first author.

Practical Applications

ChemCrow receives a prompt from the user, plans ahead how to solve the task, selects the relevant tools, and iteratively refines its strategy based on the outcome(s) of each step. This methodical approach ensures that ChemCrow doesn’t only work off theory but is also grounded in practical application for real-world interaction with laboratory environments.

By democratizing access to complex chemical knowledge and processes, ChemCrow lowers the barrier to entry for non-experts while augmenting the toolkit available to veteran chemists. This can accelerate research and development in pharmaceuticals, materials science, and beyond, making the process more efficient and safer.

Source: SciTechDaily, Author: EPFL

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A quick glance at the news headlines each morning might convey that the world is in crisis. Challenges include climate-change threats to human infrastructure and habitats; cyberwarfare by state and nonstate actors attacking energy sources and health care systems; and the global water crisis, which is compounded by the climate crisis. Perhaps the biggest challenge is the rapid advance of artificial intelligence and what it means for humanity.

As people grapple with those and other issues, they typically look to policymakers and business leaders for answers. However, no true solutions can be developed and implemented without the technical expertise of engineers.

Encouraging visionary, futuristic thinking is the function of the  Engineering Research Visioning Alliance. ERVA is an initiative of the U.S. National Science Foundation’s Directorate for Engineering, for which I serve as principal investigator. IEEE is one of several professional engineering societies that are affiliate partners.

Engineers are indispensable architects

Engineers are not simply crucial problem-solvers; they have long proven to be proactive architects of the future. For example, Nobel-winning physicists discovered the science behind the sensors that make modern photography possible. Engineers ran with the discovery, developing technology that NASA could use to send back clear pictures from space, giving us glimpses of universes far beyond our line of sight. The same tech enables you to snap photos with your cellphone.

As an engineer myself, I am proud of our history of not just making change but also envisioningit.

In the late 19th century, electrical engineer Nikola Tesla had envisioned wireless communication, lighting, and power distribution.

As early as 1900, civil engineer John Elfreth Watkins predicted that by 2000 we would have such now-commonplace innovations as color photography, wireless telephones, and home televisions (and even TV dinners), among other things.

“If we are going to successfully tackle today’s most vexing global challenges, engineers cannot be relegated to playing a reactive role.”

Watkins embodied an engineer’s curiosity and prescience, but too often today, we spend the lion’s share of our time with technical tinkering and not enough on the bigger picture.

If we are going to successfully tackle today’s most vexing global challenges, engineers cannot be relegated to playing a reactive role. We need to completely reimagine how nearly everything works. And because complex problems are multifaceted, we must do so in a multidisciplinary fashion.

We need big ideas, future-focused thinking with the foresight to transform how we live, work, and play—a visionary mindset embraced and advanced by engineers who leverage R&D to solve problems and activate discoveries. We need a different attitude from that of the consummate practitioners we typically imagine ourselves to be. We need the mindset of the futurist.

Futuristic thinking transforms society

A futurist studies current events and trends to determine not just predictions but also possibilities for the future. The term futurist has a long connection with science fiction, going back to the early 20th century, personified in such figures as writer H.G. wells.

While many literary figures’ predictions have proven fanciful (though some, like Elfreth’s, have come true), engineers and scientists have engaged in foresight for generations, introducing new ways to look at our world, and transforming society along the way.

Futuristic thinking pushes the boundaries of what we can currently imagine and conceive. In an era of systemic crises, there is a seemingly paradoxical but accurate truth: It has become impractical to think too pragmatically.

It is especially counterintuitive to engineers, as we are biased toward observable, systematic thinking. But it is a limitation we have overcome through visionary exploits of the past—and one we must overcome now, when the world needs us.

Overcoming systematic thinking

Four times each year, ERVA convenes engineers, scientists, technologists, ethicists, social scientists, and federal science program leads to engage in innovative visioning workshops. We push hard and ask the experts to expand their thinking beyond short-term problems and think big about future possibilities. Some examples of challenges we have addressed—and the subsequent comprehensive reports on recommended research direction for visionary, futuristic thinking—are:

• The Role of Engineering to Address Climate Change. Our first visioning event considered how engineers can help mitigate the effects of rising global temperatures and better reduce carbon emissions. We envisioned how we could use artificial intelligence and other new technologies, including some revolutionary sensors, to proactively assess weather and water security events, decarbonize without disruptions to our energy supply, and slow the pace of warming.
• Engineering R&D Solutions for Unhackable Infrastructure.  We considered a future in which humans and computing systems were connected using trustworthy systems, with engineering solutions to self-identity threats and secure systems before they become compromised. Solutions for unhackable infrastructure should be inherent rather than bolted-on, integrated across connected channels, and activated from the system level to wearables. Actions must be taken now to ensure trustworthiness at every level so that the human element is at the forefront of future information infrastructure.
• Engineering Materials for a sustainable Future. In our most recent report, we discussed a future in which the most ubiquitous, noncircular materials in our world—concrete, chemicals, and single-use packaging—are created using sustainable materials. We embraced the use of organic and reusable materials, examining what it is likely to take to shift production, storage, and transportation in the process. Again, engineers are required to move beyond current solutions and to push the boundaries of what is possible.

ERVA is tackling new topics in upcoming visioning sessions on areas as diverse as the future of wireless competitiveness, quantum engineering, and improving women’s health

We have an open call for new visioning event ideas. We challenge the engineering community to propose themes for ERVA to explore so we can create a road map of future research priorities to solve societal challenges. Engineers are needed to share their expertise, so visit our website to follow this critical work. It is time we recaptured that futurist spirit.

Related video

Source: Spectrum.ieee.org

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This has happened many times in our planet’s history, including 41,000 years ago in an event called the Laschamps excursion.

Cosmic rays are high-energy particles, usually protons or atomic nuclei, that travel through space at relativistic speeds. Normally, they’re deflected into space and away from Earth by the planet’s magnetic shield. But the shield is a natural phenomenon and its strength fluctuates, as does its orientation. When that happens, cosmic rays strike the Earth’s atmosphere.

That creates a shower of secondary particles called cosmogenic radionuclides. These isotopes become embedded in sediments and ice cores and even in the structure of living things like trees. There are different types of these isotopes, including ones like Calcium 41 and Carbon 14.

Some of the isotopes are stable, and some are radioactive. The radioactive ones have half-lives ranging from only 20 minutes (Carbon 11) up to 15.7 million years (Xenon 129.)

When Earth’s shield weakens, more of these isotopes reach the planet’s surface and collect in sediments and ice. By studying these cores and sediments, scientists can determine the magnetic shield’s history. Their observations show that Earth experienced a geomagnetic excursion or reversal 41,000 years ago. It’s called the  Laschamps excursion after the Laschamps lava flows in France, where geomagnetic anomalies revealed its occurrence.

Every few hundred thousand years, the Earth’s magnetic poles flip. North becomes South and vice versa. In between those major events are more minor events called excursions. During excursions, the poles shift around for a while without swapping places. The excursions weaken the Earth’s shield and can last from a few thousand to tens of thousands of years. When that happens, more cosmic rays strike the atmosphere, creating more radionuclides that shower down onto Earth.

Scientists often focus on one particular radioactive isotope in paleomagnetic studies. Beryllium 10  has a relatively long half-life of 1.36 million years and tends to accumulate on the soil surface.

Sanja Panovska is a researcher at GFZ Potsdam, Germany, who studies geomagnetism. At the recent European Geosciences Union (EGU) General Assembly 2024, Panovska presented new research on the Laschamps excursion. She found that during the Laschamps excursion, production of Be 10 was twice as high as normal.

To understand the Laschamps excursion more thoroughly, Panovska combined cosmogenic radionuclide and paleomagnetic data to reconstruct the Earth’s magnetic field at the time. She found that when the field decreased in strength, it also shrank. The transition from normal field to reversed field took about 250 years, and it stayed flipped for about 440 years. During the transition, the Earth’s shield weekend to as little as 5% of its normal strength. When it was fully reversed, it was at about 25% of its regular strength. This weakening allowed more Be 10 and other cosmogenic radionuclides to reach Earth’s surface.

These radionuclides do more than collect in sediments and ice. Some of them are radioactive. The weakening of the shield also weakened the ozone layer, letting more UV radiation reach Earth’s surface. The high-altitude atmosphere also cooled, which changed the wind flows. This could’ve caused drastic changes on the Earth’s surface.

For these reasons, the Laschamps event has been linked to the extinction of the Neanderthals, the extinction of Australian megafauna, and even to the appearance of cave art. Those links haven’t withstood scientific scrutiny, but that doesn’t mean that events like the Laschamps event aren’t hazardous. If it occurred now, it would knock out our power grids. The Earth’s equatorial region would light up with aurorae.

“Understanding these extreme events is important for their occurrence in the future, space climate predictions, and assessing the effects on the environment and on the Earth system,” Panovska said.

Scientists are learning that the magnetic shield isn’t static. There are anomalies. One of them is the South Atlantic Anomaly, a region where the magnetic field is weakest near Earth. When satellites pass over this region, they’re exposed to higher levels of ionizing radiation. The anomaly is likely caused by a reservoir of dense rock inside Earth, illustrating how complex the magnetic shield is.

Scientists are uncertain about what effect the cosmic rays have on life when the magnetic shield is weak. It’s tempting to correlate extinctions with events like the Laschamps excursion when they line up temporally. But the poles have shifted, weakened, and reversed many times and life is still here and still thriving.

If humanity lasts long enough, we’ll go through one of these reversals. Then we’ll know.

Related video:

Source: Universetoday.com, Author: Evan Gough

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Were there really dinosaurs?

Scientists have identified nearly a thousand different genera (types) of dinosaurs, although some are represented by so few bones that some people consider their identification as questionable. There is no doubt that dinosaurs once lived on Earth and in certain places seem to have been numerous. Paleontologists have found evidence of their existence in sediments from all continents, including Antarctica and have found evidence that includes bones, eggs, nests, and footprints. Dinosaur bones, footprints and other trace fossils have been identified by the thousands, and are especially prevalent in the US, Argentina, Spain, France, Russia, China, Mongolia and North Africa.

Skeletons on display in museums are rarely the original findings, but are replicas. The original bones are too valuable and delicate to be exposed to the public and are usually stored in special chambers that can only be accessed by professional scientists. Furthermore, the skeletons of museums are usually an assembled combination of replica bones from various specimens, which sometimes come from distant places. Despite this, these replicas can be considered reasonably reliable and in fact, some are complete specimens, such as the Tyrannosaurus rex “Sue” (photo 1) in the Chicago Field Museum. The animations on television, however, are much more speculative, especially as regards skin color, physiology, and behavior.

Were all dinosaurs large?

The study of dinosaur bones, eggs and footprints provides valuable information about their size, physiology, social behavior, and habitat. While dinosaurs are known to be the largest land animals to have inhabited the planet, the average Diplodocus being 115 ft long (35 m) from head to tail (photo 2), not all dinosaurs were giants. Some species were small, similar in size to a sheep or dog. For example, Struthiomimus (photo 3) was the size of an ostrich, and Compsognathus (photo 4) was no bigger than a rooster. Often the species exhibited in museums are the largest and are usually represented in threatening postures intended to impress visitors. This has distorted the image of dinosaurs in the minds of many people.

Where did dinosaurs live?

Dinosaurs appear to have been well adapted to their habitats, which spanned the globe, from rivers to forests to deserts. How do we know? The dinosaurs were buried in sediments (sand, mud), which later became sedimentary rocks. Some characteristics of these sedimentary rocks are used to identify specific environments such as beaches, rivers, swamps, lakes and arid deserts. Other fossils associated with these deposits (fossil plants, shells, microfossils, etc…) also provide valuable information on the environmental conditions. Thus, by studying the sediments in which the dinosaurs were buried, paleontologists can learn about the environments (paleoenvironments) in which they lived.

What did dinosaurs eat?

We know what dinosaurs ate by studying their teeth. Most dinosaurs were herbivores while some were carnivorous and fed on small animals, fish and other dinosaurs. Size did not always determine what an animal ate. For instance, the largest known dinosaur is Argentinosaurus (photo 5), a sauropod that probably weighed 100 tons.

Argentinosaurus was herbivorous, like other large sauropods. On the other hand, Spinosaurus (photo 6) and Tyrannosaurus rex, had an approximate weight of 7-8 tons each and were the largest carnivores to roam the planet.

Two other sources of information about what dinosaurs ate are fossil stomach contents and coprolites (fossilized excrement). The bones and teeth of fossilized animals have been found in the stomach of some dinosaurs, which shows that these animals were part of their diet. Inside coprolites from what are thought to be Titanosaur sauropods scientists have found conifer leaves, palm leaves and, surprisingly, grass. The presence of grass is puzzling because it is assumed that these plants did not evolve until after the extinction of the dinosaurs.

What was the origin of the dinosaurs?

According to the theory of evolution, dinosaurs originated from ancestral animals, called archosaurs (photo 7), through a gradual process of accumulation of mutations and natural selection. Their remains in the geologic column appear in the layers of rock that paleontologists call the Mesozoic (Triassic, Jurassic and Cretaceous), which according to conventional geologic time scale, corresponds to 250 to 65 million years ago.

If Darwinism were true, we should find organisms of less complexity and diversity in the early Triassic rocks, and forms of higher complexity and diversity in the Cretaceous layers. This would fit the evolutionary model of increasing complexity and diversity over time.

However, the reality found in the rock record is completely different. Although dinosaurs have been studied for almost two hundred years, their origins remain uncertain. Dinosaur fossils appear suddenly, without connection to any known ancestor and disappear just as abruptly in the fossil record. If the process of macroevolution were true, we would expect to find a gradual arrival, but in fact the opposite is observed. Dinosaurs seem to appear fully formed, well adapted to their environment and highly diversified. Thus, the theory of evolution does not seem to adequately explain the origin of these animals.

Creationists do not accept the evolutionary interpretation. Instead, most creation scientists suggest that dinosaurs were part of God’s original creation, which later, as in the case of other animals, suffered genetic variations leading to carnivorous ways.

Why did dinosaurs go extinct?

Though there is still debate among scientists, the conventional belief is that dinosaurs died out as the result of a meteor impact and the resulting environmental disasters that it caused. Great volcanic eruptions and earthquakes are thought to have accompanied the impact, which would have been quite catastrophic. The volcanic eruptions would have blown huge amounts of gases and dust into the air. This, along with dust from the impact itself, would have blocked enough sunlight to cause a shift in global temperatures, which would have been difficult for cold-blooded animals to survive, leading to their ultimate decline.

Most Christians, however, believe that dinosaurs were destroyed during the flood. Others believe that this particular group of animals had been altered so drastically by sin that they were not allowed on the ark and so their kind was totally lost.

Related videos:

Where there dinosaurs on earth???

Source: Grisda.org, Author: Raul Esperante, PhD

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Researchers at the University of New South Wales (UNSW) in Australia have successfully engineered protein filaments produced by bacteria so that they can conduct electricity and even harness it using moisture from the air. This interdisciplinary research, comprising protein engineering and nanoelectronics, could one day help scientists develop ‘green electronics,’ a university press release said. 

Modern-day electronics, which are ubiquitous, are made using energy-intensive processes and extremely toxic components. These are required to facilitate the movement of electrons within the device and get work done. 

On the other hand, multiple events in nature also require electron movement. For instance, in the process of photosynthesis that plants use to make their food, chlorophyll moves electrons across various protein molecules. Bacterial systems also transfer electrons across membranes using conductive filaments called nanowires. 

Engineering bacteria for nanowires

Bacterial nanowires can conduct electricity and can potentially be used to devise sensing systems. However, after being harvested from bacteria, these nanowires are hard to modify and, therefore, have limited functionality. 

“To overcome these limitations, we genetically engineered a fiber using the bacteria E. coli,” says Lorenzo Travaglini, a postdoctoral researcher at UNSW who was involved in the work. 

We modified the DNA of E.coli  so that the bacteria not only produced the proteins that it needed to survive but also built the specific protein we had designed, which we then engineered and assembled into nanowires in the lab,” Travaglini explained in the press release. 

Interestingly, this additional molecule that makes the nanowires highly conductive is haem, an iron-based circular structure commonly found in animal blood and used to transport oxygen to different body parts. 

Making electricity from the air

The UNSW team furthered the research on bacterial nanowires, which showed that when haem molecules are arranged closely together, they can also perform electron transfer. Travaglini and his team integrated haem into their engineered filaments, hoping that the electrons would jump between the haem molecules if placed sufficiently close to each other. 

By measuring the conductance of the filaments in the presence and absence of haem molecules, the researchers confirmed that the iron-based molecule was making the protein conductive. 

During their extensive tests, the researchers found that the electric current was stronger when the ambient conditions were between 20 and 30 percent humidity. 

When the tests were repeated with increasing amounts of conductive material sandwiched between the electrodes, the researchers confirmed that humidity created a charge gradient across the material and generated additional current without applying additional potential. 

The researchers then devised a humidity sensor that generated electric current even when one exhaled on it. 

The team is now exploring how the properties of their proteins can be tuned by changing the haem’s structure or the filament’s environment. In one experiment, the researchers are using light-sensitive molecules to facilitate electron transfer. 

Travagliini highlighted that the research was still in its early stages and would take some time to become part of everyday electronics. 

“It’s really matter of translation,” he added in the press release. “We don’t know how long exactly it’s going to take, but we can see that we are going in the right direction.”

The research findings were published in the journal small.

Source: Interestingengineering.com, Author: Ameya Paleja

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The findings show that the brain has finely tuned head direction signals, similar to those found in rodents, crucial for orientation and navigation. This research is particularly relevant for understanding navigation impairments in diseases like Parkinson’s and Alzheimer’s and could influence future navigational technologies in robotics and AI.

Key Facts:

1. Innovative Measurement Techniques: The study employed mobile EEG and motion capture technologies to measure brain activity during movement, a novel approach in human subjects.
2. Directional Brain Signals Identified: Researchers pinpointed specific neural signals that help humans maintain orientation, akin to an ‘internal GPS’ system.
3. Implications for Disease and Technology: The study’s insights have potential applications in understanding neurodegenerative diseases and enhancing navigational aids in both AI and robotics.

Researchers at the University of Birmingham and Ludwig Maximilian University of Munich have for the first time been able to pinpoint the location of an internal neural compass which the human brain uses to orientate itself in space and navigate through the environment.  

The research identifies finely tuned head direction signals within the brain. The results are comparable to neural codes identified in rodents and have implications for understanding diseases such as Parkinson’s and Alzheimers, where navigation and orientation are often impaired.  

Measuring neural activity in humans while they are moving is challenging as most technologies available require participants to remain as still as possible. In this study, the researchers overcame this challenge by using mobile EEG devices and motion capture. 

First author Dr Benjamin J. Griffiths said: “Keeping track of the direction you are heading in is pretty important. Even small errors in estimating where you are and which direction you are heading in can be disastrous.

“We know that animals such as birds, rats and bats have neural circuitry that keeps them on track, but we know surprisingly little about how the human brain manages this out and about in the real world.” 

A group of 52 healthy participants took part in a series of motion-tracking experiments while their brain activity was recorded via scalp EEG. These enabled the researchers to monitor brain signals from the participants as they moved their heads to orientate themselves to cues on different computer monitors.  

In a separate study, the researchers monitored signals from 10 participants who were already undergoing intercranial electrode monitoring for conditions such as epilepsy. 

All the tasks prompted participants to move their heads, or sometimes just their eyes, and brain signals from these movements were recorded from EEG caps, which measure signals from the scalp, and the intracranial EEG (iEEG), which records data from the hippocampus and neighbouring regions. 

After accounting for ‘confounds’ in the EEG recordings from factors such as muscle movement or position of the participant within the environment, the researchers were able to show a finely tuned directional signal, which could be detected just before physical changes in head direction among participants.  

Dr Griffiths added: “Isolating these signals enables us to really focus on how the brain processes navigational information and how these signals work alongside other cues such as visual landmarks.

” Our approach has opened up new avenues for exploring these features, with implications for research into neurodegenerative diseases and even for improving navigational technologies in robotics and AI.” 

In future work, the researchers plan to apply their learning to investigate how the brain navigates through time, to find out if similar neuronal activity is responsible for memory. 

Source: University of Birmingham , Author: Beck Lockwood

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