Lifeboat Foundation

Zhongli Pan is the recipient of the 2023 Distinguished Service Award by the Rice Technical Working Group, which will be presented at the 2023 RTWF Conference on February 20–23. The award recognizes individuals who have given distinguished long-term service to the rice industry in areas of research, education, international agriculture, administration and industry rice technology.

Post-harvest losses are common in the global food and agricultural industry. Research shows that storage grain pests can cause serious post-harvest losses, almost 9% in developed countries to 20% or more in developing countries. To address this problem, Zhongli Pan, an adjunct professor in the Department of Biological and Agricultural Engineering, has developed a potential solution.

Pan’s recent project using an IoT (Internet of Things) based smart wireless technology to remotely detect early insect activity in storage, processing, handling and transportation may solve the insect infestation related challenges for the agricultural industry. The technology uses a novel device called SmartProbe – designed by Pan and his team using wireless sensors and cameras – and leverages cloud computing to monitor and predict insect occurrences. This could help control insect pest, reduce food loss and the fumigants used in agricultural products today. Ragab Gebreil, a project scientist in Pan’s lab, is the co-inventor of this technology.

Reconfigurable antennas—those that can tune properties like frequency or radiation beams in real time, from afar—are integral to future communication network systems, like 6G. But many current reconfigurable antenna designs can fall short: they dysfunction in high or low temperatures, have power limitations or require regular servicing.

To address these limitations, electrical engineers in the Penn State College of Engineering combined electromagnets with a compliant mechanism, which is the same mechanical engineering concept behind binder clips or a bow and arrow. They published their proof-of-concept reconfigurable compliant mechanism-enabled patch antenna today (Feb. 13) in Nature Communications.

“Compliant mechanisms are engineering designs that incorporate elements of the materials themselves to create motion when force is applied, instead of traditional rigid body mechanisms that require hinges for motion,” said corresponding author Galestan Mackertich-Sengerdy, who is both a doctoral student and a full-time researcher in the college’s School of Electrical Engineering and Computer Science (EECS). “Compliant mechanism-enabled objects are engineered to bend repeatedly in a certain direction and to withstand harsh environments.”

Will the AI chatbots eat Google’s lunch?

Opera announced a new feature that will be added to its browser’s sidebar. Called ‘shorten’, the tool is a ChatGPT-powered tool that can be used to generate summaries of webpages and articles. The blog also displays a short demo video that gives us a glimpse of how ChatGPT will be integrated in the browser.

Song Lin, Co-CEO of Opera, said in the blog post, “In more than 25 years of our company’s history, we have always been at the forefront of browser innovation. Whether inventing browser tabs or providing our users with built-in access to generative AI tools, we always push the limits of what’s possible on the web. Following the mass interest in generative AI tools, we believe it’s now time for browsers to step up and become the gateway to an AI-powered web”.

The results highlight some potential strengths and weaknesses of ChatGPT.

Some of the world’s biggest academic journal publishers have banned or curbed their authors from using the advanced chatbot, ChatGPT. Because the bot uses information from the internet to produce highly readable answers to questions, the publishers are worried that inaccurate or plagiarised work could enter the pages of academic literature.

Several researchers have already listed the chatbot as a co-author in academic studies, and some publishers have moved to ban this practice. But the editor-in-chief of Science, one of the top scientific journals in the world, has gone a step further and forbidden any use of text from the program in submitted papers.

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To launch another pair of Starlink stacks to orbit this week, SpaceX will break its all-time launch pad turnaround record. The first launch, Starlink Group 5–4, is currently scheduled to lift off at 12:10 AM EST (05:10 UTC) on Sunday, Feb. 12, pending a forecasted 20% chance of acceptable weather.

If the launch target holds, the mission will break the record for both Space Launch Complex 40 (SLC-40) at the Cape Canaveral Space Force Station in Florida and the overall record across all three Falcon 9 launch pads. The second mission, Starlink Group 2–5, is slated to launch at 8:32 AM PST (16:32 UTC) on Wednesday, Feb. 15 from Space Launch Complex 4 East (SLC-4E) at Vandenberg Space Force Base in California.

From A to B, check out this week’s awesome tech stories from around the web.

Top 10 upcoming future technologies | trending technologies | 10 upcoming tech.

Future technologies are currently developing at an acclerated rate. Future technology ideas are being converted into real life at a very fast pace.

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SUMMARY Researchers at the George Washington University, together with researchers at the University of California, Los Angeles, and the deep-tech venture startup Optelligence LLC, have developed an optical convolutional neural network accelerator capable of processing large amounts of information, on the order of petabytes, per second. This innovation, which harnesses the massive parallelism of light, heralds a new era of optical signal processing for machine learning with numerous applications, including in self-driving cars, 5G networks, data-centers, biomedical diagnostics, data-security and more.

THE SITUATION Global demand for machine learning hardware is dramatically outpacing current computing power supplies. State-of-the-art electronic hardware, such as graphics processing units and tensor processing unit accelerators, help mitigate this, but are intrinsically challenged by serial data processing that requires iterative data processing and encounters delays from wiring and circuit constraints. Optical alternatives to electronic hardware could help speed up machine learning processes by simplifying the way information is processed in a non-iterative way. However, photonic-based machine learning is typically limited by the number of components that can be placed on photonic integrated circuits, limiting the interconnectivity, while free-space spatial-light-modulators are restricted to slow programming speeds.

THE SOLUTION To achieve a breakthrough in this optical machine learning system, the researchers replaced spatial light modulators with digital mirror-based technology, thus developing a system over 100 times faster. The non-iterative timing of this processor, in combination with rapid programmability and massive parallelization, enables this optical machine learning system to outperform even the top-of-the-line graphics processing units by over one order of magnitude, with room for further optimization beyond the initial prototype.