The new era of cooperative education in india!
“Tribhuvan” Sahkari University, through its innovative HUB-and-SPOKE model, will ensure nationwide reach and accessibility by integrating sectoral schools and leveraging strategic partnerships to build a robust Pan-India ecosystem of technical and skill development institutions.
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To detect the quantum tornado in momentum space, the Würzburg team enhanced a well-known technique called ARPES (angle-resolved photoemission spectroscopy). “ARPES is a fundamental tool in experimental solid-state physics. It involves shining light on a material sample, extracting electrons, and measuring their energy and exit angle. This gives us a direct look at a material’s electronic structure in momentum space,” explains Ünzelmann. “By cleverly adapting this method, we were able to measure orbital angular momentum. I’ve been working with this approach since my dissertation.”
ARPES is rooted in the photoelectric effect, first described by Albert Einstein and taught in high school physics. Ünzelmann had already refined the method in 2021, gaining international recognition for detecting orbital monopoles in tantalum arsenide. Now, by integrating a form of quantum tomography, the team has taken the technique a step further to detect the quantum tornado — another major milestone. “We analyzed the sample layer by layer, similar to how medical tomography works. By stitching together individual images, we were able to reconstruct the three-dimensional structure of the orbital angular momentum and confirm that electrons form vortices in momentum space,” Ünzelmann explains.
This compares some of the ringworlds, centrifuges, space stations, and ships that use spin to make gravity. It also try’s to show how the variables of artificial gravity are used to make centripetal acceleration into spin gravity.
Calculator used: https://www.artificial-gravity.com/sw/SpinCalc/
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REFERENCES
1. Hill, Paul R.; Schnitzer, Emanuel (1962 September). “Rotating Manned Space Stations.” In, Astronautics (vol. 7, no. 9, p. 14
18). Reston, Virginia, USA: American Rocket Society / American Institute of Aeronautics and Astronautics.
2. Gilruth, Robert R. (1969). “Manned Space Stations – Gateway to our Future in Space.” In S. F. Singer (Ed.), Manned.
Laboratories in Space (p. 1–10). Berlin, Germany: Springer-Verlag.
3. Gordon, Theodore J.; Gervais, Robert L. (1969). “Critical Engineering Problems of Space Stations.” In S. F. Singer (Ed.).
Manned Laboratories in Space (p. 11–32). Berlin, Germany: Springer-Verlag.
4. Stone, Ralph W. (1973). “An Overview of Artificial Gravity.” In A. Graybiel (Ed.), Fifth Symposium on the Role of the.
Vestibular Organs in Space Exploration (NASA SP-314, p. 23–33). Pensacola, Florida, USA, 19–21 August 1970.
Washington, DC, USA: NASA
5. Cramer, D. Bryant (1985). “Physiological Considerations of Artificial Gravity.” In A. C. Cron (Ed.), Applications of Tethers in.
Space (NASA CP-2364, vol. 1, p. 3·95–3·107). Williamsburg, Virginia, USA, 15–17 June 1983. Washington, DC, USA:
NASA.
6. Graybiel, Ashton (1977). “Some Physiological Effects of Alternation Between Zero Gravity and One Gravity.” In J. Grey (Ed.).
Space Manufacturing Facilities (Space Colonies): Proceedings of the Princeton / AIAA / NASA Conference, May 7–9, 1975
7. Hall, Theodore W. “Artificial Gravity in Theory and Practice.” International Conference on Environmental Systems, 2016, www.artificial-gravity.com/ICES-2016–194.pdf.
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SOCIAL
Twitter: https://twitter.com/OverviewEfect.
Instagram: https://www.instagram.com/overviewefects/
Facebook: https://www.facebook.com/profile.php?id=61552024642764
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ATTRIBUTION
Mass Effect music from @MrHulthen Check it out and his channel here: https://www.youtube.com/watch?v=57-xIuu4Vv.
“Citadel (Mass Effect)” (https://skfb.ly/6CLEX) by Yanez Designs is licensed under Creative Commons Attribution.
“Babylon 5 Station (Babylon 5)” (https://skfb.ly/6pFJp) by uperesito is licensed under Creative Commons Attribution.
“Halo Ring” (https://skfb.ly/orU8C) by Inditrion Dradnon is licensed under Creative Commons Attribution.
“Empire State Building” (https://skfb.ly/BGwU) by Microsoft is licensed under Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/).
“MCRN Tachi [Expanse TV Show]” (https://skfb.ly/o6JGy) by Jakub. Vildomec is licensed under Creative Commons Attribution.
“endurance spaceship” (https://skfb.ly/6TnFK) by devanshujha is licensed under Creative Commons Attribution.
“Discovery 1″ (https://skfb.ly/6oRCD) by uperesito is licensed under Creative Commons Attribution.
“Soviet Nuclear Computer Terminal” (https://skfb.ly/prtFw) by PIPO is licensed under Creative Commons Attribution.
“Hail Mary Ship” by MallocArray https://www.printables.com/model/232479-hail-mary-ship/files.
“Death Star — Star Wars” (https://skfb.ly/oqGZX) by Quiznos323.
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This cross-sectional study evaluates associations between depressive symptoms and amyloid pathology by age, sex, education, and APOE genotype.
Artificial intelligence in various forms has been used in medicine for decades — but not like this. Experts predict that the adoption of large language models will reshape medicine. Some compare the potential impact with the decoding of the human genome, even the rise of the internet. The impact is expected to show up in doctor-patient interactions, physicians’ paperwork load, hospital and physician practice administration, medical research, and medical education.
Most of these effects are likely to be positive, increasing efficiency, reducing mistakes, easing the nationwide crunch in primary care, bringing data to bear more fully on decision-making, reducing administrative burdens, and creating space for longer, deeper person-to-person interactions.
Which of these technologies excites you the most? CHAPTERS:00:00 20 Space Megastructures That Will Transform Our Future01:15:51 15 Robotics TechnologiesThat…
Posted in cybercrime/malcode, education, robotics/AI | Leave a Comment on Artificial Intelligence and the “Great Machine” Problem: Avoiding Technology Oversimplification in Homeland Security and Emergency Management
This research note deploys data from a simulation experiment to illustrate the very real effects of monolithic views of technology potential on decision-making within the Homeland Security and Emergency Management field. Specifically, a population of national security decision-makers from across the United States participated in an experimental study that sought to examine their response to encounter different kinds of AI agency in a crisis situation. The results illustrate wariness of overstep and unwillingness to be assertive when AI tools are observed shaping key situational developments, something not apparent when AI is either absent or used as a limited aide to human analysis. These effects are mediated by levels of respondent training. Of great concern, however, these restraining effects disappear and the impact of education on driving professionals towards prudent outcomes is minimized for those individuals that profess to see AI as a full viable replacement of their professional practice. These findings constitute proof of a “Great Machine” problem within professional HSEM practice. Willingness to accept grand, singular assumptions about emerging technologies into operational decision-making clearly encourages ignorance of technological nuance. The result is a serious challenge for HSEM practice that requires more sophisticated solutions than simply raising awareness of AI.
Keywords: artificial intelligence; cybersecurity; experiments; decision-making.
This document intends to provide a summary of the cybersecurity threats in Japan with reference to globally observed cyber landscape. It looks at various kinds of cyberattacks their quantum and impact as well as specific verticals that are targeted by various threat actors.
As in February, 2024, in Japan, an organisation faces an average of 1,003 attacks per week, with FakeUpdates being the top malware. Most malicious files are delivered via email, and Remote Code Execution is the most common vulnerability exploit. In recent times, major Japanese incidents include a sophisticated malware by a nation state, attacks on Nissan and JAXA, and data breaches at the University of Tokyo and CASIO. Globally, incidents include Ukrainian media hacks, a ransomware attack on U.S. schools, and disruptions in U.S. healthcare due to cyber-attacks. The document also covers trends in malware types, attack vectors, and impacted industries over the last 6 months.
The details provide an overview of the threat landscape and major incidents in Japan and globally, highlighting the prevalence of attacks, common malware types, and impact on various industries and organisations. The information described should create awareness and help businesses and government organisation prepare well to safely operate in a digital environment.
As an initial step, we selected ARDs associated with hallmarks of aging. These included a total of 83 diseases linked to one or more hallmarks of aging, based on the taxonomy put forward in ref. 4 (Supplementary Table 2). Support for this taxonomy comes from multiple sources. Analyses of electronic health records from general practice and hospitalizations identified more than 200 diseases with incidence rates increasing with chronological age6,22. Researchers linked a subset of these ARDs to specific hallmarks of aging using several approaches: mining 1.85 million PubMed abstracts on human aging, identifying shared genes in the genome-wide association study catalog, conducting gene set enrichment analysis and analyzing disease co-occurrence networks within each hallmark4.
We confirmed the co-occurrence of ARDs within each hallmark in 492,257 participants from the UK Biobank study23. The presence of one ARD increased the risk of developing another ARD related to the same hallmark, with clustering coefficients ranging from 0.76 for LOP-specific ARDs to 0.92 for SCE-specific ARDs. These findings corroborated the hallmark-specific clustering of ARDs (Extended Data Figs. 3 and 4)23.
In time-to-event analyses of UK Biobank and FPS participants without these ARDs at baseline (n ranging from 477,325 to 492,294 in the UK Biobank and from 278,272 to 286,471 in the FPS, depending on the social disadvantage indicator and ARD), social disadvantage—indicated by education and adult SES (neighborhood deprivation)—was associated with a higher risk of developing ARDs. In the UK Biobank, the age-, sex-and ethnicity-adjusted hazard ratio for developing any ARD was 1.31 (95% confidence interval (CI) 1.29–1.33) for individuals with low compared with high education. For individuals with high versus low adult SES, the hazard ratio was 1.21 (95% CI 1.20–1.23). In the FPS, the corresponding hazard ratios were 1.28 (95% CI 1.25–1.31) and 1.23 (95% CI 1.20–1.27), respectively.
This simple animation shows the principle of Atomic Layer Deposition (ALD) using the molecules trimethyl aluminum (TMA) and water (H2O). At the end of this animation 1 monolayer (1 Angstrom ~ 10^−10 m)of Al2O3 is grown.
You may use this video for teaching /instructional purpose. We request you to please acknowledge the Banerjee Group at Washington University in St. Louis (http://research.engineering.wustl.edu/~parag.banerjee/) while using this video.
