A somewhat systematic review of previous systematic reviews and meta-analyses about nutrition and Alzheimer's
May 2023, phys.org
The team conducted a review of systematic reviews aka meta-analysis studies around the topic of nutrition in disease. (So their's is a meta-meta-analysis.)
This is why kids need to go to school:
There is a risk that a systematic review of multiple meta-data studies could artificially weight specific study findings if they are included multiple times across the different meta-analyses.
What may be important to know is that both of these meta-analyses included the same study from 2015, titled "Intakes of fish and polyunsaturated fatty acids and mild-to-severe cognitive impairment risks: A dose-response meta-analysis of 21 cohort studies," which is itself a meta-analysis that could contain studies included in other meta-analyses covered by the current study.
Note: This is 100% related to the AI-feeding-AI problem we're not even beginning to talk about yet -- because when you use meta-analysis of past meta-analyses, you risk multiplying the dirty data.
via International Education College of Zhejiang Chinese Medical University, Hangzhou: Inmaculada Xu Lou et al, Effect of nutrition in Alzheimer's disease: A systematic review, Frontiers in Neuroscience (2023). DOI: 10.3389/fnins.2023.1147177
Do measurements produce the reality they show us?
Aug 2023, phys.org
The observable values of a physical system depend on the dynamics of the measurement interaction by which they are observed. "This is a major step towards explaining the meaning of 'superposition' in quantum mechanics".
"Our results show that the physical reality of an object cannot be separated from the context of all its interactions with the environment, past, present and future, providing strong evidence against the widespread belief that our world can be reduced to a mere configuration of material building blocks," said Hofmann.
I've never heard it this way -
Fully resolved measurements require a complete randomization of the system dynamics; this corresponds to a superposition of all possible system dynamics.
"Context-dependent realities can explain a wide range of seemingly paradoxical quantum effects. We are now working on better explanations of these phenomena. Ultimately, the goal is to develop a more intuitive understanding of the fundamental concepts of quantum mechanics that avoids the misunderstandings caused by a naïve belief in the reality of microscopic objects," said Hofmann.
Hiroshima University: Tomonori Matsushita et al, Dependence of measurement outcomes on the dynamics of quantum coherent interactions between the system and the meter, Physical Review Research (2023). DOI: 10.1103/PhysRevResearch.5.033064
From stock markets to brain scans, new research harmonizes hundreds of scientific methods to understand complex systems
Sep 2023, phys.org
They looked at hundreds of different methods for measuring interaction patterns in complex system, and worked out which ones are most useful for understanding a given system. They call it the scientific orchestra, and each method is an instrument, and they thought maybe some instruments are better for certain kinds of data, so they tried to find out.
We're talking about methods like Brownian motion, coupled maps, coupled oscillators, simulated fMRI, simulated climate, wave equations, or uncorrelated noise to understand data like stock markets, river flow, brain waves, earthquakes.
In total, we applied our 237 methods to more than 1,000 datasets. By analyzing how these methods behave when applied to such diverse scientific systems, we found a way for them to "play in harmony" for the first time.
They found that the methods were grouped differently than what we traditionally think, and that when properly orchestrated, the full ensemble of scientific methods demonstrated improved performance over any single method on its own.
via Centre for Complex Systems, The University of Sydney: Oliver M. Cliff et al, Unifying pairwise interactions in complex dynamics, Nature Computational Science (2023). DOI: 10.1038/s43588-023-00519-x
Five factors that assess well-being of science predict support for increasing US science funding
Sep 2023, phys.org
Drawing on 13 questions in APPC's 2022 nationally representative Annenberg Science Knowledge survey (ASK) survey of 1,154 U.S. adults, researchers identified five factors that form a Factors Assessing Science's Self-Preservation (FASS) model. The model can be used to assess the extent to which public perceptions align with the self-presentation of science and scientists live up to the ways in which they define themselves and their work to the public.
- credible
- prudent
- unbiased
- self-correcting
- beneficial
via Annenberg Public Policy Center of the University of Pennsylvania: Yotam Ophir et al, Factors Assessing Science's Self-Presentation model and their effect on conservatives' and liberals' support for funding science, Proceedings of the National Academy of Sciences (2023). DOI: 10.1073/pnas.2213838120
Artificial intelligence predicts the future of artificial intelligence research
Oct 2023, phys.org
(Asimov's Foundation no?)
An algorithm that not only assists researchers in orienting themselves systematically but also predictively guides them in the direction in which their own research field is likely to evolve.
Science4Cast is a graph-based representation of knowledge which becomes more complex over time as more scientific articles are published. Each node in the graph represents a concept in AI, and the connections between nodes indicate whether and when two concepts were studied together.
For example, the question "What will happen" can be described as a mathematical question about the further development of the graph. Science4Cast is fed with real data from over 100,000 scientific publications spanning a 30-year period, resulting in a total of 64,000 nodes.
via Max-Planck Institute for the Science of Light in Erlangen: Mario Krenn et al, Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network, Nature Machine Intelligence (2023). DOI: 10.1038/s42256-023-00735-0
