Bibliographic record
Abstract
The purpose of this chapter is to show the feasibility of using ad-hoc networks as an enabler of brain spectroscopy. Ad-hoc networks have many applications. The application which this chapter explains provides full mobility in everyday environment using a near-infrared light sensor designed to monitor brain function in humans. Multiple wireless networks employing several different protocols are used for data carriage and provide new freedom to conduct tests in real environment outside a lab. An Ad-hoc network (Bluetooth) is one of the wireless networks used to support the application. The value of this application is to measure the changes in the concentration of oxyhemoglobin (HbO2) and deoxyhemoglobin (Hb) in tissues in the real-life environment. This might lead to better understanding of tissue pathologies. This type of application was not available before. A fully mobile functional brain spectroscopy system has been developed to allow the possibility of testing subjects to be monitored in their real environment. To test this hypothesis, communication software was developed to allow for the collection of physiological data from a mobile near-infrared sensor via a mobile telephone that has a Bluetooth support. The developed application is used to track the changes in the concentrations of HbO2 and Hb during various activities and send the data to a computer at a remote monitoring site. The specific aims of this application have been to build a fully mobile system to monitor the concentrations of HbO2 and Hb in near real time, to monitor the concentrations of HbO2 and Hb during smoking, as well as to analyze the gathered data, and to try to understand the correlation between HbO2 and Hb during smoking. Performance and data accuracy were the key for this application to provide the sought value. Java portability allows the developed application to run on a wide range of operating systems and devices. Java Standard Edition (J2SE) was used for server code; Java Micro Edition (J2ME) was used to run code in the phone; C language was used to build the Bluetooth code and the protocol in the sensor; and Eclipse was used as the integrated development environments (IDE) to build and debug the application. Java has native network support. It is possible to create applications to support different kinds of networks and protocols. Java has native libraries that support wired and wireless communications. It supports Bluetooth, WiFi, and more. Several popular network protocols and standards are also supported. By default, Java libraries support Transmission Control Protocol (TCP), User Datagram Protocol (UDP), and binary stream communications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".