Public Release Research Articles

The conversational AI "ChatGPT" outperforms medical students on a physiology university examination.

Artificial intelligence (AI) has gained massive interest with the public release of the conversational AI "ChatGPT," but it also has become a matter of concern for academia as it can easily be misused. We performed a quantitative evaluation of the performance of ChatGPT on a medical physiology university examination. Forty-one answers were obtained with ChatGPT and compared to the results of 24 students. The results of ChatGPT were significantly better than those of the students; the median (IQR) score was 75% (66-84%) for the AI compared to 56% (43-65%) for students (P < 0.001). The exam success rate was 100% for ChatGPT, whereas 29% (n = 7) of students failed. ChatGPT could promote plagiarism and intellectual laziness among students and could represent a new and easy way to cheat, especially when evaluations are performed online. Considering that these powerful AI tools are now freely available, scholars should take great care to construct assessments that really evaluate student reflection skills and prevent AI-assisted cheating.NEW & NOTEWORTHY The release of the conversational artificial intelligence (AI) ChatGPT has become a matter of concern for academia as it can easily be misused by students for cheating purposes. We performed a quantitative evaluation of the performance of ChatGPT on a medical physiology university examination and observed that ChatGPT outperforms medical students obtaining significantly better grades. Scholars should therefore take great care to construct assessments crafted to really evaluate the student reflection skills and prevent AI-assisted cheating.

Assessing the Impact of Generative Pre-Trained Transformers on AI Literacy and Public Awareness in India

Generative Pre-Trained Transformers (GPTs), a class of Artificial Intelligence (AI) models capable of producing human-like responses, have become widely adopted since their release. In India, this development raises questions about whether GPTs have influenced public awareness and interest in AI. Understanding this influence is crucial, particularly in the context of AI literacy. This study examines the impact of GPT adoption on creating increased awareness and public interest in AI in India. AI literacy is assessed using the four-factor framework proposed by Ng et al., (2021): knowledge and understanding of AI, uses and applications, evaluation and creation of new solutions, and ethical considerations. Information-seeking behavior serves as the theoretical foundation for examining changes in public interest across these dimensions. Google search volumes from 2020 to 2024 are analyzed to measure public engagement, with data divided into two periods: 2.5 years prior to and 1.5 years following the initial public release of ChatGPT in November 2022. Welch’s t-test is applied to assess changes in search volumes across these time periods. The results indicate a statistically significant increase in interest across all four aspects of AI literacy. The adoption of GPTs has significantly boosted public engagement with AI literacy in India, enhancing awareness and interest across all facets of AI knowledge and application.

The S-PLUS Ultra-Short Survey: First data release

Context. This paper presents the first public data release of the S-PLUS Ultra-Short Survey (USS), a photometric survey with short exposure times, covering approximately 9300 deg2 of the Southern sky. The USS utilizes the Javalambre 12-band magnitude system, including narrow, medium, and broad-band filters targeting prominent stellar spectral features. The primary objective of the USS is to identify bright, extremely metal-poor (EMP; [Fe/H] ≤ −3) and ultra-metal-poor (UMP; [Fe/H] ≤ −4) stars for further analysis using medium- and high-resolution spectroscopy. Aims. This paper provides an overview of the survey observations, calibration method, data quality, and data products. Additionally, it presents the selection of EMP and UMP candidates. Methods. The data from the USS were reduced and calibrated using the same methods as presented in the S-PLUS DR2. An additional step was introduced, accounting for the offset between the observed magnitudes off the USS and the predicted magnitudes from the very low-resolution Gaia XP spectra. Results. This first release contains data for 163 observed fields totaling ~324 deg2 along the Celestial Equator. The magnitudes obtained from the USS are well-calibrated, showing a difference of ~15 mmag compared to the predicted magnitudes by the GaiaXPy toolkit. By combining colors and magnitudes, 140 candidates for EMP or UMP have been identified for follow-up studies. Conclusions. The S-PLUS USS DR1 is an important milestone in the search for bright metal-poor stars, with magnitudes in the range 10 &lt; r ≤ 14. The USS is an ongoing survey; in the near future, it will provide many more bright metal-poor candidate stars for spectroscopic follow-up.

Silencing the Giant: Evidence of Active Galactic Nucleus Feedback and Quenching in a Little Red Dot at z = 4.13

The James Webb Space Telescope (JWST) has uncovered a ubiquitous population of dust-obscured compact sources at z ≳ 4. Many of these objects exhibit signs of active galactic nucleus (AGN) activity, making their study crucial for understanding the formation of supermassive black holes (SMBHs) and their growth with host galaxies. In this work, we examine low and medium-resolution JWST/NIRSpec spectra from the JADES GTO public data release in the GOODS-N field of a red, luminous (M B ∼ −22.2 mag) and compact (<500 pc) source at z = 4.13. The rest-optical (λ rest > 4000 Å) continuum of this source is strongly dominated by a massive (log10[M */M ⊙] ∼ 10.6), quenched (log10[sSFR/yr−1] < −11) galaxy, as indicated by the clear presence of a Balmer break and stellar absorption lines. Star formation history modeling reveals a starburst episode followed by rapid quenching about 200 Myr ago. The spectrum shows extremely broad (FWHM ∼2500 km s−1) Hα emission and elevated optical line ratios, indicating an actively accreting SMBH. Moreover, our work has potentially revealed clear AGN signatures in the rest-UV in little red dots for the first time via the detection of a strong Lyα emission and a broad Mg ii doublet. The derived black hole mass of log10(M BH/M ⊙) ∼ 7.3 results in M BH/M * ∼ 0.04%, consistent with the local relations, unlike the elevated ratios in other high-z reddened AGN. Finally, we use JWST data from AGN at z = 4–10 to explore an evolutionary link between high-z reddened AGN, early quiescent galaxies, and local ellipticals.

Updated Cosmological Constraints in Extended Parameter Space with Planck PR4, DESI Baryon Acoustic Oscillations, and Supernovae: Dynamical Dark Energy, Neutrino Masses, Lensing Anomaly, and the Hubble Tension

We present updated constraints on cosmological parameters in a 12-parameter model, extending the standard six-parameter ΛCDM by including dynamical dark energy (DE; w 0, w a ), the sum of neutrino masses (∑m ν ), the effective number of non-photon radiation species (N eff), the lensing amplitude scaling (A lens), and the running of the scalar spectral index (α s ). For cosmic wave background (CMB) data, we use the Planck Public Release (PR) 4 (2020) HiLLiPoP and LoLLiPoP likelihoods, Planck PR4+Atacama Cosmology Telescope (ACT) DR6 lensing, and Planck 2018 low-ℓ TT likelihoods, along with DESI DR1 baryon acoustic oscillations (BAO) and Pantheon+ and DESY5 uncalibrated Type Ia supernovae (SNe) likelihoods. Key findings are the following: (i) Contrary to DESI results, CMB+BAO+Pantheon+ data include a cosmological constant within 2σ, while CMB+BAO+DESY5 excludes it at over 2σ, indicating the dynamical nature of DE is not yet robust. Potential systematics in the DESY5 sample may drive this exclusion. (ii) Some data combinations show a 1σ+ detection of nonzero ∑m ν , indicating possible future detection. We also provide a robust upper bound of ∑m ν ≲ 0.3 eV (95% confidence limit (CL)). (iii) With CMB+BAO+SNe, A lens = 1 is included at 2σ (albeit not at 1σ), indicating no significant lensing anomaly in this extended cosmology with Planck PR4 likelihoods. (iv) The Hubble tension persists at 3.2 to 3.9σ, suggesting these simple extensions do not resolve it. (v) The S 8 tension with Dark Energy Survey Year 3 weak lensing is reduced to 1.4σ, likely due to additional parameters and the Planck PR4 likelihoods.

Significance of the smaller scales for hypersonic turbulent boundary layers with focused laser differential interferometry

While designed to suppress disturbances located away from its beam foci, focused laser differential interferometry (FLDI) is known to have some integration of signals along its optical axis. This is especially true for longer-wavelength signals, although smaller-scale flow structures also have a non-infinitesimal sensitivity length. This study investigates the performance of FLDI in a hypersonic turbulent boundary layer to better understand its behavior. FLDI simulations were conducted using both full-scale direct numerical simulation (DNS) and spatially averaged DNS inputs to directly assess the influence of smaller flow structures on FLDI measurements. The full-scale FLDI results indicate that integration along the optical axis likely results in lower FLDI amplitudes than for true point measurements. Comparison with the spatially averaged FLDI simulations reveals the significance of small-scale structures in FLDI signal roll-off and root mean square amplitudes. Further, the influence of FLDI setup parameters on the response across the frequency spectrum are analyzed. Circular, Gaussian beams with smaller widths are verified to present increased performance relative to elliptical or uniform-intensity beams. Also, measurements using distinct differentiation directions suggest an experimental way to measure turbulence isotropy scales. These results have notable implications for understanding hypersonic turbulent boundary-layer dynamics and interpreting experimental data. Distribution Statement A: Approved for Public Release; Distribution is Unlimited. PA# AFRL-2024-4678; Cleared 08/23/2024.

Effectiveness of endoscopic papillectomy with stent placement in pancreatic and bile ducts for treating duodenal papillary adenoma: a retrospective study

BackgroundDuodenal papillary adenoma, a potentially malignant benign tumor is primarily treated with endoscopic papillectomy. Despite its efficacy, endoscopic papillectomy has a high complication rate. This study investigates whether pancreatic duct and common bile duct stent placement can mitigate these complications.MethodsIn a retrospective observational analysis, 79 patients with duodenal papillary adenoma, treated with endoscopic papillectomy at our center, were studied. The cohort included patients who underwent endoscopic papillectomy with no stents placement, common bile duct stent placement alone, pancreatic duct stent placement alone, or stents placement in both ducts. We assessed the outcomes of endoscopic papillectomy, including complete resection rate and recurrence rate as the primary and secondary outcomes respectively. In the meantime, the incidence of complications were also analysed as the safety outcomes.ResultsComplete resection rates did not significantly differ between patients with or without stent placement (85.7% P group vs. 89.2% N-P group, P = 0.64). Early complication rates were similar across groups. However, significant reduction in common bile duct stenosis was observed in the stenting group (0% B group vs. 10.5% N-B group, P = 0.03). Furthermore, stent placement correlated with lower adenoma recurrence rates during follow-up (2.4% P group vs. 16.2% N-P group, P = 0.03; 2.4% B group vs. 15.8% N-B group, P = 0.04).ConclusionsPancreatic duct and common bile duct stent placement in endoscopic papillectomy may decrease late complications, particularly common bile duct stenosis, and reduce the recurrence of duodenal papillary adenoma.Trial registrationThis study received approval from the Institutional Review Board and Ethics Committee of Beijing Friendship Hospital (Approval No. BFHHZS20230203), and retrospectively registered in www.ClinicalTrials.gov (NCT06301048, Initial Release date: 02/18/2024, Last Public Release date: 03/03/2024).

An Early Snapshot of Attitudes Toward Generative Artificial Intelligence in Physical Therapy Education

Introduction. Generative artificial intelligence (AI) is rapidly gaining popularity across health care, education, and society. The purpose of this study was to assess perceptions and use of generative AI in academic physical therapy (PT). Review of Literature. Generative AI became one of the fastest-growing technologies ever after the public release of ChatGPT in November 2022. Early data indicate that attitudes toward generative AI in higher education are mixed and rapidly evolving, with significant ethical concerns around use and potential misuse. There are no published studies investigating perceptions and use of generative AI in PT education. Subjects. A total of 175 surveys were completed and analyzed. Respondents included PT educators, administrators, and students. Methods. An anonymous, online survey on use and perception of AI was distributed through email and social media. Descriptive statistics and cross-tabulations were performed to analyze respondent characteristics and responses to survey questions. Results. Most respondents (61.1%) reported they did not use generative AI during the 2022-2023 academic year, whereas 35.4% were generative AI users. More than 40% of respondents were optimistic or very optimistic toward generative AI. Users of AI were more likely to report an optimistic or very optimistic disposition toward AI compared with nonusers. AI users were more likely to agree or completely agree that generative AI has more benefits than drawbacks compared with nonusers. Discussion and Conclusion. Results of this survey suggest that, despite the rapid uptake of generative AI in society, many PT educators and students harbor reservations and uncertainties toward its use. Artificial intelligence users were less likely to hold negative perceptions toward it and were more likely to find it useful. Understanding use and perceptions of generative AI in PT education may inform strategies to promote innovation, policy-making, and ethical integration of this new and rapidly evolving technology.

The Dark Energy Survey Supernova Program: Light Curves and 5 Yr Data Release

We present griz photometric light curves for the full 5 yr of the Dark Energy Survey Supernova (DES-SN) program, obtained with both forced point-spread function photometry on difference images (DiffImg) performed during survey operations, and scene modelling photometry (SMP) on search images processed after the survey. This release contains 31,636 DiffImg and 19,706 high-quality SMP light curves, the latter of which contain 1635 photometrically classified SNe that pass cosmology quality cuts. This sample spans the largest redshift (z) range ever covered by a single SN survey (0.1 < z < 1.13) and is the largest single sample from a single instrument of SNe ever used for cosmological constraints. We describe in detail the improvements made to obtain the final DES-SN photometry and provide a comparison to what was used in the 3 yr DES-SN spectroscopically confirmed Type Ia SN sample. We also include a comparative analysis of the performance of the SMP photometry with respect to the real-time DiffImg forced photometry and find that SMP photometry is more precise, more accurate, and less sensitive to the host-galaxy surface brightness anomaly. The public release of the light curves and ancillary data can be found at github.com/des-science/DES-SN5YR and doi:10.5281/zenodo.12720777.

Editorial Preview

Effective from Volume 3, JIWE has transitioned to a triannual publication release effort. Specifically, releases occur each February, June, and October. This particular October 2024 release contains 12 papers within the regular section that covers a broad range of application concerning Machine Learning (ML), Artificial Intelligence (AI), Data Mining (DM), Software Engineering, Recommender Systems, Cybersecurity, Healthcare, and other key areas in web engineering. Additionally, this edition presents a captivating collection of 7 papers curated by our Thematic Editor, Dr. Ji-Jian Chin, under the theme "Pervasive Computing." In his editorial, Dr. Ji-Jian Chin highlights cutting-edge research on the integration of computing into everyday environments. Moreover, these papers are aligned to some of the United Nations Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being) through advancements in healthcare technologies, SDG 9 (Industry, Innovation, and Infrastructure) through software and systems innovation research, and SDG 16 (Peace, Justice, and Strong Institutions) through contributions to privacy and cybersecurity research.

Easy and accurate protein structure prediction using ColabFold.

Since its public release in 2021, AlphaFold2 (AF2) has made investigating biological questions, by using predicted protein structures of single monomers or full complexes, a common practice. ColabFold-AF2 is an open-source Jupyter Notebook inside Google Colaboratory and a command-line tool that makes it easy to use AF2 while exposing its advanced options. ColabFold-AF2 shortens turnaround times of experiments because of its optimized usage of AF2's models. In this protocol, we guide the reader through ColabFold best practices by using three scenarios: (i) monomer prediction, (ii) complex prediction and (iii) conformation sampling. The first two scenarios cover classic static structure prediction and are demonstrated on the human glycosylphosphatidylinositol transamidase protein. The third scenario demonstrates an alternative use case of the AF2 models by predicting two conformations of the human alanine serine transporter 2. Users can run the protocol without computational expertise via Google Colaboratory or in a command-line environment for advanced users. Using Google Colaboratory, it takes <2 h to run each procedure. The data and code for this protocol are available at https://protocol.colabfold.com .

Sustaining Ethical ePortfolio Practices: Insights from AAEEBL’s Task Force Year 4

This paper recognises the importance of digital ethics when using ePortfolios and provides information about the development of a comprehensive resource from the Association for Authentic, Experiential and Evidence-based Learning (AAEEBL) Digital Ethics in ePortfolios Task Force’s four-year work. With the public release of ChatGPT addressing ethical challenges in education is critical. Over three years, the global Task Force diligently drafted, refined, and published a suite of digital ethics principles, strategies, scenarios, and resources, which is available as open source. In the fourth year (July 2022-June 2023), the Task Force formed three sub-groups to manage the growing number of objectives. The Development group explored intersections between principles and generative artificial intelligence (GenAI), while evaluating data responsibility. The Outreach group disseminated the work through engaging workshops and publications, which also allowed the wider community feedback opportunities to further enhance the resources. Furthermore, the Research group undertook the ePortfolio Mapping Project to shed light on labour dynamics within ePortfolio practice across North America. The survey instrument was then adapted for data collection in Australia, and New Zealand. The paper highlights the significant achievements of these collective efforts, shares preliminary research findings, and offers valuable insights. It aims to promote ethical ePortfolio usage to enhance teaching, learning and assessment. Practitioner Notes Recognising potential ethical challenges in digital learning is critical. ePortfolios enable integrated learning experiences to meet a range of pedagogical objectives, including the tracking of learning processes as well as end artefacts. Learning how to apply ethical decision-making in using ePortfolios is essential. Comprehensive resources have been developed to assist educators, students, professional staff, and other digital pedagogy stakeholders. Research is underway into current ePortfolio practices which will inform future thinking, advocacy, and awareness.

Implementation and validation of face de-identification (de-facing) in ADNI4.

Recent technological advances have increased the risk that de-identified brain images could be re-identified from face imagery. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a leading source of publicly available de-identified brain imaging, who quickly acted to protect participants' privacy. An independent expert committee evaluated 11 face-deidentification ("de-facing") methods and selected four for formal testing. Effects of de-facing on brain measurements were comparable across methods and sufficiently small to recommend de-facing in ADNI. The committee ultimately recommended mri_reface for advantages in reliability, and for some practical considerations. ADNI leadership approved the committee's recommendation, beginning in ADNI4. ADNI4 de-faces all applicable brain images before subsequent pre-processing, analyses, and public release. Trained analysts inspect de-faced images to confirm complete face removal and complete non-modification of brain. This paper details the history of the algorithm selection process and extensive validation, then describes the production workflows for de-facing in ADNI. ADNI is implementing "de-facing" of MRI and PET beginning in ADNI4. "De-facing" alters face imagery in brain images to help protect privacy. Four algorithms were extensively compared for ADNI and mri_reface was chosen. Validation confirms mri_reface is robust and effective for ADNI sequences. Validation confirms mri_reface negligibly affects ADNI brain measurements.

CAVITY: Calar Alto Void Integral-field Treasury surve. I. First public data release

The Calar Alto Void Integral-field Treasury surveY (CAVITY) is a legacy project aimed at characterising the population of galaxies inhabiting voids, which are the most under-dense regions of the cosmic web, located in the Local Universe. This paper describes the first public data release (DR1) of CAVITY, comprising science-grade optical data cubes for the initial 100 out of a total of sim 300 galaxies in the Local Universe ($0.005 &lt; z &lt; 0.050$). These data were acquired using the integral-field spectrograph PMAS/PPak mounted on the 3.5m telescope at the Calar Alto observatory. The DR1 galaxy sample encompasses diverse characteristics in the color–magnitude space, morphological type, stellar mass, and gas ionisation conditions, providing a rich resource for addressing key questions in galaxy evolution through spatially resolved spectroscopy. The galaxies in this study were observed with the low-resolution V500 set-up, spanning the wavelength range 3745–7500 with a spectral resolution of 6.0 (FWHM). Here, we describe the data reduction and characteristics and data structure of the CAVITY datasets essential for their scientific utilisation, highlighting such concerns as vignetting effects, as well as the identification of bad pixels and management of spatially correlated noise. We also provide instructions for accessing the CAVITY datasets and associated ancillary data through the project's dedicated database.

Standardised formats and open-source analysis tools for the MAGIC telescopes data

Instruments for gamma-ray astronomy at Very High Energies (E>100GeV) have traditionally derived their scientific results through proprietary data and software. Data standardisation has become a prominent issue in this field both as a requirement for the dissemination of data from the next generation of gamma-ray observatories and as an effective solution to realise public data legacies of current-generation instruments. Specifications for a standardised gamma-ray data format have been proposed as a community effort and have already been successfully adopted by several instruments. We present the first production of standardised data from the Major Atmospheric Gamma-ray Imaging Cherenkov (MAGIC) telescopes. We converted 166h of observations from different sources and validated their analysis with the open-source software Gammapy. We consider six data sets representing different scientific and technical analysis cases and compare the results obtained analysing the standardised data with open-source software against those produced with the MAGIC proprietary data and software. Aiming at a systematic production of MAGIC data in this standardised format, we also present the implementation of a database-driven pipeline automatically performing the MAGIC data reduction from the calibrated down to the standardised data level. In all the cases selected for the validation, we obtain results compatible with the MAGIC proprietary software, both for the manual and for the automatic data productions. Part of the validation data set is also made publicly available, thus representing the first large public release of MAGIC data. This effort and this first data release represent a technical milestone toward the realisation of a public MAGIC data legacy.

AI in Criminal Justice Education: Assessing Educator’s Attitudes and Experiences

Since the public release of OpenAI’s ChatGPT and similar large language models in late 2022, generative artificial intelligence (AI) has become a significant societal force. Discourse about these developments through OpEds and blog posts have raised critical questions for educators about the promises and pitfalls of AI integration into higher education. Through an online survey administered to criminal justice educators (N = 68), the current study explores attitudes about and experiences with AI, and beliefs about the practical challenges AI presents. Results indicate criminal justice educators are both excited but also uncomfortable with the implications of AI for higher education. Respondents were generally concerned about the implications of AI for academic integrity and student development. Interestingly, respondents who had actually used AI tools expressed higher levels of excitement and comfort and lower levels of concern, suggesting that exposure to AI tools may play a key role in assuaging educators’ fears and concerns regarding AI tools. Additional findings and implications are discussed.

JWST/NIRSpec spectroscopy of intermediate-mass quiescent galaxies at z ~ 3-4

ABSTRACT We present the analysis of three intermediate-mass quiescent galaxies (QGs) with stellar masses of ${\sim} 10^{10}\, \mathrm{M}_{\odot }$ at redshifts $z\sim 3\!-\!4$ using NIRSpec low-resolution spectroscopy. Utilizing the spectral energy distribution fitting code bagpipes, we confirm these target galaxies are consistent with quiescent population, with their specific star formation rates falling below 2 dex the star-forming main sequence at the same redshifts. Additionally, we identify these QGs to be less massive than those discovered in previous works, particularly prior to the JWST era. Two of our target galaxies exhibit the potentially blended $\mathrm{ H} \, {\alpha }$ + [N ii] emission line within their spectra with signal-to-noise ratio ${\gt} 5$. We discuss whether this feature comes from an active galactic nucleus (AGN) or star formation, although future high-resolution spectroscopy is required to reach a conclusion. One of the target galaxies is covered by JWST/NIRCam imaging of the Public Release IMaging for Extragalactic Research survey. Using the 2D profile fitting code galfit, we examine its morphology, revealing a disc-like profile with a Sérsic index of $n=1.1 \pm 0.1$. On the size–mass relation, we find a potential distinction between less massive ($\log _{10}{(M_*/\mathrm{M}_\odot)}\lt 10.3$) and massive ($\log _{10}{(M_*/\mathrm{M}_\odot)}\gt 10.3$) QGs in their evolutionary pathways. The derived quenching time-scales for our targets are less than $1 \, {\rm Gyr}$. This may result from these galaxies being quenched by AGN feedback, supporting the AGN scenario of the emission line features.

The Pristine survey. XXIII. Data Release 1 and an all-sky metallicity catalogue based on Gaia DR3 BP/RPspectro-photometry

We used the spectro-photometric information of $ million stars from Data Release 3 (DR3) to calculate synthetic, narrow-band, metallicity-sensitive $CaHK$ magnitudes that mimic the observations of the Pristine survey, a survey of photometric metallicities of Milky Way stars that has been mapping more than 6,500\,deg$^2$ of the northern sky with the Canada-France-Hawaii Telescope since 2015. These synthetic magnitudes were used for an absolute recalibration of the deeper Pristine photometry and, combined with broadband information, synthetic and Pristine $CaHK$ magnitudes were used to estimate photometric metallicities over the whole sky. The resulting metallicity catalogue is accurate down to $ and is particularly suited for the exploration of the metal-poor Milky Way ($ We make available here the catalogue of synthetic $CaHK_ syn $ magnitudes for all stars with BP/RP information in DR3, as well as an associated catalogue of more than $ million photometric metallicities for high signal-to-noise FGK stars. This paper further provides the first public data release of the Pristine catalogue in the form of higher quality recalibrated Pristine $CaHK$ magnitudes and photometric metallicities for all stars in common with the BP/RP spectro-photometric information in DR3. We demonstrate that, when available, the much deeper Pristine data greatly enhance the quality of the derived metallicities, in particular at the faint end of the catalogue ($G_ BP 16$). Combined, both photometric metallicity catalogues include more than two million metal-poor star candidates ($ phot &lt;-1.0$) as well as more than 200,000 and sim 8,000 very and extremely metal-poor candidates ($ phot &lt;-2.0$ and $&lt;-3.0$, respectively). Finally, we show that these metallicity catalogues can be used efficiently, among other applications, for Galactic archaeology, to hunt for the most metal-poor stars, and to study how the structure of the Milky Way varies with metallicity, from the flat distribution of disk stars to the spheroid-shaped metal-poor halo.

Navigating the light and shadow of scientific publishing faced with machine learning and generative AI.

The public release of ChatGPT in November 2022 sparked a boom and public interest in generative artificial intelligence (AI) that has led to journals and journal families hastily releasing generative AI policies, ranging from asking authors for acknowledgement or declaration to the outright banning of use. Here, we briefly discuss the basics of machine learning, generative AI, and how it will affect scientific publishing. We focus especially on potential risks and benefits to the scientific community as a whole and journals specifically. While the concerns of editors, for example about manufactured studies, are valid, some recently implemented or suggested policies will not be sustainable in the long run. The quality of generated text and code is quickly becoming so high that it will not only be impossible to detect the use of generative AI but would also mean taking a powerful tool away from researchers that can make their life easier every day. We discuss the history and current state of AI and highlight its relevance for medical publishing and pain research. We provide guidance on how to act now to increase good scientific practice in the world of ChatGPT and call for a task force focusing on improving publishing pain research with use of generative AI.

Obfuscation mechanism for simultaneous public event information release and private event information hiding in discrete event systems

In this paper, we study the problem of simultaneous public event information release and private event information hiding in discrete event systems modeled by partially observed non-deterministic finite automata, where the public and private event information is respectively defined as unobservable fault and secret events. The notion of D-C-compossibility is introduced to characterize whether a system simultaneously conceals the occurrences of secret events while releasing the occurrences of fault events. When such a property does not hold, we investigate its enforcement through a defensive function. This function takes each observable event of a system as input and generates a suitably modified event sequence at the system's output using event deletion, insertion, or substitution. Specifically, our focus is on prioritizing the concealment of the secret events while maximizing the detection of the fault events through the use of defensive functions. The notion of D-C-enforceability that refers to the capability of a defensive function to employ a strategy for manipulating observations of a system to enforce D-C-compossibility is proposed. That is, a D-C-enforcing defensive function ensures that all occurrences of fault events can be revealed while concealing all occurrences of secret events. A D-C-diagnoser construction is proposed to enumerate all feasible defensive actions following system behavior. By taking advantage of the D-C-diagnoser, we obtain a necessary and sufficient condition for D-C-enforceability, along with a corresponding obfuscation strategy for defensive functions.