Scientific Goals Research Articles

High Elevation Radiation Array (HERA) detectors for airborne thunderstorm investigations

A high-energy atmospheric physics phenomenon, referred to as a terrestrial gamma ray flash (TGF), is associated with lightning and produces large bursts of energetic photon radiation. TGFs will be investigated using a suite of gamma-ray instruments designed and constructed to fly on ten United States Air Force (USAF) WC-130J Hurricane Hunter aircraft as part of an aircrew ionization study led by the Air Force Institute of Technology (AFIT) and the United States Air Force School of Aerospace Medicine (USAFSAM), in cooperation with the 53rd Weather Reconnaissance Squadron (WRS). Each instrument consists of one NaI and one plastic detector, a GPS timing device, and an instrument computer that performs data acquisition. High Elevation Radiation Array (HERA) detectors will be employed to maximize the chances of observing TGFs near their source and to gain a better understanding of their origin, mechanism, ubiquity, and to assess potential hazards posed to military and commercial aircrew and passengers. The HERA program, deployed on 10 separate Air Force aircraft over a multi-year campaign, will result in thousands of observational flight hours and be the largest concerted effort to date to observe TGFs in situ through aircraft observations. In this paper, we give an overview of the scientific goals of this campaign and how the HERA instruments have been designed to meet those goals. We include a detailed description of the HERA instrument, along with mass model and signal processing simulations.

Development of the mobile application “Fascinating Onomastics” based on semantic base of knowledge

This research, funded under the BR11765535 “Development of Scientific and Linguistic Foundations and IT Resources to Expand the Functions and Improve the Culture of the Kazakh Language” project, is aimed at the need to develop the scientific and linguistic foundations of the Kazakh language with a special focus on anthroponymy. Through the use of descriptive and historical methods, the research creates a semantic knowledge base for Kazakh proper names. This extensive database forms the basis of the innovative mobile application "Fascinating Onomastics", developed using the Flutter 3.0 framework. The application serves as an innovative resource allowing users to study Kazakh anthroponymy with features such as name search, favorites and the ability to add new names. The study revealed about 100 properties that affect the structure of proper names, contributing to a deeper understanding of Kazakh onomastics. The technical and economic effectiveness of the application is highlighted by its potential to increase cultural awareness, promote cultural tourism and be used as an economically viable product. In addition, this study has a high scientific and technical status, offering an unprecedented contribution to the study of Kazakh onomastics and cultural heritage. The application "Fascinating Onomastics" with its rich semantic features and interface in the Kazakh language is becoming an important milestone in achieving scientific, cultural and educational goals.

LINGUISTIC ECONOMY AND SYSTEMATIC CONSTRUCTION OF LANGUAGE

The article provides general information about language phenomena, artificial intelligence and their connection with knowledge, thinking, memory, etc. In addition, the approaches and opinions of scientists on these topics are presented and explained with the help of examples. The author states that the movement of thinking and the formation of thought is associated with cognitive activity, a process that ensures the reflection of reality directly in the mind. It is known that the formation of speech units and their understanding are the result of mental activity. The performance of this activity is directly related to the condition of obtaining linguistic knowledge. There is no way to know reality without linguistic knowledge, to lyrically express logical structures that reflect information about the current event-events. However, at the intersection of cognitive science, six fields of science are adjacent, and a single scientific goal is to be occupied with the search for a solution to the problems of concentration, processing and application of knowledge in natural and artificial systems. It seems-that interrelation between these areas does not take place in the same way, whereas, Artificial Intelligence and anthropological philosophy are directly related.Meanwhile, human cognitive activity should be considered not as a reflection of being as a mirror, but as a phenomenon that reflects the inner or outer essence of objects. The mental structure formed in the process of cognition –the content of the concept is occupied by character-features that have a clear appearance of exactly this type, and the same features lead in the linguistic reality of the concept.

A Review on Graphene Analytical Sensors for Biomarker-based Detection of Cancer.

The engineering of nanoscale materials has broadened the scope of nanotechnology in a restricted functional system. Today, significant priority is given to immediate health diagnosis and monitoring tools for point-of-care testing and patient care. Graphene, as a one-atom carbon compound, has the potential to detect cancer biomarkers and its derivatives. The atom-wide graphene layer specialises in physicochemical characteristics, such as improved electrical and thermal conductivity, optical transparency, and increased chemical and mechanical strength, thus making it the best material for cancer biomarker detection. The outstanding mechanical, electrical, electrochemical, and optical properties of two-dimensional graphene can fulfil the scientific goal of any biosensor development, which is to develop a more compact and portable point-of-care device for quick and early cancer diagnosis. The bio-functionalisation of recognised biomarkers can be improved by oxygenated graphene layers and their composites. The significance of graphene that gleans its missing data for its high expertise to be evaluated, including the variety in surface modification and analytical reports. This review provides critical insights into graphene to inspire research that would address the current and remaining hurdles in cancer diagnosis.

Differential optical transfer function (dOTF) broad-spectrum wavefront sensing using integral field unit spectroscopy

Context. Detecting and characterizing Earth-like exoplanets is a critical scientific goal for the next generation of telescopes. However, current direct imaging instruments are hindered by evolving noncommon path aberrations (NCPAs), which lead to persistent speckles in the images. The differential optical transfer function (dOTF) is an image-based, noniterative, and model-independent wavefront sensor that combines differential images to estimate the complex field of the pupil. Aims. Given the objective of spectrally characterizing exoplanets to discern their composition and potentially detect biosignatures, we aim to explore the integration of the dOTF sensing method with an integral field unit (IFU) spectrograph. An IFU spectrograph generates a spectral data cube that contains diverse spectral information within a single dataset. Combining these two concepts is expected to improve the efficiency with which NCPAs can be spectrally measured and characterized. Methods. This Letter presents an implementation strategy for integrating dOTF and IFU spectroscopy data, enabling estimations of the electric field at various wavelengths simultaneously. Results. This method is specifically optimized to provide the most effective combination with minimal hardware requirements. It involves the use of multiple simultaneous deformable mirror actuator displacements in conjunction with an adapted Lyot stop employing a circular variable filter. Conclusions. The dOTF wavefront sensor coupled with an IFU spectrograph promises to be rapid and efficient, capable of measuring NCPAs across the entire spectrum provided by the spectrograph. Because the combination of dOTF and IFU spectroscopy is immediately applicable, it is of potential interest for various present-day on-sky instruments, as well as future IFU-based instruments coupled to the next generation of extremely large telescopes (ELTs).

B-mode polarization forecasts for GreenPol

We present tensor-to-scalar ratio forecasts for GreenPol, a hypothetical ground-based B-mode experiment aiming to survey the cleanest regions of the Northern Galactic Hemisphere at five frequencies between 10 and 44 GHz. Its primary science goal would be to measure large-scale cosmic microwave background (CMB) polarization fluctuations at multipoles ℓ ≲ 500, and thereby constrain the primordial tensor-to-scalar ratio r. The observations for the suggested experiment would take place at the Summit Station (72 ° 34N, 38 ° 27W) on Greenland, at an altitude of 3216 m above sea level. For this paper we simulated various experimental setups, and derived limits on the tensor-to-scalar ratio after CMB component separation using a Bayesian component separation implementation called Commander. When combining the proposed experiment with Planck HFI observations for constraining polarized thermal dust emission, we found a projected limit of r < 0.02 at 95% confidence for the baseline configuration. This limit is very robust with respect to a range of important experimental parameters, including sky coverage, detector weighting, foreground priors, among others. Overall, GreenPol would have the possibility to provide deep CMB polarization measurements of the Northern Galactic Hemisphere at low frequencies.

The Next Generation Deep Extragalactic Exploratory Public (NGDEEP) Survey

We present the Next Generation Deep Extragalactic Exploratory Public (NGDEEP) Survey, a deep slitless spectroscopic and imaging Cycle 1 JWST treasury survey designed to constrain feedback mechanisms in low-mass galaxies across cosmic time. NGDEEP targets the Hubble Ultra Deep Field (HUDF) with NIRISS slitless spectroscopy ( flim,line,5σ≈1.2× 10−18 erg s−1 cm−2) to measure metallicities and star formation rates (SFRs) for low-mass galaxies through the peak of the cosmic SFR density (0.5 < z < 4). In parallel, NGDEEP targets the HUDF-Par2 parallel field with NIRCam ( mlim,5σ=30.6−30.9 ) to discover galaxies to z > 12, constraining the slope of the faint end of the rest-ultraviolet luminosity function. NGDEEP overlaps with the deepest HST Advanced Camera for Surveys optical imaging in the sky, F435W in the HUDF ( mlim,F435W=29.6 ) and F814W in HUDF-Par2 ( mlim,F814W=30 ), making this a premier HST+JWST deep field. As a treasury survey, NGDEEP data are public immediately, and we will rapidly release data products and catalogs in the spirit of previous deep-field initiatives. In this paper we present the NGDEEP survey design, summarize the science goals, and detail plans for the public release of NGDEEP reduced data products.

Artificial Intelligence: A Way to Promote Innovation

The theme of artificial intelligence is how to use it to make computers useful in solving problems concerning health. We interpret the data obtained by diagnosing various diseases, such as various types of cancer, diabetes, etc. The largest scientific goal of information construction—the theory of processing intelligence. It is the science and engineering of manufacturing. Intelligent machines, especially an intelligent computer. This work presents artificial intelligence (A.I.). Study how to make computers that have some characteristics of the human mind. A.I. systems are now routinely used in the economy, Medicine, and the military. They also have broad data that can potentially solve many problems in clinical trials. This article provides an overview of A.I. and its innovations. It is one of the cutting-edge technologies shaping the future of pharmacy. Various advanced systems, such as mathematics, machining performance, cloud computing, and algorithm design, have accelerated the development of methods that can be used to analyze, interpret, and make predictions using these data sources. We can learn how to get machines to solve problems by observing others.There has been sporadic research growth in two main areas: genomics and digital Medicine. This article examines the introduction, definition, history, applications, and innovation in pharmacy.

To the Question of the Conjunction of the Cognitive Potential of the Dialectical Method and the Systematic Approach

The presented text is devoted to the study, systematization and integration of economic knowledge in the aspect of researching the possibilities of coupling the cognitive potential of the dialectical method and systems approach in the context of identifying the features of the evolution process of complex weakly structured macro-objects, which are in different phase states of their being. As a scientific goal, the problem of researching the possible limits and potential of applying the dialectical method and the system approach in the context of identifying the features of the process of evolution of economic systems, mediated by the phase nature of the process of social production. Based on the cognitive potential of the dialectical method, system analysis, supplemented by certain elements of the method of ascent from the abstract to the concrete, it is proved that the conjunction of the cognitive potential of these tools, ultimately allows a comprehensive qualitative presentation of the evolution of economic systems; the approach proposed by the authors fully fits into the synergistic paradigm of modern science, as its basis is the study of qualitative transformations in macroobjects, that are in a dynamic nonequilibrium state, for which the multivariate nature of elements is inherent.

Improving Photometric Redshift Estimation for Cosmology with LSST Using Bayesian Neural Networks

We present results exploring the role that probabilistic deep learning models can play in cosmology from large-scale astronomical surveys through photometric redshift (photo-z) estimation. Photo-z uncertainty estimates are critical for the science goals of upcoming large-scale surveys such as the Legacy Survey of Space and Time (LSST); however, common machine learning methods typically provide only point estimates and lack uncertainties on predictions. We turn to Bayesian neural networks (BNNs) as a promising way to provide accurate predictions of redshift values with uncertainty estimates. We have compiled a galaxy data set from the Hyper Suprime-Cam Survey with grizy photometry, which is designed to be a smaller-scale version of large surveys like LSST. We use this data set to investigate the performance of a neural network and a probabilistic BNN for photo-z estimation and evaluate their performance with respect to LSST photo-z science requirements. We also examine the utility of photo-z uncertainties as a means to reduce catastrophic outlier estimates. The BNN outputs the estimate in the form of a Gaussian probability distribution. We use the mean and standard deviation as the redshift estimate and uncertainty. We find that the BNN can produce accurate uncertainties. Using a coverage test, we find excellent agreement with expectation—67.2% of galaxies between 0 < 2.5 have 1σ uncertainties that cover the spectroscopic value. We also include a comparison to alternative machine learning models using the same data. We find the BNN meets two out of three of the LSST photo-z science requirements in the range 0 < z < 2.5.

Modelling science return from the lunar crater radio telescope on the far side of the moon.

The era following the separation of CMB photons from matter, until the emergence of the first stars and galaxies, is known as the Cosmic Dark Ages. Studying the electromagnetic radiation emitted by neutral hydrogen having the 21 cm rest wavelength is the only way to explore this significant phase in the Universe's history, offering opportunities to investigate essential questions about dark matter physics, the standard cosmological model andinflation. Due to cosmological redshift, this signal is now only observable at frequencies inaccessible from the Earth's surface due to ionospheric absorption and reflection. With the Lunar Crater Radio Telescope (LCRT), we aim to conduct unprecedented measurements of the sky-averaged redshifted signal spectrum in the 4.7-47 MHz band, by deploying a 350 m diameter parabolic reflector mesh inside a lunar crater on the far side of the Moon and suspending a receiver at its focus. This work discusses the feasibility of the LCRT science goals through the development of a science model, with emphasis on post-processing techniques to extract the Dark Ages signal from the galactic foreground dominating the expected raw data. This model can be used to vary critical instrument and mission parameters to understand their effect on the quality of the retrieved signal. This article is part of a discussion meeting issue 'Astronomy from the Moon: the next decades (part 2)'.

Optical properties of Y dwarfs observed with the Gran Telescopio Canarias

Our science goals are to characterise the optical properties of Y dwarfs and to study their consistency with theoretical models. A sample of five Y dwarfs was observed with three optical and near-infrared instruments at the 10.4\,m Gran Telescopio Canarias. Deep near-infrared ($J$- or $H$-band) and multicolour optical images ($z$-, $i$-, $r$-, $g$-, $u$-bands) of the five targets and a low-resolution far-red optical spectrum for one of the targets were obtained. One of the Y dwarfs, WISE J173835.53+273258.9 (Y0), was clearly detected in the optical ($z$- and $i$-bands) and another, WISE J182831.08+265037.7 (Y2), was detected only in the $z$-band. We measured the colours of our targets and found that the $z-J$ and $i-z$ colours of the Y dwarfs are bluer than those of mid- and late-T dwarfs. This optical blueing has been predicted by models, but our data indicates that it is sharper and happens at temperatures about 150 K warmer than expected. The culprit is the K I resonance doublet, which weakens more abruptly in the T- to Y-type transition than expected. Moreover, we show that the alkali resonance lines (Cs I and K I) are weaker in Y dwarfs than in T dwarfs; the far-red optical spectrum of WISE J173835.53+273258.9 is similar to that of late-T dwarfs, but with stronger methane and water features; and we noted the appearance of new absorption features that we propose could be due to hydrogen sulphide. Last but not least, in 2014, WISE J173835.53+273258.9 presented a bluer $i-z$ colour than in 2021 by a factor of 2.8 (significance of 2.5sigma ). Thanks to our deep optical images, we found that the 2014 $i$-band spectrum was contaminated by a galaxy bluer than the Y dwarf. The optical properties of Y dwarfs presented here pose new challenges to the modelling of grain sedimentation in extremely cool objects. The weakening of the very broad K I resonance doublet due to condensation in dust grains is more abrupt than theoretically anticipated. Consequently, the observed blueing of the $z-J$ and $i-z$ colours of Y dwarfs with respect to T dwarfs is more pronounced than predicted by models and could boost the potential of upcoming deep large-area optical surveys regarding their ability to detect extremely cool objects.

Responsible Research Assessment Should Prioritize Theory Development and Testing Over Ticking Open Science Boxes

We appreciate the initiative to seek for ways to improve academic assessment by broadening the range of relevant research contributions and by considering a candidate’s scientific rigor. Evaluating a candidate's ability to contribute to science is a complex process that cannot be captured through one metric alone. While the proposed changes have some advantages, such as an increased focus on quality over quantity, the proposal's focus on adherence to open science practices is not sufficient, as it undervalues theory building and formal modelling: A narrow focus on open science conventions is neither a sufficient nor valid indicator for a “good scientist” and may even encourage researchers to choose easy, pre-registerable studies rather than engage in time-intensive theory building. Further, when in a first step only a minimum standard for following easily achievable open science goals is set, most applicants will soon pass this threshold. At this point, one may ask if the additional benefit of such a low bar outweighs the potential costs of such an endeavour. We conclude that a reformed assessment system should put at least equal emphasis on theory building and adherence to open science principles and should not completely disregard traditional performance metrices.

The New Era of Machine Learning: A Curse or a Boon for Society

Abstract: Machine Learning (ML) &amp; Artificial Intelligence (AI) is changing how we live and work by permeating more and more industries. Although artificial intelligence (AI) has great promise for improving efficiency and accuracy in decision-making, it also poses serious concerns about how AI will affect society and the nature of labor in the future. Our goal in this study article is to investigate the possible effects of AI's widespread use on employment, inequality, ethics, and privacy. We'll look at current developments in AI and assess how they affect different sectors, such as the job market, healthcare, finance, and education. Additionally, the article will evaluate the possible risks that come with AI and go over possible mitigation strategies. Through an analysis of AI's current state and future social implications, this research attempts to offer a thorough and informed view of what work will look like in an AI-driven world. Keywords: Societal Impact, Role, Ethical Considerations, Influence on Specific Industries, Potential, Conclusion The goal of the computer science and engineering field of artificial intelligence (AI) is to build intelligent computers that are capable of carrying out tasks that would typically require human intelligence. Language comprehension, pattern identification, and decision-making are all part of this activity. AI systems are now widely used across many industries, including healthcare, banking, education, and retail, thanks to the growth of AI technology. The broad use of AI has the potential to help society greatly in a number of ways, such as increased decision-making accuracy and efficiency, better patient outcomes in the medical field, and enhanced financial outcomes in the banking industry. But as AI advances, significant questions concerning its effects on society and the direction of business are brought up. AI systems have the ability to change the power

Decolonizing and Diversifying Research in Cognitive Development

ABSTRACT This article serves as an introduction to the Special Issue on “Decolonizing and Diversifying Research in Cognitive Development.” The Special Issue comprises six articles: two articles are empirical articles that focus on executive function development in under-represented environments, two articles address barriers pathways toward behavioral change in advancing toward diversification and decolonization; and two articles focus on reconciling sampling and reporting practices with the scientific goal of generalizability. Though different lenses, each article identifies important practices and considerations for broadening the study of cognitive development to faithfully represent children’s diverse environments and trajectories via developing methods and practices in situ and sampling judiciously across diverse settings. The series culminates in a commentary via Bard and Keller, who discuss each article and articulate future pathways toward decolonization and diversification.

Selection of the Family Electric Car Based on Objective and Subjective Criteria—Analysis of a Case Study of Polish Consumers

In accordance with the policies of European Union countries, including Poland, users of combustion vehicles are and will be encouraged in the coming years to purchase BEVs (battery electric vehicles) through various restrictions, burdens, and incentives. In this context, it is important to choose a BEV that meets consumer needs. The practical aim of the article was to analyse BEVs used in households and to select a car with the highest utility for such a household located in a specific city in Poland. The scientific goal was to analyse the impact of subjective criteria and the imprecision of judgements on the results of the multi-criteria assessment of BEVs. The research used the PVM-VSI (Preference Vector Method—Vector Space of Increments) method, which allows for examining the impact of subjective criteria on the assessment results. Moreover, by examining the deviations of the assessments, the PVM-VSI method also allows for measuring the imprecision of subjective judgements. The study showed that including subjective criteria in the decision-making model may have a decisive impact on the obtained ranking of alternatives. In the study using objective and subjective criteria, Nissan Ariya ranked first. However, in the ranking based solely on objective criteria, Kia Niro EV won, and the winner of the first ranking took the last place. In the study, the imprecision of judgements did not have a significant impact on the order of vehicles in the ranking. Based on utilities and standard deviations, it was found that only if the decision-maker was prone to gambling would there be a slight switch between alternatives.

The Potential of Participatory Social Economics: A Framework and Feminist Perspective

Recurrent economic, ecological and democratic crises provoke opportunities to critically examine previously unquestioned assumptions about economies, an effort which can include those people on which the theory and practice are usually designed and tested. This article claims that there is a role for participatory research (PR) approaches to play for social economists seeking to understand and support local problem-solving, especially in social economy activities and socioecological change processes. The objective of the article is twofold: first, a simple framework for understanding the different manifestations of PR will be presented based on the degree to which a project has (a) scientific or transformative goals, and (b) hierarchical or democratic decision-making. Second, this text will discuss the kind of PR that is ethically and epistemologically compatible with social economic scholarship. This theoretical argument is grounded in feminist, Black and Indigenous principles of (1) highlighting subjective and socially-embedded perspectives, (2) critically interrogating power dynamics within research contexts and processes, and (3) ethically engaging participants in collaborative research. Such an approach aims to dismantle the hierarchical binary between scientific and non-scientific knowledges, with social economists actively contributing to collective problem-solving efforts rather than acting as detached observers and planners.

Rethinking Civic Science Funding to Better Support Community Engagement

Civic science funders and grantmakers play a key role in supporting community engagement efforts. However, current funding policies are often misaligned with the realities of community engagement. Collaboration with community partners moves at the speed of trust, and trust requires time, relationship-building, and persistence. Yet, many civic science grants have short timelines, burdensome reporting requirements, and are focused on quantitative outcomes. To realize the goals of civic science, grantmakers need to shift their practices to provide more flexible and long-term funding. This means extending funding periods from one year to multiple years, creating open communication channels to streamline and reduce reporting requirements and frequency, and valuing relationship-building as an important outcome.

Ethical considerations in utilizing artificial intelligence for analyzing the NHGRI’s History of Genomics and Human Genome Project archives

Understanding “how to optimize the production of scientific knowledge” is paramount to those who support scientific research—funders as well as research institutions—to the communities served, and to researchers. Structured archives can help all involved to learn what decisions and processes help or hinder the production of new knowledge. Using artificial intelligence (AI) and large language models (LLMs), we recently created the first structured digital representation of the historic archives of the National Human Genome Research Institute (NHGRI), part of the National Institutes of Health. This work yielded a digital knowledge base of entities, topics, and documents that can be used to probe the inner workings of the Human Genome Project, a massive international public-private effort to sequence the human genome, and several of its offshoots like The Cancer Genome Atlas (TCGA) and the Encyclopedia of DNA Elements (ENCODE). The resulting knowledge base will be instrumental in understanding not only how the Human Genome Project and genomics research developed collaboratively, but also how scientific goals come to be formulated and evolve. Given the diverse and rich data used in this project, we evaluated the ethical implications of employing AI and LLMs to process and analyze this valuable archive. As the first computational investigation of the internal archives of a massive collaborative project with multiple funders and institutions, this study will inform future efforts to conduct similar investigations while also considering and minimizing ethical challenges. Our methodology and risk-mitigating measures could also inform future initiatives in developing standards for project planning, policymaking, enhancing transparency, and ensuring ethical utilization of artificial intelligence technologies and large language models in archive exploration.Author Contributions:&amp;nbsp;Mohammad Hosseini: Investigation; Project Administration; Writing – original draft; Writing – review &amp;amp; editing.&amp;nbsp;Spencer Hong: Conceptualization, Data curation, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review &amp;amp; editing.&amp;nbsp;Thomas Stoeger: Conceptualization; Investigation; Project Administration; Supervision; Writing – original draft; Writing – review &amp;amp; editing.&amp;nbsp;Kristi Holmes: Funding acquisition, Supervision, Writing – review &amp;amp; editing.&amp;nbsp;Luis A. Nunes Amaral: Funding acquisition, Supervision, Writing – review &amp;amp; editing.&amp;nbsp;Christopher Donohue: Conceptualization, Project administration, Resources, Supervision, Writing – original draft, Writing – review &amp;amp; editing.&amp;nbsp;Kris Wetterstrand: Conceptualization, Funding acquisition, Project administration.

The model-as-a-resource paradigm for geoscience digital ecosystems

A long-term goal of environmental science and Earth observation is to enable the creation of a “Model Web” of semantically interconnected data and models. Geospatial models are usually exposed on the Web as services accessible through heterogeneous interfaces. However, such services, which represent instances of the paradigm called Model-as-a-Service (MaaS), cannot be easily exploited beyond their original use as defined by the service provider. To overcome this important limitation and better support transparency, reproducibility, replicability and reusability of the model (following the Open Science paradigm), we investigated the adoption of a Model-as-a-Resource (MaaR) approach, in which a model is considered a generic digital resource that, as such, can play different roles in different potential use cases. The proposed MaaR framework can play an important enabling role in the realization of those digital ecosystems that generate environmental knowledge. The main challenges and opportunities are discussed in the manuscript.