ethicalabs/Research-Intent-Collab huggingface dataset:
Benchmark Dataset for Precipitation Forecasting by Post-Processing the Numerical Weather Prediction

Precipitation forecasting is an important scientific challenge that has wide-reaching impacts on society. Historically, this challenge has been tackled using numerical weather prediction (NWP) models, grounded on physics-based simulations. Recently, many works have proposed an alternative approach, using end-to-end deep learning (DL) models to replace physics-based NWP models. While these DL methods show improved performance and computational efficiency, they exhibit limitations in long-term forecasting and lack the explainability. In this work, we present a hybrid NWP-DL workflow to fill the gap between standalone NWP and DL approaches. Under this workflow, the outputs of NWP models are fed into a deep neural network, which post-processes the data to yield a refined precipitation forecast. The deep model is trained with supervision, using Automatic Weather Station (AWS) observations as ground-truth labels. This can achieve the best of both worlds, and can even benefit from future improvements in NWP technology. To facilitate study in this direction, we present a novel dataset focused on the Korean Peninsula, termed KoMet (Korea Meteorological Dataset), comprised of NWP outputs and AWS observations. For the NWP model, the Global Data Assimilation and Prediction Systems-Korea Integrated Model (GDAPS-KIM) is utilized. We provide analysis on a comprehensive set of baseline methods aimed at addressing the challenges of KoMet, including the sparsity of AWS observations and class imbalance. To lower the barrier to entry and encourage further study, we also provide an extensive open-source Python package for data processing and model development. Our benchmark data and code are available at https://github.com/osilab-kaist/KoMet-Benchmark-Dataset.
Quality of Life and Patient-Reported Outcomes in Patients With Osteosarcoma

Patients diagnosed with osteosarcoma undergo intensive multimodality treatment that can lead to long-term adverse effects, significantly impacting various aspects of daily living. To objectively assess the Health-Related Quality of Life (HR-QoL) in pediatric and adult populations, several Patient-Reported Outcome Measurements (PROMs) are available. However, these questionnaires often exhibit substantial variability in the domains and items they encompass, frequently failing to address aspects that are particularly important after osteosarcoma treatment. A systematic review was conducted to identify the most frequently used questionnaires concerning QoL in pediatric and adult patients with osteosarcoma and to examine the diverse domains and subdomains of QoL assessed by these questionnaires to identify gaps in their coverage, to recommend suitable instruments for an upcoming European trial within the Fighting Osteosarcoma Through European Research (FOSTER) Consortium. English-language literature published since 1980 in PubMed was reviewed. One hundred twenty-eight articles were initially screened for eligibility. Sixty-three original articles were included in the qualitative synthesis. An overview from review articles was given. Selected studies displayed substantial heterogeneity in terms of their objectives, target populations, age ranges, follow-up time, and number of patients included. None of the questionnaires covered all age groups and addressed all important aspects following osteosarcoma treatment. To comprehensively address as many relevant aspects as possible, a combination of questionnaires is suggested. For the adult population, it is recommended to use the EORTC-QLQ-C30 questionnaire together with the Body Image Scale (BIS), while for pediatric patients, the PedsQl-generic and PedsQl-cancer-specific questionnaires and BIS (.
Optimization-Based Eukaryotic Genetic Circuit Design (EuGeneCiD) and Modeling (EuGeneCiM) Tools: Computational Approach to Synthetic Biology

Synthetic biology has the potential to revolutionize the biotech industry and our everyday lives and is already making an impact. Developing synthetic biology applications requires several steps including design and modeling efforts which may be performed by in silico tools. In this work, we have developed two such tools, Eukaryotic Genetic Circuit Design (EuGeneCiD) and Modeling (EuGeneCiM), which use optimization concepts and bioparts including promotors, transcripts, and terminators in designing and modeling genetic circuits. EuGeneCiD and EuGeneCiM preclude problematic designs leading to future synthetic biology application development pipelines. EuGeneCiD and EuGeneCiM are applied to developing 30 basic logic gates as genetic circuit conceptualizations which respond to heavy metal ions pairs as input signals for Arabidopsis thaliana. For each conceptualization, hundreds of potential solutions were designed and modeled. Demonstrating its time-dependence and the importance of including enzyme and transcript degradation in modeling, EuGeneCiM is used to model a repressilator circuit.
AI in Education: Adaptive Learning Systems and Intelligent Tutoring

Artificial Intelligence (AI) has the potential to revolutionize education by transforming traditional learning environments into dynamic, adaptive, and personalized experiences. This paper explores how AI can reshape education through adaptive learning systems and intelligent tutoring systems. It investigates the technologies driving these advancements, examines their impact on educational outcomes, and addresses the challenges and opportunities associated with their implementation.
Sustainable Reporting: Upaya Korporasi Mengevaluasi Corporate Social Responsibility

Any discussion of social responsibilities reporting necessarily needs to consider what the responsibilities of organizations are. Are businesses responsible to their direct owners( shareholders) alone, or do they owe a duty to the wider community in which they operate? Certainly, many organizations are making public statements to the effect that they consider that they do have responsibilities to parties other than just the shareholders. A .firm should voluntarily discloses information publicly about its social and environmental performance that implies the managers are acknowledging that they are accountable to abroad group of stakeholders in relation not to only their financial performance, but also their social and environmental performance. Those environmental aspects should be described in o lot of progress in their social publicly responsibilities reporting, hoping that all shareholders know completely the whole position and financial performance, risks, business prospects and corporate sustainability. The accountability of corporate must disclose their socio economic environmental accounting as a broader of Corporate Social Responsibility (CSR), which contain of social maping, that shows harmonization of goals between corporate and peoples.
Strategic Decision Analysis for Investment Portfolios: Computational Risk Assessment in Transportation Asset Management

This study presents a strategic decision-making framework for portfolio investment by applying computational risk assessment methods to the transportation sector in Indonesia. Focusing on two key players—PT. Adi Sarana Tbk (ASSA) and PT. Blue Bird Tbk (BIRD)—the research evaluates their individual and combined risk-return profiles using daily stock return data from December 2023 to November 2024. A hybrid analytical approach is employed, integrating traditional financial metrics with advanced computational techniques such as Monte Carlo simulation and Value at Risk (VaR) modeling to support managerial decision-making. The analysis reveals contrasting performance patterns: BIRD exhibits marginally positive expected returns (0.04%) but higher downside risk exposure, whereas ASSA shows negative average returns (-0.06%) yet demonstrates lower volatility and reduced extreme loss potential. Portfolio optimization results demonstrate that a diversified allocation of 60% ASSA and 40% BIRD generates improved risk-adjusted returns, achieving a positive expected return while maintaining lower overall risk compared to the individual assets. By incorporating monetary VaR estimates, this study enhances the practical relevance of risk analytics for portfolio managers, particularly in navigating volatile emerging markets. The findings underscore the importance of strategic asset allocation, business model differentiation, and quantitative risk modeling in constructing resilient investment portfolios. This research contributes both methodologically and managerially by offering a robust, replicable framework for strategic decision analysis in transportation asset management and beyond.
Error-correcting codes for adiabatic quantum computation

Errors corrected
Hesitating at the door: differences in perceptions between genders and generations on sexual and reproductive health and rights in Kaski, Nepal

This case study describes issues of sexual and reproductive health and rights from the perspectives of young people and provides an analysis of the context in which the Family Planning Association of Nepal (FPAN) is providing youth friendly services and comprehensive sexuality education.\ud The achievements of IPPF's A+ project to realise youth sexual rights have helped to raise awareness of youth friendly services, and the learning in the youth programme can help to achieve more of a rights-based approach in the longer term.\ud In order both to go beyond information, addressing sex and sexuality in a non-biological more emotional way, and to analyse and address gender discrimination and power dynamics in the community further, the project needs time. The A+ project is providing learning to the broader youth programme and feeding into advocacy in both youth friendly services and comprehensive sexuality education (discussed in Johnson, V et al. 2013, Love, Sexual Rights and Young People, IPPF).
The Impact of Social Media on the Mental Health of Young Children and Adolescents

Introduction Adolescents are among the most active users of emerging digital technologies, especially social media platforms. The rapid development of social media has significantly reshaped how young people communicate, build relationships, and shape their identities. At the same time, questions about the impact of social media use on mental health have raised due to increasing rates of anxiety, depression, sleep problems, and disturbed body image. This review explores the intricate association between social media engagement and adolescent mental health. It highlights both disadvantages, as well as possible benefits of social media use. Aim of the study This review examines the influence of social media on adolescents and its diverse connection with mental health.While the current body of research provides valuable insights, it also reveals unexplored areas that should shape the direction of future investigations. Materials and methods We searched the following databases: PubMed, Web of Science, Google Scholar, clinical trial registry platforms, using key terms such as “social media”, “mental health”, “social media addiction”, “adolescents”. Conclusion This review highlights both the risks and benefits of adolescent social media use, emphasising its impact on mental health.While over-consumption is linked to anxiety, depression, and sleep disruption, social media can also promote support, self-expression, and connection. Understanding factors is essential for developing effective clinical, educational, and policy-based interventions.
Occupational Health and Safety Practices at Workplace during COVID-19 Pandemic

Coronavirus Disease 2019 (COVID-19) a highly contagious viral respiratory disease has been declared as pandemic due to its global spread across most countries. Social isolation (i.e. physical distancing) and strict embracement of personal hygiene are few demonstrated preventive methods of COVID-19 transmission. All countries, based on the recommendations of World Health Organization (WHO), have adopted lockdown strategy (promoting physical distancing) to prevent COVID-19 transmission in the community including workplaces. In the interest of economic sustenance, many countries have partially relaxed the lockdown policies, to resume selective functioning of factories / organizations / institutes / workplaces. However, in the absence of appropriate occupational health and safety policies, workplaces are a potential threat for COVID-19 transmission &amp; outbreak. The following document reviews the conventional hierarchy of occupational safety and health control measures (i.e. engineering controls, administrative controls and PPE), necessary to prevent COVID-19 outbreaks at workplace, based on the current scientific evidences on COVID-19.
AQUAPONICS: A SUSTAINABLE TECHNOLOGY FOR AQUACULTURE AND AGRICULTURE FOOD SECURITY

Aquaponics are one of green technologies and that would allow for the production of multiple food products like fish and vegetables in one complete system. Aquaponic systems also have potential as a sustainable food security practice as the production of vegetables and fish can be done simultaneously. The process also supports global Sustainable Development Goals (SDG); SDG 1 (no poverty) and SDG 2 (zero hunger), with the implementation of aquaponic systema by local communities. The aquaponic concept is characterised by the recycling of fish waste into beneficial products and as a source of nutrients and natural fertilisers for plant growth. The synergy between the plants, fish and bacteria in the soil and fish waste benefits all components in the system and promotes a circular economy. This review emphasises the types of aquaponic systems, the fish that are suitable for aquaponic cultivation, the types of vegetables that are suitable for aquaponic cultivation and the benefits of the application of aquaponic systems. Aquaponic systems, help promote a sustainable environment, for the recycling of fish and plant waste into fertilisers that maximise resources use and production yields. In this manner, the system helps the local community improve socioeconomic and income generation as well as supports national and global food security agenda.
Genetic Discrimination and Health Insurance

According to US law, insurance companies can lawfully differentiate individual health insurance premiums on the basis of non-genetic medical information, but not on the basis of genetic information. The article reviews the case for such genetic exceptionalism. First, I critically assess some standard justifications. Next, I scrutinize an argument appealing to the view that genetically based premium differentiation expresses that persons do not all merit equal concern and respect. In the final section, I argue that even if genetic exceptionalism is unjustified, there is a forceful luck egalitarian argument against basing premiums on genetic risks, to wit, that this tends to make some individuals worse off than others as a result of the bad brute luck involved in having a genetically determined, above-average risk of developing health problems.
TaikoNation: Patterning-focused Chart Generation for Rhythm Action Games

Generating rhythm game charts from songs via machine learning has been a problem of increasing interest in recent years. However, all existing systems struggle to replicate human-like patterning: the placement of game objects in relation to each other to form congruent patterns based on events in the song. Patterning is a key identifier of high quality rhythm game content, seen as a necessary component in human rankings. We establish a new approach for chart generation that produces charts with more congruent, human-like patterning than seen in prior work.
IVOA Recommendation: Data Model for Astronomical DataSet Characterisation

This document defines the high level metadata necessary to describe the physical parameter space of observed or simulated astronomical data sets, such as 2D-images, data cubes, X-ray event lists, IFU data, etc.. The Characterisation data model is an abstraction which can be used to derive a structured description of any relevant data and thus to facilitate its discovery and scientific interpretation. The model aims at facilitating the manipulation of heterogeneous data in any VO framework or portal. A VO Characterisation instance can include descriptions of the data axes, the range of coordinates covered by the data, and details of the data sampling and resolution on each axis. These descriptions should be in terms of physical variables, independent of instrumental signatures as far as possible.   Implementations of this model has been described in the IVOA Note available at: http://www.ivoa.net/Documents/latest/ImplementationCharacterisation.html   Utypes derived from this version of the UML model are listed and commented in the following IVOA Note: http://www.ivoa.net/Documents/latest/UtypeListCharacterisationDM.html   An XML schema has been build up from the UML model and is available at: http://www.ivoa.net/xml/Characterisation/Characterisation-v1.11.xsd
Lindorm TSDB: A Cloud-Native Time-Series Database for Large-Scale Monitoring Systems

Internet services supported by large-scale distributed systems have become essential for our daily life. To ensure the stability and high quality of services, diverse metric data are constantly collected and managed in a time-series database to monitor the service status. However, when the number of metrics becomes massive, existing time-series databases are inefficient in handling high-rate data ingestion and queries hitting multiple metrics. Besides, they all lack the support of machine learning functions, which are crucial for sophisticated analysis of large-scale time series. In this paper, we present Lindorm TSDB, a distributed time-series database designed for handling monitoring metrics at scale. It sustains high write throughput and low query latency with massive active metrics. It also allows users to analyze data with anomaly detection and time series forecasting algorithms directly through SQL. Furthermore, Lindorm TSDB retains stable performance even during node scaling. We evaluate Lindorm TSDB under different data scales, and the results show that it outperforms two popular open-source time-series databases on both writing and query, while executing time-series machine learning tasks efficiently.
The Great Inequality In A Hamiltonian Planetary Theory

The Jupiter-Saturn 2:5 near-commensurability is analyzed in a fully analytic Hamiltonian planetary theory. Computations for the Sun-Jupiter-Saturn system, extending to the third order of the masses and to the 8th degree in the eccentricities and inclinations, reveal an unexpectedly sensitive dependence of the solution on initial data and its likely nonconvergence. The source of the sensitivity and apparent lack of convergence is this near-commensurability, the so-called great inequality. This indicates that simple averaging, still common in current semi-analytic planetary theories, may not be an adequate technique to obtain information on the long-term dynamics of the Solar System.   Preliminary results suggest that these difficulties can be overcome by using resonant normal forms.
Suicide prediction with natural language processing of electronic health records

Abstract                 Suicide attempts are one of the most challenging psychiatric outcomes and have great importance in clinical practice. However, they remain difficult to detect in a standardised way to assist prevention because assessment is mostly qualitative and often subjective. As digital documentation is increasingly used in the medical field, Electronic Health Records (EHRs) have become a source of information that can be used for prevention purposes, containing codified data, structured data, and unstructured free text. This study aims to provide a quantitative approach to suicidality detection using EHRs, employing natural language processing techniques in combination with deep learning artificial intelligence methods to create an algorithm intended for use with medical documentation in German. Using psychiatric medical files from in-patient psychiatric hospitalisations between 2013 and 2021, free text reports will be transformed into structured embeddings using a German trained adaptation of Word2Vec, followed by a Long-Short Term Memory (LSTM) – Convolutional Neural Network (CNN) approach on sentences of interest. Text outside the sentences of interest will be analysed as context using a fixed size ordinally-forgetting encoding (FOFE) before combining these findings with the LSTM-CNN results in order to label suicide related content. This study will offer promising ways for automated early detection of suicide attempts and therefore holds opportunities for mental health care.
A New Dynamical Mean-Field Dynamo Theory and Closure Approach

We develop a new nonlinear mean field dynamo theory that couples field growth to the time evolution of the magnetic helicity and the turbulent electromotive force, $\emfb$. We show that the difference between kinetic and current helicities emerges naturally as the growth driver when the time derivative of $\emfb$ is coupled into the theory. The solutions predict significant field growth in a kinematic phase and a saturation rate/strength that is magnetic Reynolds number dependent/independent in agreement with numerical simulations. The amplitude of early time oscillations provides a diagnostic for the closure.
Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network

Mucopolysaccharidosis (MPS) is a group of lysosomal storage diseases (LSD), characterized by the deficiency of a lysosomal enzyme responsible for the degradation of glycosaminoglycans (GAG). This deficiency leads to the lysosomal accumulation of partially degraded GAG. Nevertheless, deficiency of a single lysosomal enzyme has been associated with impairment in other cell mechanism, such as apoptosis and redox balance. Although GAG analysis represents the main biomarker for MPS diagnosis, it has several limitations that can lead to a misdiagnosis, whereby the identification of new biomarkers represents an important issue for MPS. In this study, we used a system biology approach, through the use of a genome-scale human metabolic reconstruction to understand the effect of metabolism alterations in cell homeostasis and to identify potential new biomarkers in MPS. In-silico MPS models were generated by silencing of MPS-related enzymes, and were analyzed through a flux balance and variability analysis. We found that MPS models used approximately 2286 reactions to satisfy the objective function. Impaired reactions were mainly involved in cellular respiration, mitochondrial process, amino acid and lipid metabolism, and ion exchange. Metabolic changes were similar for MPS I and II, and MPS III A to C; while the remaining MPS showed unique metabolic profiles. Eight and thirteen potential high-confidence biomarkers were identified for MPS IVB and VII, respectively, which were associated with the secondary pathologic process of LSD. In vivo evaluation of predicted intermediate confidence biomarkers (β-hexosaminidase and β-glucoronidase) for MPS IVA and VI correlated with the in-silico prediction. These results show the potential of a computational human metabolic reconstruction to understand the molecular mechanisms this group of diseases, which can be used to identify new biomarkers for MPS.