ethicalabs/Research-Intent-Collab huggingface dataset:
The JCMT Transient Survey: Data Reduction and Calibration Methods

Though there has been a significant amount of work investigating the early stages of low-mass star formation in recent years, the evolution of the mass assembly rate onto the central protostar remains largely unconstrained. Examining in depth the variation in this rate is critical to understanding the physics of star formation. Instabilities in the outer and inner circumstellar disk can lead to episodic outbursts. Observing these brightness variations at infrared or submillimetre wavelengths sets constraints on the current accretion models. The JCMT Transient Survey is a three-year project dedicated to studying the continuum variability of deeply embedded protostars in eight nearby star-forming regions at a one month cadence. We use the SCUBA-2 instrument to simultaneously observe these regions at wavelengths of 450 $μ$m and 850 $μ$m. In this paper, we present the data reduction techniques, image alignment procedures, and relative flux calibration methods for 850 $μ$m data. We compare the properties and locations of bright, compact emission sources fitted with Gaussians over time. Doing so, we achieve a spatial alignment of better than 1" between the repeated observations and an uncertainty of 2-3\% in the relative peak brightness of significant, localised emission. This combination of imaging performance is unprecedented in ground-based, single dish submillimetre observations. Finally, we identify a few sources that show possible and confirmed brightness variations. These sources will be closely monitored and presented in further detail in additional studies throughout the duration of the survey.
The dark side of transparency: Does the Nigeria extractive industries transparency initiative help or hinder accountability and corruption control?

Abstract   This study explores the dark side of transparency by problematizing the Nigeria Extractive Industries Transparency Initiative (NEITI) as a transparency, accountability and anti-corruption initiative in Nigeria. It does this by interrogating the underlying assumptions that transparency in the form of increased information disclosure inevitably leads to enhanced accountability and reduced corruption. Theoretic insights are drawn from the transparency literature as well as from the International Accounting Standards Board's framework for financial reporting. The findings enable a more nuanced understanding of transparency – where and when transparency works, and where and when it may lead to unintended outcomes. They show how increased information disclosure conceals and legitimises the weak and corrupt reporting systems and practices of government agencies. They highlight the importance of understandability of information disclosed as a key requirement of transparency. They illustrate that transparency is a complex social process by highlighting the means by which the government tries to gain control of the NEITI organisation and how NEITI's ability to operate effectively is dependent on the political will of the government in power. The findings also demonstrate that the instrument through which transparency is enacted is itself a central actor in the transparency process as historical corruption within the NEITI bureaucracy as well as the opacity of NEITI as an organisation lead to outcomes of distrust, uncertainty and doubt amongst NEITIs target audience.
A Machine Learning-Based Error Mitigation Approach for Reliable Software Development on IBM’s Quantum Computers

Quantum computers have the potential to outperform classical computers for some complex computational problems. However, current quantum computers (e.g., from IBM and Google) have inherent noise that results in errors in the outputs of quantum software executing on the quantum computers, affecting the reliability of quantum software development. The industry is increasingly interested in machine learning (ML)--based error mitigation techniques, given their scalability and practicality. However, existing ML-based techniques have limitations, such as only targeting specific noise types or specific quantum circuits. This paper proposes a practical ML-based approach, called Q-LEAR, with a novel feature set, to mitigate noise errors in quantum software outputs. We evaluated Q-LEAR on eight quantum computers and their corresponding noisy simulators, all from IBM, and compared Q-LEAR with a state-of-the-art ML-based approach taken as baseline. Results show that, compared to the baseline, Q-LEAR achieved a 25% average improvement in error mitigation on both real quantum computers and simulators. We also discuss the implications and practicality of Q-LEAR, which, we believe, is valuable for practitioners.
&ldquo;Pop-Up Systems&rdquo;&mdash;Innovative Sport and Exercise-Oriented Offerings for Promoting Physical Activity in All-Day Schools

In all-day schools and schools with extended education, children have the opportunity to spend the whole day at school. Full-day attendance at school requires a reorientation of extended time. Therefore, it is important to consider how children spend their time between lessons and identify conducive opportunities. As part of the project &ldquo;Sport in the School Environment&rdquo;, multiple mobile pop-up facilities were installed at several all-day schools in Zurich, Switzerland, for a period of 3&ndash;6 weeks. These facilities included &ldquo;Parkour&rdquo;, &ldquo;Pumptrack&rdquo;, &ldquo;Skatepark&rdquo;, and &ldquo;Streetsoccer&rdquo;. The aim was to find out whether these installations offer an opportunity for physical activity (PA). At the same time, social aspects of the students using these facilities as part of their all-day school attendance were observed. The following questions were investigated: How, by whom, and in which contexts are the installations used? The use of the facilities was evaluated content-analytically and descriptively. It was shown that the facilities are used intensively and in very different ways. The main focus of the pop-up facilities is on the use of the facilities and the creation of opportunities for children and young people to exercise and meet up with each other.
KG-FLoc: Knowledge Graph-Enhanced Fault Localization in Secondary Circuits via Relation-Aware Graph Neural Networks

This paper introduces KG-FLoc, a knowledge graph-enhanced framework for secondary circuit fault localization in intelligent substations. The proposed KG-FLoc innovatively formalizes secondary components (e.g., circuit breakers, disconnectors) as graph nodes and their multi-dimensional relationships (e.g., electrical connections, control logic) as edges, constructing the first comprehensive knowledge graph (KG) to structurally and operationally model secondary circuits. By reframing fault localization as a knowledge graph link prediction task, KG-FLoc identifies missing or abnormal connections (edges) as fault indicators. To address dynamic topologies and sparse fault samples, KG-FLoc integrates two core innovations: (1) a relation-aware gated unit (RGU) that dynamically regulates information flow through adaptive gating mechanisms, and (2) a hierarchical graph isomorphism network (GIN) architecture for multi-scale feature extraction. Evaluated on real-world datasets from 110 kV/220 kV substations, KG-FLoc achieves 97.2% accuracy in single-fault scenarios and 93.9% accuracy in triple-fault scenarios, surpassing SVM, RF, MLP, and standard GNN baselines by 12.4–31.6%. Beyond enhancing substation reliability, KG-FLoc establishes a knowledge-aware paradigm for fault diagnosis in industrial systems, enabling precise reasoning over complex interdependencies.
Collaborative Applications over Peer-to-Peer Systems - Challenges and Solutions

Emerging collaborative Peer-to-Peer (P2P) systems require discovery and utilization of diverse, multi-attribute, distributed, and dynamic groups of resources to achieve greater tasks beyond conventional file and processor cycle sharing. Collaborations involving application specific resources and dynamic quality of service goals are stressing current P2P architectures. Salient features and desirable characteristics of collaborative P2P systems are highlighted. Resource advertising, selecting, matching, and binding, the critical phases in these systems, and their associated challenges are reviewed using examples from distributed collaborative adaptive sensing systems, cloud computing, and mobile social networks. State-of-the-art resource discovery/aggregation solutions are compared with respect to their architecture, lookup overhead, load balancing, etc., to determine their ability to meet the goals and challenges of each critical phase. Incentives, trust, privacy, and security issues are also discussed, as they will ultimately determine the success of a collaborative P2P system. Open issues and research opportunities that are essential to achieve the true potential of collaborative P2P systems are discussed.
Bayesian optimization assisted unsupervised learning for efficient intra-tumor partitioning in MRI and survival prediction for glioblastoma patients

Glioblastoma is profoundly heterogeneous in microstructure and vasculature, which may lead to tumor regional diversity and distinct treatment response. Although successful in tumor sub-region segmentation and survival prediction, radiomics based on machine learning algorithms, is challenged by its robustness, due to the vague intermediate process and track changes. Also, the weak interpretability of the model poses challenges to clinical application. Here we proposed a machine learning framework to semi-automatically fine-tune the clustering algorithms and quantitatively identify stable sub-regions for reliable clinical survival prediction. Hyper-parameters are automatically determined by the global minimum of the trained Gaussian Process (GP) surrogate model through Bayesian optimization(BO) to alleviate the difficulty of tuning parameters for clinical researchers. To enhance the interpretability of the survival prediction model, we incorporated the prior knowledge of intra-tumoral heterogeneity, by segmenting tumor sub-regions and extracting sub-regional features. The results demonstrated that the global minimum of the trained GP surrogate can be used as sub-optimal hyper-parameter solutions for efficient. The sub-regions segmented based on physiological MRI can be applied to predict patient survival, which could enhance the clinical interpretability for the machine learning model.
LncRNAs and epigenetic regulation of vascular endothelium: genome positioning system and regulators of chromatin modifiers

Epigenetic mechanisms regulate the cell type-specific expression of endothelial-enriched genes. A major question has been how chromatin modifiers without inherent sequence specificity can be targeted to genomic coordinates. Recently, long noncoding RNAs (lncRNAs) have emerged as candidates for specifying genomic positioning for chromatin modifiers. However, lncRNAs function by a number of mechanisms in both the nucleus and the cytoplasm. Recent studies indicate the existence of endothelial-enriched lncRNAs. This review discusses lncRNA regulation in endothelial cells with a focus on four recently described nuclear-enriched lncRNAs: MANTIS, LEENE, STEEL, and GATA6-AS. This emerging work on these lncRNAs contributes to our understanding of epigenetic regulation in the vascular endothelium with links to important themes in endothelial biology, including angiogenesis and shear stress.
The protection of sexual and reproductive health in European human rights law: perspectives from the Council of Europe

The recent recognition of a ‘right to sexual and reproductive health’ in human rights law and the volatility of national legal safeguards on such issues (as highlighted by recent events in Europe), call for a discussion on how sexual and reproductive health is currently protected in European human rights law. This article will thus, explore whether and how human rights bodies of the Council of Europe protect individuals’ sexual and reproductive health through their monitoring procedures. It will analyse the case-law of the European Court of Human Rights and that of the European Committee of Social Rights since 2000, to finally conclude whether or not these indirectly enable the recognition of a right to sexual and reproductive health in the future of the Council of Europe.
Method of local‐scaling transformations and density functional theory in quantum chemistry. II. The procedure for reproducing a many‐electron wave function from x‐ray diffraction data on one‐electron density

AbstractThe computational procedure for reconstructing a many‐electron wave function on the basis of x‐ray diffraction data of one‐electron density is proposed within the method of local‐scaling transformations.
Additional file 2 of Beyond coverage: a qualitative study exploring the perceived impact of Gabon’s health insurance plan on access to and quality of prenatal care

Additional file 2. Health professionals Interview guide
Integration of NoSQL Databases for Analyzing Spatial Information in Geographic Information System

Geography Information Systems allow you to manage and analyze spatial data over the past two decades have represented a practical solution for generating scenarios for decision-making. Over the years the amount of spatial information has been increasing result of the digitization of historical collections and the use of satellite images ("Raster") and aerial photography. In addition the new digital technologies spatial modeling make such systems need to manage large amounts of data. In this article the use of new managers databases called NoSQL especially raises the MongoDB version which is characterized by the management of information through documents for the creation of a GIS that has the quality especially be comprised of satellite images stored for more than a decade.
Development of Guanfacine Extended-Release Dosing Strategies in Children and Adolescents with ADHD Using a Physiologically Based Pharmacokinetic Model to Predict Drug–Drug Interactions with Moderate CYP3A4 Inhibitors or Inducers

Guanfacine extended-release (GXR) is an orally administered, non-stimulant treatment for children and adolescents with attention-deficit/hyperactivity disorder (ADHD) and is primarily metabolized by the 3A4 isozyme of cytochrome P450 (CYP3A4). The results of clinical pharmacokinetic (PK) studies indicate that guanfacine is sensitive to drug-drug interactions (DDIs) perpetrated by strong inhibitors and inducers of CYP3A4.The aim was to provide guidance on the possible requirement for GXR dose adjustment in children and adolescents with ADHD by predicting DDIs following co-administration with moderate CYP3A4 inhibitors and inducers.A physiologically based PK model for GXR orally administered to healthy adults was developed based on physicochemical, in vitro and clinical PK data. The model was validated using clinical PK data for co-administration of GXR with ketoconazole (strong CYP3A4 inhibitor) or rifampicin (strong CYP3A4 inducer).Model predictions indicated that co-administration of GXR with the moderate CYP3A4 inhibitors erythromycin 500 mg three times a day or fluconazole 200 mg daily (q.d.) increased the guanfacine area under the plasma concentration-time curve (AUC) by 2.31-fold or 1.98-fold, respectively, compared with GXR monotherapy. The moderate CYP3A4 inducer efavirenz 400 mg or 600 mg q.d. was predicted to reduce guanfacine AUC to 58 or 33% of its value for GXR monotherapy, respectively.Without the requirement for additional clinical studies, the following GXR dose recommendations were developed and approved for US labeling for use in children and adolescents with ADHD: (1) decrease GXR to 50% of the usual target dose when it is co-administered with strong or moderate CYP3A4 inhibitors; (2) consider titrating GXR up to double the usual target dose over 1-2 weeks when it is co-administered with strong or moderate CYP3A4 inducers.
Performances on simulator and da Vinci robot on subjects with and without surgical background

Objective: To assess whether previous training in surgery influences performance on da Vinci Skills Simulator and da Vinci robot. Material and methods: In this prospective study, thirty-seven participants (11 medical students, 17 residents, and 9 attending surgeons) without previous experience in laparoscopy and robotic surgery performed 26 exercises at da Vinci Skills Simulator. Thirty-five then executed a suture using a da Vinci robot. Results: The overall scores on the exercises at the da Vinci Skills Simulator show a similar performance among the groups with no statistically significant pair-wise differences (p < .05). The quality of the suturing based on the unedited videos of the test run was similar for the intermediate (7 (4, 10)) and expert group (6.5 (4.5, 10)), and poor for the untrained groups (5 (3.5, 9)), without statistically significant difference (p < .05). Conclusion: This study showed, for subjects new to laparoscopy and robotic surgery, insignificant differences in the scores at the da Vinci Skills Simulator and at the da Vinci robot on inanimate models.
ChemInform Abstract: Predicting Surface Phase Transitions from ab initio Based Statistical Mechanics and Thermodynamics

AbstractChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.
Practices of corporate social responsibility and sustainable systems work in Peruvian companies issuing sustainability reports

Through a literature review, this exploratory study seeks to determine whether the practices related to its colaborators, who report as part of its action responsible Peruvian companies issuing sustainability reports can be considered sustainable management practices of human resources. To this end, it was used the approach of sustainable work systems as a general approach. It was found that some of the practices of responsible management of human resources that implement the analyzed companies address the human dimensions of sustainability, although not all dimensions are considered equally or similar depth.
AI Safety, Uncontrollability, and Existential Risk: A Critical Review of the Superintelligence Problem

Generation X, broadly defined as individuals born between 1965 and 1980, represents one of the least-studied yet psychologically complex cohorts in contemporary generational research. Often described as the “forgotten” or “invisible” generation—positioned between the culturally dominant Baby Boomers and the digitally fluent Millennials—Generation X developed a distinctive psychological profile shaped by a unique confluence of sociohistorical pressures. These include the latchkey phenomenon, the collapse of institutional trust following Watergate and the Cold War, repeated economic disruptions, and the transitional experience of bridging an analogue world with an emerging digital civilization.  This paper presents a systematic psychosocial analysis of Generation X, focusing on four core psychological traits: (1) autonomous self-sufficiency rooted in unsupervised childhood, (2) defensive pessimism as a functional cognitive strategy, (3) irony and sarcasm as emotional coping mechanisms, and (4) loyalty to competence over institutional authority. Drawing on developmental psychology, sociological generational theory, cognitive science, and recent empirical evidence, we argue that Generation X’s psychological architecture is not merely a product of neglect but a sophisticated adaptive response to high-contingency environments. Furthermore, the cohort’s role as an intergenerational bridge—linking predigital culture with the algorithmically mediated present—remains underappreciated in both academic discourse and popular culture. The findings carry important implications for organizational psychology, leadership studies, and intergenerational communication.
Psychometric Alignment: Capturing Human Knowledge Distributions via Language Models

Language models (LMs) are increasingly used to simulate human-like responses in scenarios where accurately mimicking a population's behavior can guide decision-making, such as in developing educational materials and designing public policies. The objective of these simulations is for LMs to capture the variations in human responses, rather than merely providing the expected correct answers. Prior work has shown that LMs often generate unrealistically accurate responses, but there are no established metrics to quantify how closely the knowledge distribution of LMs aligns with that of humans. To address this, we introduce "psychometric alignment," a metric that measures the extent to which LMs reflect human knowledge distribution. Assessing this alignment involves collecting responses from both LMs and humans to the same set of test items and using Item Response Theory to analyze the differences in item functioning between the groups. We demonstrate that our metric can capture important variations in populations that traditional metrics, like differences in accuracy, fail to capture. We apply this metric to assess existing LMs for their alignment with human knowledge distributions across three real-world domains. We find significant misalignment between LMs and human populations, though using persona-based prompts can improve alignment. Interestingly, smaller LMs tend to achieve greater psychometric alignment than larger LMs. Further, training LMs on human response data from the target distribution enhances their psychometric alignment on unseen test items, but the effectiveness of such training varies across domains.
An analysis of the impact of content-generation-based AI design tools on the visual arts

Abstract                   With the development of artificial intelligence, the use of generative adversarial networks in deep learning can generate good visual art based on content, and at the same time, it can reduce the problems of noisy texture, transition migration, and image distortion that exist in content-based image generation. The article proposes a multi-view image generation architecture (DrawGAN) based on generative adversarial networks and builds an AI design tool on this basis to explore the effect and analysis of the design tool for content generation based on generative adversarial networks on visual art. Through experimental testing, the model proposed in this paper, after the constraints of the relevant loss function, the generated image content is compatible with the input image, the style is consistent with the style of the target image, and the image generated by the base DrawGAN has a high degree of reproducibility. In addition, the average score of visual art images generated using the model proposed in this paper is 8.25, which is 2.26 points higher than that of images generated by traditional methods. To sum up, the DrawGAN model proposed in this paper is a valuable tool in the generation of visual art images.