ethicalabs/Research-Intent-Collab huggingface dataset:
Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts

Abstract                                   Objective                   Limited research exists in predicting first-time suicide attempts that account for two-thirds of suicide decedents. We aimed to predict first-time suicide attempts using a large data-driven approach that applies natural language processing (NLP) and machine learning (ML) to unstructured (narrative) clinical notes and structured electronic health record (EHR) data.                                                   Methods                   This case-control study included patients aged 10–75 years who were seen between 2007 and 2016 from emergency departments and inpatient units. Cases were first-time suicide attempts from coded diagnosis; controls were randomly selected without suicide attempts regardless of demographics, following a ratio of nine controls per case. Four data-driven ML models were evaluated using 2-year historical EHR data prior to suicide attempt or control index visits, with prediction windows from 7 to 730 days. Patients without any historical notes were excluded. Model evaluation on accuracy and robustness was performed on a blind dataset (30% cohort).                                                   Results                   The study cohort included 45 238 patients (5099 cases, 40 139 controls) comprising 54 651 variables from 5.7 million structured records and 798 665 notes. Using both unstructured and structured data resulted in significantly greater accuracy compared to structured data alone (area-under-the-curve [AUC]: 0.932 vs. 0.901 P &amp;lt; .001). The best-predicting model utilized 1726 variables with AUC = 0.932 (95% CI, 0.922–0.941). The model was robust across multiple prediction windows and subgroups by demographics, points of historical most recent clinical contact, and depression diagnosis history.                                                   Conclusions                   Our large data-driven approach using both structured and unstructured EHR data demonstrated accurate and robust first-time suicide attempt prediction, and has the potential to be deployed across various populations and clinical settings.
Adaptive Energy Saving and Mobility Support IPv6 Routing Protocol in Low-Power and Lossy Networks for Internet of Things and Wireless Sensor Networks

Internet of Things (IoT) is an interconnection of physical objects that can be controlled, monitored and exchange information from remote locations over the internet while been connected to an Application Programme Interface (API) and sensors. It utilizes low-powered digital radios for communication enabling millions and billions of Low-power and Lossy Network (LLN) devices to communicate efficiently via a predetermined routing protocol. Several research gaps have identified the constraints of standardised versions of IPv6 Routing Protocol for Low Power and Lossy Networks evidently showing its lack of ability to handle the growing application needs and challenges. This research aims to handle routing from a different perspective extending from energy efficiency, to mobility aware and energy scavenging nodes thereby presenting numerous improvements that can suit various network topologies and application needs. Envisioning all the prospects and innovative services associated with the futuristic ubiquitous communication of IoT applications, we propose an adaptive Objective Function for RPL protocol known as Optimum Reliable Objective Function (OR-OF) having a fuzzy combination of five routing metrics which are chosen based on system and application requirements. It is an approach which combines the three proposed implemented Objective Functions within this thesis to enable the OR-OF adapt to different routing requirements for different IoT applications. The three proposed OFs are Energy saving Routing OF, Enhanced Mobility Support Routing OF and Optimized OF for Energy Scavenging nodes. All proposed OFs were designed, implemented, and simulated in COOJA simulator of ContikiOS, and mathematical models were developed to validate simulated results. \ud Performance Evaluation indicated an overall improvement as compared with the standardised versions of RPL protocols and other related research works in terms of network lifetime with an average of 40%, packet delivery ratio of 21%, energy consumption of 82% and End-to-End Delay of 92%.
Laser parameter optimization and experimental study of end-pumped continuous wave Tm，Ho∶YLF lasers

The output performance of a laser diode end-pumped continuous wave Tm，Ho∶YLF laser is studied and the laser parameters are optimized. Based the quasi-three-level rate equations，the relation formulus between the output power and the input power is deduced with consideration of the energy transfer up-conversion. The laser parameters such as the concentration of doped ions，the crystal length，the pump-to-mode size ratio and the transmittance of output coupler are optimized by numerical analysis. The output power as a function of pump power is obtained in experiment. Comparison of experimental and theoretical results shows that the theoretical results are valid.
Crystal structure prediction with host-guided inpainting generation and foundation potentials

This work presents the crystal host-guided generation (CHGGen) framework, which uses a conditional inpainting method on a symmetrized host structure for generating symmetric crystals and a foundation potential for structure optimization.
Notice: Jing‐Gang Gai, Yi Ren, Density functional theory study on the reactions of X<sup>−</sup> with CH<sub>3</sub>SY (X, Y = F, Cl, Br, I)<i>International Journal of Quantum Chemistry</i>(2006)

AbstractThe original article to which this erratum refers was published in the International Journal of Quantum ChemistryInt J Quantum Chem&amp;;par;2006).
Signal Processing on Directed Graphs

This paper provides an overview of the current landscape of signal processing (SP) on directed graphs (digraphs). Directionality is inherent to many real-world (information, transportation, biological) networks and it should play an integral role in processing and learning from network data. We thus lay out a comprehensive review of recent advances in SP on digraphs, offering insights through comparisons with results available for undirected graphs, discussing emerging directions, establishing links with related areas in machine learning and causal inference in statistics, as well as illustrating their practical relevance to timely applications. To this end, we begin by surveying (orthonormal) signal representations and their graph frequency interpretations based on novel measures of signal variation for digraphs. We then move on to filtering, a central component in deriving a comprehensive theory of SP on digraphs. Indeed, through the lens of filter-based generative signal models, we explore a unified framework to study inverse problems (e.g., sampling and deconvolution on networks), statistical analysis of random signals, and topology inference of digraphs from nodal observations.
A lava flow simulation experience oriented to disaster risk reduction, early warning systems and response during the 2021 volcanic eruption in Cumbre Vieja, La Palma

AbstractLava flows are one of the hazards involved in a volcanic eruption, and although they rarely cause the loss of human life, they are highly destructive in terms of damage to property and economic activity. Therefore, the management of volcanic disasters requires fast and accurate information on the behaviour and evolution of the flows, mainly related to their extension, displacement, and trajectory. This was the case during the disaster linked to the volcanic event that occurred on the island of La Palma in the Cumbre Vieja area at the end of 2021, which lasted eighty-five days. This paper describes part of the work performed by many different groups to provide predictive information aimed at feeding the early warning system set up during the disaster. This case shows the experience in the use of a probabilistic simulation algorithm implemented in the Q-LavHA plugin for the QGIS software, which is both easily accessible and applicable, to analyze its features in detail, as well as its predictive capacity. The results show that the model can efficiently and quickly satisfy the demand for this type of information, and its high similarity value is also validated by the Kappa index.
The simulated features of heliospheric cosmic-ray modulation with a time-dependent drift model. I - General effects of the changing neutral sheet over the period 1985-1990

A time-dependent drift model is used to simulate the heliospheric modulation of galactic cosmic rays, with emphasis on the effects of the wavy heliospheric neutral sheet (HNS) as a function of time during the period 1985-1990. The model predicted a clearly defined time difference between the time when minimum modulation occurred at earth and at radial distances farther away from the sun, indicating that the HNS plays an important part in establishing this observed time delay. The movements of the Voyager 1 and 2 and Pioneer 10 spacecraft were simulated in order to calculate instantaneous radial and latitudinal gradients. The time dependencies of these gradients were found to follow the observations well. The model study indicates that the HNS cannot be ignored, especially during periods of low to moderate solar activity, when the modulation of cosmic rays is described.
The Hot and Energetic Universe: The astrophysics of supernova remnants and the interstellar medium

The study of both supernova remnants and the hot and cold phases of the interstellar medium are essential for understanding the final stages of stellar evolution and their feedback on the evolution of galaxies through injection of energy and heavy elements. These studies are also crucial for understanding the physics of supernovae, their cosmological implication, and the origin of galactic cosmic rays. The unique capabilities of Athena+ will allow us to explore a new parameter space. Spatially-resolved high-resolution spectroscopy using Athena+ X-IFU of young remnants will allow to characterize individual parcels of ejected material in the line of sight in terms of kinematics, ionization and composition, providing access to the three dimensional geometry of the explosion. Athena+ will also allow studying shock physics and particle acceleration in supernova remnants, as well as their interaction with their environment. Athena+ X-IFU will also characterize the ionization mechanisms competing in forming the complex structures of the hot interstellar medium, likely to keep the echo of past star formation activity, both in our Galaxy and nearby ones. For the first time the dust and gas of the densest cold medium, like in the Galactic Centre environment, will be studied. Athena+ X-IFU will observe, along with the Mg K and Si K edges, which are the main tracers of the silicates content of the ISM, the Fe K edge with unprecedented sensitivity and energy-resolution. This will allow us to study for the first time the nature of Fe-bearing dust in such regions.
Artificial intelligence in urological malignancy diagnosis and prognosis: current status and future prospects

Artificial intelligence (AI) is transforming the diagnostic landscape of malignant tumors in the urinary system, including prostate cancer, bladder cancer, and renal cell carcinoma (RCC). By integrating imaging, pathology, and molecular data, AI enhances the precision and reproducibility of tumor detection, grading, and risk stratification. In prostate cancer, AI-assisted multiparametric Magnetic resonance imaging (MRI) and digital pathology systems improve lesion localization and Gleason scoring. For bladder cancer, deep learning-based cystoscopy and radiomics models from Computed tomography/magnetic resonance imaging (CT/MRI) enable real-time lesion segmentation and non-invasive biomarker prediction, such as Programmed Cell Death-Ligand 1 (PD-L1) expression. In RCC, AI, combined with CT/MRI and multi-omics data, aids in subtype classification and prognostic prediction, supporting personalized therapy. However, despite these promising advances, challenges such as data standardization, model generalizability, interpretability, and regulatory compliance hinder AI's clinical translation. This review outlines the current state of AI in urological cancer diagnosis and prognosis, its technological innovations, and the clinical challenges and opportunities that lie ahead.
A novel genomic rearrangement signature to predict poor survival among women with high grade serous ovarian cancer.

5509  Background: Resistance to platinum-based chemotherapy is a major cause of disease progression and mortality among women with high grade serous ovarian cancer (HGSOC). It is not known whether patterns of genomic rearrangement are predictive of clinical outcome in HGSOC. Methods: This was a retrospective cohort analysis of whole genome sequences from 80 HGSOC tumors. Genomic rearrangements were identified and categorized by size and type (inversion, duplication, deletion, or translocation). Non-negative matrix factorization was then used to extract rearrangement signatures. Wilcoxon rank-sum test was used for comparison of continuous variables. Univariate and multivariate analyses were performed using Cox proportional hazards models. Results: A rearrangement signature characterized by 10 kilobase to 10 megabase duplications and deletions was identified. The median overall survival (OS) was 22.5 months (95% CI, 20.1 to 33.5 months) in the Sig-High group versus 46.0 months (95% CI, 27.7 to 80.6 months) in the Sig-Low group (hazard ratio, 2.13; 95% CI, 1.27 to 3.55; P=0.004). Exploration of clinical variables showed a significantly higher median signature contribution in platinum-resistant disease than platinum-sensitive disease (20.0% vs 9.1%, p=0.007). Multivariate analysis showed a hazard ratio for death of 2.1 associated with the Sig-High group (Table). Validation of this signature was performed using HGSOC copy number data from the Cancer Genome Atlas. In this cohort, the median OS was 38.8 months (95% CI, 36.7 to 44.5 months) in the Sig-High group versus 49.5 months (95% CI, 45.2 to 56.3 months) in the Sig-Low group (hazard ratio, 1.44; 95% CI, 1.14 to 1.82; P=0.024). Conclusions: A genomic rearrangement signature is associated with chemoresistance and poor prognosis in HGSOC. Prediction of poor survival outcomes could allow early identification of women who may be candidates for clinical trials. [Table: see text]
Additive Margin in Contrastive Self-Supervised Frameworks to Learn Discriminative Speaker Representations

accepted at Odyssey 2024: The Speaker and Language Recognition Workshop. arXiv admin note: text overlap with arXiv:2306.03664
Artificial Intelligence Generated Content (AIGC) in Empowering Vocational English Teaching Evaluation Systems

The integration of Artificial Intelligence Generated Content (AIGC) into vocational English teaching has the potential to revolutionize the evaluation process, making it more efficient, personalized, and insightful. This paper explores the current landscape of AIGC in education, with a focus on its application in vocational English teaching evaluation systems. Through a systematic literature review, we analyze how AIGC can enhance existing evaluation frameworks, the challenges it poses, and the future direction of its integration into educational practices.
How climate scientists and communication professionals view public engagement with science: Perspectives, practices and constraints

IntroductionThis article examines understandings of public engagement in science by scientists and communicators in four universities in Portugal and Spain.MethodsBased on mixed-methods research, we present an integrative analysis of practices and perceptions of climate change scientists and communication professionals, and identify critical barriers for them to further public engagement.ResultsMore similarities than differences are found between the four universities and the two countries but there are important differences between the two professional groups. Universities and the scientific community at large tend to privilege forms of science communication that are focused on dissemination of information and on self-promotion. Over the last decade there are signs of positive developments, even though some aspects of science communication practice appear to have worsened.DiscussionDespite the advancements presented this article Portuguese and Spanish universities are not yet making the most of the potential inherent to public engagement. This raises important questions for research and practice. Although several interviewees acknowledge a normative requirement of having citizens participate more deeply in science, they stay short of fulfilling such commitment. This may be partly due to implementation barriers but also because scientists and especially communication professionals make a relatively narrow reading of the potential benefits of engaging the public in science.
ASSESSING CONFLICT RESOLUTION AND PEACE BUILDING STRATEGIES IN DEMOCRATIC SYSTEM IN NORTH EAST NIGERIA

This project investigated conflict resolution and peace building strategies in a democratic system, using three states from the Northeast part of Nigeria Bauchi State, Yobe State and Adamawa state Nigeria, as a case study. The data generated in the field survey was analyzed using statistical methods. Simple descriptive statistics were used to analyze the data using percentages and frequencies, charts, tables, and others. 384 questionnaires were administered to the population residing in the study area. The study reveals a slight male majority, a concentration of working-age individuals, diverse perceptions of conflict, trans-boundary awareness, prolonged conflict existence, and causative factors including economic challenges. Occasional conflicts involve farmers, herdsmen, political parties, and tribes, with resolution strategies ranging from military intervention to meetings and consultants. Respondents perceive conflict resolution as temporary, highlighting the complexity of the enduring conflict scenario. Hence, this study suggests that societies and governments, both at the national and international levels, should allocate additional resources and exert greater efforts towards peace building, which serves as a proactive mechanism for preventing conflicts. Instead of waiting for conflicts to arise and then investing substantial resources to mitigate or resolve them, it is advisable to prioritize preemptive measures.
A Computational Framework for Prioritization of Events in Fault Tree Analysis Under Interval-Valued Probabilities

In probabilistic safety assessment (PSA), the assessed events can be prioritized using risk importance measures, which are functions of the events' failure probabilities. These probabilities can be uncertain, and consequently the resulting prioritization can be uncertain too. In this paper, we present a framework for computing the impacts of this uncertainty, which is modeled by interval-valued probabilities that establish lower and upper bounds, within which the probability may vary. Specifically, we make pairwise comparisons between events so that an event is said to dominate another if its risk importance measure is at least as high for all event probabilities that are within their respective intervals, and strictly higher for some probabilities. The dominance relations establish a partial order which can be visualized as a directed acyclic graph. We illustrate our method by analyzing the fault tree that represents the residual heat removal system of a nuclear reactor. The results for this fault tree with 31 events and 147 minimal cut sets was solved in seconds using a tailored algorithm. Theoretical properties of the algorithm suggest that much larger models can still be solved in reasonable time.
Tailoring Privacy: Performance Analysis of Differential Privacy for Personalized Federated Learning (slides)

This webinar session will explore personalized federated learning (PFL), a cutting-edge method that creates customized models for diverse client needs, enhancing model convergence through meta-learning. We'll address the significant challenges associated with information leakage and unveil a differential privacy (DP) based PFL framework. Our approach designs a privacy budget allocation scheme rooted in the Rényi Differential Privacy composition theory. Throughout the session, we will discuss the formulation of convergence bounds applicable to both convex and non-convex loss functions. This analysis leads to optimal strategies for determining the most effective model size and achieving the best balance among communication rounds, convergence performance, and privacy considerations. Supported by thorough evaluations on various real-life datasets, our findings corroborate our theoretical predictions and serve as a practical guide for developing DP-PFL algorithms. This webinar is perfect for researchers, data scientists, and practitioners eager to deepen their knowledge and enhance the privacy and efficiency of their federated learning projects. Join us to gain valuable insights into how to design PFL systems and ensure robust privacy protections while maintaining high performance.
A database of calculated solution parameters for the recently released AlphaFold predicted protein structures

Abstract         Recent spectacular advances by AI programs in 3D structure predictions from protein sequences have revolutionized the field in terms of accuracy and speed. The resulting "folding frenzy" has already produced predicted protein structure databases for the entire human and other organisms' proteomes. However, rapidly ascertaining a predicted structure's reliability based on measured properties in solution should be considered. Shape-sensitive hydrodynamic parameters such as the diffusion and sedimentation coefficients (D0t(20,w),s0(20,w)) and the intrinsic viscosity ([η]) can provide a rapid assessment of the overall structure likeliness, and SAXS would yield the structure-related pair-wise distance distribution function p(r) vs. r. Using the extensively validated UltraScan SOlution MOdeler (US-SOMO) suite we have calculated from the AlphaFold structures the corresponding D0t(20,w), s0(20,w), [η], p(r) vs. r, and other parameters. Circular dichroism spectra were also computed. The resulting US-SOMO-AF database should aid in rapidly evaluating the consistency in solution of AlphaFold predicted protein structures.
Electronic nursing documentation interventions to promote or improve patient safety and/or quality care in an acute setting

Research has demonstrated that the implementation of electronic nursing documentation interventions can enhance the precision and comprehensiveness of records, thereby providing a significant benefit. Electronic documentation systems help to reduce errors resulting from factors such as indecipherable handwriting, incomplete or absent documentation, and transcription errors. ‎In May 2022, a comprehensive research was conducted in CINAHL, MEDLINE, Cochrane, and Web of Science to identify relevant literature for their study. The study focuses on the use of electronic health systems in nursing, specifically in the documentation of care plans, records, reports, and charts. Individuals utilizing any given database employ the tools at their disposal to conduct searches for document abstracts and titles. This rapid review comprised one cross-sectional, one randomised control trial, two quasi-experimental, one qualitative, and one cohort study. The US, Taiwan, and Jordan each held two tests. The six trials included a medical-surgical and telemetry hospital with 446 beds, an acute care hospital with 1037 beds, a medical-surgical unit, and an urgent care clinic. The results revealed that electronic nursing documentation has improved quality of nursing.