ethicalabs/Research-Intent-Collab huggingface dataset:
Biomechanical Finite Element Simulation of Cochlear Implant Surgical Robot Drilling Through Direct Cochlear Access

AbstractRobotic drilling through direct cochlear access for cochlear implantation (CI) is currently a prominent area in the field of CI surgery. However, the safety of drilling remains an issue due to the limited space within the facial recess. Estimation and control of drilling force are critical to prevent drill‐bit breakthrough, excessive heat generation, and mechanical damage to the facial nerve. The bone drilling process presents a favorable application scenario for finite element (FE) analysis. In this study, a 3D FE model of temporal bone drilling is established for the first time to predict the drilling force. The self‐developed CI surgical robot validates these findings through experimental drilling. The experimental results show that the drilling process can be divided into three stages, and the corresponding force curve can be divided into six zones. The overall trend of temporal bone drilling is two force peaks and one trough in the middle. The drilling force is positively correlated with the feed rate. The FE results found to be in good agreement with the experimental results. Numerical simulation can be utilized as an invaluable tool for assisting in predicting the position of drilling tools and optimizing drilling parameters in the future.
Land use changes and abiotic aspects as basic conditions for conservation of biodiversity in a tropical montane cloud forest (Guatemala)

The tropical montane cloud forests of Guatemala need to be protected for their high number of ecological functions and endemic flora and fauna. For that, a complete land use potential and mosaic landscape evaluation was carried out with special consideration of the resources   and  . Processes regarding the forest conversion were investigated, as well as studies with respect to the soil resource potential. The main objectives were the analysis of mosaic landscapes changes. Satellite imaging supported analysis of the land use changes, as well as the determination of the potential of use within the context of the degradation processes, soil evaluation and soil hazards. This has contributed to the achievement of the following aims: (1) to characterise the chemical, physical and biological changes of the soil by traditional milpa land use system in the highlands of Guatemala (Sierra Yalijux); (2) spatial evaluation of the forest cover changes in the same area; (3) illustration of the interrelationship between forest conversion, soil degradation and changes of the micro climate and their theoretically effects on biodiversity. The inclusion of the investigated soil quality and soil differentiation within the nutrient turnover in the tropical mountainous cloud forests might be used as a basis for selection of protected areas as well as for recommendations for reforestation. At the investigation site in Guatemala, many changes occur concerning forest conversion, soil quality and micro climate. Soil quality declines with increasing land use intensity. The soil degradation as a result of agricultural use is high. For example, carbon and nitrogen are reduced in the following consecutive habitats: primary forest > milpa 15 years > milpa 25 years > milpa 60 years > secondary scrub > fallow land. An increase of soil temperature is identified after logging, determined by a false-time-series, which leads to effects on the composition of soil organisms.
Advancing optical neural networks: photonic crystal perceptrons for ultra-fast AI computing

This paper presents what we believe to be a novel approach to designing an artificial neuron, specifically a basic perceptron, using two-dimensional photonic crystals for the first time. By utilizing a square lattice of silicon rods in an air background, the proposed structure demonstrates potential for enhanced processing speed, reduced energy consumption, and scalability for more complex AI applications. Various design examples, including threshold detection, linear classification, logical AND gates, and Iris flower classification, are presented to validate the effectiveness of the proposed photonic crystal neuron. The results show that the proposed structure can efficiently solve several computational problems, paving the way for the development of next-generation optical processors for AI applications.
Synthetic biology of genetic circuits

The combination of positive and negative feedback loops has been shown to increase the robustness of oscillations. Such breakthrough has enabled to understand the importance of that dual control in a few biological systems. The reason is that most biological systems are non-linear. One of the obstacles that must be overcome when dealing with non-linear systems, even if they are simple, is that the use of intuition to predict its behavior is no longer valid. The comprehension of the behavior of the system can only be achieved mathematical modeling and computational simulations This bachelor thesis aims to develop a mathematical model of the relaxoscillator, a gene regulatory network in which two genes with identical promoters are regulated by the same activator and repressor. At the same time, the binding of those depends on the concentrations of two inducers: arabinose and IPTG, which correspond to the control parameters of the system. The obtained model, derived from the chemical reactions, was simulated under different inducer concentrations in an attempt to comprehend the long term behavior of the system. The results show that varying these inducer concentrations allows to tune the period and the amplitude of the oscillations observed in the system. In order to analyze changes in the long term behavior of the system it will be required to include a third control parameter, the transcription repressor rate, so that the system displays different dynamic behaviors. The analysis of the simulations indicates the presence of a supercritical Hopf bifurcation for a given value of the transcription repressor rate that would explain the transition between damped oscillations and persistent oscillations. Nevertheless, due to the theoretical nature of the project, experimental studies as well as two-parameter bifurcation analysis should be performed in order to confirm such hypothesis and gain understanding of the behavior of the system as a function of inducers concentrations.
A rich bounty of AGN in the 9 square degree Bootes survey: high-z obscured AGN and large-scale structure

We use observations from the 9 square degree multiwavelength survey in Bootes to identify hundreds of obscured active galactic nuclei (AGN) with high redshifts (z > 0.7), luminosities (L_bol > 10^45 ergs/s), and moderate obscuring columns (N_H > 10^22 cm^-2), and to measure the clustering properties of X-ray AGN at z > 1. In the Bootes region, shallow (5 ks) Chandra X-ray observations have detected ~4,000 X-ray sources, and the same region has been mapped with deep optical imaging and by Spitzer IRAC, which detects ~300,000 point sources, of which ~30,000 have detections in all four IRAC bands, for which we can select AGN on the basis of their mid-IR colors. With the MMT/Hectospec we have obtained modest resolution optical spectra for about half the X-ray sources (out to z > 3) and ~20,000 galaxies (out to z = 0.7). With this multiwavelength data we select >400 AGN per square degree (compared to 12 per square degree from SDSS). Among a sample of IRAC-selected AGN we identify 641 candidate obscured objects based on their R band and IRAC luminosities. We use X-ray stacking techniques to verify that they are obscured AGN and measure their absorbing column densities. We also measure the three-dimensional two-point correlation function for X-ray selected AGN.
Reproducibility in Computational Materials Science: Lessons from 'A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials'

Main text: 15 pages, 1 table, 1 figure
Thermodynamic circuits: Modeling chemical reaction networks with nonequilibrium conductance matrices

11 pages, 2 figures, last paper of the series
A Review of Generative AI and DevOps Pipelines: CI/CD, Agentic Automation, MLOps Integration, and Large Language Models

This paper provides an overview of recent advancements and discussions surrounding Generative AI and its intersection with DevOps practices, leveraging a collection of contemporary research and articles. The increasing adoption of AI agents and machine learning models is transforming various aspects of software development and operations. This paper presents a comprehensive review of Generative AI applications in DevOps automation, covering 50 key research works published between 2023-2025. We analyze the transformative impact of AI-driven solutions across the software development lifecycle, including code generation, infrastructure management, continuous integration/delivery, and Kubernetes operations.  The convergence of generative artificial intelligence (AI), intelligent agents, and automation is revolutionizing DevOps and cloud-native software engineering. This paper provides a comprehensive review of how generative AI and agentic workflows are transforming the development, deployment, and operation of modern software systems. We examine the integration of AI-driven automation in continuous integration and continuous deployment (CI/CD) pipelines, the adoption of cloud-native technologies such as Docker and Kubernetes, and the emergence of Infrastructure as Code (IaC) and progressive delivery models. The study highlights the benefits of these advancements, including increased efficiency, enhanced reliability, and accelerated innovation, while also addressing the challenges of security, compliance, observability, and skill development. By synthesizing insights from recent research and industry practice, this paper identifies the top terms, theories, and algorithms shaping the field and offers a forward-looking perspective on the evolution of AI-driven DevOps through 2029. The findings aim to guide researchers, practitioners, and organizations in leveraging the transformative potential of intelligent automation for resilient and adaptive software engineering. The review systematically examines how generative AI enhances deployment efficiency, monitoring capabilities, and overall development workflows while addressing challenges in cloud-native environments. Our analysis reveals emerging trends in AI agents for DevOps, containerized AI solutions, and the integration of large language models with existing DevOps toolchains.
Real-time flood and water level forecasting using AI-based models for early warning and disaster risk reduction

The increasing frequency and intensity of floods pose a significant threat to lives, property, and infrastructure. Real-time flood forecasting is crucial for early warning systems and disaster risk reduction. However, traditional forecasting methods often have limitations in terms of accuracy and timeliness.This paper, developed under the framework of AI4Copernicus 5th Calls project, presents a data-driven approach for real-time flood and water level forecasting using AI and machine learning algorithms. The proposed system is based on a hybrid model that combines multiple machine learning algorithms, including DLinear/NLinear, LSTM Hindsight Modelling, and FLEX. The system is trained on historical data on hydrological and meteorological features, and is able to predict water levels at river gauging stations up to the next 9 hours.The system has been tested on data from the Lamone River in Italy, and has been shown to achieve a mean-absolute-error of only a few (
Modulation of Galactic Cosmic Rays in the Inner Heliosphere over Solar Cycles

Abstract                The 11- and 22-year modulation of galactic cosmic rays (GCRs) in the inner heliosphere is studied using a numerical model developed by Qin and Shen in 2017. Based on the numerical solutions of Parker’s transport equations, the model incorporates a modified Parker heliospheric magnetic field, a locally static time-delayed heliosphere, and a time-dependent diffusion coefficients model in which an analytical expression of the variation of magnetic turbulence magnitude throughout the inner heliosphere is applied. Furthermore, during solar maximum, the solar magnetic polarity is determined randomly with the possibility of A &gt; 0 decided by the percentage of the solar north polar magnetic field being outward and the solar south polar magnetic field being inward. The computed results are compared at various energies with several GCR observations, e.g., the Interplanetary Monitoring Platform                   8 (IMP 8), EPHIN on board the Solar and Heliospheric Observatory (SOHO), Ulysses, and Voyager 1 and 2, and they show good agreement. We show that our model has successfully reproduced the 11- and 22-year modulation cycles.
Molecular dynamics simulation of sodium diffusion in amorphous carbon using optimized reactive force field

A reactive force fieldhas been optimized to study sodium intercalation and diffusion into graphitic carbon at high temperature. The resulting force field reproduces training data and calculates new data with moderate accuracy. It has been applied in computer simulations of atomistic models representing sodium diffusion in graphitic carbon. In this thesis, the force field is further employed to simulate sodium intrusion in generated amorphous carbon models at room temperature. The objective is to qualitatively evaluate the ability of the force field to describe sodium interaction in relevant carbon structures at lower temperature. Amorphous carbon models have been generated by the liquid-quench routine. The radial distribution functions of these a-C give a good fit to the experimental data. The successive peaks shows that the structures contain largely of sp2 hybridized bonds, suggesting the carbon atoms are fused in rings. Results from the computed angle distribution functions reveal that the structures are mostly comprised of six-membered rings, with small amount of five- and seven-membered rings. Sodium atoms of two different densities are subsequently inserted into the amorphous structures. For the case of low density, sodium atoms enter the structures through the insertion sites of low energy, occupying these sites and preventing further insertion. At higher density, the sodium atoms exert a higher pressure on the carbon structure. More sodium atoms are able to enter the structure through insertion sites of higher energy. These simulation results demonstrate that the force field is able to qualitatively describe sodium diffusion into amorphous carbon.
Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review (Preprint)

BACKGROUND                         Mental disorders are normally diagnosed exclusively on the basis of symptoms, which are identified from patients’ interviews and self-reported experiences. To make mental health diagnoses and monitoring more objective, different solutions have been proposed such as digital phenotyping of mental health (DPMH), which can expand the ability to identify and monitor health conditions based on the interactions of people with digital technologies.                                                                       OBJECTIVE                         This article aims to identify and characterize the sensing applications and public data sets for DPMH from a technical perspective.                                                                       METHODS                         We performed a systematic review of scientific literature and data sets. We searched 8 digital libraries and 20 data set repositories to find results that met the selection criteria. We conducted a data extraction process from the selected articles and data sets. For this purpose, a form was designed to extract relevant information, thus enabling us to answer the research questions and identify open issues and research trends.                                                                       RESULTS                         A total of 31 sensing apps and 8 data sets were identified and reviewed. Sensing apps explore different context data sources (eg, positioning, inertial, ambient) to support DPMH studies. These apps are designed to analyze and process collected data to classify (n=11) and predict (n=6) mental states/disorders, and also to investigate existing correlations between context data and mental states/disorders (n=6). Moreover, general-purpose sensing apps are developed to focus only on contextual data collection (n=9). The reviewed data sets contain context data that model different aspects of human behavior, such as sociability, mood, physical activity, sleep, with some also being multimodal.                                                                       CONCLUSIONS                         This systematic review provides in-depth analysis regarding solutions for DPMH. Results show growth in proposals for DPMH sensing apps in recent years, as opposed to a scarcity of public data sets. The review shows that there are features that can be measured on smart devices that can act as proxies for mental status and well-being; however, it should be noted that the combined evidence for high-quality features for mental states remains limited. DPMH presents a great perspective for future research, mainly to reach the needed maturity for applications in clinical settings.
Underlying Deep Learning Networks Diagnosis Evaluation and Generative Adversarial Network Data Augmentation Based on a Benchmark Accident Dataset

Abstract                Nuclear energy plays an important role in the global energy supply, particularly as a key source of low-carbon electricity, while ensuring the safe operation of nuclear power plants (NPPs) is crucial. Given the significant impact of human-caused errors on three serious nuclear accidents in history, artificial intelligence (AI) has been increasingly interested assisting operators in NPP accident diagnosis. However, the field has lacked a benchmarking effort for underlying deep learning networks based on public datasets for accident diagnosis, as well as research on time-series data augmentation efforts for accident data. Based on the first open-source NPP accident dataset NPPAD, which covers all kinds of accident data of pressurized water reactors and has been validated with Fukushima nuclear accident data for reliability, this study explored accident diagnostic techniques based on Multilayer Perceptions (MLPs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Transformers, and data augmentation techniques based on conditional Generative Adversarial Networks (cGANs) and Time-series Generative Adversarial Networks (TimeGANs). Experimental results show Transformers have the best classification ability for accident types. In addition, the standardization of data has a large impact on the classification effect, and standardization in data preprocessing will make all models substantially more effective. For data augmentation, TimeGANs exhibit higher performance compared to cGANs in generating fake datasets and can better capture the dynamic features of time-series data. This paper provides a reference for the application of deep learning through the study of three parts: a comprehensive comparison of underlying deep learning networks, data standardization processing, and data augmentation.
On the Calibration of Large Language Models and Alignment

to be published in findings of EMNLP-2023
Real-Time Electroencephalogram Data Visualization Using Generative AI Art

This study is the result of the need to research the visualization of brainwaves. The aim is based on the idea of using generative AI art systems as a method. Data visualization is an important part of understanding the evolution of the world around us. It offers the ability to see a representation that goes beyond numbers. Generative AI systems have gained the possibility of helping the process of visualizing data in new ways. This specific process includes real-time-generated artistic renderings of these data. This real-time rendering falls into the field of brainwave visualization, with the help of the EEG (electroencephalogram), which can serve here as input data for Generative AI systems. The brainwave measurement technology as a form of input to real-time generative AI systems represents a novel intersection of neuroscience and art in the field of neurofeedback art. The main question this paper hopes to address is as follows: How can brainwaves be effectively fed into generative AI art systems, and where can the outcome lead, in terms of progress? EEG data were successfully integrated with generative AI to create interactive art. The installation provided an immersive experience by moving the image with the change in the user’s mental focus, demonstrating the impact of EEG-based art.
Food Security and Biodiversity Conservation in the context of sustainable agriculture: The role of Agricultural Extension

Accomplishing household food security simultaneously with biodiversity conservation, particularly on communal farm lands, constitutes a great challenge in South Africa. This is because biodiversity species are being threatened on lands wherein agricultural production is done in the name of securing food availability. The general threats to biodiversity are in the forms of deforestation and habitat fragmentation, encroachment, pollution, invasion of alien species, wild fires, logging, and hunting. Over time, agriculture emerges the greatest threat to biodiversity. Using this framework, this paper presents a scientific argument, backed with empirical evidences, by exploring the role that agricultural extension can play to realise the goals of biodiversity conservation on South African communal and farm lands. Drawing on relevant published works, this paper argues that extension is particularly well positioned to address both food security and biodiversity conservation concerns through the instruments of linkages, local knowledge facilitation, social capital and education.Keywords: Agricultural extension, Biodiversity conservation, Food security, Sustainable agriculture
TherapyView: Visualizing Therapy Sessions with Temporal Topic Modeling and AI-Generated Arts

This work extends our prior empirical work on topic modeling (arxiv:2204.10189) to now provide an interpretable and interactive data visualization platform with AI-generated artworks as a concrete user scenario for therapists