ethicalabs/Research-Intent-Collab huggingface dataset:
Error Correction Using Quantum Computation

Quantum Error Correction (QEC) is an important technique for protecting quantum information against decoherence and errors. This involves the design and implementation of algorithms and techniques to minimize error rates and increase the stability of quantum circuits. One of the key parameters in QEC is the distance of the error- correcting code, which determines the number of errors that can be corrected. Another important parameter is the error probability, which quantifies the likelihood of errors occurring in the quantum system. In this context, the goal of a simulation sweeps like the one performed in the code is to study the performance of the QEC code     for different values of the distance and error probability, and to optimize the code for maximum accuracy. By varying these parameters and observing the performance of the code, researchers can gain insights into how to design better codes and improve the reliability of quantum computing systems. We also discuss the challenges that need to be addressed for quantum computing to realize its potential in solving practical Error-correction problems.
Fishery-dependent livelihoods, conservation and sustainable use of biodiversity: the case of marine and coastal protected areas in India

ISBN 978-93-80802-08-04
Modulation over a 22-year cosmic ray cycle: On the tilt angles of the heliospheric current sheet

Abstract   The ‘tilt angle’ of the wavy current sheet in the hehosphere has been used very successfully as a proxy for solar activity in cosmic ray (CR) modulation models with particle drifts included. The “Hoeksema tilt angles α” are calculated using two potential field methods: (1) The classic ‘line-of-sight’ computation and (2) the more recent ‘radial’ photospheric field computation. The question of which set of ‘tilt angles’ is the more appropriate to use in modulation models is addressed by utilizing them in a comprehensive time-dependent drift model applied to the 22-year modulation cycle of 1.2 GeV cosmic ray electrons. We found that the ‘classic’ α are better suited than the ‘new’ α as a proxy for solar activity for periods of decreasing α and vice versa for periods of increasing α. By comparing the model with data it was shown that the set of α with the smallest rate of change over a period of decreasing or increasing solar activity provides the best compatibility with CR observations.
DIGITAL SURVEILLANCE AND PRIVACY RIGHTS: A SOCIOLOGICAL INQUIRY

In the contemporary digital age, the rapid expansion of surveillance technologies poses significant challenges to individual privacy rights, with profound sociological implications. This article explores the relationship between digital surveillance and privacy rights from a sociological perspective, analyzing how surveillance systems are reshaping social behavior, power structures, and the concept of privacy in both the public and private spheres. By examining the role of government and corporate actors in surveillance practices, the study addresses the legal, ethical, and social consequences of privacy infringement. The paper further explores the cultural and psychological impacts on individuals and communities, highlighting the tensions between security and personal freedom. Through theoretical analysis and empirical data, the article provides a nuanced understanding of the dynamics of surveillance and its effects on modern society.
Conflict Resolution Learning Model As A Strategic Effort in Building Peace Amidst Indonesia's Diversity

As a pluralistic country, peace still seems to be a mere wishful thinking. Because all this time conflicts that are rooted in pluralism have injured and damaged the peace that was actually agreed upon by the founders of the nation. The purpose of this research was to find out the conflict resolution learning model as a strategic effort in building peace in the midst of Indonesia's diversity.  The method used in this research was a library method or approach. Data collection techniques in this research used documentation and literature. The data obtained were then analyzed in depth and presented descriptively. The results of this research indicated that strategic efforts to build peace in the midst of Indonesia's diversity are carried out by educational institutions through the application of conflict resolution learning models. The application of the conflict resolution learning model aims to train students' skills in solving critical problems, communication, and interpersonal skills better. Conflict resolution methods can optimize student competence, especially training social values, social sensitivity and problem solving skills. In relation to the diversity of the Indonesian nation, there is a significant influence from the use of conflict resolution learning models on students' multicultural attitudes. The multicultural attitude of students who take part in learning with the conflict resolution learning model obtains good results.
Comparing Terraform and Ansible for Infrastructure as Code: A Containerized Full-Stack Web Application Case Study

This thesis compares Terraform and Ansible for Infrastructure as Code (IaC) through a containerized full-stack web application case study. Three ap- proaches are tested: Terraform-only, Ansible-only, and hybrid implementa- tions, measuring their performance in infrastructure provisioning, application deployment, and resource cleanup. The thesis tracks deployment times and reliability, error rates, and operational complexity across Azure cloud infras- tructure using Docker Compose for consistent testing. Insights into each tool’s strengths and limitations are provided. The findings offer practical guidance for developers and organizations with containerized applications, helping them choose the most suitable IaC approach for their deployment needs without requiring complex orchestration platforms.
Discovery of knowledge flow in science

Recognizing and understanding knowledge flow between scientists is valuable for science. Discovering, managing, and utilizing such knowledge are advanced services of the e-science knowledge grid environment.
Ion Mobility Mass Spectrometry Guided Modeling with AlphaFold and Rosetta Improves Protein Complex Structure Prediction

Abstract                 Ion mobility mass spectrometry (IM-MS) provides valuable structural information about protein shape and size through collision cross section (CCS). However, it lacks atomic level structural detail. While AlphaFold has been successful in predicting monomeric protein structure, it can struggle with modeling protein complexes. To address these limitations, we developed a method that integrates IM-MS data with AlphaFold and Rosetta to improve complex structure prediction. Our approach uses experimental CCS data to guide the assembly of AlphaFold predicted subunits using a Rosetta docking pipeline and evaluating the resulting complexes with a newly developed score. Using this strategy, we were able to improve root mean square deviation (RMSD) values for 26 of 38 (68%) complexes compared to AlphaFold-Multimer. Furthermore, 16 of these systems improved significantly from greater than 4 Å RMSD to less than 4 Å. This method demonstrates a robust approach to overcome limitations in complex assembly modeling.                                    Table of Contents Graphic                                                               In this integrative modeling work, protein complex structures were modeled by combining AlphaFold predicted subunits with Rosetta docking. Collision cross section data from ion-mobility mass spectrometry were used as evaluation constraints and docked models were scored using the IM-complex score. The best scoring models generally represent accurate protein complex structures.
Secured Healthcare Data Sharing Through Wireless Networks for Mobile Computing Using Trust-Bat-Adaptive Homomorphic Crypto Routing Protocol

Abstract         The Wireless Sensor Network's (WSN's) sensor nodes commonly communicate via mobiles, which have been widely regarded as the most prevalent and perhaps most important communication technology. Due to their self-configuration, ease of handling, and adaptability, WSNs have become more popular in mobile computing. The WSN is set up with extra nodes to transport data. As a result of their short memory, poor throughput, restricted capacity, and other limitations, sensor networks become notoriously difficult to secure. WSN has been grappling with how to maintain network security in mobile computing while using as little power as possible in current history. So, we present a novel trust-bat-adaptive homomorphic crypto routing protocol (TBAHCRP) to overcome these difficulties. Initially, we gather healthcare datasets from the medical server and deploy a 5G (fifth-generation) network to transfer these datasets from sending node to receiving node. The encryption and decryption are performed by employing the proposed technique and it also performs clustering and routing functionalities. A multi-gradient spider monkey optimization (MG-SMO) approach is applied to optimize the received data for mobile applications. Finally, performance metrics like security level, memory usage, energy efficiency, and energy consumption of the proposed technique are examined and compared with other classical techniques to prove our work with the greatest efficiency. These metrics are depicted in graphical form by using the MATLAB tool.
Sparse Erroneous Vehicular Trajectory Compression and Recovery via Compressive Sensing

Vehicle tracking information is necessary to enable safety communication systems and intelligent transportation systems. Compression technologies with high efficiency and low complexity provide a promising approach to address the transmission and computing problems in vehicle tracking applications. Especially, vehicular trajectory with sparse errors that happened in the measurement sensing process poses a great challenge on traditional compression algorithms. In this paper, we analyze and design a compressive sensing (CS) based erroneous trajectory compression and recovery algorithm for vehicle tracking scenario. Moreover, some theoretical bounds for the proposed recovery optimization problem are analyzed and proved. The CS-based method proposed in this paper could not only achieve a fairly high compression rate and recovery accuracy, but fit the bandwidth mismatch between the road side unit (RSU) and on board unit (OBU). In another aspect, the Kalman filtering (KF) technology is applied for further optimizing the system performance, e.g. mean square error (MSE). Extensive simulations with real vehicular trajectories are carried out, which shows that CS-based compression algorithm achieves relatively high compression performance compared to some state-of-the-art trajectory compression algorithms.
Coupled trace element and SIMS sulfur isotope geochemistry of sedimentary pyrite: Implications on pyrite growth of Caixiashan Pb–Zn deposit

Abstract   Colloform pyrite with core-rim texture is commonly deposited in carbonate platforms associated with the sulfide ores such as the Caixiashan Pb–Zn deposit. However, the genesis of colloform pyrite in Pb–Zn deposits, its growth controls and their geological implication are insufficiently understood. Integration of in-situ trace element and SIMS sulfur isotopes has revealed geochemical variations among these pyrite layers. These colloform pyrite occur as residual phases of core-rim aggregates, the cores are made up of very fine-grained anhedral pyrite particles, with some rims being made up of fine-grained and poorly-crystallized pyrite, while the other rims were featured with euhedral cubic pyrite, which are cemented by fine-grained calcite and/or dolomite with minor quartz. Sulfur isotope analysis shows that some well-preserved rims have negative δ34S values (–28.12‰ to –0.49‰), whereas most of the cores and rims have positive δ34S values (>0 to +44.28‰; peak at +14.91‰). Integrating with the methane and sulfate were observed in previous fluid inclusion study, we suggest that the 34S depleted rims were initially formed by bacteria sulfate reduction (BSR), whereas the positive δ34S values were resulted from the sulfate reduction driven by anaerobic methane oxidation (AOM). The well-developed authigenic pyrite and calcite may also support the reaction of AOM. Combined with petrographic observations, trace element composition of the colloform pyrite reveals the incorporation and precipitation behavior of those high abundance elements in the pyrite: Pb and Zn were present as mineral inclusion and likely precipitated before Fe, as supported by the time-resolved Pb–Zn signal spikes in most of the analyzed pyrite grains. Other metals, such as Hg, Co and Ni, may have migrated as chloride complexes and entered the pyrite lattice. Arsenic and Sb, generally influenced by complex-forming reactions rather than substitution ones, could also enter the pyrite lattice, or slightly predate the precipitation of colloform pyrite as mineral inclusions, which are controlled by their hydrolysis constant in the ore fluids. The colloform pyrite may have grown inward from the rims. The successive BSR reaction process would enrich H232S in the overlying water column but reduce the metal content, the nucleation of these pyrite rims was featured by strongly negative sulfur isotopes. The following AOM process should be activated by deformation like the turbidity sediment of the mudstone as the sulfide deposition are associated with fault activities that caused the emission of methane migration upward and simultaneously replenishing the metal in the column. The higher AOM reaction rate and the higher metal supply (not only Fe, but with minor other metals such as Pb and Zn) caused by sediment movement enhanced the metal concentration within the pyrite lattice.
Catastrophic Forgetting as Accessibility Collapse: A Three-Level Framework for Knowledge Persistence in Continual Learning

Catastrophic forgetting is commonly interpreted as the irreversible erasure of previously acquired knowledge during sequential learning. In this work, we investigate an alternative perspective: that forgetting may arise not from complete destruction of task representations but from a loss of accessibility to preserved information. We introduce a three-level framework separating knowledge storage, representation, and accessibility, and evaluate each component through a series of continual-learning experiments on sequential CIFAR-100 classification using ResNet-18. Our analysis combines checkpoint persistence, linear probing, representation geometry, classifier-reset recovery, and layer-wise recoverability experiments. We observe complete behavioral forgetting of earlier tasks, with task accuracy collapsing from 54.8% to 0%, while linear probe performance retains approximately 76% of the original representational information. Furthermore, retraining only the final classifier restores 75.7% of the original task performance without modifying the backbone network. Layer-wise analysis reveals that early and intermediate layers preserve highly recoverable task information despite severe degradation at later stages. Projection-energy and principal-angle analyses indicate that retained knowledge persists as distributed high-dimensional representations rather than through preservation of a small dominant subspace. These findings suggest that catastrophic forgetting is better characterized as an accessibility failure than complete representational erasure, and that substantial task-relevant information remains embedded within neural representations even after functional forgetting has occurred.
Integrated genomic analysis for prediction of survival for patients with liver cancer using The Cancer Genome Atlas

To evaluate the prognostic power of different molecular data in liver cancer.Cox regression screen and least absolute shrinkage and selection operator were performed to select significant prognostic variables. Then the concordance index was calculated to evaluate the prognostic power. For the combination data, based on the clinical cox model, molecular features that better fit the model were combined to calculate the concordance index. Prognostic models were built based on the arithmetic summation of the significant variables. Kaplan-Meier survival curve and log-rank test were performed to compare the survival difference. Then a heatmap was constructed and gene set enrichment analysis was performed for pathway analysis.The mRNA data were the most informative prognostic variables in all kinds of omics data in liver cancer, with the highest concordance index (C-index) of 0.61. For the copy number variation, methylation and miRNA data, the combination of molecular data with clinical data could significantly boost the prediction accuracy of the molecular data alone (P < 0.05). On the other hand, the combination of clinical data with methylation, miRNA and mRNA data could significantly boost the prediction accuracy of the clinical data itself (P < 0.05). Based on the significant prognostic variables, different prognostic models were built. In addition, the heatmap analysis, survival analysis, and gene set enrichment analysis validated the practicability of the prognostic models.In all kinds of omics data in liver cancer, the mRNA data might be the most informative prognostic variable. The combination of clinical data with molecular data might be the future direction for cancer prognosis and prediction.
Extracting the multimodal fingerprint of urban transportation networks

Urban mobility increasingly relies on multimodality, combining the use of bicycle paths, streets, and rail networks. These different modes of transportation are well described by multiplex networks. Here we propose the overlap census method which extracts a multimodal profile from a city's multiplex transportation network. We apply this method to 15 cities, identify clusters of cities with similar profiles, and link this feature to the level of sustainable mobility of each cluster. Our work highlights the importance of evaluating all the transportation systems of a city together to adequately identify and compare its potential for sustainable, multimodal mobility.
Gibbs posterior for variable selection in high-dimensional classification and data mining

In the popular approach of "Bayesian variable selection" (BVS), one uses prior and posterior distributions to select a subset of candidate variables to enter the model. A completely new direction will be considered here to study BVS with a Gibbs posterior originating in statistical mechanics. The Gibbs posterior is constructed from a risk function of practical interest (such as the classification error) and aims at minimizing a risk function without modeling the data probabilistically. This can improve the performance over the usual Bayesian approach, which depends on a probability model which may be misspecified. Conditions will be provided to achieve good risk performance, even in the presence of high dimensionality, when the number of candidate variables "$K$" can be much larger than the sample size "$n$." In addition, we develop a convenient Markov chain Monte Carlo algorithm to implement BVS with the Gibbs posterior.
Analysis of workplace compliance measurements of asbestos by the U.S. Occupational Safety and Health Administration (1984–2011)

The United States Occupational Safety and Health Administration (OSHA) maintains the Chemical Exposure Health Data (CEHD) and the Integrated Management Information System (IMIS) databases, which contain quantitative and qualitative data resulting from compliance inspections conducted from 1984 to 2011. This analysis aimed to evaluate trends in workplace asbestos concentrations over time and across industries by combining the samples from these two databases. From 1984 to 2011, personal air samples ranged from 0.001 to 175 f/cc. Asbestos compliance sampling data associated with the construction, automotive repair, manufacturing, and chemical/petroleum/rubber industries included measurements in excess of 10 f/cc, and were above the permissible exposure limit from 2001 to 2011. The utility of combining the databases was limited by the completeness and accuracy of the data recorded. In this analysis, 40% of the data overlapped between the two databases. Other limitations included sampling bias associated with compliance sampling and errors occurring from user-entered data. A clear decreasing trend in both airborne fiber concentrations and the numbers of asbestos samples collected parallels historically decreasing trends in the consumption of asbestos, and declining mesothelioma incidence rates. Although air sampling data indicated that airborne fiber exposure potential was high (>10 f/cc for short and long-term samples) in some industries (e.g., construction, manufacturing), airborne concentrations have significantly declined over the past 30 years. Recommendations for improving the existing exposure OSHA databases are provided.
Additional file 1 of How are ontologies implemented to represent clinical practice guidelines in clinical decision support systems: protocol for a systematic review

Additional file 1. PRISMA 2015 checklist
Applications of integral transforms composition method (ITCM) to wave-type singular differential equations and index shift transmutations

In the paper we study applications of integral transforms composition method (ITCM) for obtaining transmutations via integral transforms. It is possible to derive wide range of transmutation operators by this method. Classical integral transforms are involved in the integral transforms composition method (ITCM) as basic blocks, among them are Fourier, sine and cosine-Fourier, Hankel, Mellin, Laplace and some generalized transforms. The ITCM and transmutations obtaining by it are applied to deriving connection formulas for solutions of singular differential equations and more simple non-singular ones. We consider well-known classes of singular differential equations with Bessel operators, such as classical and generalized Euler-Poisson-Darboux equation and the generalized radiation problem of A.Weinstein. Methods of this paper are applied to more general linear partial differential equations with Bessel operators, such as multivariate Bessel-type equations, GASPT (Generalized Axially Symmetric Potential Theory) equations of A.Weinstein, Bessel-type generalized wave equations with variable coefficients,ultra B-hyperbolic equations and others. So with many results and examples the main conclusion of this paper is illustrated: the integral transforms composition method (ITCM) of constructing transmutations is very important and effective tool also for obtaining connection formulas and explicit representations of solutions to a wide class of singular differential equations, including ones with Bessel operators.
Masked Vision and Language Modeling for Multi-modal Representation Learning

In this paper, we study how to use masked signal modeling in vision and language (V+L) representation learning. Instead of developing masked language modeling (MLM) and masked image modeling (MIM) independently, we propose to build joint masked vision and language modeling, where the masked signal of one modality is reconstructed with the help from another modality. This is motivated by the nature of image-text paired data that both of the image and the text convey almost the same information but in different formats. The masked signal reconstruction of one modality conditioned on another modality can also implicitly learn cross-modal alignment between language tokens and image patches. Our experiments on various V+L tasks show that the proposed method, along with common V+L alignment losses, achieves state-of-the-art performance in the regime of millions of pre-training data. Also, we outperforms the other competitors by a significant margin in limited data scenarios.