ethicalabs/Research-Intent-Collab huggingface dataset:
Integrating Lean Six Sigma with blockchain technology for quality management – a scoping review of current trends and future prospects

PurposeThe purpose of this research is to study and analyze the literature that integrates Lean Six Sigma (LSS) approach with blockchain technology in different sectors for improved quality management.Design/methodology/approachThis study presents a scoping review on the application of integrated LSS and blockchain technology in the manufacturing and healthcare sector. Further, the authors examined existing blockchain-based solutions on a variety of dimensions, including application area, technical approach, methodology, application scenario, various blockchain platforms, purpose, and monitoring parameters. The authors study LSS approaches in detail, as well as the key benefits that blockchain technology can enable. Finally, the authors discuss significant research problems to be addressed in order to develop a highly efficient, resilient, and secure quality management framework using blockchain technology.FindingsIt has been observed that the adoption of blockchain technology for quality management and assurance is influenced by several factors such as transaction execution speed, throughput, latency. Also, prior blockchain-based solutions have neglected to leverage the benefits of LSS methodologies for effective quality management.Originality/valueThis is the first study to explores the influence of blockchain technology on quality management and assurance in manufacturing and healthcare industry. Furthermore, prior research has not examined how integrating the LSS methodology with blockchain technology can aid in the control of product quality management.
Landform Monitoring and Warning Framework Based on Time Series Modeling of Topographic Databases

Global Positioning System (GPS) and geodetic control networks are used today for analyzing and monitoring time-dependent crustal deformations, providing a series of accurate positional measurements to deliver information on positional changes and deformations that have occurred. Still, such networks present a low-resolution dispersal of positional measures, and do not take into account various physical constraints that affect the terrain’s seismic behavior. An alternative form of spatio-temporal infrastructure that is feasible and practical to establish might involve the use of Digital Terrain Model (DTM) databases. These databases use higher positional resolutions, and are exhibiting an increasing level of positional and height accuracy. Still, when comparing temporal DTMs, the separation of actual physical phenomena from data-related ambiguities is essential in the framework of spatio-temporal analysis. This paper proposes the use of a hierarchical  co-modeling of different DTM databases for the task of landform monitoring. Analyses showed promising results, pointing to the feasibility of the proposed methodology in monitoring and quantifying topographic-related spatio-temporal phenomena, such as landslides and change detection, thus facilitating a reliable and precise landform monitoring and warning framework for geomorphodynamic analyses.
Integration of Unmanned Aerial Vehicle and Internet of Things Sensor Networks in Agriculture: An Artificial Intelligence–Driven Analytical Review for Smart Farming

The two most important challenges for contemporary agriculture are enhancing production and ensuring resources are utilized effectively. The demands placed upon precision agriculture are often not met by traditional forms of cultivation, particularly in regard to speed, data analysis, and realization. One breakthrough option is the integration of Internet of Things (IoT) networks and unmanned aerial vehicles (UAVs). To collect detailed information on soil moisture, temperature, and plant health across large regions, ground sensors and UAV platforms work together. Processing this vast and diverse dataset in real time is very challenging; however, artificial intelligence (AI) bridges this gap through the application of machine learning algorithms to analyze historical data, detect trends, and identify anomalies. Early detection of diseases, pests, and nutrient deficiencies is facilitated through AI-based systems, like multispectral sensor-based systems on UAVs. In addition, by foretelling water requirements and minimizing wastage, AI-based insights optimize irrigation, promoting water saving and enhancing yield. Data security, transparency, and trust of such systems are also enhanced through advancements in explainable AI and federated learning. AI-powered UAV–IoT networks usher in precision-oriented, eco-friendly, and sustainable agriculture through the propagation of a data-driven culture.
Promoting Health &amp; Preventing Disease: How Will We Meet the Challenge of Behavior Change?

Approximately half of all causes of mortality in the United States are linked to behavioral factors such as smoking, diet, alcohol use, sedentary lifestyle and accidents (McGinnis &amp; Foege, 1993). One very promising strategy for promoting health and preventing disease, therefore, is to help people change behaviors that increase disease risk—but helping people do this has proven to be a very difficult and challenging problem. One would think that if we informed people of their risks they would rush home and, in the interests of good health, change behaviors that caused the risk. Some people do, but most do not (Smedley &amp; Syme, 2000).
Scientific Autonomy, Academic Freedom, and Social Research in the United States

AbstractThis article connects the sociology of science to discussions of academic freedom and scientific autonomy, asking how social science is shaped by politics and extrascientific forces and how...
Knowledge Discovery from Earth Science Data

Mining the knowledge from climate data is one of the important issues due the rapid development and the extensive use of data acquisition technology. Earth science data increases tremendously, it is containing a large amount of information and knowledge. In this paper we used earth science data which is downloaded from the NASA website. The Earth science data has characteristics of mass, high dimensions, spatial co-relations. The data consists of time series measurements of various climate variables such as temperature, pressure, precipitation, humidity, direction and speed of wind and so forth. Any sudden change in parameters cause change in weather and also changes in parameters patterns behaviour. These sudden changes in parameters pattern do not conform to the general behaviour of the data set, these nonconforming patterns are often referred to as anomalies. Anomalies in the climatic variable cause ecosystem disturbance events such as forest fires, droughts, floods, and deforestation. Anomaly detection is a problem of finding unexpected patterns in a data set. In June 2013, a multi-day cloud burst centred on the North Indian state of Uttarakhand caused devastating floods and landslides in the country's worst natural disaster. Some neighbours area of Uttarakhand was experienced heavy rainfall. In this paper, we show the co-relations between the climatic parameters and analysed all parameters that affects the climates with the help of our proposed algorithm, which has the capability of discovering anomalies in the data set, in addition, analyse and identify significant changes or anomalies in the Uttarakhand climate data set. We also proposed anomalies detection algorithm based on Min-Max Method.
Radiation outer boundary conditions and near-to-far field signal transformations for the Bardeen-Press equation

Several theoretical and astrophysical problems - including gravitational-wave modeling for extreme mass-ratio inspirals - require accurate time-domain solutions of the spin-weight $s=-2$ Teukolsky equation in Boyer-Lindquist coordinates. Because such simulations are performed on finite computational domains, they typically introduce an artificial outer boundary where nontrivial boundary conditions must be imposed. If these conditions are inaccurate, then spurious reflections and slowly-growing unphysical modes may corrupt long-time evolutions. We develop and implement exact radiation outer boundary conditions for the Bardeen-Press equation (a harmonic moment of the $a=0$ Teukolsky equation), making the artificial boundary transparent at any finite radius. We also construct near-to-far field teleportation kernels that map field data recorded at finite radius $r_1$ to the data reaching $r_2 > r_1$. The possible choice $r_2 = \infty$ corresponds to asymptotic waveform evaluation, that is propagation of the data to future null infinity. We show that both boundary and teleportation kernels are well approximated by exponential sums, with associated error bounds. Implemented in a time-domain solver, our kernel-based boundary conditions eliminate unphysical late-time growth and give the correct late-time decay rates, affording efficient long-duration simulations for waveform modeling and related blackhole perturbation calculations.
A Review of Bangla Natural Language Processing Tasks and the Utility of Transformer Models

Bangla -- ranked as the 6th most widely spoken language across the world (https://www.ethnologue.com/guides/ethnologue200), with 230 million native speakers -- is still considered as a low-resource language in the natural language processing (NLP) community. With three decades of research, Bangla NLP (BNLP) is still lagging behind mainly due to the scarcity of resources and the challenges that come with it. There is sparse work in different areas of BNLP; however, a thorough survey reporting previous work and recent advances is yet to be done. In this study, we first provide a review of Bangla NLP tasks, resources, and tools available to the research community; we benchmark datasets collected from various platforms for nine NLP tasks using current state-of-the-art algorithms (i.e., transformer-based models). We provide comparative results for the studied NLP tasks by comparing monolingual vs. multilingual models of varying sizes. We report our results using both individual and consolidated datasets and provide data splits for future research. We reviewed a total of 108 papers and conducted 175 sets of experiments. Our results show promising performance using transformer-based models while highlighting the trade-off with computational costs. We hope that such a comprehensive survey will motivate the community to build on and further advance the research on Bangla NLP.
Schur-type invariants of branched G-covers of surfaces

Fix a finite group $G$ and a conjugacy invariant subset $C\subseteq G$. Let $Σ$ be an oriented surface, possibly with punctures. We consider the question of when two homomorphisms $π_1(Σ) \to G$ taking punctures into $C$ are equivalent up to an orientation preserving diffeomorphism of $Σ$. We provide an answer to this question in a stable range, meaning that $Σ$ has enough genus and enough punctures of every conjugacy type in $C$. If $C$ generates $G$, then we can assume $Σ$ has genus 0 (or any other constant). The main tool is a classifying space for (framed) $C$-branched $G$-covers, and related homology classes we call branched Schur invariants, since they take values in a torsor over a quotient of the Schur multiplier $H_2(G)$. We conclude with a brief discussion of applications to $(2+1)$-dimensional $G$-equivariant TQFT and symmetry-enriched topological phases.
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning

It is desirable to combine the expressive power of deep learning with Gaussian Process (GP) in one expressive Bayesian learning model. Deep kernel learning showed success in adopting a deep network for feature extraction followed by a GP used as function model. Recently,it was suggested that, albeit training with marginal likelihood, the deterministic nature of feature extractor might lead to overfitting while the replacement with a Bayesian network seemed to cure it. Here, we propose the conditional Deep Gaussian Process (DGP) in which the intermediate GPs in hierarchical composition are supported by the hyperdata and the exposed GP remains zero mean. Motivated by the inducing points in sparse GP, the hyperdata also play the role of function supports, but are hyperparameters rather than random variables. We follow our previous moment matching approach to approximate the marginal prior for conditional DGP with a GP carrying an effective kernel. Thus, as in empirical Bayes, the hyperdata are learned by optimizing the approximate marginal likelihood which implicitly depends on the hyperdata via the kernel. We shall show the equivalence with the deep kernel learning in the limit of dense hyperdata in latent space. However, the conditional DGP and the corresponding approximate inference enjoy the benefit of being more Bayesian than deep kernel learning. Preliminary extrapolation results demonstrate expressive power from the depth of hierarchy by exploiting the exact covariance and hyperdata learning, in comparison with GP kernel composition, DGP variational inference and deep kernel learning. We also address the non-Gaussian aspect of our model as well as way of upgrading to a full Bayes inference.
<i>The responsibility to protect</i>: is anyone interested in humanitarian intervention?

In a post‐September 11 and post‐Iraq world, many people and states are having second thoughts about the importance and risks of humanitarian intervention. Does any one care any longer? Observers ha...
Phylogenetic trees and other evolutionary diagrams in biology textbooks and their importance in secondary science education

Diagrams describing relationship between organisms, and their overall evolution, commonly in the form of phylogenetic trees or other evolutionary diagrams, have become a part of even lower secondary biology textbooks. These diagrams can help promote basic science literacy, yet their design may also strengthen misconceptions about evolution. Therefore, based on the content analysis of 112 Czech biology textbooks for secondary schools (ISCED levels 2 and 3), characteristics of introduced phylogenetic trees and other evolutionary diagrams were recorded and evaluated according to the cladistics to see if their construction supports the current scientific understanding of evolution. The content analysis indicates that the design of nearly half of all diagrams in current lower (ISCED 2) and upper secondary (ISCED 3) textbooks promotes ladder thinking. More than 80% of all diagrams were not accompanied by instructions on how to read them, meaning that students did not have sufficient scaffolding to understand them. Mainly ISCED 3 textbooks did not introduce additional problem tasks that would support the use of the diagrams in the lessons. Therefore, authors of textbooks should focus more on the construction quality of these diagrams while also supporting their correct application during the educational process. This is likely to prevent a further increase in student misconceptions.
Identity construction in digital communication for public engagement in science

This article explores identity construction in citizen science web texts. Keyness and concordance analyses show that these texts reflect, construct and negotiate identity construction in various ways to ultimately support citizen participation in scientific processes. Scientists primarily construct a professional identity through self-representation markers (‘we’ pronouns). They present themselves as credible professionals, aligned with the socially established values and ideologies of the scientific community. In addition, they appear to create a collective identity to promote empathy and to encourage and maintain citizen engagement in scientific processes. However, processes of indexicality and relationality – that is positioning and dialogism – reveal that the construction of a professional identity is consistently made more relevant than a collective expert-crowd identity, thus exposing overt power asymmetries. Even when dialogue between experts and nonexperts is encouraged, all the monologic and dialogic interactions established by the hypertext structure of the project sites reflect power imbalances.
Adolescent pregnancies in the Amazon Basin of Ecuador: a rights and gender approach to adolescents&#x2019; sexual and reproductive health

In the Andean region of Latin America over one million adolescent girls get pregnant every year. Adolescent pregnancy (AP) has been associated with adverse health and social outcomes, but it has also been favorably viewed as a pathway to adulthood. AP can also be conceptualized as a marker of inequity, since it disproportionately affects girls from the poorest households and those who have not been able to attend school. Using results from a study carried out in the Amazon Basin of Ecuador, this paper explores APs and adolescents&#x2019; sexual and reproductive health from a rights and gender approach. The paper points out the main features of a rights and gender approach, and how it can be applied to explore Aps. Afterward it describes the methodologies (quantitative and qualitative) and main results of the study, framing the findings within the rights and gender approach. Finally, some implications that could be generalizable to global reserach on APs are highlighted. The application of the rights and gender framework to explore APs contributes to a more integral view of the issue. The rights and gender framework stresses the importance of the interaction between rights-holders and duty-bearers on the realization of sexual and reproductive rights, and acknowledges the importance of gender&#x2013;power relations on sexual and reproductive decisions. A rights and gender approach could lead to more integral and constructive interventions, and it could also be useful when exploring other sexual and reproductive health matters.
Breakthrough of  error correction in quantum computing

By using a superconducting processor, Google Quantum AI group demonstrated that a logic qubit realized by surface code of quantum error correction performs better when the number of physical qubits increases. Two surface codes to encode logic qubits for scaling are realized experimentally, with multiple cycles of quantum error correcting assisted by ancillary qubits. This result can be considered as an important step toward fault-tolerant quantum computers.  In this review paper, we  introduce briefly the mechanism of quantum error correction. As an example, Shor’s nine-qubit error correction code is explained. Then, the new experiments of Google quantum AI group are introduced to show their significance in scaling. The advances in other quantum error correction experiments are also reviewed. Finally, the development of quantum computers is discussed.
Research on Probabilistic Safety Assessment (PSA) Method of Safety Class Distributed Control System (DCS) in Nuclear Power Plant Based on Fuzzy Markov Process

Abstract                Safety class DCS (Distributed Control System) in nuclear power plant is characterized by multi-state interaction, complex function and unknown fault mechanism, which limits the use of tradition PSA (Probabilistic Safety Assessment) method such as event tree and fault tree. An improved dynamic probabilistic security simulation method is proposed based on fuzzy Markov process to quantitative evaluation the risk of Safety class DCS in nuclear power plant, which resolved the failure uncertainty problem without in-depth understanding of failure mechanism. Firstly, the fuzzy fault tree reliability evaluation model is established based on fuzzy theory and FTA (Fault Tree Analysis), which describes the probability and degree of failure with fuzzy number. Then, the fuzzy fault tree is transformed into fuzzy Markov chain, this model is described mathematically, and the reliability indexes are simulated and analyzed with Monte Carlo simulation method, the change of system safety performance in long-term operation is obtained. Finally, the scientificity and effectiveness is verified by the example of redundant analog output module of safety class DCS in nuclear plant.
Dependence of the cosmic microwave background lensing power spectrum on the matter density

The anisotropies in the cosmic microwave background (CMB) provide our best laboratory for testing models of the formation and evolution of large-scale structure. The rich features in the cosmic microwave background anisotropy spectrum, in combination with highly precise observations and theoretical predictions, also allow us to simultaneously constrain a number of cosmological parameters. As observations have progressed, measurements at smaller angular scales have provided increasing leverage. These smaller angular scales provide sensitive measures of the matter density through the effect of gravitational lensing. In this work we provide an analytic explanation of the manner in which the lensing of CMB anisotropies depends on the matter density, finding that the dominant effect comes from the shape of the matter power spectrum set by the decay of small-scale potentials between horizon crossing and matter-radiation equality.
Deep learning-driven survival prediction in pan-cancer studies by integrating multimodal histology-genomic data

Abstract                Accurate cancer prognosis is essential for personalized clinical management, guiding treatment strategies and predicting patient survival. Conventional methods, which depend on the subjective evaluation of histopathological features, exhibit significant inter-observer variability and limited predictive power. To overcome these limitations, we developed cross-attention transformer-based multimodal fusion network (CATfusion), a deep learning framework that integrates multimodal histology-genomic data for comprehensive cancer survival prediction. By employing self-supervised learning strategy with TabAE for feature extraction and utilizing cross-attention mechanisms to fuse diverse data types, including mRNA-seq, miRNA-seq, copy number variation, DNA methylation variation, mutation data, and histopathological images. By successfully integrating this multi-tiered patient information, CATfusion has become an advanced survival prediction model to utilize the most diverse data types across various cancer types. CATfusion’s architecture, which includes a bidirectional multimodal attention mechanism and self-attention block, is adept at synchronizing the learning and integration of representations from various modalities. CATfusion achieves superior predictive performance over traditional and unimodal models, as demonstrated by enhanced C-index and survival area under the curve scores. The model’s high accuracy in stratifying patients into distinct risk groups is a boon for personalized medicine, enabling tailored treatment plans. Moreover, CATfusion’s interpretability, enabled by attention-based visualization, offers insights into the biological underpinnings of cancer prognosis, underscoring its potential as a transformative tool in oncology.