Paulina Carmona Rodriguez, Maria de la Paz Ramos-Lara
The Large Millimeter Telescope Alfonso Serrano (LMT), is the largest millimeter radio telescope in the world, and was founded in 2006. This radio telescope is the final product of a collaboration agreement between Mexico and the United States in the 1990s. It is located on top of an extinct volcano in Mexico at an altitude of 4600 meters above sea level. In 2018, the University of Manchester and the Rutherford Appleton Laboratory signed an agreement with the Instituto Nacional de Astrofisica Optica y Electronica (INAOE) to train Mexican astronomers in high-frequency radio receiver construction techniques by designing an innovative device called Collaborative Heterodyne Amplifier Receiver for Mexico (CHARM) designed to work at a frequency of 345 GHz. The research team, composed of British and Mexican technicians and scientists, installed CHARM at the LMT in 2019 and began testing the equipment until the COVID-19 pandemic shut it down in March 2020. This paper describes the collaboration process between Mexico and the United Kingdom, facilitated by a British institution dedicated to supporting scientific projects in developing countries, the Global Challenges Research Fund (GCRF).
A complex multi-state redundant system with preventive maintenance subject to multiple events is considered. The online unit can undergo several types of failures: internal and those provoked by external shocks. Multiple degradation levels are assumed so as internal and external. Degradation levels are observed by random inspections and if they are major, the unit goes to repair facility where preventive maintenance is carried out. This repair facility is composed of a single repairperson governed by a multiple vacation policy. This policy is set up according to the operational number of units. Two types of task can be performed by the repairperson, corrective repair and preventive maintenance. The times embedded in the system are phase type distributed and the model is built by using Markovian Arrival Processes with marked arrivals. Multiple performance measures besides of the transient and stationary distribution are worked out through matrix-analytic methods. This methodology enables us to express the main results and the global development in a matrix-algorithmic form. To optimize the model costs and rewards are included. A numerical example shows the versatility of the model.
Emily Marshman, Alexandru Maries, Chandralekha Singh
One hallmark of expertise in physics is the ability to translate between different representations of knowledge and use the representations that make the problem-solving process easier. In quantum mechanics, students learn about several ways to represent quantum states, e.g., as state vectors in Dirac notation and as wavefunctions in position and momentum representation. Many advanced students in upper-level undergraduate and graduate quantum mechanics courses have difficulty translating state vectors in Dirac notation to wavefunctions in the position or momentum representation and vice versa. They also struggle when translating the wavefunction between the position and momentum representations. The research presented here describes the difficulties that students have with these issues and how research was used as a guide in the development, validation, and evaluation of a Quantum Interactive Learning Tutorial (QuILT) to help students develop a functional understanding of these concepts. The QuILT strives to help students with different representations of quantum states as state vectors in Dirac notation and as wavefunctions in position and momentum representation and with translating between these representations. We discuss the effectiveness of the QuILT from in-class implementation and evaluation.
To investigate the impact of cross-grained sentiments on user feature representation and address the issue of data sparsity, this paper proposes a Personalized Recommendation Algorithm Integrating Cross-Grained Sentiment and Rating Interaction Features (ICSR). The algorithm begins by employing a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model and a Bi-GRU (Bidirectional Gated Recurrent Units) network to derive feature vectors from user and item reviews. Sentiment dictionaries and attention mechanisms are then applied to assign appropriate weights to the review features of users and items, respectively. To capture a richer set of sentiment features, a cross-grained sentiment feature fusion module is introduced. This module leverages an LDA (Latent Dirichlet Allocation) model and dependency syntactic analysis techniques to extract fine-grained sentiment features, while a word2vec pre-trained model and sentiment dictionaries are used to capture coarse-grained sentiment features. These features are then fused to form comprehensive cross-grained sentiment representations. Finally, rating interaction features are extracted using matrix factorization techniques, and all features are integrated and fed into a DeepFM model for rating prediction. Experimental results on Amazon datasets demonstrate that the proposed ICSR algorithm significantly outperforms baseline algorithms in terms of recommendation performance.
Mauro Marino-Jiménez, Norma Sánchez-Chávez, Yenny Rivero-Fortón
et al.
Student performance, disciplinary innovation and teaching methodology occupy the main concerns of educational research. Therefore, there is a greater interest in gamification strategies, where digital tools facilitate the development of competitive activities and strengthening of learning. One example of this idea is the use of video games created for non-educational purposes, where disciplinary strategies and/or social skills can be developed. In this paper, the game Among Us is used to develop an educational experience at higher education. Its use helps to develop a methodology for the identification and analysis of fallacies, according to their recurrency and effectiveness. The result of this learning experience led to a greater comprehension about the use of fallacies, favorable perceptions about the use of games for educational experience, and a deeper reflection about social intelligence in the students.
History of scholarship and learning. The humanities, Social Sciences
Diego D. Díaz-Guerra, Marena de la C. Hernández-Lugo, Yunier Broche-Pérez
et al.
IntroductionEvaluating neurocognitive functions and diagnosing psychiatric disorders in older adults is challenging due to the complexity of symptoms and individual differences. An innovative approach that combines the accuracy of artificial intelligence (AI) with the depth of neuropsychological assessments is needed.ObjectivesThis paper presents a novel protocol for AI-assisted neurocognitive assessment aimed at addressing the cognitive, emotional, and functional dimensions of older adults with psychiatric disorders. It also explores potential compensatory mechanisms.MethodologyThe proposed protocol incorporates a comprehensive, personalized approach to neurocognitive evaluation. It integrates a series of standardized and validated psychometric tests with individualized interpretation tailored to the patient’s specific conditions. The protocol utilizes AI to enhance diagnostic accuracy by analyzing data from these tests and supplementing observations made by researchers.Anticipated resultsThe AI-assisted protocol offers several advantages, including a thorough and customized evaluation of neurocognitive functions. It employs machine learning algorithms to analyze test results, generating an individualized neurocognitive profile that highlights patterns and trends useful for clinical decision-making. The integration of AI allows for a deeper understanding of the patient’s cognitive and emotional state, as well as potential compensatory strategies.ConclusionsBy integrating AI with neuro-psychological evaluation, this protocol aims to significantly improve the quality of neurocognitive assessments. It provides a more precise and individualized analysis, which has the potential to enhance clinical decision-making and overall patient care for older adults with psychiatric disorders.
Ana Joksimović, Danijela Arsenov, Milan Borišev
et al.
This study aimed to characterise the chemical properties of fullerenol nanoparticles (FNP) and zinc oxide nanoparticles (ZnO nano), as well as their physiological and molecular effects following foliar application on Arabidopsis thaliana. Additionally, we explored the potential synergistic impact of combining ZnO nano with FNPs to enhance plant resilience under drought stress conditions. Chemical characterisation confirmed the successful formation of a stable FNP-ZnO aggregate. The previously established biostimulatory effects of fullerenol at micromolar concentrations were reaffirmed, highlighting its unique chemical properties. We demonstrated that a low dose (10 mg/L) of ZnO nano, used as a foliar application for the first time in Arabidopsis thaliana, positively influenced drought stress acclimatization. Our findings indicate that FNP and ZnO alleviate oxidative stress by mitigating the impact of reactive oxygen species (ROS), modulating antioxidant enzyme activities, and stabilising redox balance. Photosynthetic performance, stomatal conductance and water-use efficiency were optimized, particularly through fullerenol application, due to its unique antioxidative and hygroscopic properties. We further analyzed the expression of selected drought-response genes involved in ABA-dependent and ABA-independent water deficit acclimation in Col-0 wild-type and pp2ca-1 drought hypersensitive mutant backgrounds. Our results revealed distinct gene expression changes in response to nanoparticle treatments, demonstrating modulation of ABA signaling and stress-related transcription factors. The combined application of FNP and ZnO exhibited unique, synergistic protective effects in drought acclimation. Future research will further elucidate the direct mechanisms linking these physiological outcomes to specific nanoparticle properties, paving the way for innovative strategies in sustainable agriculture.
Mateusz Grzyb, Mateusz Krzyziński, Bartłomiej Sobieski
et al.
This project explores the application of Natural Language Processing (NLP) techniques to analyse United Nations General Assembly (UNGA) speeches. Using NLP allows for the efficient processing and analysis of large volumes of textual data, enabling the extraction of semantic patterns, sentiment analysis, and topic modelling. Our goal is to deliver a comprehensive dataset and a tool (interface with descriptive statistics and automatically extracted topics) from which political scientists can derive insights into international relations and have the opportunity to have a nuanced understanding of global diplomatic discourse.
Under the widely accepted but unproven assumption that the one-dimensional S=1 antiferromagnetic Heisenberg model has a unique gapped ground state, we prove that the model belongs to a nontrivial symmetry-protected topological (SPT) phase. In other words, we rigorously rule out the possibility that the model has a unique gapped ground state that is topologically trivial. To be precise, we assume that the models on open finite chains with boundary magnetic field have unique ground states with a uniform gap and prove that the ground state of the infinite chain has a nontrivial topological index. This further implies the presence of a gapless edge excitation in the model on the half-infinite chain and the existence of a topological phase transition in the model that interpolates between the Heisenberg chain and the trivial model.
Brett R. Lane, Amy E. Kendig, Christopher M. Wojan
et al.
Invasive plants, which cause substantial economic and ecological impacts, acquire both pathogens and beneficial microbes in their introduced ranges. Communities of fungal endophytes are known to mediate impacts of pathogens on plant fitness but few studies have examined the temporal dynamics of fungal communities on invasive plants. The annual grass Microstegium vimineum, an invader of forests and riparian areas throughout the eastern United States, experiences annual epidemics of disease caused by Bipolaris pathogens. Our objective was to characterize the dynamics of foliar fungal communities on M. vimineum over a growing season during a foliar disease epidemic. First, we asked how the fungal community in the phyllosphere changed over 2 months that corresponded with increasing disease severity. Second, we experimentally suppressed disease with fungicide in half of the plots and asked how the treatment affected fungal community diversity and composition. We found increasingly diverse foliar fungal communities and substantial changes in community composition between timepoints using high-throughput amplicon sequencing of the internal transcribed spacer 2 region. Monthly fungicide application caused shifts in fungal community composition relative to control samples. Fungicide application increased diversity at the late-season timepoint, suggesting that it suppressed dominant fungicide sensitive taxa and allowed other fungal taxa to flourish. These results raise new questions regarding the roles of putative endophytes found in the phyllosphere given the limited number of pathogens known to cause disease on M. vimineum in its invasive range.
Systems thinking can shed light on important relationships and conditions that affect community engagement activities. While robust tools like the community capitals framework and the sustainable livelihoods approach provide valuable context for engagement projects, additional insights can stem from models that describe the ebb and flow of different types of capital. This paper uses a well-studied ecosystem model called adaptive renewal (AR) to contextualize heritage-related challenges and opportunities in four rural communities on the Canadian prairies. Based on a reflective case-study analysis, we applied the AR model to focus group and semistructured interview data collected as part of a Museums Association of Saskatchewan (MAS) project aimed at using local heritage assets to build sociocultural and environmental capacity and attract investment. The MAS project identified four themes that could be addressed through training and policy changes, including concerns about funding, limited human resources, a lack of public services, and a desire to preserve and build on memories. By mapping each community onto the AR model, we uncovered additional insights about community resilience and other heritage-related challenges and opportunities. The AR model is likely to be a valuable tool for planning or assessing community engagement projects because it reflects the dynamic nature of socioeconomic and cultural relationships that affect community dynamics and local well-being.
AbstractYarn-shaped supercapacitors (YSCs) are becoming promising energy-supply units with decent mechanical flexibility to be integrated into e-textiles in various shapes and locations. However, a robust YSC configuration that can provide long-term and reliable power output, especially after rigorous weaving and knitting processes, as well as all kinds of end uses, is yet to be established. Most YSCs today still suffer from short-circuiting upon length, primarily due to the structure failure of gel electrolyte that also works as the separator. Herein, we report the incorporation of separator threads in a twisted YSC, to withstand repetitive mechanical deformations. Separator threads are wrapped outside of yarn electrodes as a scaffold to accommodate gel electrolyte, while chemistry and wrapping density of these threads are investigated. With processing parameters optimized, we present an YSC configuration that can bear mechanical deformations along almost all directions, leading to reliable power units in woven or knit fabrics.
AbstractMachine-learned interatomic potentials based on local environment descriptors represent a transformative leap over traditional potentials based on rigid functional forms in terms of prediction accuracy. However, a challenge in their application to ionic systems is the treatment of long-ranged electrostatics. Here, we present a highly accurate electrostatic Spectral Neighbor Analysis Potential (eSNAP) for ionic α-Li3N, a prototypical lithium superionic conductor of interest as a solid electrolyte or coating for rechargeable lithium-ion batteries. We show that the optimized eSNAP model substantially outperforms traditional Coulomb–Buckingham potential in the prediction of energies and forces, as well as various properties, such as lattice constants, elastic constants, and phonon dispersion curves. We also demonstrate the application of eSNAP in long-time, large-scale Li diffusion studies in Li3N, providing atomistic insights into measures of concerted ionic motion (e.g., the Haven ratio) and grain boundary diffusion. This work aims at providing an approach to developing quantum-accurate force fields for multi-component ionic systems under the SNAP formalism, enabling large-scale atomistic simulations for such systems.
Technological advancements in education have turned the idea of machines as teachers into a reality. To better understand this phenomenon, the present study explores how college students develop expectations (or anticipations) about a machine teacher, particularly an AI teaching assistant. Specifically, the study examines whether students’ previous experiences with online courses taught by a human teacher would influence their expectations about AI teaching assistants in future online courses. An online survey was conducted to collect data from college students in the United States. Findings indicate that positively experienced social presence of a human teacher helps develop positive expectations about an AI teaching assistant. The study provides meaningful implications and contributions to our understanding of a machine agent in education.
Derek K.L. Tsang, Ryan J. Wang, Oliver De Sa
et al.
The small intestinal epithelial barrier inputs signals from the gut microbiota in order to balance physiological inflammation and tolerance, and to promote homeostasis. Understanding the dynamic relationship between microbes and intestinal epithelial cells has been a challenge given the cellular heterogeneity associated with the epithelium and the inherent difficulty of isolating and identifying individual cell types. Here, we used single-cell RNA sequencing of small intestinal epithelial cells from germ-free and specific pathogen-free mice to study microbe-epithelium crosstalk at the single-cell resolution. The presence of microbiota did not impact overall cellular composition of the epithelium, except for an increase in Paneth cell numbers. Contrary to expectations, pattern recognition receptors and their adaptors were not induced by the microbiota but showed concentrated expression in a small proportion of epithelial cell subsets. The presence of the microbiota induced the expression of host defense- and glycosylation-associated genes in distinct epithelial cell compartments. Moreover, the microbiota altered the metabolic gene expression profile of epithelial cells, consequently inducing mTOR signaling thereby suggesting microbe-derived metabolites directly activate and regulate mTOR signaling. Altogether, these findings present a resource of the homeostatic transcriptional and cellular impact of the microbiota on the small intestinal epithelium.
Diseases of the digestive system. Gastroenterology
This paper aims to provide an overview of the ethical concerns in artificial intelligence (AI) and the framework that is needed to mitigate those risks, and to suggest a practical path to ensure the development and use of AI at the United Nations (UN) aligns with our ethical values. The overview discusses how AI is an increasingly powerful tool with potential for good, albeit one with a high risk of negative side-effects that go against fundamental human rights and UN values. It explains the need for ethical principles for AI aligned with principles for data governance, as data and AI are tightly interwoven. It explores different ethical frameworks that exist and tools such as assessment lists. It recommends that the UN develop a framework consisting of ethical principles, architectural standards, assessment methods, tools and methodologies, and a policy to govern the implementation and adherence to this framework, accompanied by an education program for staff.