Related Information Research Articles

Dimensionless correlation of mass transfer on a frosted flat plate with thickness, mass, heat flux measurements

The aim of the study is to investigate frosting on the horizontal flat plate which has uniform air velocity distribution upstream. While the air temperature is 21.9 °C, the relative humidity is 80% and the air velocity is 0.7 m/s; frost is formed on the flat plate at surface temperatures of −15.1, −11.2, −7.1 °C, then under these conditions frost thickness, frost mass are measured. Continuous heat flux measurements under frosting which is very rare in literature are performed, too. Considering only the initial part of the frosting process at high plate temperature first an increase in heat flux then a continuous decrease is observed. A new dimensionless mass transfer correlation that does not require frost surface temperature and additionally includes dimensionless plate length is presented. The mass transfer correlation is based on dimensionless numbers of Reynolds, Fourier, specific humidity, dimensionless temperature, and dimensionless plate length which are derived from air temperature, air velocity, air relative humidity, plate temperature, plate length, plate width, and time information. In obtaining the correlation, frost mass measurements from previous researchers were added to the presented experimental results to develop a correlation valid over a wide range. As a result of the broad data set, the ranges at which the correlation is valid; air velocity of 0.7–2.5 m/s, air temperature of 5–22 °C, plate temperature of −5 to −25 °C, air relative humidity of 50%–83.73% and plate length of 85.6–300 mm. Nondimensionalizing the plate length has increased the prediction capability of the correlation.

Abstract 4840: Creating a culturally sensitive manual to increase clinical trial participation among African Americans and African Nationals

Abstract Background: Racial and ethnic minorities are immensely underrepresented in clinical trial studies in the United States. Black/African Americans, Latinos/as, Asians, American Indians, and Pacific Islanders make up roughly 30% of all clinical trials (CT) participation across all race groups. Being inclusive of all populations in CT ensures that everyone has access to modern medicines and treatments, but also, that these latest treatments work and react well to all people. Methods: We developed a manual on clinical trials with a target focus to African Americans and African Nationals. We hosted two focus groups with Blacks/African Americans in which we found they are more comfortable getting cancer related information through trusted community services, such as coming from churches. We have connected with over 80 Pastors who agreed to collaborate in clinical trials education. Results: Through outreach and engagement to African American churches in Los Angeles County, we have distributed a copy of this clinical trials educational manual to 90 churches to be disseminated during their services. Our office of Community Outreach and Engagement (COE) regularly attend community health and resource events and provide hard copies of the clinical trials manual at tables/booths designated for our participation. We have attended 50 community events and have disseminated about 1,900 copies of the CT manual so far this year 2023. This manual is also available for free download on our cancer center’s website, in which over 100 toolkits have been downloaded within the last year. Conclusions: By creating a culturally sensitive and adapted educational manual and partnering with trusted services in the community such as faith-based organizations, we can begin to break down barriers to clinical trials participation among all race and ethnic minority groups. Citation Format: Carolina Aristizabal, Eduardo Ibarra, Lourdes A. Baezconde-Garbanati. Creating a culturally sensitive manual to increase clinical trial participation among African Americans and African Nationals [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4840.

Roles of KCNA2 in Neurological Diseases: from Physiology to Pathology.

Potassium voltage-gated channel subfamily a member 2 (Kv1.2, encoded by KCNA2) is highly expressed in the central and peripheral nervous systems. Based on the patch clamp studies, gain-of function (GOF), loss-of-function (LOF), and a mixed type (GOF/LOF) variants can cause different conditions/disorders. KCNA2-related neurological diseases include epilepsy, intellectual disability (ID), attention deficit/hyperactive disorder (ADHD), autism spectrum disorder (ASD), pain as well as autoimmune and movement disorders. Currently, the molecular mechanisms for the reported variants in causing diverse disorders are unknown. Consequently, this review brings up to date the related information regarding the structure and function of Kv1.2 channel, expression patterns, neuronal localizations, and tetramerization as well as important cell and animal models. In addition, it provides updates on human genetic variants, genotype-phenotype correlations especially highlighting the deep insight into clinical prognosis of KCNA2-related developmental and epileptic encephalopathy, mechanisms, and the potential treatment targets for all KCNA2-related neurological disorders.

Digital Transformation of Tax Law Principles

Objective: to search for conceptual issues, features and prospects of development of tax law principles and tax obligation institute in the context of modern digitalization trends.Methods: the study is based on a set of general legal (analysis and synthesis, induction and deduction) and special-legal (formal-legal, comparativelegal) methods of cognition.Results: the main features of tax law principles characterizing their digital development were defined, namely: the expansion of the list of such principles and the changes in their content, predominantly at the level of institutional principles of taxation. A conclusion is made that in the digital era the tax law principles retain their theoretical significance as the most important category of the relevant science. The author states that the digital transformation of the institutional principle of tax fulfillment independence takes place mainly in the following directions: firstly, the range of taxpayers is expanded, in respect of which the duty to calculate tax can be imposed on a tax authority; secondly, the mechanism of the tax obligation execution is changed, among other things, by increasing the tax obligations of state authorities and credit organizations.Scientific novelty: the paper assesses the approaches used in the scientific literature and normative-legal framework related to transformation of the taxation principles due to the intensive use of information and communication technologies in tax relations. The author also analyzed the digital transformation of the principles of tax obligation as one of the main tax-legal institutions.Practical significance: is due to the lagging pace of tax legislation development in the Russian Federation compared to the objective processes of creation and application of new information and communication technologies in tax relations and adjacent spheres. The main provisions and conclusions of the study can be used as a basis to improve the tax legislation, as they develop knowledge about new tax law institutions – professional income tax, tax monitoring, single tax payment, and automated simplified taxation system.

Entity alignment with fusing relation representation

Entity alignment is the task of identifying entities from different knowledge graphs (KGs) that point to the same item and is important for KG fusion. In the real world, due to the heterogeneity between different KGs, equivalent entities often have different relations around them, so it is difficult for Graph Convolutional Network (GCN) to accurately learn the relation information in the KGs. Moreover, to solve the problem regarding inadequate utilisation of relation information in entity alignment, a novel GCN-based model, joint Unsupervised Relation Alignment for Entity Alignment (URAEA), is proposed. The model first employs a novel method for calculating relation embeddings by using entity embeddings, then constructs unsupervised seed relation alignments through these relation embeddings, and finally performs entity alignment together with relation alignment. In addition, the seed entity alignments are expanded based on the generated seed relation alignments. Experiments conducted on three real-world datasets show that this approach outperforms state-of-the-art methods.

A Lightweight Channel and Time Attention Enhanced 1D CNN Model for Environmental Sound Classification

One dimension convolutional neural networks (1D CNN) that directly take raw waveforms as input has less competition than 2D CNN recognizing environmental sound. In order to overcome its disadvantages, we propose a novel lightweight 1D CNN structure by employing attention mechanism, which has significant improvement in both accuracy and computational complexity. Concretely, (1) two attention modules are constructed along channel and time dimension separately, and combined to give an intermediate feature map, which focus on key frequency band and semantically related time frame information. (2) Without increasing training overhead, snapshot ensemble is employed to further improve performance. Results from two benchmarking datasets (UrbanSound8k, ESC-10) demonstrated that: by employing attention mechanism, our model outperforms all of the previously reported 1D CNN approaches in accuracy with less parameters. Meanwhile with improved performance gain, the proposed model is superior than most of the existing spectral-based 2D CNN approaches and competitive with SOTA performance, while with orders of magnitude parameters fewer. Overall, it indicates our model is compact and has good potential in practical resource-limited applications, such as sound recognition on embedded platform.

Robust consensus tracking of one‐sided Lipschitz nonlinear multi‐agent systems under structured uncertainty and time‐varying delay

AbstractThis article delves into the intricate matter of achieving robust consensus tracking of multi‐agent systems with structured uncertainty, one‐sided Lipschitz nonlinearity and time‐varying delay. It is important to note that the time delay is both time‐varying and bounded. To solve the consensus tracking problem of multi‐agent systems, we transform it into the stability problem of an error system. Then, a Lyapunov–Krasovskii functional, incorporating the free‐weighting matrix technique and integral inequality approach, is designed to investigate the stability of the obtained error system. The Leibnitz–Newton formula and its associated relationships among terms are also taken into account. Then, two distributed control strategyies that respectively utilizes relative state and output information of neighboring agents are designed. This approach enables us to find a feasible solution to the consensus tracking problem. Finally, several numerical examples are presented to illustrate the effectiveness of the proposed theoretical results.

Generalized functional additive mixed models with (functional) compositional covariates for areal Covid-19 incidence curves

Abstract We extend the generalized functional additive mixed model to include compositional and functional compositional (density) covariates carrying relative information of a whole. Relying on the isometric isomorphism of the Bayes Hilbert space of probability densities with a sub-space of the L2, we include functional compositions as transformed functional covariates with constrained yet interpretable effect function. The extended model allows for the estimation of linear, non-linear, and time-varying effects of scalar and functional covariates, as well as (correlated) functional random effects, in addition to the compositional effects. We use the model to estimate the effect of the age, sex, and smoking (functional) composition of the population on regional Covid-19 incidence data for Spain, while accounting for climatological and socio-demographic covariate effects and spatial correlation.

A novel joint extraction model based on cross-attention mechanism and global pointer using context shield window

Relational triple extraction is a critical step in knowledge graph construction. Compared to pipeline-based extraction, joint extraction is gaining more attention because it can better utilize entity and relation information without causing error propagation issues. Yet, the challenge with joint extraction lies in handling overlapping triples. Existing approaches adopt sequential steps or multiple modules, which often accumulate errors and interfere with redundant data. In this study, we propose an innovative joint extraction model with cross-attention mechanism and global pointers with context shield window. Specifically, our methodology begins by inputting text data into a pre-trained RoBERTa model to generate word vector representations. Subsequently, these embeddings are passed through a modified cross-attention layer along with entity type embeddings to address missing entity type information. Next, we employ the global pointer to transform the extraction problem into a quintuple extraction problem, which skillfully solves the issue of overlapping triples. It is worth mentioning that we design a context shield window on the global pointer, which facilitates the identification of correct entities within a limited range during the entity extraction process. Finally, the capability of our model against malicious samples is improved by adding adversarial training during the training process. Demonstrating superiority over mainstream models, our approach achieves impressive results on three publicly available datasets.

Student Registration System

The Student Management System is involved in maintaining the details of the attendance of students. Along with this, the system can also perform various functionalities on enrolled students as well as teachers. Generated student attendance is based on classroom attendance. A daily attendance base will be maintained and specified users will be provided with a personalized username and password for proper observation and analysis of the attendance sheet along with related informations. Using this given system we can easily create a number of new batches with respect to different subjects, class timing, semester name, etc. Apart from this, we are able to add, remove and update students and teachers with respect to different batches, subjects, and semester names. We can generate normal as well as customized (with a given set of parameters) student attendance reports with relevant informations, in whichever format we like (PDF, Excel, XPS writer). This system thus greatly reduces the complexity, time taken and manual efforts required to generate as well as maintain normal attendance reports and perform related operations on students, teachers, etc. by using one centralized system for all operations

A comprehensive numerical investigation of multi-scale particle shape effects on small-strain stiffness of sands

The effects of multi-scale particle shape characteristics on the small-strain stiffness of granular soils remain controversial. This study revisits this topic using well-calibrated three-dimensional discrete-element simulations incorporating a particle roughness–embedded contact model and particles of realistic shape. Based on the numerical simulation results, the multi-scale particle shape effects on the small-strain stiffness of sands and the magnitude of Hardin's equation parameters are systematically investigated, and the underlying micro-mechanisms are also thoroughly explored. Results indicate that the small-strain stiffness increases with the increase of particles’ overall irregularity due to the increased mechanical coordination number, but decreases with the increase of particle surface roughness because of the decreased contact normal stiffness. In addition, the constant term parameter and void ratio term parameter of Hardin's equation increases and decreases linearly, respectively, with the particles’ overall regularity, but reduces and grows with the particle surface roughness, respectively. Furthermore, the stress exponent is almost unchanged with the particles’ overall regularity, but increases with the particle surface roughness, which determines the relative proportions of contacts under asperity-dominated, transitional and Hertzian stages. The study helps to advance the cross-scale understanding of multi-scale particle shape information in relation to small-strain stiffness of sands.

The link between multiplicative competitive interaction models and compositional data regression with a total

ABSTRACT This article sheds light on the relationship between compositional data (CoDa) regression models and multiplicative competitive interaction (MCI) models, which are two approaches for modeling shares. We demonstrate that MCI models are particular cases of CoDa models with a total and that a reparameterization links both. Recognizing this relation offers mutual benefits for the CoDa and MCI literature, each with its own rich tradition. The CoDa tradition, with its rigorous mathematical foundation, provides additional theoretical guarantees and mathematical tools that we apply to improve the estimation of MCI models. Simultaneously, the MCI model emerged from almost a century-long tradition in marketing research that may enrich the CoDa literature. One aspect is the grounding of the MCI specification in assumptions on the behavior of individuals. From this basis, the MCI tradition also provides credible justifications for heteroskedastic error structures – an idea we develop further and that is relevant to many CoDa models beyond the marketing context. Additionally, MCI models have always been interpreted in terms of elasticities, a method that has only recently emerged in CoDa. Regarding this interpretation, the CoDa perspective leads to a decomposition of the influence of the explanatory variables into contributions from relative and absolute information.

Has sentiment returned to the pre-pandemic level? A sentiment analysis using U.S. college subreddit data from 2019 to 2022.

As the impact of the COVID-19 pandemic winds down, both individuals and society are gradually returning to life and activities before the pandemic. This study aims to explore how people's emotions have changed from the pre-pandemic period during the pandemic to this post-emergency period and whether the sentiment level nowadays has returned to the pre-pandemic level. We collected Reddit social media data in 2019 (pre-pandemic), 2020 (peak period of the pandemic), 2021, and 2022 (late stages of the pandemic, transitioning period to the post-emergency period) from the subreddits communities in 128 universities/colleges in the U.S., and a set of school-level baseline characteristics such as location, enrollment, graduation rate, selectivity, etc. We predicted two sets of sentiments from a pre-trained Robustly Optimized BERT pre-training approach (RoBERTa) and a graph attention network (GAT) that leverages both the rich semantic information and the relational information among posted messages and then applied model stacking to obtain the final sentiment classification. After obtaining the sentiment label for each message, we employed a generalized linear mixed-effects model to estimate the temporal trend in sentiment from 2019 to 2022 and how the school-level factors may affect the sentiment. Compared to the year 2019, the odds of negative sentiment in years 2020, 2021, and 2022 are 25%. 7.3%, and 6.3% higher, respectively, which are all statistically significant at the 5% significance level based on the multiplicity-adjusted p-values. Our study findings suggest a partial recovery in the sentiment composition (negative vs. non-negative) in the post-pandemic-emergency era. The results align with common expectations and provide a detailed quantification of how sentiments have evolved from 2019 to 2022 in the sub-population represented by the sample examined in this study.

Krylov Complexity of Fermionic and Bosonic Gaussian States

AbstractThe concept of complexity has become pivotal in multiple disciplines, including quantum information, where it serves as an alternative metric for gauging the chaotic evolution of a quantum state. This paper focuses on Krylov complexity, a specialized form of quantum complexity that offers an unambiguous and intrinsically meaningful assessment of the spread of a quantum state over all possible orthogonal bases. This study is situated in the context of Gaussian quantum states, which are fundamental to both Bosonic and Fermionic systems and can be fully described by a covariance matrix. While the covariance matrix is essential, it is insufficient alone for calculating Krylov complexity due to its lack of relative phase information is shown. The relative covariance matrix can provide an upper bound for Krylov complexity for Gaussian quantum states is suggested. The implications of Krylov complexity for theories proposing complexity as a candidate for holographic duality by computing Krylov complexity for the thermofield double States (TFD) and Dirac field are also explored.

Attack reconstruction‐based resilient consensus tracking for multi‐agent systems with distributed adaptive control protocol

AbstractThis article considers the distributed secure consensus tracking control problem for multi‐agent systems (MASs) with relative output information. To tackle the potential false‐data‐injection (FDI) attacks on both the communication between physical components and the control center at the agent level, as well as the message transmission between connected agents, local sliding mode observers (SMO) and distributed observers are proposed for each agent. By introducing reconstructed signals for attacks and smooth terms to compensate the unknown input in the leader agent's dynamics, a distributed adaptive consensus protocol with relative output information is proposed. The closed‐loop stability is analyzed by introducing a novel Lyapunov function. It is shown that all closed‐loop signals are globally uniformly bounded and asymptotically consensus tracking can be achieved. Simulation results are provided to validate the theoretical findings.

Community pharmacist intervention in doxycycline self-medication for acne among pregnant women in Saudi Arabia

IntroductionSelf-medication during pregnancy remains a prevalent global phenomenon, with pregnant women often relying on their own judgment to manage symptoms. Objective: This study aimed to assess community pharmacist practice regarding self-medication requests for acne treatment in pregnant women within Saudi Arabia. MethodsA descriptive, observational cross-sectional study was conducted in Riyadh, Saudi Arabia. Community pharmacists completed a questionnaire after interacting with a simulated customer (SC) seeking doxycycline for a pregnant woman. Data were analyzed using SPSS software. ResultsOne hundred community pharmacists participated in the study. The majority were between 25 and 35 years old (56 %) and worked in chain pharmacies (81 %). Notably, 71 % requested a prescription before dispensing any medication. While 66 % advised the SC to consult a physician or dermatologist, only 15 % inquired about the pregnancy and related information of the SC's sister. ConclusionThis study suggests a positive trend in community pharmacy practice regarding self-medication requests during pregnancy. However, further improvement is needed, particularly in terms of in-depth inquiry about pregnancy status and potential risks associated with specific medications. Collaborative efforts between the Ministry of Health (MOH), Saudi Food and Drug Administration (SFDA), and pharmacy education programs are crucial to minimize inappropriate dispensing practices and ensure optimal patient safety.

Coupled translation and attitude tracking control for multi-satellite electromagnetic formation flight based on dual quaternion

Electromagnetic formation flight technology utilizes electromagnetic force and torque between satellites to control satellites’ relative position and attitude without consuming propellant, thus having broad application prospects. However, the dynamics of electromagnetic formation flying are nonlinear and strongly coupled, posing challenges to the 6-DOF control of electromagnetic formation and the magnetic dipole allocation of satellites. The frequency division multiplexing method can approximate decoupling formation dynamics by loading multi-frequency AC carriers onto satellite magnetic dipoles and is likely to achieve more control functions. A design method for AC carriers based on frequency division multiplexing is proposed to decouple formation dynamics while controlling electromagnetic force and torque. This paper further provides an analytical solution for the magnetic dipoles of the electromagnetic force/torque equations. In addition, in response to external disturbances, model uncertainty, and the inability to obtain relative velocity and angular velocity information of the formation system, this paper establishes a 6-DOF coupling model based on dual quaternion and implements tracking control using the active disturbance rejection control method. Based on the internal force characteristics of the electromagnetic formation, an expression for the equilibrium distribution of reaction wheel torque is derived to avoid some reaction wheels’ rapid accumulation of angular momentum. Numerical simulation results show the effectiveness of the proposed control method.

Unifying Structured Data as Graph for Data-to-Text Pre-Training

Abstract Data-to-text (D2T) generation aims to transform structured data into natural language text. Data-to-text pre-training has proved to be powerful in enhancing D2T generation and yields impressive performance. However, previous pre-training methods either oversimplified structured data into a sequence without considering input structures or designed training objectives tailored for a specific data structure (e.g., table or knowledge graph). In this paper, we unify different types of structured data (i.e., table, key-value data, knowledge graph) into the graph format and cast different D2T generation tasks as graph-to-text generation. To effectively exploit the structural information of the input graph, we propose a structure-enhanced pre-training method for D2T generation by designing a structure-enhanced Transformer. Concretely, we devise a position matrix for the Transformer, encoding relative positional information of connected nodes in the input graph. In addition, we propose a new attention matrix to incorporate graph structures into the original Transformer by taking the available explicit connectivity structure into account. Extensive experiments on six benchmark datasets show the effectiveness of our model. Our source codes are available at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/unid2t.

Joint Representation Learning for Retrieval and Annotation of Genomic Interval Sets.

As available genomic interval data increase in scale, we require fast systems to search them. A common approach is simple string matching to compare a search term to metadata, but this is limited by incomplete or inaccurate annotations. An alternative is to compare data directly through genomic region overlap analysis, but this approach leads to challenges like sparsity, high dimensionality, and computational expense. We require novel methods to quickly and flexibly query large, messy genomic interval databases. Here, we develop a genomic interval search system using representation learning. We train numerical embeddings for a collection of region sets simultaneously with their metadata labels, capturing similarity between region sets and their metadata in a low-dimensional space. Using these learned co-embeddings, we develop a system that solves three related information retrieval tasks using embedding distance computations: retrieving region sets related to a user query string, suggesting new labels for database region sets, and retrieving database region sets similar to a query region set. We evaluate these use cases and show that jointly learned representations of region sets and metadata are a promising approach for fast, flexible, and accurate genomic region information retrieval.

Distributed Cubature Information Filtering Method for State Estimation in Bearing-Only Sensor Network.

The problem of state estimation based on bearing-only sensors is increasingly important while existing research on distributed filtering solutions is rather limited. Therefore, this paper proposed the novel distributed cubature information filtering (DCIF) method for addressing the state estimation challenge in bearing-only sensor networks. Firstly, the system model of the bearing-only sensor network was constructed, and the observability of the system was analyzed. The sensor nodes are paired to measure relative angle information. Subsequently, the coordinated consistency theory is employed to achieve a unified state estimation of the maneuvering target. The DCIF method enhances the observability of the system, addressing the issues of large accuracy errors and divergence in traditional nonlinear filtering algorithms. Building upon the theoretical proof of consistency convergence in DCIF, four simulation experiments were conducted for comparison. These experiments validate the effectiveness and superiority of the DCIF method in bearing-only sensor networks.