Related Information Research Articles

Leveraging transformers architectures and augmentation for efficient classification of fasteners and natural language searches

A primary concern in the realm of mechanical engineering is to ensure the efficient and effective data entry of hardware devices. Fasteners are mechanical tools that rigidly connect or affix two surfaces or objects together. They are small and often different fasteners might look similar; it is therefore a long and prone-to-risk procedure to manually analyze them to classify and store their related information. With the widespread diffusion of AI frameworks in several domains, equipment manufacturers started to rely on AI technologies for these heavy tasks. Automatically classifying fasteners by type and extracting metadata from natural language questions are important tasks that fastener manufacturers and suppliers encounter. In this paper, we address these challenges. To address the first task, we introduce an augmentation methodology that starts with a small set of 3D models representing each of the 21 types of fasteners we aim to classify. This methodology efficiently generates multiple 2D images from these models. Next, we train a vision transformer using the collected data to address a single-label multi-class classification task. For the second task, we introduce a prompt-engineering technique designed for conversational agents. This technique leverages in-context knowledge to extract (metadata field, value) pairs from natural language questions. Subsequently, we tackle a question-answering task to the description fields of the extracted fasteners. Our evaluation demonstrates the effectiveness of both approaches, surpassing the baselines we tested.

Microelectrochemical Sensor Reveals Tunneling Nanotube-Mediated Intercellular Communication of Endothelial Mechanotransduction.

The intercellular communication of mechanotransduction has a significant impact on various cellular processes. Tunneling nanotubes (TNTs) have been documented to possess the capability of transmitting mechanical stimulation between cells, thereby triggering an influx of Ca2+ ions. However, the related kinetic information on the TNT-mediated intercellular mechanotransduction communication is still poorly explored. Herein, we developed a classic and sensitive Pt-functionalized carbon fiber microelectrochemical sensor (Pt/CF) to study the intercellular communication of endothelial mechanotransduction through TNTs. The experimental findings demonstrate that the transmission of mechanical stimulation from stimulated human umbilical vein endothelial cells (HUVECs) to recipient HUVECs connected by TNTs occurred quickly (<100 ms) and effectively promoted nitric oxide (NO) production in the recipient HUVECs. The kinetic profile of NO release exhibited remarkable similarity in stimulated and recipient HUVECs. But the production of NO in the recipient cell is significantly attenuated (16.3%) compared to that in the stimulated cell, indicating a transfer efficiency of approximately 16.3% for TNTs. This study unveils insights into the TNT-mediated intercellular communication of mechanotransduction.

A Survey of Adversarial Attacks: An Open Issue for Deep Learning Sentiment Analysis Models

In recent years, the use of deep learning models for deploying sentiment analysis systems has become a widespread topic due to their processing capacity and superior results on large volumes of information. However, after several years’ research, previous works have demonstrated that deep learning models are vulnerable to strategically modified inputs called adversarial examples. Adversarial examples are generated by performing perturbations on data input that are imperceptible to humans but that can fool deep learning models’ understanding of the inputs and lead to false predictions being generated. In this work, we collect, select, summarize, discuss, and comprehensively analyze research works to generate textual adversarial examples. There are already a number of reviews in the existing literature concerning attacks on deep learning models for text applications; in contrast to previous works, however, we review works mainly oriented to sentiment analysis tasks. Further, we cover the related information concerning generation of adversarial examples to make this work self-contained. Finally, we draw on the reviewed literature to discuss adversarial example design in the context of sentiment analysis tasks.

Sustainable Investment Factors Getting Prominent in India (Mechanism of ESG Funds)

Despite the escalating concerns about the environmental, social &amp; governance (ESG) factors in the corporate world, limited academic studies &amp; researches have been conducted on this subject. ESG theory &amp; practices are highly unexplored in growing economies like India. Major proportion of capital market investors do not even have adequate knowledge about the ESG funds as a sustainable investment option. Direct or indirect cause of this scenario is lack of clear dissemination of ESG related information &amp; various ESG fund alternatives available in the market. This informative article / research report aims to cover the conceptual base of ESG theme &amp; it’s prominence in the Indian market. It also highlights various categorical parameters used by SEBI to judge the ESG performance of the companies. Over &amp; above this, the basic terminologies associated with ESG like ESG rating, ERPs, ESG Indices &amp; ESG funds are also discussed in this article.

Meta-analysis on clinical efficacy of ringheaded thumbtack needle in the treatment of allergic rhinitis.

To evaluate the efficacy and safety of ringheaded thrumbtack needle in the treatment of allergic rhinitis (AR). Clinical studies about treatment of AR with ringheaded thrumbtack needle were searched from databases of CNKI, Wanfang, China Science and Technology Journal, China Biology Medicine disc, PubMed, Embase and Web of Science from their inception to November 2022. Two researchers independently screened the literature and collected related information. The total effective rate, visual analogue scale (VAS) for AR, rhinoconjunctivitis quality of life questionnaire (RQLQ), and recurrence rate were the main outcome indicators. Secondary outcome indicators included quantitative scores of symptoms and signs, 'quartering' symptom evaluation scale, etc. All the included studies were subjected to Meta-analysis using Stata software. A total of 22 clinical studies involving 1 491 participants were included. The results of Meta-analysis indicated that the total effective rate of ringheaded thrumbtack needle in the treatment of AR was higher than that of traditional Chinese and Western medicine ［RR=1.20, 95%CI (1.14, 1.26), P<0.001］, and recurrence rate is lower than conventional therapy ［OR=0.35, 95%CI (0.14, 0.89), P=0.027］. Moreover, The VAS score ［WMD=-1.30, 95%CI (-1.85, -0.75), P<0.001］ and RQLQ score ［WMD=-6.75, 95%CI (-12.74, -0.76), P=0.027］ of AR treated by ringheaded thrumbtack needle were lower than those of traditional Chinese and Western medicine. Ringheaded thrumbtack needle can improve the total effective rate, reduce the disease recurrence, and improve the symptoms of discomfort when AR outbreaks, and has no significant adverse reactions. However, the reliability is limited. Thus, it is still necessary to improve the level of evidence quality by including researches with large samples, rigorous design and international standards.

Using social media image to identify threatened elasmobranch species caught by a small-scale fishery in a data-poor area.

Sharing fishing activity via social media attracts millions of views. The data from these shares includes key information for a fisheries database such as the species present in the catches and in many cases the number of specimens caught. Many countries in the Global South do not have official information about basic data on fishing activity, especially small-scale activity. In many of such countries like Brazil, overfishing poses a significant threat to numerous threatened species, including elasmobranchs, due to the frailty of fisheries management and law enforcement. The lack of a national (Brazil) data collection program makes sharing data on social media a potential source of fisheries related information. Hence, this study aimed to verify the potential of social media data to describe artisanal fishing and identify elasmobranch species caught by a small-scale fleet in a data-poor Equatorial Southwest Atlantic region. The identification of elasmobranch species was made based on 250 videos shared by local fishers publicly on social media (Youtube™). Among these, sharks and rays were observed in 19 (8%) and 42 (17%) videos, respectively. A total of 98 specimens were recorded, of which 80% could be identified to a species level. Most of the records comprised the stingrays Hypanus berthalutzae and Hypanus guttatus, while Ginglymostoma cirratum was the most recorded shark. More than half of the elasmobranch species recorded are listed at some level of threat according to the IUCN Red List. According to the Brazilian legislation, at least 20% of the species identified in these videos are considered Endangered (EN) or Critically Endangered (CR). Therefore, fishing activity shared via social media can be considered effective in identifying threatened elasmobranch species, as well as providing data on species occurrence, number of specimens, or rare sightings. It is suggested that fishing activity shared via social media be used in parallel with traditional methods (e.g. on-board data and landing observations) to obtain fisheries information and data about endangered species in data poor areas.

The effect of political connections on the relationship between managers’ personality traits and corporate social responsibility disclosure

Purpose Corporate social responsibility (CSR) might be among the primary factors ensuring any organization’s survival, and disclosing its related information is very important. This research initially investigates the effect of managers’ behavior characteristics, including overconfidence, myopia and narcissism and corporate political ties on the disclosure of CSR. This study also aims to assess the mediating impact of political connections on the association between managerial personality traits and CSR. Design/methodology/approach The research sample included 129 listed companies on the Tehran Stock Exchange from 2013 to 2020. Behavioral managerers charecteristics. A multivariate regression method with combined data (firm-year) was used to test the research hypotheses. Findings The results show that overconfidence and managerial myopia cause the disclosure of CSR to decrease. Managers’ overconfidence and short-term attitudes lead to a decrease in the level of CSR activities of the companies and their disclosure, respectively, 0.021 and 0.025. However, the existence of narcissism in managers and having political ties by companies may lead to an increase in the disclosure of the CSR, respectively, around 0.089 and 0.02. Further findings also indicate that political connections may motivate narcissistic managers to increase CSR disclosure near 0.037. However, the results document no significant impact of political ties on the relationship between managerial overconfidence and myopia with CSR involvement. Research limitations/implications According to the findings, the authors recommend to stockholders that employing narcissistic managers and improving political connections might be two effective strategies to enhance the level of CSR engagement. One of the critical limitations of the current paper might be its generalizability. As Iran is an emerging and fossil fuel seller country, its institutional settings may significantly differ from those of developed and industrial nations. Thus, the readers of these nations must consider such an important issue. Originality/value For the first time, to the best of the authors’ knowledge, this research has investigated the moderating effect of political ties on the association between management behavioral characteristics and the level of fulfilling CSR by listed companies.

Diagnostics of Mixed-State Topological Order and Breakdown of Quantum Memory

Topological quantum memory can protect information against local errors up to finite error thresholds. Such thresholds are usually determined based on the success of decoding algorithms rather than the intrinsic properties of the mixed states describing corrupted memories. Here we provide an intrinsic characterization of the breakdown of topological quantum memory, which both gives a bound on the performance of decoding algorithms and provides examples of topologically distinct mixed states. We employ three information-theoretical quantities that can be regarded as generalizations of the diagnostics of ground-state topological order, and serve as a definition for topological order in error-corrupted mixed states. We consider the topological contribution to entanglement negativity and two other metrics based on quantum relative entropy and coherent information. In the concrete example of the two-dimensional (2D) Toric code with local bit-flip and phase errors, we map three quantities to observables in 2D classical spin models and analytically show they all undergo a transition at the same error threshold. This threshold is an upper bound on that achieved in any decoding algorithm and is indeed saturated by that in the optimal decoding algorithm for the Toric code. Published by the American Physical Society 2024

Distributed formation control of multi-UAV systems using relative information

An adaptive distributed control strategy is designed for the formation of multiple unmanned aerial vehicle (UAV) groups by using only relative measurement information. A distributed estimator is first designed to get the estimate of the consensus error. Then, an adaptive distributed controller is proposed to realize the formation tracking with the relative measurements. Based on the position and velocity information, a control barrier function (CBF) algorithm is applied to achieve the UAV formation under safe position constraints. In addition, in order to break up and reorganize multiple UAV groups, the proposed controller is designed based on the time-varying network topology. It is proved that the closed-loop system is uniformly asymptotically stability. Simulation and experiment results indicate that the proposed strategy can achieve formation control of multiple UAVs under safety constraints based on the measurement of relative measurement information.

Augmented sensing-based on-orbit configuration estimation for large-scale distributed spacecraft systems

On-orbit configuration estimation is one of the most important tasks for large-scale distributed spacecraft systems, yet incomplete measurement topologies limit the acquisition of partial inter-satellite relative navigation information. In this article, an on-orbit configuration estimation algorithm based on augmented sensing is proposed, which enables each spacecraft to extend the estimation range beyond the observation range without any communication overhead. Referring to the Bayesian inference process, the cooperative behavior of the large-scale distributed spacecraft system is applied to infer relative motion states of unobservable spacecrafts. Under the framework of scaled unscented transformations (SUT), we achieve high-accuracy configuration estimation for the large-scale distributed spacecraft system. Finally, the proposed algorithm is verified in a high-orbiting gravitational wave detector constellation with inter-satellite laser interferometers.

Anomalous origin of the coronary artery: prevalence and coronary artery disease in adults undergoing coronary tomographic angiography

BackgroundAnomalous aortic origin of a coronary artery (AAOCA) is a rare congenital coronary anomaly with the potential to cause adverse cardiac events. However, there is limited data on the association between AAOCA and coronary artery disease (CAD). Therefore, the aim of this study is to determine the prevalence and symptoms of patients with AAOCA, as well as investigate the correlation between AAOCA and CAD in a population referred for coronary computed tomographic angiography (CTA).Methods and resultsAll consecutive patients who underwent CTA from 2010 to 2021 were included. Characteristics, symptoms, coronary related adverse events and CTA information were reviewed by medical records. Separate multivariable cumulative logistic regressions were performed, using the stenosis severity in each of the four coronaries as individual responses and as a combined patient clustered response. Finally, we identified 207 adult patients with AAOCA, the prevalence of AAOCA is 0.23% (207/90,501). Moreover, this study found no significant association between AAOCA and CAD. AAOCA did not contribute to higher rates of hospitalization or adverse cardiac events, including calcification.ConclusionAAOCA is a rare congenital disease that is not associated with increased presence of obstructive CAD in adults.

Secondary Ethnic Effects in the Transition to Higher Education in Germany and Their Explanations

Given their lower socioeconomic background and achievement, immigrants and their descendants have been shown to make more ambitious decisions regarding educational transitions. While previous research has extensively analysed transitions within and after compulsory education, research on late transitions is scarce. This study focuses on the secondary effect of ethnic origin in the transition after upper secondary education in Germany, where school leavers decide between vocational education and training and higher education. Based on longitudinal data from the National Educational Panel Study, we first analyse differences by ethnic origin (Turkey, former Soviet Union, Poland, other countries) and immigrant generation (first, second, third generation) net of socioeconomic background and achievement. Compared to those of the ethnic majority, we find increased probabilities of enrolment in higher education for most ethnic groups of the first and second immigrant generation, net of differences in socioeconomic background and achievement. Second, we test four explanatory approaches for secondary ethnic effects (immigrant optimism, relative status maintenance, anticipated discrimination, and information deficits). By employing decomposition analyses for nonlinear regression models, we find that, taken together, the four explanations account for up to half of the ethnic differences in educational choice after upper secondary education. Specifically, increased levels of immigrant optimism and information deficits regarding vocational education explain a substantial share of the ethnic differences among all origin groups. For Turkish immigrants, the motive to maintain relative status in the origin country also contributes to increased enrolment rates in higher education.

Dietary perception and knowledge among cardiac patients visiting tertiary care hospital outpatient department (OPD).

To appraise the dietary perception and knowledge of cardiac patients visiting Outpatient department of a tertiary care hospital. A cross sectional study of 466 patients attending cardiac outdoor clinic at a tertiary care cardiac hospital in Rawalpindi, Pakistan were selected through convenient sampling from January to April 2021. Patients included in the study were above 18 years of age and had a cardiac disease. A structured questionnaire in English and Urdu was drafted with questions related to patient demographics, dietary perception and knowledge. Among 466 patients, 60% (n=280) were males and 40% (n=186) were females whereas 14% (n=66) of the patients were uneducated and 15% (n=68) had completed postgraduate education. In this study 23% (n=106) of the participants used internet to search nutrition related information and 72 % (n=339) of the patients consider their lifestyle to be healthy. Majority (n=261, 56%) of the patients had no idea regarding sodium consumption. 46% (n=215) of the patients had no knowledge regarding the effects of bakery items on cardiac health. Dietary Knowledge plays a predominant role in the management of cardiac disease. The study concluded that cardiac patients had inadequate knowledge regarding dietary intake in cardiac disease and has high prevalence of dietary myths and misconceptions. Strategic plan including nutritional awareness program and intensive counseling sessions should be designed to increase dietary knowledge of cardiac patients.

Adoption of Accrual Accounting in the Brazilian Public Sector: a Case Study in Light of Regulation Theory

Objective: To identify existing gaps in the adoption of full accrual accounting in the public sector, with a focus on analyzing the financial statements of the Ministry of Defense (MD), and to investigate the application of accrual accounting in the public sector, considering its complex regulations. Theoretical Framework: For the analyses, the theoretical foundation is based on the theory of regulation applicable to accounting regimes in the public sector. Methodology: The research, using the Ministry of Defense (MD) as a case study, adopted a multifaceted methodological approach, including a case study, literature review, and documentary analysis. Data sources included books, dissertations, scientific articles, and legislation. Documentary analysis focused on the MD's Financial Statements, accessed through the government portal, covering the years 2018 to 2022. The research also employed content analysis techniques, following the methodology suggested by Sampaio and Lycarião (2021), to evaluate the content of the information in relation to the established objectives. Results and Conclusions: The research identified that there is already partial adherence of the public sector to international standards, as the cash basis is still observed for revenues, and areas for improvement were noted, such as the adoption of the Statement of Comprehensive Income in the context of the public sector. Research Implications: It was observed that regulatory changes are important instruments for influencing the accountability processes of the public sector. Originality/Value: The research emphasizes the partial adoption of international public accounting standards and suggests further discussions and analysis to achieve complete adherence, confirming a gap between the implementation of standards and their practical application.

Label-Free Identification of AML1-ETO Positive Acute Myeloid Leukemia Using Single-Cell Raman Spectroscopy.

Acute myeloid leukemia (AML) is a malignant hematological tumor disease. Chromosomal abnormality is an independent prognostic factor in AML. AML with t(8:21) (q22; q22)/AML1-ETO (AE) is an independent disease group. In this research, a new method based on Raman spectroscopy is reported for label-free single-cell identification and analysis of AE fusion genes in clinical AML patients. Raman spectroscopy reflects the intrinsic vibration information of molecules in a label-free and non-destructive manner, and the fingerprint Raman spectrum of cells characterizes intracellular molecular types and relative concentration information, so as to realize the identification and molecular metabolism analysis of different kinds of cells. We collected the Raman spectra of bone marrow cells from clinically diagnosed AML M2 patients with and without the AE fusion gene. Through comparison of the average spectra and identification analysis based on multivariate statistical methods such as principal component analysis and linear discriminant analysis, the distinction between AE positive and negative sample cells in M2 AML patients was successfully achieved, and the single-cell identification accuracy was more than 90%. At the same time, the Raman spectra of the two types of cells were analyzed by the multivariate curve resolution alternating least squares decomposition method. It was found that the presence of the AE fusion gene may lead to the metabolic changes of lipid and nucleic acid in AML cells, which was consistent with the results of genomic and metabolomic multi-omics studies. The above results indicate that single-cell Raman spectroscopy has the potential for early identification of AE-positive AML.

Implementation of Discovery Learning Model to Increase Student Interest and Learning Outcomes

This research aims to increase student interest and improve learning outcomes using the discovery learning model. This research uses the Action Research method. This research method starts with the planning, action implementation, observation, and reflection stages. Data Collection Techniques and Tools This research uses tests and observations. This class action was carried out at SMAN 1 Sungai Menang to determine students' interests and learning outcomes regarding applying the discovery learning model. This research shows an increase in interest, namely from 70.36% to 84% and student learning outcomes from the first and second cycles, namely from 25.92% to 85.17%. This increase is visible after implementing the discovery learning model, which can improve students' interests and learning outcomes. This research is implemented in two cycles; in the first cycle stage, the teacher provides a stimulus for students to identify and respond to problems and encourages students' critical thinking. In the second cycle, the teacher raises enthusiasm for learning by increasing students' interest in searching for related information and changing learning methods to make them more fun and interactive, such as through discussion activities that increase students' self-confidence and knowledge.

Association not found between dietary fermentable oligosaccharides, disaccharides, monosaccharides, and polyols score and disease severity in patients with ulcerative colitis

ObjectivesWe hypothesized that a low fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAP) diet may be associated with the disease severity of ulcerative colitis (UC). Therefore, this cross-sectional study was undertaken to assess the association between FODMAP score and UC severity. MethodsIn this study, 158 patients with UC were enrolled. The disease severity was designated using the Mayo score. The diet relation information was obtained using the 160-item food frequency questionnaire. To calculate the FODMAP score, the consumption of all food items was converted to a gram per day and multiplied by the FODMAP factor. The FODMAP factor was obtained from the application developed by Monash University (Melbourne, Victoria, Australia). The association between disease severity (dependent factor) and FODMAP score tertiles (independent factors) was assessed by logistic regression adjusted for different covariates. ResultsIn the present study, the age range of participants was 18 to 64 y old, and 46.2% of patients had moderate or severe disease activity. There were significant differences in sex, body mass index, and supplement use across different tertiles of FODMAP score. There was no significant association between the FODMAP score tertiles and disease severity in the crude model and adjusted models. ConclusionsThe results of the present study showed that there was no significant association between the FODMAP score and UC severity. However, considering the limitations of the study, more studies with prospective and interventional designs using more accurate methods of dietary assessments are needed to confirm these preliminary results.

A multitask tensor-based relation network for cloth-changing person re-identification

The Cloth-changing person re-identification (CC-ReID) is more challenging than person re-identification (ReID) because of the unreliability of cloth-relevant features. Existing CC-ReID methods output the unique latent vector feature for the same pedestrian image. However, on the one hand, the latent vector feature generated by the pooling layer will lose more spatial information. On the other hand, the features that they focus on are the same for the same pedestrian image under different comparison pedestrian images, which does not consider the pedestrian pair relation information. For this, we propose a Multitask Tensor-based Relation Network (MTTRN) for CC-ReID. In MTTRN, we utilize the augmented cloth-changing identity images, human parsing images and head images to guide the model learning more fine-grained cloth-irrelevant feature cues. We propose a novel channel subspace generator and use the tensor mode-n product to generate the subspace tensor features instead of the vector feature generated by the pooling layer, which can retain more spatial feature information. Furthermore, we assume that the model will focus on different features for the same pedestrian image under different comparison pedestrian images in the same feature subspace or the same pedestrian image pair in different feature subspaces, in which the tensor feature pair relation is considered fully to mine more robust features. Extensive experiments show that our method achieves the state-of-the-art or competitive performance on three CC-ReID benchmark datasets and demonstrate the robustness of our model.

Parallel event-triggered dynamic output feedback control for nonlinear networked systems with randomly occurring multiple communication delays

This paper investigates the issue of parallel event-triggered (PET) dynamic output feedback control for networked control systems (NCSs) built by the discrete-time T–S fuzzy model. Initially, a novel PET dynamic output feedback controller is designed. Based on saving network resources and enhancing transmission efficiency, the PET strategy makes full use of relative and absolute triggering condition information. And the dynamic output feedback control can not only address unmeasurable states but also provide a better response to the internal information of the system. The random multiple communication delays and the ℓth-order Rice fading model with different channel coefficients, meanwhile, are both applied in the system. It is closer to the actual situation. Subsequently, new sufficient conditions of membership function dependence are proposed via the staircase function approximation method combined with Lyapunov stability. It guarantees that the system is exponentially mean square stable (EMSS) with H∞ performance. Ultimately, the presented results are validated using two examples. In the future, we will explore the correlative research of T–S fuzzy Markov jump NCSs.

Principle of Information Increase: An Operational Perspective on Information Gain in the Foundations of Quantum Theory

A measurement performed on a quantum system is an act of gaining information about its state. However, in the foundations of quantum theory, the concept of information is multiply defined, particularly in the area of quantum reconstruction, and its conceptual foundations remain surprisingly under-explored. In this paper, we investigate the gain of information in quantum measurements from an operational viewpoint in the special case of a two-outcome probabilistic source. We show that the continuous extension of the Shannon entropy naturally admits two distinct measures of information gain, differential information gain and relative information gain, and that these have radically different characteristics. In particular, while differential information gain can increase or decrease as additional data are acquired, relative information gain consistently grows and, moreover, exhibits asymptotic indifference to the data or choice of Bayesian prior. In order to make a principled choice between these measures, we articulate a Principle of Information Increase, which incorporates a proposal due to Summhammer that more data from measurements leads to more knowledge about the system, and also takes into consideration black swan events. This principle favours differential information gain as the more relevant metric and guides the selection of priors for these information measures. Finally, we show that, of the symmetric beta distribution priors, the Jeffreys binomial prior is the prior that ensures maximal robustness of information gain for the particular data sequence obtained in a run of experiments.