Information Input Research Articles

The Effect of Providing Information About Ca Cerviks via Whatsapp Autoreply on the Interest of Women of Childbearing Age to Conduct IVA Screening at the Bandar Health Center in 2024

The high prevalence of cancer in Indonesia needs to be observed with preventive measures and early detection through Visual Examination of Acetic Acid/IVA test. The low interest of women of childbearing age to do IVA Screening is one of the health problems. Many media have been used by the government and researchers before to socialize cervical cancer prevention to the public. In the digital era, the internet is one of the dominant information media, the most accessed is WhatsApp (95.9%). Researchers use the Whatsapp autoreply feature to provide information about Ca Cervix which can be accessed 24/7. The use of this technology is expected to be useful so that users are motivated to conduct IVA screening. The study used a quasi experiment with a pretest-posttest control group design model. The data were analyzed using the non-parametric statistical test Wilcoxon Signed Rank Test to compare the differences between the pre-test and the post-test and the Mann Whitney U Test to compare the differences between the control group and the experimental group. In the control group, the results of the wilcoxon test pre-test post-test knowledge context showed a-Symp 0.227, at the input information a-Symp 0.916, in the process of providing information a-Symp 0.083 and at Interest a-Symp 0.317, the value was >0.05 Ho was rejected, there was no difference in the pre-test post-test of the control group. In the experimental group, the results of the wilcoxon test pre-test post-test in the context of knowledge, showed a-Symp 0.000, at the input of a-Symp information 0.000, in the process of providing information a-Symp 0.000 and in the interest of a-Symp 0.000, the value <0.05 Ho was accepted, there was a difference in the pre-test post-test of the experimental group. The results of the Mann Whitney U Test showed that A-Symp was 0.000< 0.05 There was a difference in the control group and there was an increase in the experimental group that was given the treatment. The interest of women of childbearing age to do IVA screening can increase after being given information about Ca Cerviks and IVA Test through whatsapp autoreply.

P-1233. Application of Pharmacometrics in the Personalized Colistin Dosing Recommendation

Abstract Background Colistin is frequently used as a treatment for infections caused by carbapenem-resistant Gram-negative bacteria. Colistin dosing should be adjusted according to the patient's renal function because the prodrug, colistin methanesulfonate (CMS), is partially removed by the kidney. Intensive care unit (ICU) patients may require individualized dosing to prevent nephrotoxicity or treatment failure due to the highly variable pharmacokinetics (PK) of colistin.Figure 1.Example of colistin dosing recommendation program – Patient information input Methods Colistin plasma concentration data from ICU patients who received colistin from June 2019 to August 2022 in Korea University Hospital were included in the dataset for this population PK analysis. We developed a population PK model for colistin using ICU patients, and established therapeutic drug monitoring software. A population PK analysis was performed using the first-order conditional estimation method with interaction in non-linear mixed effects modeling. We used the open-source R Shiny program to establish Colistin TDM software. The structural PK model and the final estimates of both fixed-effect and random-effect parameters were translated into an R code.Figure 2.Example of colistin dosing recommendation program – Simulation outputRecommendations provide the loading and maintenance doses based on patient information. Maintenance dose depends on the predicted colistin trough and peak levels, and the interval and infusion time of colistin administration. Results We enrolled 176 colistin observations from 22 patients in the dataset. A two-compartment, first-order elimination model best described the PK of Colistin A, one-compartment, first-order elimination model for PK of Colistin B, and one-compartment, first-order elimination model for PK of CMS. In the results from the covariate analysis, GFR estimated by Cockcroft–Gault equation and albumin level was included as a statistically significant covariate of total clearance of CMS, and body weight was selected as a covariate of CMS volume of distribution. The two-compartment structure model for Colistin and CMS, the final estimates of both fixed effects and random effects from the PK model and the log-likelihood Equation (5) for estimating ηi were implemented in R script. A draft version of the software is now available at http://mychloe00.shinyapps.io/colistin_new. Conclusion We developed colistin TDM software using PK parameters in ICU patients for individualized colistin administration. Further research is needed to determine whether colistin is actually administered within the therapeutic range through the application of this software. Disclosures All Authors: No reported disclosures

Closing the Information Gap Between the Field and Scientific Literature for Improved Disease Management, with a Focus on Rice and Bacterial Blight.

A path to sustainably reduce world hunger, food insecurity, and malnutrition is to close the crop yield gap and, particularly, lower losses due to pathogens. Breeding resistant crops is key to achieving this goal, which is an effort requiring collaboration among stakeholders, scientists, breeders, farmers, and policymakers. During a disease outbreak, epidemiologists survey the occurrence of a disease after which pathologists investigate mechanisms to stop an infection. Policymakers then implement strategies with farmers and breeders to overcome the outbreak. Information flow from the field to the lab and back to the field involves several processing hubs that require different information inputs. Failure to communicate the necessary information results in the transfer of meaningless data. Here, we discuss gaps in information acquisition and transfer between the field and laboratory. Using rice bacterial blight disease as an example, we discuss pathogen biology and disease resistance to point out the importance of reporting pathogen strains that caused an outbreak to optimize the deployment of resistant crop varieties. We examine differences between infection in the field and assays performed in the laboratory to draw awareness of possible misinformation concerning plant resistance or susceptibility. We discuss key data considered useful for reporting disease outbreaks, sampling bias, and suggestions for improving data quality. We also touch on the knowledge gap in the state-of-the-art literature regarding disease dispersal and transmission. We use a recent case study to exemplify the gaps mentioned. We conclude by highlighting potential actions that may contribute to food security and to closing the yield gap. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

On the resiliency of post-crisis decoupling in higher-order economy-energy-environment nexus in high-inflation developing economies

AbstractMacroeconomic pathways of enabling decoupling of emissions from economic growth in a post-crisis period is analyzed in this study for the high-inflation developing economy of India. A novel control system internalizing the inherent stochasticity of economy-energy-environment (3E) nexus extends the interpretation of Environmental Kuznets Curve hypothesis, where this study finds that decoupling is an emergent phenomenon of a 3E system. Using Zivot-Andrews unit root test, adaptive error correction modelling and robustness analysis through information theory-based approximate entropy method, the stochastic model is found to reproduce real-world higher order phenomena more accurately than previously theorized systems. With high entropy (information content) in long-run and short-run coefficients, the stochastic model can replicate the resiliency speed in a post-crisis period, without new information input. Some key findings of policy/macroeconomic linkages include: (a) decoupling progress lies in capital building and inhibited by inflationary growth; (b) fossil fuel imports, inflation and energy-use have a whiplash effect on carbon emissions in post-crisis periods; (c) electricity and non-electric-energy have differential effect on trade, with decoupling prevalent in electricity sector only; (d) inflation opposes GDP-emission causality during business cycle movements; (e) decoupling policies should be discretized to growth and recession phases of business cycle, with inflationary fossil fuel rebounds actively disincentivized in recession periods.

Artificial Visual System for Stereo-Orientation Recognition Based on Hubel-Wiesel Model.

Stereo-orientation selectivity is a fundamental neural mechanism in the brain that plays a crucial role in perception. However, due to the recognition process of high-dimensional spatial information commonly occurring in high-order cortex, we still know little about the mechanisms underlying stereo-orientation selectivity and lack a modeling strategy. A classical explanation for the mechanism of two-dimensional orientation selectivity within the primary visual cortex is based on the Hubel-Wiesel model, a cascading neural connection structure. The local-to-global information aggregation thought within the Hubel-Wiesel model not only contributed to neurophysiology but also inspired the development of computer vision fields. In this paper, we provide a clear and efficient conceptual understanding of stereo-orientation selectivity and propose a quantitative explanation for its generation based on the thought of local-to-global information aggregation within the Hubel-Wiesel model and develop an artificial visual system (AVS) for stereo-orientation recognition. Our approach involves modeling depth selective cells to receive depth information, simple stereo-orientation selective cells for combining distinct depth information inputs to generate various local stereo-orientation selectivity, and complex stereo-orientation selective cells responsible for integrating the same local information to generate global stereo-orientation selectivity. Simulation results demonstrate that our AVS is effective in stereo-orientation recognition and robust against spatial noise jitters. AVS achieved an overall over 90% accuracy on noise data in orientation recognition tasks, significantly outperforming deep models. In addition, the AVS contributes to enhancing deep models' performance, robustness, and stability in 3D object recognition tasks. Notably, AVS enhanced the TransNeXt model in improving its overall performance from 73.1% to 97.2% on the 3D-MNIST dataset and from 56.1% to 86.4% on the 3D-Fashion-MNIST dataset. Our explanation for the generation of stereo-orientation selectivity offers a reliable, explainable, and robust approach for extracting spatial features and provides a straightforward modeling method for neural computation research.

A Nonlinear Rebalanced Control Compensation Model for Visual Information of Drivers in the Foggy Section of Expressways

To obtain the optimal driving visual guidance methods in sudden low-visibility fog environments, it is crucial to analyze the changes in visual characteristics and information demand under low-visibility foggy conditions. The paper constructs a driving visual information demand model for foggy environments based on visual information input and output, using Shannon’s theory and feedback control theory. Two types of foggy road sections with the same visibility, one with guidance lights and one without, were selected for real-vehicle experiments based on the driver’s blood pressure, heart rate, and driving gaze domain tests. The study found the following: (1) In sudden foggy environments, the amount of driving information obtained by drivers decreases instantly with a sudden drop in visibility, failing to meet the information demand for driving cognition, thereby disrupting the dynamic balance state of driving based on speed, visibility, and other road environment factors. The experiment also found that in low-visibility environments, the radius of the human eye’s visual gaze domain becomes smaller, with the gaze range mainly concentrated directly in front of the vehicle, and the lower the visibility, the smaller the gaze domain range; (2) Foggy conditions affect changes in drivers’ blood pressure and heart rate. Installing guidance lights with sufficient illumination at foggy sections to compensate for drivers’ visual information can effectively supplement the visual information required for safe driving; (3) The experiment indicates that the guidance effect of the lights is most pronounced when visibility is within the range of [50 m, 150 m]; however, when visibility is above 500 m, the presence of guidance lights can, to some extent, affect driving safety and increase the risk of accidents.

Parametric Estimation of Directional Wave Spectra from Moored FPSO Motion Data Using Optimized Artificial Neural Networks

This paper introduces a comprehensive, data-driven framework for parametrically estimating directional ocean wave spectra from numerically simulated FPSO (Floating Production Storage and Offloading) vessel motions. Leveraging a mid-fidelity digital twin of a spread-moored FPSO vessel in the Guyana Sea, this approach integrates a wide range of statistical values calculated from the time histories of vessel responses—displacements, angular velocities, and translational accelerations. Artificial neural networks (ANNs), trained and optimized through hyperparameter tuning and feature selection, are employed to estimate wave parameters including the significant wave height, peak period, main wave direction, enhancement parameter, and directional-spreading factor. A systematic correlation analysis ensures that informative input features are retained, while extensive sensitivity tests confirm that richer input sets notably improve predictive accuracy. In addition, comparisons against other machine learning (ML) methods—such as Support Vector Machines, Random Forest, Gradient Boosting, and Ridge Regression—demonstrate the present ANN model’s superior ability to capture intricate nonlinear interdependencies between vessel motions and environmental conditions.

A hitchhiker’s guide to active motion

Intelligent decisions in response to external informative input can allow organisms to achieve their biological goals while spending very little of their own resources. In this paper, we develop and study a minimal model for a navigational task, performed by an otherwise completely motorless particle that possesses the ability of hitchhiking in a bath of active Brownian particles (ABPs). Hitchhiking refers to identifying and attaching to suitable surrounding bath particles. Using a reinforcement learning algorithm, such an agent, which we refer to as intelligent hitchhiking particle (IHP), is enabled to persistently navigate in the desired direction. This relatively simple IHP can also anticipate and react to characteristic motion patterns of their hosts, which we exemplify for a bath of chiral ABPs (cABPs). To demonstrate that the persistent motion of the IHP will outperform that of the bath particles in view of long-time ballistic motion, we calculate the mean-squared displacement and discuss its dependence on the density and persistence time of the bath ABPs by means of an analytic model.Graphical abstractIllustration of an intelligent hitchhiking particle (IHP) in a bath of active Brownian particles (ABPs). The IHP fulfills a navigational task by holding on to an ABP only if its orientation points upwards, enabling persistent motion.

Multi-functional broadband diffractive neural network with a single spatial light modulator

Diffractive neural networks (DNNs) are emerging as a novel optical computing architecture that combines wave optics with deep-learning methods for high-speed parallel information processing. Herein, we report a reflection type, multi-functional, broadband DNN design. It consists of two phase-modulation layers based on a single spatial light modulator and a mirror facing it. The power efficiency of this design is more than 16 times higher than that of the cascaded structure utilizing beam splitters. It can function either as a two-layer DNN or a one-layer DNN with the other serving as an information input layer. Single- and dual-wavelength filtering and focusing, as well as spatial wavelength demultiplexing of supercontinuum, are experimentally demonstrated using the two-layer DNN, whereas the one-layer DNN is experimentally demonstrated by the classification of hand-written digits, which are input by the first layer via holographic imaging. The designed DNN could operate independently or be readily integrated with other optical systems and may find applications in spectroscopy, microscopy, and information technology.

Design of Multimodal Obstacle Avoidance Algorithm Based on Deep Reinforcement Learning

The navigation obstacle avoidance method based on deep reinforcement learning has stronger adaptability and better performance compared to traditional algorithms in complex unknown dynamic environments, and has been widely developed and applied. However, when using multimodal information input, deep reinforcement learning strategy networks extract features that differ significantly between simulated and real world environments, resulting in poor algorithm output strategies and difficulty in transferring models obtained from simulation training to actual environments. To address the aforementioned issues, this article utilizes image segmentation to narrow the gap in environmental features, integrates multimodal information, and designs a deep reinforcement learning multimodal local obstacle avoidance algorithm, MMSEG-PPO, based on proximal strategy optimization algorithms. The algorithm is then ported to practical environments for deployment and testing. The experiment shows that the algorithm proposed in this article reduces the gap between the simulation environment and the actual environment, and has better performance and generalization when transplanted to the real world environment.

MODEL OF MULTIMODAL LANGUAGE PERSONALITY OF THE INTERPRETER: NON-VERBAL ASPECT

The present article is devoted to the problem of multimodality research in the complex study of interpreter’s linguistic personality. Topicality of the article is conditioned by modern requirements to the level of interpreter competences, including multimodal interaction (remote interpreting, online interpreting of interviews, presentations, conferences, relay interpreting, etc.). The purpose of this paper is to create a model of multimodal linguistic personality and to describe the modality channels from the perspective of the interpreter’s nonverbal behaviour. The novelty of the research is considering the interpreter as a multimodal linguistic personality and identifying non-obvious channels of modality. The author considers the views of Russian and foreign researchers on understanding the nature of multimodality and the problem of its study. During the evaluative-critical analysis the author studies and describes the functional side of perceptual channels (modalities). The first stage of the study includes linguo-cognitive analysis and observation of non-verbal behaviour (kinesics, oculesics, prosodics) of the interpreters. Based on the results of the evaluation of the non-verbal behaviorur of the interpreter’s linguistic personality, the author analyses the following perceptual channels: extra-linguistic components of communication, the environment, the non-verbal behaviour, the means of input and output of information. As a result of the second stage, the classification of perceptual channels in the situation of simultaneous interpreting is presented. Taking into account the integration of the data, the researcher proposes a model of multimodal linguistic personality of the interpreter. The results of the study enrich the understanding of the problem of multimodality and contribute to the in-depth study of professional linguistic personality. The author outlines the prospects for further research on multimodality from the perspective of cognitive linguistics and non-verbal semiotics.

Evaluating emergency nurses' use of and satisfaction with a web-based patient triage app.

Evidence suggests that using a standardised triage system that categorises patients into levels of severity or acuity to prioritise their care can provide the greatest levels of patient safety. The use of a web-based app to record patient triage data can support operational decision-making, reduce the risk of errors and standardise the time it takes to record the data required for accurate triage. This article details one aspect of a three-stage research project undertaken in Thailand which aimed to improve the quality of nurse triage, reduce errors in triage decisions and decrease the time spent on triage through implementation of a patient triage app. The patient information input into the app at initial triage is automatically uploaded into the electronic patient record. The authors report the results of the third stage of the project, which aimed to analyse the feasibility of using the app by evaluating completion of patient records and user satisfaction levels.

Study of the dimensions of and countermeasures for improving the administrative efficiency of primary and secondary schools in western China: Taking MX Middle School as an example

With the deepening of education reform, improving the administrative efficiency of primary and secondary schools in Western China has become the key to promoting the balanced development of education. Taking MX Middle School as the research object, this paper analyzes the difficulties of administrative men in primary and secondary schools in western China in depth, including rigid management systems, uneven resource distributions, low information levels, insufficient incentive mechanisms and low participation of teachers and students. In view of these problems, this paper proposes a series of targeted counts and suggestions, such as optimizing the organizational structure, establishing a fair and transparent resource allocation mechanism, increasing the information input, improving the performance appraisal system and establishing a participation mechanism for teachers and students. th Improving the administrative efficiency of primary and secondary schools in Western China to promote educational equity, improve educational quality and promote the balanced development of regional education.

DEVELOPMENT OF A PROGRAM FOR CALCULATING THE PREDICTED OPERATION TIME OF GAS FILTERS

Objective: Development of software to calculate the predicted duration of protective action of gas filters for gas masks and respirators under specific operating conditions. Methods: The program for determining the predicted breakthrough time of gas filters was created using the integrated development environment Visual Studio Code. The algorithm embedded in the program is based on the mathematical equations of Wheeler-Jonas and the conversion of concentrations of chemical compounds into different dimensions. The real breakthrough time of gas filters was established on a laboratory stand for determining the absorption capacity, which meets the requirements of DSTU EN 14387:2021, and also provides the opportunity to use four test substances during testing, such as cyclohexane, sulfur dioxide, hydrogen sulfide or ammonia. Results: A software package was developed to calculate the predicted breakthrough time of gas filters for gas masks and respirators under specific operating conditions. It consists of four modules: initial data input, memory, output and visualization of information, and calculation of breakthrough time of gas filters. An algorithm for calculating the predicted level of filters breakthrough time was developed, which includes five steps: determining the hazard, concentration of harmful substances, required respirator protection class, humidity and air temperature, mode of operation; entering initial data for calculation; preliminary selection of a filter from the specified catalog located in the memory module; calculation of filter capacity and analysis with permissible values of the chemical substance with the calculation of breakthrough time; visualization of results and their storage in the memory module. Novelty: Establishing the dependency of the predicted breakthrough time of FRPA-G A1, B1, K1 filters on varying concentrations of chemicals in the air and the type of work performed, which allowed identifying a five-fold reduction in filter operation time during heavy work compared to light work. Practical Value: Results obtained by comparing the predicted breakthrough time using the developed software product with the programs of personal protective equipment manufacturers showed an error not exceeding 10%, due to the lack of accounting for uneven wear of the filter filler across its surface. Keywords: adsorption, gas filter, software, breakthrough time, substance concentration.

Dinámicas socioproductivas en sistemas agrícolas. Bases teóricos conceptuales para su estudio

Agricultural production systems are very dynamic, the use and combination of production factors by the farmer is reflected in different levels of production and family well-being. The objective of this article was to build a theoretical - conceptual reflection, for a multiple, interdisciplinary and transdisciplinary approach in the interpretation of phenomena that are not caused exclusively by one factor. For this, a systematic bibliographic review of documents hosted on Google Scholar/Google was carried out, which allowed analyzing and comparing concepts and theories associated with the topic, highlighting Bertalanffy's systems theory, Morín's complexity and Lonergan's economic dynamics. In addition, concepts associated with the research such as productivity, agricultural systems, territory and family well-being were considered. The search yielded fifty-eight documents, which were subjected to filters for pre-established exclusions. As a result of these filters, eighteen were selected for analysis, which allowed the construction of a theoretical-conceptual body for the study and analysis of socio-productive dynamics in agricultural systems, which focuses on how the inputs represented by the producer, capital, Information and agricultural inputs are related to each other and interact with other factors such as local technology, workforce, territory, environment and marketing and how these generate different levels of performance, productivity and well-being in the system for the farmer and his family. Keywords: family well-being, territory, farmer

Алгоритмизация курса начертательной геометрии как дидактическая основа обучению информационной технологий

This article discusses the principles of creation and development of new content for the courses "Descriptive geometry" and "Engineering computer graphics" of algorithmic and software solutions for graphic problems used in computer modeling applied to the training of students of pedagogical specialties on the basis of new information technologies. The article also tells about the successful solution of the most common geometric problems by students and their solution in an interactive mode. The reason is that the input of graphic information into the computer is carried out only using algorithms. Also, the algorithmization of graphic constructions should become an integral part of the new technology of teaching at the university in the course of graphic disciplines, which should be didactics-the practical basis for using a computer in the educational process as a means of teaching graphic disciplines.

Multi-Channel Lightweight Contrast Prediction Coding for Features Extraction of Radar Emitter Signals

This letter presents a novel multi-channel improved contrastive predictive coding (CPC) method to extract signals features. Specifically, to extract low pulse width radar signals features and meet the real-time requirements of signals identification, we design a lightweight encoder to realize the CPC features encoding function. Then, we construct a multi-channel CPC features decoder to mine and extract subtle individual signals features from the perspective of multi-domain and multi-channel information input. Simulation results verify the effectiveness of our proposed method, which can achieve state-of-the-art results in both accuracy and running time compared to the existing optimal methods. All our models and code are available at https://github.com/jn-z/MC-CPC.

Near-Time Digital Mapping for Geoforensic Searches

Inadequate base maps with poor scale or low resolution demonstrate the need for contemporary topographic maps when conducting geological mapping. In neotectonic regimes and areas of dynamic geomorphology, archival or large-scale maps require time-consuming, on-site manual updating while mapping bedrock and superficial geology. In contrast, stable ground conditions may have suitable legacy maps in some locations but not in others, such as where surveying is absent, incomplete or subject to legal restrictions. The geologist tasked with mapping may have to do this on short notice at their first site visit with no time to search for or create digital or physical copies of background maps on a suitable scale. The field mapper may encounter any of the above scenarios, especially the Geoforensic specialist tasked with Search and Rescue, hazard assessment or preparation and desktop study for subsequent search teams or law enforcement. Drone-derived orthoimagery and digital surface modelling can be conducted on-site in near real time to provide high resolution georeferenced maps for direct input of geological information, thus bypassing either non-existent or unsuitable base maps.

SCORE: Skill-Conditioned Online Reinforcement Learning

Solving complex long-horizon tasks through Reinforcement Learning (RL) from scratch presents challenges related to efficient exploration. Two common approaches to reduce complexity and enhance exploration efficiency are (i) integrating learning-from-demonstration techniques with online RL, where the prior knowledge acquired from demonstrations is used to guide exploration, refine representations, or tailor reward functions, and (ii) using representation learning to facilitate state abstraction. In this study, we present Skill-Conditioned Online REinforcement Learning (SCORE), a novel approach that leverages these two strategies and utilizes skills acquired from an unstructured demonstrations dataset in a policy gradient RL algorithm. This integration enriches the algorithm with informative input representations, improving downstream task learning and exploration efficiency. We evaluate our method on long-horizon robotic and navigation tasks and game environments, demonstrating enhancements in online RL performance compared to the baselines. Furthermore, we show our approach’s generalization capabilities and analyze its effectiveness through an ablation study.

Compelling reading skills in English classes through guided discovery strategy

The purpose of this paper is to present a review of how the guided discovery strategy favors the development of reading comprehension skills and at the same time allows the acquisition of vocabulary and improvement of grammar in the teaching of English as a foreign language. The problem arises from a niche in which it found that the guided discovery strategy is conducive to develop reading comprehension skills, however a problem is generated by the motivation shown in students by the type of texts used for the development of reading comprehension, based on this it have raised the development of it research, considering that several studies indicate that compelling reading has a positive influence on reading skills. The methodology used consists of a bibliographic and documentary review of academic texts based on hermeneutic analysis. In this work it address issues related to the teaching of English as a foreign language, it focus on the development of reading skills through persuasive reading, it also highlight grammatical comprehension and comprehension of information input, and it point out some of the benefits of the application of the guided discovery strategy in the classroom, thus knowing that reading comprehension is of interest as a means of developing cognitive abilities and even helps them to become familiar with different reading styles, it also constitutes a significant learning process, which in successive stages will allow them to access information quickly and efficiently, achieving an independent mastery of their abilities, knowledge and skills when learning a language.