Information Input Research Articles

The Effect Of Virginia Coconut Oil (VCO) On The Prevention Of Decubitus In Prolonged Bed Rest Patients

Decubitus is a condition where external pressure causes damage or injury to local tissue. Trauma and surgical wounds can cause damage to skin integrity, long-term pressure on the skin can also cause irritation and the formation of decubitus, People who experience nerve damage due to stroke, traumatic injury, diabetes mellitus, or coma are also more likely to experience decubitus. The purpose of this study was to determine the effect of administering virgin coconut oil (VCO) on the risk of decubitus. The research method used the Pre Experiment design method with a one group pre post test design. The population in this study were all patients in the Akasia Inpatient Room, Siti Fatimah Az-Zahra Hospital, South Sumatra Province. The total number of patients treated was 30 respondents. Sampling was carried out using the total sampling technique. The results of the study showed that there was an effect of administering virginia coconut oil on the risk of decubitus in patients with long-term bed rest, the significant value of the non-parametric statistical test (Wilcoxon) was 0.000 (p value 0.000 <0.05). Suggestions are expected as input and sources of information in providing counseling to patients who are bedridden for a long time to use pharmacological therapy in preventing decubitus, and routinely use Virginia Coconut Oil which has many benefits.

The Relationship Between Time-Related Characteristics of Visual Impairment and Psychological Symptoms in Adults Who Are Blind

Introduction The prevalence of depression among blind people is higher than in sighted populations. The purpose of this study was to investigate the associations between time-related characteristics of visual impairment (i.e., onset and duration), as well as depressive symptoms, fatigue, sleepiness, and stress, in the context of self-reported total blindness and the inability to perceive light exposure. Methods This study employed a cross-sectional design utilizing the combination of online survey and telephone survey. The Centre for Epidemiological Studies–Depression Inventory, the Fatigue Syndrome Inventory, the Epworth Sleepiness Scale, and the Perceived Stress Scale were selected for the self-reported questionnaires. For statistical analysis, independent t-tests, analysis of variance tests, Wilcoxon–Mann–Whitney tests, chi-square tests, multiple linear regression models, and multivariable ordinal logistic regressions were conducted using the R software program and its required packages. Results A total of 154 respondents completed the online and telephone surveys. Although the number of years of experiencing blindness showed negative correlations with the scores of fatigue ( r = −.16, p < .05), stress ( r = −.18, p < .05), and depressive feelings ( r = −.21, p < .05) in Model 1, the impacts of the onset of blindness on the depressive symptoms (β = −.02, p = .570) and the presence of severe depressive symptoms, OR = 1.42, 95% CI [0.49, 4.19], p = .518, were insignificant in Model 2. Discussion Blindness seems to be regarded as a more severe chronic condition which affects individuals’ psychological status. Given the effects of the limited range of social activities and daily tasks and the dramatically reduced accessible visual information inputs, the severe depressive symptoms in this group can be understood. Implications for Practitioners Regular counseling and psychological services monitoring depressive symptoms, chronic fatigue, and severe distress should be continued even after the completion of rehabilitation programs.

The Assessment of Land Suitability for Urban Expansion and Renewal for Coastal Urban Agglomerations: A Pilot Study of the Guangdong-Hong Kong-Macao Greater Bay Area

Effectively and rationally allocating land resources, while coordinating urban expansion with internal renewal strategies, is crucial for achieving high-quality regional development in coastal urban agglomerations. Land-use suitability assessment (LSA) is a key method for coastal land-use planning, but it is primarily used to delineate ecological redlines or areas for urban expansion, often overlooking the spatial analysis needed for urban renewal. This is particularly critical in coastal urban agglomerations facing land scarcity and ecological fragility. Here, we combined land use and the Analytical Hierarchical Process (to consider stakeholder priorities) in a Minimum cumulative resistance model (MCRM) to determine suitable coastal urban growth and renewal based on a suite of 12 indicators relevant to development intensity and stock space. Application to the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) indicates a dominance of the Ecological Buffer Zone (70.5%), and the available stock space in the GBA comprises only 9.2% of the total area. Our modeling framework tailored different development strategies for different cities: Huizhou and Zhaoqing had space for urban expansion to varying degrees, while other cities were found to be suitable for urban renewal due to low stock space and high development intensity. Our modeling approach, incorporating stakeholder input and objective evaluation of geographic land-use information, can assist planners in improving ecological security while promoting high-quality developments in coastal areas.

Test-Retest Reliability of Dynamic Subjective Visual Vertical and Visual Dependency in Older Adults Using Virtual Reality Methods.

The perception of verticality is formed through the integration of multisensory gravitational information, including somatosensory, visual, and vestibular inputs. Older adults exhibit visual dependency (VD) as they rely more on visual information to compensate for reduced somatosensory and vertical perception. Increased VD is associated with falls, and the dynamic subjective visual vertical (SVV) is used to assess VD. However, the measurement reliability of dynamic SVV and VD using virtual reality (VR), which has garnered considerable attention in recent years, remains unclear. Therefore, our purpose in this study was to assess the test-retest reliability of dynamic SVVs and VDs using a VR method. We evaluated static and dynamic SVV of 40 older adults using a smartphone-based VR system (SVR-SVV). Dynamic SVV consisted of numerous spheres on the background rotating clockwise (CW-SVV) or counterclockwise (CCW-SVV). Each SVV measurement consisted of one set of 10 trials. VD was calculated as the mean value of dynamic SVV minus the mean values of static SVV. A re-test was conducted after one week. Reliability was analyzed using Bland-Altman plots and intraclass correlation coefficients (ICC 2, k) for each SVV measure. We observed no systematic bias in any of the SVV values, which were 0.1° (SD = 2.8°), 13.3° (SD = 8.3°), -12.8° (SD = 6.9°), and 15.7° (SD = 8.1°) for static SVV, CW-SVV, CCW-SVV, and VD, respectively. Test-retest reliability was good for static SVV (ICC = .817, p < .001), CW-SVV (ICC = .896, p < .001) and excellent for CCW-SVV (ICC = .914, p < .001), VD (ICC = .937, p < .001). The dynamic SVV and VD measurements using SVR-SVV demonstrated good test-retest reliability. Moreover, the SVR-SVV is more portable than conventional methods, making it highly useful in clinical practice.

Self-Powered Handwritten Letter Recognition Based on a Masked Triboelectric Nanogenerator for Intelligent Personal Protective Equipment.

As one of the most important ways of human-machine interfaces, the touchpad has excellent input convenience. Input devices for extreme environments require simpler structures and diverse inputs to ensure information inputs. This paper proposed a self-powered flexible input panel with single-channel output for the input recognition of all 26 letters, and a paper mask was implemented to cover the triboelectric nanogenerator (TENG) board and obtain more complicated electrical signal features. Based on the change of the triboelectric output of the mask, neural network models with different combinations of layers were designed and optimized, and the highest recognition rate of 88.7% for all letters and 100% recognition accuracy for some letters were achieved among the five testers. For letters with low recognition rates, a specific writing specification was further proposed to improve the accuracy of model recognition. These results facilitate the application of the proposed input panel as a flexible wearable device and personal protective equipment for extreme environments including chemical, biological, radiological, nuclear (CBRN) scenarios or aerospace.

Environmental uncertainty and entrepreneurial orientation in collectivist and individualist cultures: evidence from Brazil and Belgium

ABSTRACT A fundamental premise of the information processing approach in entrepreneurship theory is that the distribution and processing of information is key to opportunity identification. We propose that collectivist-individualist cultural values influence the efficacy of information processing leading to different levels of entrepreneurial orientation (EO). Specifically, we submit that collectivism enhances shared information processing and goal setting in a dynamic environment, while individualism facilitates creativity and divergent thinking that can help EO if sufficient informational input is available. To empirically verify our theory, we study the relationships using survey and archival data from a sample of 487 firms in six industries in Brazil and Belgium, two countries that contrast in collectivism-individualism. Consistent with our predictions, we find higher EO in the collectivist culture of Brazil under high environmental dynamism and low information availability, and this difference is increased by strategic planning.

Nurses’ Experiences with the Use of Electronic Nursing Record: A Qualitative Study

Background: The electronic nursing record has contributed to improving nursing practice and ensuring nursing quality, but it is still incomplete in supporting nursing workflow. The purpose of this study is to explore the experience of using the electronic nursing record by nurses working in frontline care for trauma, emergency, and infectious disease-exposed patients.Methods: The study employed a qualitative descriptive design and qualitative content analysis. In-depth interviews were conducted with 10 nurses working in emergency, trauma, and critical care units in the tertiary hospital.Results: The nurses’ experiences with the use of the electronic nursing record emerged into three subjects (improvements to electronic nursing record implementation, benefits of electronic nursing record implementation, electronic nursing record user factors), five categories (information finding and sharing function, information input function, facilitating efficient communication, ensuring record quality, need for education), and eight subcategories.Conclusions: Electronic nursing record supports the visualization of special information such as emergency and infection, and has the advantage of facilitating communication between medical staff. Both support better clinical decision-making. However, system instability and low nursing friendliness delay nursing work in the process of inputting, confirming, and sharing information. Copy-andpaste practices, poor quality of records, and insufficient electronic nursing record education were identified as factors to be changed on the user side. Improvement of nursing-friendly interfaces, optimization of nursing workflows, and participation of nurses in the improvement process are necessary. We should also focus on developing electronic nursing record education programs.

Quantitative method for the probability of structural damage based on moment theory

In the context of uncertainty in the inverse problem of damage identification, this study proposes a moment theory based probabilistic damage identification (MbPDI) method. Noise errors in the identification process are treated as uncertain parameters, while structural stiffness parameters are treated as functions of noise errors. A limit state function for damage identification is defined, and its probability is computed. Utilizing the concept of moment theory, the expansion series is performed for the limit state function around the most probable point of failure, with gradient information derived using sensitivity matrices. Based on the gradient information and uncertainty inputs, damage probabilities for various elements can be obtained. Additionally, this paper addresses the calculation of structural damage probabilities under the non-normal distribution of random variables by transforming non-normal variables into equivalent normal variables. The proposed probability index and expected index of structural damage in the probabilistic damage identification method has a clear physical meaning, and the calculation has high accuracy. The proposed method is validated through a mathematical example and a numerical simulation. Meanwhile, the influence of damage extent, noise level, and modal order on identification results are analyzed. Experimental validation of the method is also presented.

Maternal education and child mortality: Evidence from developing countries

This paper evaluates how maternal education affects child mortality in the context of 52 low- and middle-income countries. The sister fixed-effects model is employed, where we compare the child mortality outcome between biological sisters who attain different numbers of educational years. We find that an additional year of maternal education is linked with a 0.4 percentage point decrease in the likelihood of child mortality (a 4.3 % reduction relative to the sample mean). Additionally, the mechanism analysis reveals that improved women’s labor market outcome, increased access to information, assortative matching, and health input allocation are possible pathways to the link between maternal education and child mortality.

Multisensory perceptual and causal inference is largely preserved in medicated post-acute individuals with schizophrenia.

Hallucinations and perceptual abnormalities in psychosis are thought to arise from imbalanced integration of prior information and sensory inputs. We combined psychophysics, Bayesian modeling, and electroencephalography (EEG) to investigate potential changes in perceptual and causal inference in response to audiovisual flash-beep sequences in medicated individuals with schizophrenia who exhibited limited psychotic symptoms. Seventeen participants with schizophrenia and 23 healthy controls reported either the number of flashes or the number of beeps of audiovisual sequences that varied in their audiovisual numeric disparity across trials. Both groups balanced sensory integration and segregation in line with Bayesian causal inference rather than resorting to simpler heuristics. Both also showed comparable weighting of prior information regarding the signals' causal structure, although the schizophrenia group slightly overweighted prior information about the number of flashes or beeps. At the neural level, both groups computed Bayesian causal inference through dynamic encoding of independent estimates of the flash and beep counts, followed by estimates that flexibly combine audiovisual inputs. Our results demonstrate that the core neurocomputational mechanisms for audiovisual perceptual and causal inference in number estimation tasks are largely preserved in our limited sample of medicated post-acute individuals with schizophrenia. Future research should explore whether these findings generalize to unmedicated patients with acute psychotic symptoms.

A Battery‐Free Wireless Tactile Sensor for Multimodal Force Perception

AbstractMultimodal tactile sensors, as key information input channel in human‐machine interactions, have faced the significant challenges including high power‐consumption, multimodal data fusion, and wireless transmission. In this work, a battery‐free multimodal wireless tactile sensor (TC‐MWTS) based on tribo‐capacitive coupled effect for normal and shear force fusion sensing is proposed, which is enabled by a 3D structure combining a triboelectric sensor and a capacitive sensor coupled with an inductive coil. A triboelectric sensor equipped with contact‐discharge structures exhibits 25‐fold wireless signal enhancement compared to conventional triboelectric sensors. Based on the characteristics of dual time‐frequency domain information existing in the wireless signals, both normal and shear forces can simultaneously be converted into voltage amplitude V and eigenfrequency f, respectively, without crosstalk and complex decoupling signals. The TC‐MWTS exhibits a maximum sensitivity of 2.47 V kPa−1 for normal force from 2 to 30 kPa and a sensitivity of 0.28 MHz N−1 for shear force between 0.3 and 1.0 N. Finally, the excellent sensing capability of TC‐MWTS to sense complex multidimensional forces in human‐machine interaction is demonstrated. This work innovatively proposes a new mechanism and methodology for effectively fusing and processing multimodal tactile information, which may drive the tremendous development of low‐power multimodal tactile sensing system.

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, particularly, losses due to pathogens. Breeding resistant crops is key to achieving this goal, 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 of the yield gap.

SIGEPS: A System for Managing the Health Internship Program.

The healthcare sector's demand for a skilled workforce necessitates effective management of internship and residency programs. This study introduces the System for Managing the Health Internship Program (SIGEPS), aimed at addressing internship and residency management challenges in Joinville's municipal health network. The method of this study focused on developing a computational solution with applied and exploratory objectives. The research adopted a User-Centered Development approach, involving seven stages from scope determination to system implementation. Key stages included selecting Google's free technological tools, developing forms for information input, integrating these into a Google Sheets spreadsheet, and creating a website for schedule display. The primary result was the successful development of SIGEPS, which automated the process from initial requests to scheduling and analysis of healthcare institutions and displayed schedules online. The system offers several advantages over previous models, such as concurrent scheduling by multiple employees, process automation, enhanced information transparency, and improved efficiency with significant time savings. Future enhancements include integrating decision support systems and enabling data analysis reports through dashboards to further reduce scheduling efforts and enhance professional efficiency. This study demonstrates the feasibility of developing and implementing a free computational solution using the Google platform for managing internships and residencies in the healthcare sector.

Advanced Global Prototypical Segmentation Framework for Few-Shot Hyperspectral Image Classification.

With the advancement of deep learning, related networks have shown strong performance for Hyperspectral Image (HSI) classification. However, these methods face two main challenges in HSI classification: (1) the inability to capture global information of HSI due to the restriction of patch input and (2) insufficient utilization of information from limited labeled samples. To overcome these challenges, we propose an Advanced Global Prototypical Segmentation (AGPS) framework. Within the AGPS framework, we design a patch-free feature extractor segmentation network (SegNet) based on a fully convolutional network (FCN), which processes the entire HSI to capture global information. To enrich the global information extracted by SegNet, we propose a Fusion of Lateral Connection (FLC) structure that fuses the low-level detailed features of the encoder output with the high-level features of the decoder output. Additionally, we propose an Atrous Spatial Pyramid Pooling-Position Attention (ASPP-PA) module to capture multi-scale spatial positional information. Finally, to explore more valuable information from limited labeled samples, we propose an advanced global prototypical representation learning strategy. Building upon the dual constraints of the global prototypical representation learning strategy, we introduce supervised contrastive learning (CL), which optimizes our network with three different constraints. The experimental results of three public datasets demonstrate that our method outperforms the existing state-of-the-art methods.

Hearing, balance, and imaging assessment in adolescent Menière's disease: A retrospective analysis.

To retrospectively analyze clinical features in adolescent Menière's disease (MD). The medical records of adolescents with MD (11-17 years old) from May 2014 to March 2023 in Shandong Provincial ENT Hospital were retrospectively analyzed, including clinical features, a battery of auditory and vestibular function tests, sensory organization test, and imaging assessments. Patients with recurrent vertigo of childhood (RVC) were as controls. Compared with RVC, adolescent MD showed higher pure tone average threshold (p < .001), lower speech discrimination score (p = .014), and lower otoacoustic emission pass rates (p = .005). Adolescents with MD exhibited significant reduction in equilibrium score (Conditions 1, 5, and 6; p1 = .035; p5 = .033; p6 = .003), composite sensory score (p = .014), and vestibular sensory score (p = .029). Adolescents with bilateral MD exhibited worse performance in equilibrium score and strategy score compared to adolescents with unilateral MD. For the affected ear, the more severe endolymphatic hydrops detected by gadolinium-enhanced magnetic resonance imaging, the higher the auditory brainstem response threshold (r = .850, p = .007), and the lower the otoacoustic emission pass rate (r = -.976, p < .001). Adolescent MD has similar vestibular information inputs with that of RVC, but the ability for the nerve center to use these clues to maintain balance is worse in adolescents with MD. There were potential differences in vestibular weights in adolescents with unilateral and bilateral MD, also potential effects on vision and proprioception. Level 4.

Information Inputs and Outputs of Quality Planning Processes and the Possibilities of Their Improvement through Digital Transformation

Purpose: The article aims to identify the key information inputs of quality planning processes, analyse their availability in the company practice, and propose framework measures for their effective digital transformation. Methodology/Approach: The quality planning process was divided into 10 sub-processes, and a total of 90 information inputs and outputs were identified. Based on the importance evaluation 20 most important information inputs of quality planning were determined. The average digitalisation degree and maturity of the used storage systems were analysed in company practice for these information inputs. The average degree of availability of these inputs was then evaluated, leading to framework proposals for improvement. Findings: The analysis revealed that many key information inputs of quality planning processes are not sufficiently available in practice. Consequently, framework measures to improve the availability of information for quality planning processes were proposed. Research Limitation/Implication: This article provides valuable insights for companies aiming to enhance competitiveness through more effective quality planning in accordance with Industry 4.0 principles. Originality/Value of paper: The paper identifies the information inputs and outputs of quality planning processes and offers framework suggestions for effectively incorporating digital transformation into these processes.

Interpretable feature-temporal transformer for short-term wind power forecasting with multivariate time series

The inherent randomness and volatility of wind power generation present significant challenges to the reliable and secure operation of the power system. Therefore, it is crucial to have interpretable wind power forecasting (WPF) to ensure seamless grid integration and effective risk assessment. Existing forecasting models often focus on improving WPF performance and ignore the interpretability of the model, resulting in ambiguous forecasting. In this paper, the interpretable feature-temporal transformer (IFTT) for short-term wind power forecasting with multivariate time series is presented. The model uses an encoder-decoder architecture to effectively integrate historical information and future prior information from multiple variables. The designed decoupled feature-temporal self-attention (DFTA) module and variable attention network (VAN) effectively realize the interpretability of temporal information and multi-variable inputs while extracting important features. The Auxiliary Forecasting Network (AFN) plays a key role in providing pseudo-future wind speed predictions, which serve as an essential input for the model's decoder, and enhancing forecasting accuracy through multi-task learning. Experimental results on multiple datasets in different geographical locations show that the proposed algorithm is superior to various advanced methods. Besides, the interpretability of the IFTT model offers valuable insights for ensuring the safety of wind power utilization and supporting informed risk decision-making.

Natural language processing in at-risk mental states: enhancing the assessment of thought disorders and psychotic traits with semantic dynamics and graph theory.

Verbal communication has key information for mental health evaluation. Researchers have linked psychopathology phenomena to some of their counterparts in natural-language-processing (NLP). We study the characterization of subtle impairments presented in early stages of psychosis, developing new analysis techniques and a comprehensive map associating NLP features with the full range of clinical presentation. We used NLP to assess elicited and free-speech of 60 individuals in at-risk-mental-states (ARMS) and 73 controls, screened from 4,500 quota-sampled Portuguese speaking citizens in Sao Paulo, Brazil. Psychotic symptoms were independently assessed with Structured-Interview-for-Psychosis-Risk-Syndromes (SIPS). Speech features (e.g.sentiments, semantic coherence), including novel ones, were correlated with psychotic traits (Spearman's-ρ) and ARMS status (general linear models and machine-learning ensembles). NLP features were informative inputs for classification, which presented 86% balanced accuracy. The NLP features brought forth (e.g. Semantic laminarity as 'perseveration', Semantic recurrence time as 'circumstantiality', average centrality in word repetition graphs) carried most information and also presented direct correlations with psychotic symptoms. Out of the standard measures, grammatical tagging (e.g. use of adjectives) was the most relevant. Subtle speech impairments can be grasped by sensitive methods and used for ARMS screening. We sketch a blueprint for speech-based evaluation, pairing features to standard thought disorder psychometric items.

The Application Artificial Intelligence-Assisted Robot System in Nursing Follow-up of Discharged Patients.

To construct a robot intelligent discharge follow-up platform and explore its application effects in clinical discharge follow-up scenarios Applying intelligent voice technology to build a robot intelligent discharge follow-up platform, replacing nurses in completing telephone calls, interactive communication, feedback collection, and information input during follow-up. Compare the work efficiency and manpower investment between robots and manual follow-up. Compared with the manual telephone follow-up method, the application of robot intelligent discharge follow-up platform resulted in a higher follow-up rate, fewer follow-up hours and nurse manpower. The robot intelligent discharge follow-up platform can release nursing manpower and time, improve the follow-up rate of discharged patients.

Identifying critical nodes in interdependent networks by GA-XGBoost

Once a critical node is destroyed, the interdependent network is prone to experience severe cascading failure. Due to the coupling, traditional methods are challenging to apply to interdependent networks. Here, we propose a novel comprehensive model based on machine learning. The main work of this data-driven approach is to train the model on a small set of nodes (5 % of the graph) and do the critical node identification on the rest. We collect node centrality indicators to describe the node features and provide informative input data from different dimensions. The uniform node sampling is improved to cluster oversampling, which combines K-means and Synthetic Minority Over-sampling Technique (SMOTE) to select and recreate uniformly distributed training samples. We optimize the XGBoost based on the Genetic algorithm (GA) to overcome the instability of manual parameters. Kendall's τ correlation coefficient, Jaccard similarity coefficient, R2, and RMSE are used as the model performance evaluation metrics. Experiment results confirm that the proposed GA-XGBoost model outperforms others, demonstrating higher adaptability and stability in various situations. The heuristic algorithm-optimized machine learning model offers a viable solution for identifying critical nodes in interdependent networks, which is of great significance for controlling virus propagation and preventing failures.