Application and Feasibility Study of Integrated Nursing Information Construction in Nephrology Nursing

With the continuous deepening of medical reforms and the continuous attempts and explorations of various management models, the traditional health care model is undergoing tremendous changes, and patients’ needs for medical institutions are becoming more and more comprehensive. Medical institutions are meeting the needs of providing medical services to patients at the same time. It is even more necessary to change our thinking and enhance the service concept. This article is based on case-based deep learning hospital nursing business process reengineering and the application and feasibility study of integrated nursing information construction in nephrology nursing. This article uses the literature analysis method, the social survey method, and other methods to discuss the construction of integrated nursing information. On the one hand, the content of this article uses the concept of process reengineering to analyze the current development status and existing problems of the hospital care industry and find countermeasures to solve problems. On the other hand, the main research content of this article is the construction of integrated nursing information and its analysis of the application and feasibility of nursing in the nephrology department. At the same time, under the background of the rapid development of the mobile Internet, we will carry out extended thinking on the continuous transformation of the construction of nursing information. According to the survey results, 87.5% of patients in the nephrology department are dissatisfied with the current hospital’s work efficiency, and 85.7% of the nursing staff in the nephrology department are generally satisfied with the information management of the current department. After the implementation of the hospital information integration system, patient satisfaction is as high as 98.2%, and the satisfaction of medical staff reached 94.2%. The construction of integrated nursing information has played a great role in the application of nephrology nursing.

Towards a rapid assessment of highway slope disasters by using multidisciplinary techniques

Mega-earthquakes and extreme climate events accompanied by intrinsic fragile geology lead to numerous landslides along mountain highways in Taiwan, causing enormous life and economic losses. In this study, a system for rapid slope disaster information integration and assessment is proposed with the aim of providing information on landslide occurrence, failure mechanisms, and subsequent landslide-affected areas to the highway authority rapidly. The functionality of the proposed system is deployed into three units: (1) geohazard rapid report (GeoPORT I), (2) multidisciplinary geological survey report (GeoPORT II), and (3) site-specific landslide simulation report (GeoPORT III). After landslide occurrence, the seismology-based monitoring network rapidly provides the initial slope disaster information, including preliminary location, event magnitude, earthquake activity, and source dynamics, within an hour. Within 3 days of the landslide, a multidisciplinary geological survey is conducted to collect high-precision topographical, geological, and remote-sensing data to determine the possible failure mechanism. After integrating the aforementioned information, a full-scale three-dimensional landslide simulation based on the discrete element method is performed within 10 days to reveal the failure process and to identify the areas potentially affected by subsequent disasters through scenario modeling. Overall, the proposed system can promptly provide comprehensive and objective information to relevant authorities after the event occurrence for hazard assessment. The proposed system was validated using a landslide event in the Central Cross-Island Highway of Taiwan.

A study of brain functional network and alertness changes in temporal lobe epilepsy with and without focal to bilateral tonic–clonic seizures

Background Temporal lobe epilepsy (TLE) is commonly refractory. Epilepsy surgery is an effective treatment strategy for refractory epilepsy, but patients with a history of focal to bilateral tonic-clonic seizures (FBTCS) have poor outcomes. Previous network studies on epilepsy have found that TLE and idiopathic generalized epilepsy with generalized tonic-clonic seizures (IGE-GTCS) showed altered global and nodal topological properties. Alertness deficits also were found in TLE. However, FBTCS is a common type of seizure in TLE, and the implications for alertness as well as the topological rearrangements associated with this seizure type are not well understood. Methods We obtained rs-fMRI data and collected the neuropsychological assessment data from 21 TLE patients with FBTCS (TLE- FBTCS), 18 TLE patients without FBTCS (TLE-non- FBTCS) and 22 controls, and constructed their respective functional brain networks. The topological properties were analyzed using the graph theoretical approach and correlations between altered topological properties and alertness were analyzed. Results We found that TLE-FBTCS patients showed more serious impairment in alertness effect, intrinsic alertness and phasic alertness than the patients with TLE-non-FBTCS. They also showed significantly higher small-worldness, normalized clustering coefficient (γ) and a trend of higher global network efficiency (gE) compared to TLE-non-FBTCS patients. The gE showed a significant negative correlation with intrinsic alertness for TLE-non-FBTCS patients. Conclusion Our findings show different impairments in brain network information integration, segregation and alertness between the patients with TLE-FBTCS and TLE-non-FBTCS, demonstrating that impairments of the brain network may underlie the disruptions in alertness functions.

Visually Improved Digital Media Communication Using Virtual Reality Technology and Digital Twin

Virtual reality (VR) has advanced as a collaborative, realistic, and creative computation technique in recent decades. With organizations becoming digitally more focused and employees’ experience changed by technology, manager’s face and continue to confront several obstacles in the digital transformation process. Recent advances in information integration have been made possible by implementing the improved digital twin (DT) paradigm and its use in the workspace. To solve these problems, simulated convergence, realistic dynamic computational decision-making, and other tools are effective. This helps to complete activities with physical models and records. Thereby, this paper presents a Visually Improved Digital Media Communication Framework (VIDMCF) using VR technology and DT. Incorporating all information, displaying the whole procedure, avoiding challenges, closing loops, optimizing repetitive processes, and making complex decisions in real-time can be aided by reproducing physical systems in the virtual design and adding VR and digital mirror twin to the output of digital media. The proposed model can achieve connectivity and convergence among the realistic atmosphere and the digital environment’s virtual system in cyber-real-space harmony over the life cycle.

B-FGC-Net: A Building Extraction Network from High Resolution Remote Sensing Imagery

Deep learning (DL) shows remarkable performance in extracting buildings from high resolution remote sensing images. However, how to improve the performance of DL based methods, especially the perception of spatial information, is worth further study. For this purpose, we proposed a building extraction network with feature highlighting, global awareness, and cross level information fusion (B-FGC-Net). The residual learning and spatial attention unit are introduced in the encoder of the B-FGC-Net, which simplifies the training of deep convolutional neural networks and highlights the spatial information representation of features. The global feature information awareness module is added to capture multiscale contextual information and integrate the global semantic information. The cross level feature recalibration module is used to bridge the semantic gap between low and high level features to complete the effective fusion of cross level information. The performance of the proposed method was tested on two public building datasets and compared with classical methods, such as UNet, LinkNet, and SegNet. Experimental results demonstrate that B-FGC-Net exhibits improved profitability of accurate extraction and information integration for both small and large scale buildings. The IoU scores of B-FGC-Net on WHU and INRIA Building datasets are 90.04% and 79.31%, respectively. B-FGC-Net is an effective and recommended method for extracting buildings from high resolution remote sensing images.

An information integration account for causal generalizations in uncertain causal networks

In reasoning about common cause networks, given that a cause generates an effect, people often need to infer how likely the cause generate another effect. This causal generalization question has not systematically been investigated in previous research. We propose the information integration account for causal generalizations in uncertain casual networks with dichotomized continuous variables. It predicts that causal generalization is the joint function of conditional probabilities of causal links and cause strength indicated by the proportion of present collateral effects. Two experiments investigated causal generalizations in uncertain causal networks with and without probability distributions, respectively. It was found that in the presence of probability distributions there was the joint effect of conditional probability and cause strength on causal generalization; in the absence of probability distributions causal generalization depend only on cause strength. The overall response pattern favors the information integration account over the other alternative accounts.

Training-Associated Superior Visuomotor Integration Performance in Elite Badminton Players after Adjusting for Cardiovascular Fitness

Badminton is recognized as the fastest racket sport in the world based on the speed of the birdie which can travel up to 426 km per hour. On the badminton court, players are not only required to track the moving badminton birdie (visual tracking and information integration) but also must anticipate the exact timing to hit it back (temporal estimation). However, the association of training experience related to visuomotor integration or temporal prediction ability remains unclear. In this study, we tested this hypothesis by examining the association between training experience and visuomotor performances after adjusting for age, education, and cardiovascular fitness levels. Twenty-eight professional badminton players were asked to perform a compensatory tracking task and a time/movement estimation task for measuring visuomotor integration and temporal prediction, respectively. Correlation analysis revealed a strong association between training experience and performance on visuomotor integration, indicating badminton training may be promoted to develop visuomotor integration ability. Furthermore, the regression model suggests training experience explains 32% of visuomotor integration performances. These behavioral findings suggest badminton training may facilitate the perceptual–cognitive performance related to visuomotor integration. Our findings highlight the potential training in visuomotor integration may apply to eye–hand coordination performance in badminton sport.

Encoding-specificity instead of online integration of real-world spatial regularities for objects in working memory

In daily life scenarios, most objects are not independent of each other; rather, they show a high degree of spatial regularity (e.g., beach umbrellas appear above beach chairs, not under them). Previous studies have shown a benefit of spatial regularities in visual working memory (VWM) performance of real-world objects, termed the spatial regularity effect. However, the mechanisms underlying this effect remain unclear. The spatial regularity effect can be explained by an “encoding-specificity” hypothesis or a “perception-alike” hypothesis. The former suggests that spatial regularity will enhance the visual encoding process but will not operate in information integration during VWM maintenance, while the latter suggests that spatial regularity will play a role in both the visual encoding and VWM maintenance processes. We tested these two hypotheses by investigating whether VWM integrates sequentially presented real-world objects by focusing on the existence of the spatial regularity effect. In Experiment 1, we manipulated the presentation (simultaneous vs. sequential) and regularity (with vs. without regularity) of memory arrays among pairs of real-world objects. The spatial regularity of memory objects improved the VWM performance in simultaneous presentation trials, but not in sequential presentation trials. In Experiment 2, we examined whether overburdened memory load hindered the spatial regularity effect in sequential presentation trials. We again found an absence of the spatial regularity effect, regardless of the memory load. These results suggest that participants were unable to integrate real-world objects into pairs based on spatial regularity during the VWM maintenance process. Therefore, the present results support the “encoding-specificity” hypothesis, implying that although the spatial regularity of real-world objects can enhance the efficiency of the encoding process in VWM, VWM cannot exploit spatial regularity to help organize sampled sequential information into meaningful groups.

Movement tracking of cognitive processes: A tutorial using mousetrap

Movement tracking is a novel process tracing method promising unique access to the temporal dynamics of cognitive processes. The method involves high-resolution tracking of the hand or handheld devices, e.g., a computer mouse, while they are used to make a choice. In contrast to other process tracing methods, which mostly focus on information acquisition, movement tracking focuses on the processes of information integration and preference formation. In this article, we present a tutorial to movement tracking of cognitive processes with the mousetrap R package. We will address all steps of the research process from design to interpretation, with a particular focus on data processing and analysis. Using a representative working example, we will demonstrate how the various steps of movement tracking analysis can be implemented with mousetrap and provide thorough explanations on their theoretical background and interpretation. Finally, we present a list of recommendations to assist researchers in addressing their own research question using movement tracking of cognitive processes.