Reference Object Research Articles

A core competency evaluation index system for the graded use of clinical nurse specialists: A Delphi study.

To describe a grading system that can be used to evaluate core competency of clinical nurse specialists (CNSs) at different levels. Evaluate core competence of CNSs at different levels reflects the quality of nursing and the development of the nursing profession. This research employed the Delphi method. The STROBE checklist for observational cross-sectional studies was followed to report this research study. This study consisted of two main phases: a literature review and semistructured interviews. Individual semistructured interviews were conducted with 11 healthcare experts and two patients. Two rounds of questionnaire surveys were administered to 21 nursing experts using the Delphi method. The CNSs were classified as primary, intermediate or advanced based on their years of work, professional titles and educational qualifications. The graded competency evaluation system consisted of five first-level indicators (clinical practice, consulting guidance and teaching, scientific research innovation, management and discipline development, and ethical decision-making), 15 second level indicators, and 40 third-level indicators. The authority coefficients (Cr) of the experts were .865 and .901. The Kendall's concordance coefficients of the three-level indicators were .417, .289 and .316 for primary CNSs; .384, .294 and .337 for intermediate CNSs; and .489, .289 and .239 for advanced CNSs. The graded use evaluation system in clinical practice initially involves a comprehensive evaluation of the core abilities of CNSs. This is a tool for cultivating and grading the abilities of specialised nurses that can promote a practical upwards spiral. The evaluation system can promote the scientific management and continuous improvement of CNSs in clinical nursing and can serve as a practical and objective reference for the effective management and development of CNSs. Patients participated in the data collection process, during which they shared their health-seeking experience with our research team.

Is Front associated with Above and Back with Below? Association between Allocentric Representations of Spatial Dimensions.

Previous research has revealed congruency effects between different spatial dimensions such as right and up. In the audiovisual context, high-pitched sounds are associated with the spatial dimensions of up/above and front, while low-pitched sounds are associated with the spatial dimensions of down/below and back. This opens the question of whether there could also be a spatial association between above and front and/or below and back. Participants were presented with a high- or low-pitch stimulus at the time of the onset of the visual stimulus. In one block, participants responded according to the above/below location of the visual target stimulus if the target appeared in front of the reference object, and in the other block, they performed these above/below responses if the target appeared at the back of the reference. In general, reaction times revealed an advantage in processing the target location in the front-above and back-below locations. The front-above/back-below effect was more robust concerning the back-below component of the effect, and significantly larger in reaction times that were slower rather than faster than the median value of a participant. However, the pitch did not robustly influence responding to front/back or above/below locations. We propose that this effect might be based on the conceptual association between different spatial dimensions.

WaRENet: A Novel Urban Waterlogging Risk Evaluation Network

In this article, we propose a novel urban waterlogging risk evaluation network (WaRENet) to evaluate the risk of waterlogging. The WaRENet distinguishes whether an urban image involves waterlogging by classification module, and estimates the waterlogging risk levels by multi-class reference objects detection module (MCROD). First, in the waterlogging scene classification, ResNet combined with Se-block is used to identify the waterlogging scene, and lightweight gradient-weighted class activation mapping (Grad-CAM) is also integrated to roughly locate overall waterlogging areas with low computational burden. Second, in the MCROD module, we detect reference objects, e.g., cars and persons in waterlogging scenes. The positional relationship between water depths and reference objects serves as risk indicators for accurately evaluating waterlogging risk. Specifically, we incorporate switchable atrous convolution (SAC) into YOLOv5 to solve occlusions and varying scales problems in complex waterlogging scenes. Moreover, we construct a large-scale urban waterlogging dataset called UrbanWaterloggingRiskDataset (UWRDataset) with 6,351 images for waterlogging scene classification and 3,217 images for reference objects detection. Experimental results on the dataset show that our WaRENet outperforms all comparison methods. The waterlogging scene classification module achieves accuracy of 95.99%. The MCROD module obtains mAP of 54.9%, while maintaining a high processing speed of 70.04 fps.

Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features.

Schizophrenia (SZ) is a prevalent mental disorder that imposes significant health burdens. Diagnostic accuracy remains challenging due to clinical subjectivity. To address this issue, we explore magnetic resonance imaging (MRI) as a tool to enhance SZ diagnosis and provide objective references and biomarkers. Using deep learning with graph convolution, we represent MRI data as graphs, aligning with brain structure, and improving feature extraction, and classification. Integration of multiple modalities is expected to enhance classification. Our study enrolled 683 SZ patients and 606 healthy controls from 7 hospitals, collecting structural MRI and functional MRI data. Both data types were represented as graphs, processed by 2 graph attention networks, and fused for classification. Grad-CAM with graph convolution ensured interpretability, and partial least squares analyzed gene expression in brain regions. Our method excelled in the classification task, achieving 83.32% accuracy, 83.41% sensitivity, and 83.20% specificity in 10-fold cross-validation, surpassing traditional methods. And our multimodal approach outperformed unimodal methods. Grad-CAM identified potential brain biomarkers consistent with gene analysis and prior research. Our study demonstrates the effectiveness of deep learning with graph attention networks, surpassing previous SZ diagnostic methods. Multimodal MRI's superiority over unimodal MRI confirms our initial hypothesis. Identifying potential brain biomarkers alongside gene biomarkers holds promise for advancing objective SZ diagnosis and research in SZ.

Unified protocol to evaluate intraoral scanner resolution, trueness and precision: the RTP-protocol

Over the past decade, studies published on the evaluation of intraoral scanners (IOSs) have mainly considered two parameters, precision and trueness, to determine accuracy. The third parameter, resolution, not much studied, seems essential for an application in dentistry. Objective: The objective of this preliminary study is to create an original method – a Resolution-Trueness- Precision (RTP) protocol to evaluate these three main parameters – resolution trueness and precision – at the same time. Material and Method: A ceramic tip with particular and calibrated dimensions is determined as the reference object and its mesh recorded with a scanning microtomograph, and compared with the one extracted to the IOS. It is the particular geometric shape of the object that will make it possible to simultaneously assess: resolution, trueness and precision. Results: The results have shown a mean resolution of 79.2 μm, a mean for trueness of 17.5 and a mean for precision of 12.3 μm. These values are close to previous results published for this camera. So, the RTP protocol is the first including the three parameters at the same time. Simple, fast and precise, its application can be useful for comparisons between IOSs within research laboratories or test organizations. Finally, this study could be a first step to create a reference kit for practitioners allowing them to control the quality of their IOS over time.

Exploiting Image Processing and Artificial Intelligence Techniques for the Determination of Antimicrobial Susceptibility

Antimicrobial susceptibility tests, achieved through the use of antibiotic-impregnated disks in a suitable laboratory environment, are conducted to determine which antibiotics are effective against the bacteria present in the body of an infected patient. The Kirby–Bauer method, a type of disk diffusion antimicrobial susceptibility test, is currently widely applied in microbiology laboratories due to its proven effectiveness. In our study, we developed an algorithm that utilizes image processing techniques to detect the inhibition zones of bacteria. A certain color depth acts as the threshold for the inhibition zone, with its radius determined according to the size of the reference object. This approach facilitates the measurement of inhibition zones and employs machine learning and deep learning to categorize antibiograms, followed by determination of whether a bacterium on the disk is sensitive or resistant to the antibiotics applied. The focus of this research is creating an automated interpretation system for antimicrobial susceptibility testing using the disk diffusion technique, thus simplifying the measurement and interpretation of inhibition zone sizes.

Информационная среда для индустриального и научно-познавательного туризма с применением ГИС

The subject of the stated research is conditioned by the need to develop modern methods and technologies of information support of the tourism industry of the regional profile taking into account its specifics and features. Awareness and comfortable conditions of tourist service are the key factors of successful promotion and sustainable development of this sphere. In the near future, domestic tourism has great prospects, it concerns many territories of the country, and the Southern Urals is no exception. The article is a continuation of research work related to the collection and analysis of geodata, as well as the visualization of the information environment. This study shows how to graphically visualize information on the basis of the development of an interactive scheme project with the possibility of overlaying it on a scalable satellite map in the field of industrial, scientific-cognitive tourism and architectural tourism on the example of a separate layer "Country of Cities" for the objects located in the South Urals. In the research on the basis of the program utility "Grid cartogram of reference archaeological objects" the design concept of the interface functionality of utility "KROT-1.0" ("Cartogram of reference objects of tourism. Version 1.0") is developed and tested as an interactive cartogram on Internet platforms. Methodology wise, the research process consists of 2 stages: pre-project or data collection with the formation of a database catalog and design and research, with the implementation of results, consisting in the development of the design concept of the utility and its integration with a web resource developed on the platform of non-commercial CMS; analysis and comparison of the results obtained on different platforms, publication or placement of the developed interactive schemes on internal servers and Internet platforms with further prospects for the development of mobile applications for the provision of information environment in different spheres of regional tourism.

What is Wrong with Raman Effect?

This paper provides a holistic picture of the Raman effect that had not been explained in the original research by Raman by showing the entire picture of what happens when a photon from a light source collides with a molecule from a reference object.

Estimation accuracy of bean bags as portion size estimation aids for amorphous foods

Objective: A study was undertaken to explore the portion size estimation accuracy of bean bags as low-cost volumetric portion size estimation aids for amorphous foods. Designs: Three observational, cross-sectional and three experimental/quasi-experimental developmental evaluation sub-studies were carried out. Settings: Observational, cross-sectional: one retirement village and four schools. Experimental/quasi-experimental: one South African university. Participants: Observational, cross-sectional: elderly persons, adult women (school educators), schoolchildren. Experimental/quasi-experimental: university students. In total N = 541; > 3 800 observations. Variables measured: Using a standardised set of bean bags (test object; volume range: 60–625 ml), volumes of different amorphous foods (reference objects: actual foods or representations) in varying portion sizes had to be estimated. Accuracy (outcome measure) was perfect if volumes of test and reference object were identical. Acceptable estimation accuracy allowed for misestimation by one bean bag size. Test–retest reproducibility was also assessed. Analysis: Descriptive statistics (proportions perfect and acceptable accuracy). Results: Across the sub-studies, perfect accuracy ranged from 22–65% depending on participants, reference food and portion size. Irrespective thereof, acceptable accuracy was noted in > 70% of observations. Reproducibility varied (range: 28–67% agreement). Conclusions and implications: Perfect portion size estimation of amorphous foods remains challenging. When misestimation by 60–125 ml still serves the purpose of a dietary assessment, bean bags show promise for cost-effective food volume quantification, especially on group level in resource-limited settings.

CerviLearnNet: Advancing cervical cancer diagnosis with reinforcement learning-enhanced convolutional networks

Women tend to face many problems throughout their lives; cervical cancer is one of the most dangerous diseases that they can face, and it has many negative consequences. Regular screening and treatment of precancerous lesions play a vital role in the fight against cervical cancer. It is becoming increasingly common in medical practice to predict the early stages of serious illnesses, such as heart attacks, kidney failure, and cancer, using machine learning-based techniques. To overcome these obstacles, we propose the use of auxiliary modules and a special residual block, to record contextual interactions between object classes and to support the object reference strategy. Unlike the latest state-of-the-art classification method, we create a new architecture called the Reinforcement Learning Cancer Network, “RL-CancerNet”, which diagnoses cervical cancer with incredible accuracy. We trained and tested our method on two well-known publicly available datasets, SipaKMeD and Herlev, to assess it and enable comparisons with earlier methods. Cervical cancer images were labeled in this dataset; therefore, they had to be marked manually. Our study shows that, compared to previous approaches for the assignment of classifying cervical cancer as an early cellular change, the proposed approach generates a more reliable and stable image derived from images of datasets of vastly different sizes, indicating that it will be effective for other datasets.

Integration into 3D Printing for Image Processing using AI ML

The challenges of physical measurement for prosthetic development and highlight how our integrated tool offers a remote solution to address these challenges, promoting accessibility, convenience, and active participation in the prosthetic design process. Our Measurement Module for Prosthetic Hand Dimensions is a comparison of Image Processing and Distance Measurement Methods The creation of a module to precisely measure finger dimensions for the creation of prosthetic hands is the main goal of this research project. The module provides distance measurement and image-based input as two different ways to get dimensions. Users place their hands at predetermined distances from a screen in the distance measurement option, while users submit photographs of their hands with reference objects for scale in the image based input option. In order To provide a proper fit and functionality, the collected dimensions are an essential component of the prosthetic hand construction process. The study contrasts the efficiency of distance measurement methods employing cameras or other cameras with image processing techniques like edge recognition and contour analysis. Accuracy, usability, and efficiency are among the factors that are assessed to identify the best method for acquiring dimensions. OpenCV for image processing, TensorFlow for machine learning-based analysis, Tkinter for UI design, Mediapipe for landmark identification and hand tracking, and NumPy for numerical operations are just a few of the open-source libraries that are used in this module. The module's possible influence on improving the creation and customization of prosthetic hands is also covered. By offering insights into effective and precise dimension measurement methodologies for customized prosthetic hand design, the study's findings advance the area of prosthetics

The impact of environmental protection tax on corporate performance: A new insight from multi angles analysis

This study investigates the practical effects of adopting the environmental protection tax (EPT) policy on corporate performance in China. The analysis uses the Difference in Differences (DID) approach based on a quasi-natural experiment scenario. The findings indicate there is a negative impact of implementing the EPT policy on the financial performance of corporations, and the conclusion remains unchanged despite exhaustive robustness testing. The negative impact can be partly attributed to corporate technology innovation inputs. Meanwhile, enterprise property rights, pollution, and technical levels also substantially influence the implementation effect of the legislation. However, implementing this policy has improved corporations’ environmental performance and established its efficacy in enhancing their sustainable capabilities. This study comprehensively explores the impact of environmental control legislation on business performance, spanning financial, environmental, and social dimensions. Corresponding findings offer valuable insights into how firms react to environmental legislation and adjust to the external environment. Meanwhile, it also provides an objective reference for the comprehensive green transformation.

Constructing an early-warning model for mortality risk in heat stroke patients based on Fisher discriminant analysis

Objective: To establish an early warning model to assess the mortality risk of patients with heat stroke disease. Methods: The case data of patients diagnosed with heat stroke disease admitted to the comprehensive ICU of Shanshan County from January 2016 to December 2020 were selected. According to the short-term outcome (28 days) of patients, they were divided into death group (20 cases) and survival group (53 cases) . The relevant indicators with statistically significant differences between groups within 24 hours after admission were selected. By drawing the subject work curve (ROC) and calculating the area under the curve, the relevant indicators with the area under the curve greater than 0.7 were selected, Fisher discriminant analysis was used to establish an assessment model for the death risk of heat stroke disease. The data of heat stroke patients from January 1, 2021 to December 2022 in the comprehensive ICU of Shanshan County were collected for external verification. Results There were significant differences in age, cystatin C, procalcitonin, platelet count, CKMB, CK, CREA, PT, TT, APTT, heart rate, respiratory rate and GLS score among the groups. Cystatin C, CKMB, CREA, PT, TT, heart rate AUC area at admission was greater than 0.7. Fisher analysis method is used to build a functional model. Results: The diagnostic sensitivity, specificity and AUC area of the functional model were 95%, 83% and 0.937 respectively. The external validation results showed that the accuracy of predicting survival group was 85.71%, the accuracy of predicting death group was 88.89%. Conclusion: The early warning model of heat stroke death constructed by ROC curve analysis and Fisher discriminant analysis can provide objective reference for early intervention of heat stroke.

THE REFERENCE GAREVKA GRANITE (YENISEI RIDGE, WESTERN MARGIN OF THE SIBERIAN CRATON): THE FINAL ATTEMPT TO VERIFY PALEOPROTEROZOIC Pb/U ISOTOPIC AGE BY M.I. VOLOBUEV

The article unravels a confusing history of ideas about the age of the Garevka granite pluton, which is considered as a reference object for the Precambrian of the Yenisei ridge. Initially, the Paleoproterozoic (1750 Ma) age of granite was determined by M.I. Volobuev and co-authors half a century ago using Pb/U isotope analyses of zircons and orthites. This dating is widely used up to recent to substantiate the Early Precambrian age of the metamorphic rocks of the Trans-Angara region. In 2003, V.A. Vernikovsky and his colleagues published data on the Neoproterozoic (752±3 Ma) age of the massif, obtained with modern technique of isotopic analysis. However, some of experts on the geology of the Yenisei ridge considered both isotopic ages correct, believing that the eastern part of the massif comprises Paleoproterozoic gneissic granite. Our investigation revealed the following: V.A. Vernikovsky and M.I. Volobuev indeed have collected their granite samples at the western and at the eastern parts of the pluton correspondingly. Krasnoyarsk geologists map the Garevka pluton as part of the Neoproterozoic Glushikha granite suite, but use to apply its Paleoproterozoic dating to substantiate the early Precambrian age of the host rocks. M.I. Volobuev obtained ten mainly discordant Pb/U analyses of the Garevka granites, and six of them form an explicable combination consistent with the age of 1750 Ma. The only concordant analysis with a known sampling point is decisive for the entire system. Thus, to fix the problem, it was necessary to reproduce this analysis using modern isotope techniques. We have dated (SHRIMP) a granite sample collected at the same point. In addition, three more samples from different parts of the massif were analysed to exclude the possible presence of ancient domains in it. The isotope ratios of all analysed zircons form a concordant cluster with an age of 762±7 Ma. The Garevka pluton comprises a separate mappable body composed of biotite leucogranites with fluorite. Granites do not replace some older rocks, but crystallized from the melt, ascended from deep crust. The question about possible Paleoproterozoic age of the Garevka granite pluton or any part of it is closed. Ideas about the wide distribution of Archean and Paleoproterozoic gneisses in the Trans-Angara region require additional geochronological verification. To date, the presence of such rocks has been reliably established here only at two local points.

Calibration‐Free Height Estimation for Person

AbstractDemand for use of indoor and outdoor surveillance cameras to prevent crime, analyze customer behavior in commercial facilities and other purposes has been increasing. For these types of applications, it is important to be able to estimate information that cannot be observed directly from the images, using the image data. One such type of information is the height of people appearing in the video. People's heights can be used, for example, to help identify a suspect in a crime investigation or as an attribute to understand customers better for marketing. However, many conventional technologies for estimating height from images have required special calibration, or the acquisition of other information in addition to the video. This paper proposes a method for estimating a person's height using only an image, without the need for any special calibration. The key points of the proposed method involve the following two processes. In the first process, a reference object of known size and the person are recognized and the person's height is estimated based on the relative sizes of the person and reference object in the image. The second process involves selecting frames from the video that are appropriate for use in the estimation process. In practical experiments using real video, the method was able to estimate people's heights with an average estimation error of 1.9%. © 2024 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Reducing vibration and noise in honeycomb structure applied to automobile integrated water tank

This work uses modal acoustic transfer vector (MATV) technology in conjunction with vibration response analysis to determine optimal target optimization parameters for automobile integrated water tanks. The effects of square and hexagonal reinforcement honeycomb structures on the vibration damping performance of water tanks are analyzed, using an untreated smooth plane as a reference object. The study investigates the effects of honeycomb height, honeycomb length, and honeycomb thickness on the tank’s vibration damping performance. The results indicate that a hexagonal structure is more effective than a square structure in improving structural stiffness and reducing structural vibration. The height of the honeycomb structure is particularly effective in suppressing normal vibration of the structural surface, especially when the height exceeds 8 mm. A response surface model is developed based on the edge length, wall thickness, and height of the honeycomb, following the Box-Behnken central combinatorial test design principle. Considering the effect of interference of additional parts, the noise reduction is found to be most effective with a honeycomb side length L = 12 mm, height H = 4.91 mm, and wall thickness T = 2.5 mm. These parameters can be a guide for creating a new honeycomb reinforcement structure to better achieve vibration and noise reduction in automobile water tanks.

Algebraic Recognition Approach in IoT Ecosystem

The solution to the problem of identifying objects in the IoT ecosystem of the Aral region is analyzed. The problem of constructing a correct algorithm with linear closure operators of a model for calculating estimates for identifying objects in the IoT ecosystem of the Aral region is considered. An algorithm operator is developed, which is considered correct for the problem Z, is the sum of q operators of the assessment calculation model, and is described by a set of numerical parameters 3·n·m·q, where n is the number of specified features, m is the number of reference objects, and q is the set of recognized objects. Within the framework of the algebraic approach, several variants of linear combinations of recognition operators are constructed, the use of which gives the correct answer on the control material, and this is proven in the form of theorems. The constructed correct recognition algorithms, which are the easiest to use, where there is no optimization procedure, make it possible to quickly solve the issue of identifying incoming information flows in the IoT ecosystem of the Aral region.

Intelligent Lighting System Using Color-Based Image Processing for Object Detection in Robotic Handling Applications

In applications reliant on image processing, the management of lighting holds significance for both precise object detection and efficient energy utilization. Conventionally, lighting control involves manual switching, timed activation or automated adjustment based on illuminance sensor readings. This research introduces an embedded system employing image processing methodologies for intelligent ambient lighting, focusing specifically on reference-color-based illumination for object detection and positioning within robotic handling scenarios. Evaluating the system’s efficacy entails analyzing the illuminance levels and power consumption through a tailored experimental setup. To minimize illuminance, the LED-based lighting system, controlled via pulse-width modulation (PWM), is calibrated using predetermined red, green, blue and yellow (RGBY) reference objects, obviating the need for external sensors. Experimental findings underscore the significance of color choice in detection accuracy, highlighting yellow as the optimal color requiring minimal illumination. Successful object detection based on color is demonstrated at an illuminance level of approximately 50 lx, accompanied by energy savings contingent upon ambient lighting conditions.

Order Distribution and Routing Optimization for Takeout Delivery under Drone–Rider Joint Delivery Mode

Order distribution and routing optimization of takeout delivery is a challenging research topic in the field of e-commerce. In this paper, we propose a drone–rider joint delivery mode with multi-distribution center collaboration for the problems of limited-service range, unreasonable distribution, high delivery cost, and tight time windows in the takeout delivery process. The model is constructed with the minimum delivery cost and the overall maximum customer satisfaction as the objective function, and a two-stage heuristic algorithm is designed to solve the model. In the first stage, Euclidean distance is used to classify customers into the regions belonging to different distribution centers, and the affinity propagation (AP) clustering algorithm is applied to allocate orders from different distribution centers. The second stage uses an improved tabu search algorithm for route optimization based on specifying the number of rider and drone calls. This paper takes China’s Ele.me and Meituan takeout as the reference object and uses the Solomon data set for research. The experimental results show that compared with the traditional rider delivery mode, the drone–rider joint delivery mode with multiple distribution center collaboration can effectively reduce the number of riders used, lower the delivery cost, and improve the overall customer satisfaction.

A generalized simplicial model and its application.

Higher-order structures, consisting of more than two individuals, provide a new perspective to reveal the missed non-trivial characteristics under pairwise networks. Prior works have researched various higher-order networks, but research for evaluating the effects of higher-order structures on network functions is still scarce. In this paper, we propose a framework to quantify the effects of higher-order structures (e.g., 2-simplex) and vital functions of complex networks by comparing the original network with its simplicial model. We provide a simplicial model that can regulate the quantity of 2-simplices and simultaneously fix the degree sequence. Although the algorithm is proposed to control the quantity of 2-simplices, results indicate it can also indirectly control simplexes more than 2-order. Experiments on spreading dynamics, pinning control, network robustness, and community detection have shown that regulating the quantity of 2-simplices changes network performance significantly. In conclusion, the proposed framework is a general and effective tool for linking higher-order structures with network functions. It can be regarded as a reference object in other applications and can deepen our understanding of the correlation between micro-level network structures and global network functions.