Scaling Coefficients Research Articles

Aging related changes in cardiovascular system in healthy female population: photoplethysmography method and DFA analysis

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Ministry of Science Technological Development and Innovation Background The cardiovascular system undergoes dynamic changes during the biological aging in humans, leading to chronic cardiovascular and cerebrovascular diseases. Purpose Developing the novel method for non-invasive monitoring of age induced changes in arterial vessels. Methodology The pilot study was performed on 58 women, in good health, non-smokers, between the ages of 20 and 70, divided into three age groups. All subjects underwent arterial blood pressure measurement using a photoplethysmography sensor. Recordings were performed in left carotid artery and the left index finger area. Arterial blood flow waveform was then analyzed using DFA analysis, calculating the values of the scalar coefficients α1 and α2. Based on the waveform time interval shift between the standing and supine positions, the pulse wave velocity (PWV) ratio was determined. Results Systolic and diastolic arterial pressure showed a slight linear increase with the age, but without significant difference between the three age categories (p > 0.5). Also, the PWV ratio between the supine and standing positions did not show a statistically significant increase between the age groups. Only scalar coefficients ratio α1/α2 demonstrated a significant difference in value between all age categories. Conclusion In subjects included in this pilot study, only the scalar coefficients ratio varied significantly with the age, indicating the occurrence of structural changes in the cardiovascular system caused by biological aging.

Development, validation and reliability testing of the hospice care environment scale

BackgroundWHO stated the environment is an important factor affecting the development of hospice care. The environment is the sum of factors affecting behavior besides the individual factors. Currently, a scale to comprehensively assess the hospice environment of nurse is still lacking. This study aimed to develop an instrument to investigate the environmental factors affecting hospice care of nurses.MethodsLiterature review and a semi-structured interview were conducted to form the items pool of the Hospice Care Environment Scale. Two rounds of Delphi expert consultation were conducted by 16 experts to revise the scale dimensions and entries to form the Hospice Care Environment Scale. A psychometric evaluation was then performed among 530 oncology nurses in a large tertiary oncology hospital in Hubei Province. The 500 valid questionnaires were randomly divided into two groups in a 1:1 ratio, sample 1 (n1 = 250) for item screening and sample 2 (n2 = 250) for quality evaluation of the resulting scale. Item analysis, reliability analysis, validity analysis and acceptability analysis were performed.ResultThe Hospice Care Environment Scale consists of two dimensions and 13 entries. The Cronbach’s α coefficient of the Hospice Care Environment Scale was 0.970, and the Cronbach’s α coefficient of the two dimensions were 0.952 and 0.969, respectively, with the Item-content validity index and average Scale- content validity index of the scale was both 1.000. The validation factor analysis showed the standardized path coefficients of each item were basically above 0.5, and the factor structure model was stable and suitable. The average completion time of the scale was about 3 min, which had good feasibility.ConclusionThe Hospice Care Environment Scale to assess the environment of hospice care services, has good content and construct validity and reliability. This scale can provide guidance to evaluate the hospice care environment.

Blind source separation algorithm for noisy hydroacoustic signals based on decoupled convolutional neural networks

Wireless communication technology has been widely used in marine engineering, marine ranching and Marine environmental monitoring. However, structural redundancy and functional confusion exist in applying neural networks in signal separation technology in underwater communication environments, which can result in a slower rate of signal separation and lead to confusion of parameters during transfer learning. Based on this, an end-to-end, internal functionally structured decoupled neural network (D-CNN) blind source separation (BSS) model is proposed in this paper, which can realize a neural network BSS algorithm with a well-defined structure and function. The one-dimensional convolutional neural network layer is used in algorithm to automatically extract observed signal's features, based on the features, and there are two generation modules of separation matrix and scaling coefficients. Then the two modules can be used to separate the observed signal and adjust the signal coefficients to obtain the separated signal. Finally the transfer learning technique is used to generalize the model, which reduces the transfer cost of the model in different application scenarios. Experimental results show that when the communication distance is set to 0.02 km–2 km, the MSE of independent signal and related signal can be reduced by 14.24% and 14.95% respectively compared with the nearest Neural FCA algorithm. The results prove that the proposed algorithm can accurately estimate the source signal and improve the signal reception quality.

Estimating and partitioning evapotranspiration in a film mulched cropland with shallow groundwater by the improved dual source model

Evapotranspiration (ET) is a key link in terrestrial water cycle and influenced by complex environmental factors in cropland. Accurate assessment of crop ET over heterogeneous underlying surfaces remains challenging. In this study, the dual source Shuttleworth-Wallace (SW) model was modified to estimate λET (latent heat corresponding to ET) through incorporating the effects of film mulching and shallow groundwater. λET and latent heat from transpiration (λT) were measured in a maize field in northwest China during 2017 and 2018 to assess the performance of the original (SW) and modified (SWM) models. Results indicate that the overestimation of latent heat from soil evaporation (λE) due to ignoring film mulching could be obviously mitigated by SWM. Besides, considering capillary rise from shallow groundwater in SWM improved not only λET estimates but also the accuracy of canopy resistance parameter. The SWM model outperformed the SW model that the slopes of fitting lines for λET and λT increased from 0.60 to 0.79 to 0.77–0.99 at daily scale and Nash-Sutcliffe Efficiency coefficient exceeded 0.86. Relative error and RMSE were decreased to below 0.15 and 19.88 W m−2, respectively. The estimations of λET and λT by SWM were also improved at hourly scale. Seasonal variation and partition of λET between two years were comparable and average T/ET for the entire growing season was about 78 %-84 %, which was largest at the heading stage. Path analysis on the responses of λET to the environmental factors recognized the greatest direct effect of crop height or leaf area index, and identified the significantly direct control of shallow groundwater on λT in this area. The study presented an improved dual source model to better quantify λET, which would benefit water resources management for film mulched croplands with shallow groundwater.

The Eye in the Sky—A Method to Obtain On-Field Locations of Australian Rules Football Athletes

The ability to overcome an opposition in team sports is reliant upon an understanding of the tactical behaviour of the opposing team members. Recent research is limited to a performance analysts’ own playing team members, as the required opposing team athletes’ geolocation (GPS) data are unavailable. However, in professional Australian rules Football (AF), animations of athlete GPS data from all teams are commercially available. The purpose of this technical study was to obtain the on-field location of AF athletes from animations of the 2019 Australian Football League season to enable the examination of the tactical behaviour of any team. The pre-trained object detection model YOLOv4 was fine-tuned to detect players, and a custom convolutional neural network was trained to track numbers in the animations. The object detection and the athlete tracking achieved an accuracy of 0.94 and 0.98, respectively. Subsequent scaling and translation coefficients were determined through solving an optimisation problem to transform the pixel coordinate positions of a tracked player number to field-relative Cartesian coordinates. The derived equations achieved an average Euclidean distance from the athletes’ raw GPS data of 2.63 m. The proposed athlete detection and tracking approach is a novel methodology to obtain the on-field positions of AF athletes in the absence of direct measures, which may be used for the analysis of opposition collective team behaviour and in the development of interactive play sketching AF tools.

Wind energy resource assessment and wind turbine selection analysis for sustainable energy production

The objective of this study is to perform an analysis to determine the most suitable type of wind turbine that can be installed at a specific location for electricity generation, using annual measurements of wind characteristics and meteorological parameters. Wind potential analysis has shown that the analyzed location is suitable for the development of a wind farm. The analysis was carried out for six different types of wind turbines, with a power ranging from 1.5 to 3.0 MW and a hub height set at 80 m. Wind power potential was assessed using the Weibull analysis. The values of the scale coefficient c were determined, and a large monthly variation was observed, with values ranging from 1.92 to 8.36 m/s and an annual value of 4.95 m/s. Monthly values for the shape coefficient k varied between 0.86 and 1.53, with an annual value of 1.07. Additionally, the capacity factor of the turbines was determined, ranging from 17.75 to 22.22%. The Vestas turbine, with a nominal power of 2 MW and a capacity factor of 22.22%, proved to be the most efficient wind turbine for the specific conditions of the location. The quantity of greenhouse gas emissions that will be reduced if this type of turbine is implemented was also calculated, considering the average CO2 emission intensity factor (kg CO2/kWh) of the national electricity system.

Cross Cultural Dynamics of Violence Against Women in Nigeria A Multivariate Analysis Approach

The purpose of the study was to examine Cross Cultural Dynamics of Violence against Women in Nigeria. A multivariate analysis approaches. To achieve this purpose, three research question were raised to guide the study. Literature were reviewed conceptually, empirically and theoretically according to the focus of the study. The study adopted a survey research design with stratified random sampling techniques used to choose participating states in two geopolitical zones, South-South and South -East. A total of 6 states were chosen randomly for the study. The researchers purposively selected 200 respondents from each state, thus having a total of 1200 respondents for the study. A questionnaire title ‘Violence Against Women Scale (VAWS)’ was the instrument that was used for data collection. The instrument was developed by the researchers and the face validity was ascertained using experts in Measurement and Evaluation and the reliability of the instrument was ascertained using Cronbach Alpha technique. The coefficient of the sub scale was high for the instrument to be adjusted reliable. The data collection was done using Google form administered to various people in different states as well as face to face contacts. The data collected were analysed using factor, cluster analysis and one way analysis of Variance (ANOVA) and the results showed that four dimensions were obtained explaining a total of 62.001% of the observed variation in the scale. More so, the result showed that for physical violence Cross River State indigenes are more violent physically compared to other six states followed by Enugu state indigene and then Anambra and Akwa Ibom State. For social violence, Akwa Ibom indigenes are more socially violent followed by Cross Riveriana and Anambra people. For sexual violence, Anambra indigenes are more involved followed by Enugu people, Cross River and then Anambra. Finally, for economic violence, Anambra is more involved, followed by Cross River, and Akwa Ibom and Enugu state. It was recommended that that strong legal enforcement should be carried out, properly monitored to bring perpetrators to books among others.

Computer aided design of a single degree-of-freedom six-bar mechanism via Monte Carlo layered optimization method

Planar single degree of freedom (DOF) 6-bar mechanisms, characterized by simple control and high stiffness, find extensive applications across various robotic systems. Nevertheless, the augmented quantity of design variables presents challenges in mechanism design, particularly when confronted with multiple constraints. This paper presents the implementation of computer-aided design for a single DOF 6-bar mechanism. Considering the characteristics of the 6-bar mechanism, a classified kinematics modeling method is proposed. Subsequently, a constraint index system is established, encompassing trajectory, posture, performance, and additional auxiliary variable constraints. A Monte Carlo layered optimization method is then proposed. The introduction of the constraint scaling coefficient divides the optimization process into multiple layers, wherein the constraint conditions are dynamically adjusted at each layer. The Monte Carlo method is integrated to screen initial values and determine the number of variables in each iteration, facilitating efficient optimization of the mechanism under multiple constraints. Building on this foundation, mechanism design software is developed to diminish reliance on experience. Numerous examples demonstrate the rapid acquisition of 6-bar mechanisms satisfying multiple constraints through the proposed method, showcasing exceptional computational efficiency. This study serves as a reference for different users seeking to accomplish the efficient design of 6-bar mechanisms.

On the modeling of compressible viscous fluids via Burgers and Oldroyd equations

AbstractThe paper develops some generalizations of the Burgers and the Oldroyd equations for the dynamics of fluids. To account for compressibility, in addition to viscosity, first the Oldroyd derivative is replaced with the Truesdell derivative. Consequently, the two equations can be given a linear form within the Lagrangian formulation. Furthermore, possible anisotropies are modeled by replacing some (scalar) coefficients with tensors. To emphasize the compressibility property, generalizations are established to allow for nonzero longitudinal viscosity. Next, the thermodynamic consistency is investigated by regarding both types of equations as rate equations, of second order and first order. The requirements on the parameters entering the two equations are derived while the linearity of the two equations allow the free energy potential be quadratic. The Oldroyd equation is found to be compatible via appropriate restrictions of the tensor coefficients, through different pairs of free energy and entropy production.

Development and psychometric evaluation of "Caring Ability of Mother with Preterm Infant Scale" (CAMPIS): a sequential exploratory mixed-method study.

Caring ability is one of the most important indicators regarding care outcomes. A valid and reliable scale for the evaluation of caring ability in mothers with preterm infants is lacking. The present study was conducted with the aim of designing and psychometric evaluation of the tool for assessing caring ability in mothers with preterm infants. A mixed-method exploratory design was conducted from 2021 to 2023. First the concept of caring ability of mothers with preterm infants was clarified using literature review and comparative content analysis, and a pool of items was created. Then, in the quantitative study, the psychometric properties of the scale were evaluated using validity and reliability tests. A maximum likelihood extraction with promax rotation was performed on 401 mothers with the mean age of 31.67 ± 6.14years to assess the construct validity. Initial caring ability of mother with preterm infant scale (CAMPIS) was developed with 64 items by findings of the literature review, comparative content analysis, and other related questionnaire items, on a 5-point Likert scale to be psychometrically evaluated. Face, content, and construct validity, as well as reliability, were measured to evaluate the psychometric properties of CAMPIS. So, the initial survey yielded 201 valid responses. The three components: 'cognitive ability'; knowledge and skills abilities'; and 'psychological ability'; explained 47.44% of the total observed variance for CAMPIS with 21 items. A subsequent survey garnered 200 valid responses. The confirmatory factor analysis results indicated: χ2/df = 1.972, comparative fit index (CFI) = 0.933, and incremental fit index (IFI) = 0.933. These results demonstrate good structural, convergent, discriminant validity and reliability. OMEGA, average inter-item correlation (AIC), intraclass correlation coefficients (ICC) for the entire scale were at 0.900, 0.27 and 0.91 respectively. Based on the results of the psychometric evaluation of CAMPIS, it was found that the concept of caring ability in the Iranian mothers with preterm infants is a multi-dimensional concept, which mainly focuses on cognitive ability, technical ability, and psychological ability. The designed scale has acceptable validity and reliability characteristics that can be used in future studies to assess this concept in the mothers of preterm infants.

Development of a blended teaching quality evaluation scale (BTQES) for undergraduate nursing based on the Context, Input, Process and Product (CIPP) evaluation model: A cross-sectional survey

ObjectiveDeveloping a nursing undergraduate blended teaching quality evaluation tool based on the Context, Input, Process and Product model and evaluating its reliability and validity. BackgroundBlended teaching is a commonly used teaching method in medical education, but there are limited tools available to effectively measure the quality of blended teaching. DesignA Delphi study and cross-sectional study. MethodsUsing the Context, Input, Process and Product model as the theoretical framework, a questionnaire was developed through literature review, expert consultation and pre-survey. From April to July 2023, 448 students from a certain university were selected as the research subjects and the questionnaire was examined for reliability and validity through a survey method. ResultsThe blended teaching quality evaluation scale with 35 items includes four dimensions Context, Input, Process and Product. The content validity and reliability of the blended teaching quality evaluation scale are both good, with a content validity index of 0.934 for the total scale and a content validity index of 0.750–1.00 for each item. The SEM shows that χ2/df = 6.89, RMSEA = 0.115, CFI = 0.882, NFI=0.865, RFI= 0.855, IFI = 0.882, TLI = 0.873. The Cronbach’s α coefficient of the total scale is 0.991 and the Cronbach’s α coefficient for each dimension is 0.944–0.984. ConclusionThe scale is based on the characteristics of blended learning and quality evaluation covers all aspects of teaching. It can accurately evaluate the quality of teaching, evaluate the problems in the teaching process based on the teaching quality score and propose reasonable teaching improvement suggestions based on the weak links in the teaching process.

Effects of subgrade spatial variability on critical strains and effectiveness of geogrid reinforcement in flexible pavement

The objective of this study is to assess the impact of spatial variability in the subgrade layer on the critical response of pavements and the effectiveness of geogrid reinforcement, employing the random field finite difference analysis (RFFDA). A comprehensive parametric study was conducted to examine the influence of two crucial factors: the coefficient of variation (COVE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ ext{COV}}}_{E}$$\\end{document}) and scale of fluctuation (SOF) of the subgrade modulus. Further investigation was conducted to uncover the statistical and mechanical mechanisms underlying the impact of subgrade spatial variability with emphasis on the critical strain distributions and their correlation with both the overall modulus and the local spatial variability of the key influence zone. Furthermore, this study explored the influence of subgrade spatial variability on the effectiveness of geogrid in reducing critical strains, considering various placement positions and geogrid moduli. The following main conclusions are drawn: (a) subgrade spatial variability has a substantial amplifying effect on critical pavement strains due to low modulus dominating effect, (b) there exists a worst value of SOF that results in the most unfavorable statistics of critical subgrade strain, (c) the effect of subgrade spatial variability on critical subgrade strain is more pronounced compared to its effect on critical asphalt strain, (d) the mean value of critical subgrade strain in RFFDA can be significantly underestimated when assuming fixed location for the strain, and (e) the effectiveness of geogrid in reducing critical strains is impacted by subgrade spatial variability, with the impact varying with the type of critical strain and geogrid location. Specifically, when placed at the base course–subgrade interface, the ability of geogrid to reduce critical subgrade strain is significantly compromised due to the subgrade spatial variability.

Hadronic Light-by-Light Corrections to the Muon Anomalous Magnetic Moment

We review the hadronic light-by-light (HLbL) contribution to the muon anomalous magnetic moment. Upcoming measurements will reduce the experimental uncertainty of this observable by a factor of four; therefore, the theoretical precision must improve accordingly to fully harness such an experimental breakthrough. With regards to the HLbL contribution, this implies a study of the high-energy intermediate states that are neglected in dispersive estimates. We focus on the maximally symmetric high-energy regime and in-quark loop approximation of perturbation theory, following the method of the OPE with background fields proposed by Bijnens et al. in 2019 and 2020. We confirm their results regarding the contributions to the muon g−2. For this, we use an alternative computational method based on a reduction in the full quark loop amplitude, instead of projecting on a supposedly complete system of tensor structures motivated by first principles. Concerning scalar coefficients, mass corrections have been obtained by hypergeometric representations of Mellin–Barnes integrals. By our technique, the completeness of such kinematic singularity/zero-free tensor decomposition of the HLbL amplitude is explicitly checked.

Using interoperable nursing care data to improve outcomes for multiple traumas patients with Impaired Physical Mobility.

To demonstrate how interoperable nursing care data can be used by nurses to create a more holistic understanding of the healthcare needs of multiple traumas patients with Impaired Physical Mobility. By proposing and validating linkages for the nursing diagnosis of Impaired Physical Mobility in multiple trauma patients by mapping to the Nursing Outcomes Classification (NOC) and Nursing Interventions Classification (NIC) equivalent terms using free-text nursing documentation. A descriptive cross-sectional design, combining quantitative analysis of interoperable data sets and the Kappa's coefficient score with qualitative insights from cross-mapping methodology and nursing professionals' consensus. Cross-mapping methodology was conducted in a Brazilian Level 1 Trauma Center using de-identified records of adult patients with a confirmed medical diagnosis of multiple traumas and Impaired Physical Mobility (a nursing diagnosis). The hospital nursing free-text records were mapped to NANDA-I, NIC, NOC and NNN linkages were identified. The data records were retrieved for admissions from September to October 2020 and involved medical and nursing records. Three expert nurses evaluated the cross-mapping and linkage results using a 4-point Likert-type scale and Kappa's coefficient. The de-identified records of 44 patients were evaluated and then were mapped to three NOCs related to nurses care planning: (0001) Endurance; (0204) Immobility Consequences: Physiological, and (0208) Mobility and 13 interventions and 32 interrelated activities: (6486) Environmental Management: Safety; (0840) Positioning; (3200) Aspiration Precautions; (1400) Pain Management; (0940) Traction/Immobilization Care; (3540) Pressure Ulcer Prevention; (3584) Skincare: Topical Treatment; (1100) Nutrition Management; (3660) Wound Care; (1804) Self-Care Assistance: Toileting; (1801) Self-Care Assistance: Bathing/Hygiene; (4130) Fluid Monitoring; and (4200) Intravenous Therapy. The final version of the constructed NNN Linkages identified 37 NOCs and 41 NICs. These valid NNN linkages for patients with multiple traumas can serve as a valuable resource that enables nurses, who face multiple time constraints, to make informed decisions efficiently. This approach of using evidence-based linkages like the one developed in this research holds high potential for improving patient's safety and outcomes. In this study, there was no direct involvement of patients, service users, caregivers or public members in the design, conduct, analysis and interpretation of data or preparation of the manuscript. The study focused solely on analysing existing de-identified medical and nursing records to propose and validate linkages for nursing diagnoses.

Analyzing urban scaling laws in the United States over 115 years

The scaling relations between city attributes and population are emergent and ubiquitous aspects of urban growth. Quantifying these relations and understanding their theoretical foundation, however, is difficult due to the challenge of defining city boundaries and a lack of historical data to study city dynamics over time and space. To address this issue, we analyze scaling between city infrastructure and population across 857 metropolitan areas in the conterminous United States over an unprecedented 115 years (1900–2015) using dasymetrically refined historical population estimates, historical urban road network models, and multi-temporal settlement data to define dynamic city boundaries. We demonstrate that urban scaling exponents closely match theoretical models over a century. Despite some close quantitative agreement with theory, the empirical scaling relations unexpectedly vary across regions. Our analysis of scaling coefficients, meanwhile, reveals that contemporary cities use more developed land and kilometers of road than cities of similar population in 1900, which has serious implications for urban development and impacts on the local environment. Overall, our results provide a new way to study urban systems based on novel, geohistorical data.

Mathematics Self-efficacy among Secondary Level Students

Low level of mathematical self-efficacy in students lessens their drive to study, which could culminate to poor mathematical outcomes. The objective of the paper was to figure out the level of mathematical self-efficacy among secondary level students. For this study quantitative paradigm was adopted, data were collected from the survey from 402 secondary school students of grade 9 to 12, randomly selected from two institutional and two community schools of Kathmandu. To measure the level of mathematics self-efficacy, the 14 items pertaining to mathematics self-efficacy were borrowed from MSEAQ prepared by May (2009). The alpha coefficient of the Self-efficacy scale was 0.91 and from the component matrix, each items have a factor loading greater than 0.4. A medium level of math self-efficacy but the lack of uniformity in mathematics self-efficacy was found. The mean mathematics self-efficacy score of male students was higher than female; the self-efficacy was slightly higher in institutional school than community school students. The mean self-efficacy score of grade 12 students was found highest and grade 9 was found least. The math self- efficacy was statistically significantly different based on gender and based on grade, but not based on types of school. The findings of this study indicate the need of stakeholders sustaining a support system that includes counselling, motivation, parental and teacher support, and other means to enhance students' mathematics self- efficacy.

A maximal overlap discrete wavelet packet transform coupled with an LSTM deep learning model for improving multilevel groundwater level forecasts

Developing precise groundwater level (GWL) forecast models is essential for the optimal usage of limited groundwater resources and sustainable planning and management of water resources. In this study, an improved forecasting accuracy for up to 3 weeks ahead of GWLs in Bangladesh was achieved by employing a coupled Long Short Term Memory (LSTM) network-based deep learning algorithm and Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) data preprocessing. The coupled LSTM-MODWPT model’s performance was compared with that of the LSTM model. For both standalone LSTM and LSTM-MODWPT models, the Random Forest feature selection approach was employed to select the ideal inputs from the candidate GWL lags. In the LSTM-MODWPT model, input GWL time series were decomposed using MODWPT. The ‘Fejér-Korovkin’ mother wavelet with a filter length of 18 was used to obtain a collection of scaling coefficients and wavelets for every single input time series. Model performance was assessed using five performance indices: Root Mean Squared Error; Scatter Index; Maximum Absolute Error; Median Absolute Deviation; and an a-20 index. The LSTM-MODWPT model outperformed standalone LSTM models for all time horizons in GWL forecasting. The percentage improvements in the forecasting accuracies were 36.28%, 32.97%, and 30.77%, respectively, for 1-, 2-, and 3-weeks ahead forecasts at the observation well GT3330001. Accordingly, the coupled LSTM-MODWPT model could potentially be used to enhance multiscale GWL forecasts. This research demonstrates that the coupled LSTM-MODWPT model could generate more precise GWL forecasts at the Bangladesh study site, with potential applications in other geographic locations globally.

A Heuristic Method for Solving Polynomial Matrix Equations

We propose a heuristic method to solve polynomial matrix equations of the type ∑k=1makXk=B, where ak are scalar coefficients and X and B are square matrices of order n. The method is based on the decomposition of the B matrix as a linear combination of the identity matrix and an idempotent, involutive, or nilpotent matrix. We prove that this decomposition is always possible when n=2. Moreover, in some cases we can compute solutions when we have an infinite number of them (singular solutions). This method has been coded in MATLAB and has been compared to other methods found in the existing literature, such as the diagonalization and the interpolation methods. It turns out that the proposed method is considerably faster than the latter methods. Furthermore, the proposed method can calculate solutions when diagonalization and interpolation methods fail or calculate singular solutions when these methods are not capable of doing so.

Graph convolutional multi-mesh autoencoder for steady transonic aircraft aerodynamics

Calculating aerodynamic loads around an aircraft using computational fluid dynamics is a user’s and computer-intensive task. An attractive alternative is to leverage neural networks (NNs) bypassing the need of solving the governing fluid equations at all flight conditions of interest. NNs have the ability to infer highly nonlinear predictions if a reference dataset is available. This work presents a geometric deep learning based multi-mesh autoencoder framework for steady-state transonic aerodynamics. The framework builds on graph NNs which are designed for irregular and unstructured spatial discretisations, embedded in a multi-resolution algorithm for dimensionality reduction. The test case is for the NASA common research model wing/body aircraft configuration. Thorough studies are presented discussing the model predictions in terms of vector fields, pressure and shear-stress coefficients, and scalar fields, total force and moment coefficients, for a range of nonlinear conditions involving shock waves and flow separation. We note that the cost of the model prediction is minimal having used an existing database.

Developing patient safety scale for hospitals.

The study aimed to contribute to the literature with a reliable and valid scale for hospitals to be used in determining the current patient safety culture and following up on its development. The study was conducted with the participation of 1137 healthcare professionals selected using the convenience sampling method in 3 secondary-care state hospitals and three research and training hospitals, one of which was affiliated with a medical faculty, and two were affiliated with the Health Sciences University. To begin with, to discover the latent structure of the items on the scale, an Exploratory Factor Analysis (EFA) was performed. Additionally, to determine the factor structure of the scale, the Confirmatory Factor Analysis (CFA) method was used. The Cronbach's alpha coefficient was calculated to check the reliability of the responses. According to Kaiser-Meyer-Olkin (KMO = 0.924) coefficient and the result of Bartlett's test of sphericity (χ 2 = 9748.777, df = 770), it was determined that the data structure was suitable for factor analysis. The Cronbach's alpha coefficient of the total scale was found to be 0.921. According to the EFA results, the scale was determined to have seven subscales, which were 1. Organizational Learning, Development, and Communication, 2. Management Support and Leadership, 3. Reporting Patient Safety Events, 4. Number of Personnel and Working Hours, 5. Response to Error, 6. Teamwork, and 7. Working Environment. The goodness-of-fit index results of the scale showed a good model fit (χ 2 / df = 3.04, RMSEA = 0.06, CFI = 0.97, NFI = 0.95, IFI = 0.97, SRMR = 0.06). The Cronbach's alpha coefficients of the subscales varied between 0.66 and 0.91. The results showed that the Patient Safety Scale for Hospitals is a valid and reliable measurement instrument for healthcare professionals.