Numerical Data Research Articles

A data‐assisted physics‐informed neural network (DA‐PINN) for fretting fatigue lifetime prediction

AbstractIn this study, we present for the first time the application of physics‐informed neural network (PINN) to fretting fatigue problems. Although PINN has recently been applied to pure fatigue lifetime prediction, it has not yet been explored in the case of fretting fatigue. We propose a data‐assisted PINN (DA‐PINN) for predicting fretting fatigue crack initiation lifetime. Unlike traditional PINN that solves partial differential equations for specific problems, DA‐PINN combines experimental or numerical data with physics equations as part of the loss function to enhance prediction accuracy. The DA‐PINN method, employed in this study, consists of two main steps. First, damage parameters are obtained from the finite element method by using critical plane method, which generates a data set used to train an artificial neural network (ANN) for predicting damage parameters in other cases. Second, the predicted damage parameters are combined with the experimental parameters to form the input data set for the DA‐PINN models, which predict fretting fatigue lifetime. The results demonstrate that DA‐PINN outperforms ANN in terms of prediction accuracy and eliminates the need for high computational costs once the damage parameter data set is constructed. Additionally, the choice of loss‐function methods in DA‐PINN models plays a crucial role in determining its performance.

ASSESSING THE READINESS OF FLEXIBLE WORKING SYSTEMS AND EMPLOYEE ENGAGEMENT : THE SIGNIFICANCE OF TRANSFORMATIONAL LEADERSHIP IN MILLENNIAL AND GENERATION Z AT THE DJKN SECRETARIAT

The need for bureaucratic reform, digital transformation, and the COVID-19 pandemic encourage the Ministry of Finance to implement a flexible working system in accordance with Minister of Finance Regulation (KMK) No. 416 of 2023. This study focuses on employees in the Secretariat of DJKN, particularly Millennials and Generation Z. This research employs mixed method that involves quantitative and qualitative aspects. The quantitative component used structured questionnaires to collect numerical data on the flexible working system, transformational leadership, and their impact on employee engagement. The qualitative component involved in-depth interviews with key organizational figures. Quantitative data analysis employed moderation regression analysis (MRA) to examine the relationship between the flexible working system and employee engagement, as well as how transformational leadership moderates this relationship. The research findings indicate that the flexible working system has a significantly positive impact on employee engagement, especially among Millennials and Generation Z. Additionally, transformational leadership significantly enhances the effectiveness of the flexible working system. Leaders who inspire and motivate their team members can amplify the positive effects of flexible work arrangements on employee engagement.

Failure analysis of Nehbandan granite under various stress states and strain rates using a calibrated Riedel–Hiermaier–Thoma constitutive model

This study presents a comprehensive procedure for determining, calibrating, and validating the Riedel–Hiermaier–Thoma (RHT) material model parameters for granite. The process involves collecting a comprehensive dataset of conventional mechanical tests conducted on various types of granite worldwide. Based on this dataset, one set of RHT material model parameters is determined. Additionally, a specific granite sample from Iran, known as Nehbandan granite, is characterized through physical and mechanical testing to obtain another set of parameters. The challenging task of determining the third set of parameters, which are difficult to obtain analytically and experimentally, is accomplished through a calibration process that iteratively adjusts the parameters based on comparisons between numerical simulation results and experimental data. To validate the determined parameters, a series of tests, including uniaxial compressive strength (UCS), triaxial compressive strength (TCS), Brazilian tensile strength, and dynamic Brazilian using split Hopkinson pressure bar (SHPB) tests, are conducted on the Nehbandan granite. These tests are also simulated using LS-Dyna software, and the numerical simulation results are compared with the corresponding experimental test results. The comparison between the numerical and experimental data serves as a means of validating and verifying the accuracy and reliability of the determined RHT material model parameters for granite. The results demonstrate the successful determination and calibration of the RHT material model parameters for granite. The model exhibits effectiveness in predicting the behavior of granite under various loading conditions. The validation process confirms the accuracy and reliability of the determined parameters through a close agreement between numerical simulations and experimental data. The findings contribute to a better understanding of granite's mechanical response and provide a reliable tool for simulating and predicting its behavior in engineering applications. The validated RHT material model parameters offer a robust framework for accurate numerical simulations, enabling engineers to make informed decisions in rock engineering projects.

Influence of radiation on the stability of MHD micropolar fluid in a vertical channel

The objective of the present article is to explore the stability of micropolar fluid flow in a vertical channel in the presence of thermal radiation and a transverse magnetic field. The generalized eigenvalue problem is numerically solved by utilizing the Chebyshev spectral collocation method, which is obtained from the perturbed state using the normal mode technique. The numerical data were compared with previously published results for particular cases. The critical modified Grashof number (Grc′) and the associated wave numbers (αc) are calculated and displayed graphically for different values of the parameters. It is noticed that the boundaries of instability may be increased or decreased with the flow governing parameters because of the presence of a magnetic field and thermal radiation.

Predicting ICU Readmission from Electronic Health Records via BERTopic with Long Short Term Memory Network Approach

Background: The increasing rate of intensive care unit (ICU) readmissions poses significant challenges in healthcare, impacting both costs and patient outcomes. Predicting patient readmission after discharge is crucial for improving medical quality and reducing expenses. Traditional analyses of electronic health record (EHR) data have primarily focused on numerical data, often neglecting valuable text data. Methods: This study employs a hybrid model combining BERTopic and Long Short-Term Memory (LSTM) networks to predict ICU readmissions. Leveraging the MIMIC-III database, we utilize both quantitative and text data to enhance predictive capabilities. Our approach integrates the strengths of unsupervised topic modeling with supervised deep learning, extracting potential topics from patient records and transforming discharge summaries into topic vectors for more interpretable and personalized predictions. Results: Utilizing a comprehensive dataset of 36,232 ICU patient records, our model achieved an AUROC score of 0.80, thereby surpassing the performance of traditional machine learning models. The implementation of BERTopic facilitated effective utilization of unstructured data, generating themes that effectively guide the selection of relevant predictive factors for patient readmission prognosis. This significantly enhanced the model’s interpretative accuracy and predictive capability. Additionally, the integration of importance ranking methods into our machine learning framework allowed for an in-depth analysis of the significance of various variables. This approach provided crucial insights into how different input variables interact and impact predictions of patient readmission across various clinical contexts. Conclusions: The practical application of BERTopic technology in our hybrid model contributes to more efficient patient management and serves as a valuable tool for developing tailored treatment strategies and resource optimization. This study highlights the significance of integrating unstructured text data with traditional quantitative data to develop more accurate and interpretable predictive models in healthcare, emphasizing the importance of individualized care and cost-effective healthcare paradigms.

Educational Revolution : Digital Project-Based Rotation Learning (DPBRL) Model to Improve Students' Critical Thinking Skills

The research aimed to develop and assess the effectiveness of the Digital Project-Based Rotation Learning (DPBRL) model in enhancing students' critical thinking skills. The study employed a research and development method, using a non-equivalent control group design during the pilot phase, a research method used to describe and measure the characteristics of populations or phenomena systematically using numerical data, involving 83 undergraduate students from Mulawarman University Guidance and Counseling Study Program, divided into two heterogeneous classes. Validation of the DPBRL model was conducted by experts in learning model design, materials, and media, with data collected through validation sheets, interviews, document analysis, questionnaires, observation, and documentation. The data was analyzed using descriptive technique and Independent Samples t-test. The results demonstrated that the DPBRL model effectively improved students' critical thinking skills and digital literacy, particularly through the use of digital modules with Flipbook Maker. The model’s rotation structure and presentation stage provided a structured learning approach, highlighting the importance of feedback. The study concluded that integrating technology and collaboration in project-based learning through the DPBRL model significantly benefited students’ critical thinking, digital literacy, and teamwork, offering a comprehensive and effective learning experience.

A multi-average based pseudo nearest neighbor classifier

Conventional k nearest neighbor (KNN) rule is a simple yet effective method for classification, but its classification performance is easily degraded in the case of small size training samples with existing outliers. To address this issue, A multi-average based pseudo nearest neighbor classifier (MAPNN) rule is proposed. In the proposed MAPNN rule, k ( k − 1 ) / 2 ( k > 1) local mean vectors of each class are obtained by taking the average of two points randomly from k nearest neighbors in every category, and then k pseudo nearest neighbors are chosen from k ( k − 1 ) / 2 local mean neighbors of every class to determine the category of a query point. The selected k pseudo nearest neighbors can reduce the negative impact of outliers in some degree. Extensive experiments are carried out on twenty-one numerical real data sets and four artificial data sets by comparing MAPNN to other five KNN-based methods. The experimental results demonstrate that the proposed MAPNN is effective for classification task and achieves better classification results in the small-size samples cases comparing to five relative KNN-based classifiers.

Evidence for large-scale climate forcing of dense shelf water variability in the Ross Sea

Antarctic Bottom Water (AABW), which supplies the lower limb of the thermohaline circulation, originates from dense shelf water (DSW) forming in Antarctic polynyas. Here, combining a long mooring record of DSW measurements with numerical simulations and satellite data, we show that significant correlation exists between interannual variability of DSW production in the Ross Sea polynyas, where DSW contributes between 20–40% of the global AABW production, and the Southern Annular Mode (SAM). The correlation is largest when the Amundsen Sea Low (ASL) is weakened and shifted east of the Ross Sea. During positive SAM phases, enhanced offshore winds and lower air temperatures over the western Ross Sea increase sea ice production and promote DSW formation, with the opposite response during negative SAM phases. These processes ultimately modulate AABW thickness in the open ocean. A projected positive shift of the SAM and eastward displacement of the ASL thus has implications for the future of DSW and AABW formation.

The Relationship between Teacher Competences, Quality of Infrastructure, Student Participation, and School Policy with Students' Learning Motivation in PJOK Subject at MA Ma'arif Dassholihin

This study aims to determine the relationship between teacher competence, quality of facilities and infrastructure, student participation and school policies in increasing student learning motivation in PJOK subjects at MA Ma’arif Darussholihin. The subjects of this study were students in grades X - XII, totaling 350 students. The sample in this study amounted to 160 students. This study uses a correlation method with a quantitative approach that aims to determine the presence and absence of a relationship between or two variables by collecting numerical data that can be measured related to teacher competence, quality of facilities and infrastructure, student participation, school policy and learning motivation. The data collection technique uses a questionnaire. Data validation uses the product moment formula with r count < r table (0.154). Reliability using Crombach Alpha, namely reliability (0.945) The analysis technique uses linear regression analysis, which will involve various descriptive statistical tests, classical assumption tests, and hypothesis testing. The results of the study showed a significant relationship between teacher competence and learning motivation. There is a significant relationship between infrastructure and learning motivation. There is a significant relationship between student participation and learning motivation. There is a significant relationship between school policies and learning motivation. There is a significant relationship between teacher competence, quality of facilities and infrastructure, student participation, school policies together with students' learning motivation.

Numerical investigation of the geometric parameters effect of helical blades installed on horizontal geo heat exchanger

In this study, we investigate the enhancement of horizontal geothermal heat exchangers equipped with helical fins on the pipe's exterior and internally ribbed turbulators. Our approach focuses on the interplay between geometry and thermal efficiency through innovative design modifications. Utilizing the finite element method, three-dimensional numerical simulations assessed the effects of varying geometric parameters such as the diameter and thickness of the fins. Our findings indicate significant increases in heat transfer efficiency with the addition of helical fins; specifically, increasing the fin diameter from 5 mm to 10 mm results in a 15 % increase in the heat transfer rate, while doubling the fin thickness from 2 mm to 4 mm enhances the rate by 10 %. These improvements are due to the expanded surface area facilitating greater heat exchange. Optimization using the desirability function approach yielded models with high performance, achieving desirability scores of 0.9879 for outlet temperature and 0.9534 for the heat transfer coefficient. This reflects the effective tuning of geometric parameters to maximize thermal performance. The study also introduces two predictive mathematical models for the outlet temperature and convective heat transfer coefficient of the U-shaped pipe equipped with these enhancements. These models, derived from extensive numerical data, provide practical tools for future design and operational applications of geothermal heat exchangers. This research advances the design and operational efficiency of geothermal heat exchange systems, establishing new benchmarks for thermal efficiency in the field with actionable insights and robust mathematical tools.

Anisogrid lattice structure in thermoplastic composite by filament gun deposition

A new method to manufacture thermoplastic composite parts has been used to produce anisogrid lattice structures. Filament gun deposition consists of a hot-melt gun loaded with narrow thermoplastic prepreg tapes. Anisogrid lattice structures have been prototyped with 3 different geometries and 5 different numbers of layers (from 4 to 8) by using a metallic pattern and E-glass/polypropylene prepregs. Scanning calorimetry and bending tests of multi-layer samples have been used to characterize thermoplastic prepregs. Anisogrid lattice structures have been tested under tensile loads. A finite element model has been used to predict mechanical stiffness of these structures by using material properties coming from the sample characterization. Numerical models have been developed with a batch-type parametric approach to rapidly evaluate the combined effect of geometric and material parameters. A good agreement has been found between experimental and numerical data with an average difference about 4%.

The Association Between Maternal Psychological Health and Human Milk Oligosaccharide Composition.

Background and Objective: Human milk oligosaccharides (HMOs) are carbohydrates abundant in human breast milk. Their composition varies widely among women, and prior research has identified numerous factors contributing to this variation. However, the relationship between maternal psychological health and HMO levels is currently unknown. Thus, our objective was to identify whether maternal stress, anxiety, or depressive symptoms are associated with HMOs. Methods: Data originated from 926 lactating individuals from the UC San Diego Human Milk Biorepository. Nineteen prevalent HMOs were assayed using high-performance liquid chromatography. Participants self-reported measures of the Edinburgh Postnatal Depression Scale (n = 495), State-Trait Anxiety Inventory S-Scale (n = 486), and/or Perceived Stress Scale (n = 493) within 60 days of their milk collection; their results were categorized using standard screening cutoffs. HMOs were assessed individually and grouped by principal component analysis (PCA), and associations with maternal psychological symptoms were analyzed using multivariable linear regression adjusted for covariates. Results: After Bonferroni correction (p < 0.002), the following HMOs significantly varied with maternal psychological distress in multivariate analysis: lacto-N-fucopentaose III (LNFP III) and lacto-N-hexaose (LNH) among Secretors with depressive symptoms and difucosyllactose (DFLac), LNFP III, and disialyl-LNH (DSLNH) among Secretors with stress. In PCA, depressive symptoms and stress were associated with one principal component among Secretors. No HMOs varied with anxiety symptoms. Conclusions: Several HMOs varied with maternal depressive symptoms and stress, suggesting a relationship between maternal psychological health and breast milk composition. Additional studies are needed to determine the impact of this variation on infant health.

A comparative analysis of dendrometric, macromorphological, and micromorphological characteristics of Pistacia atlantica subsp. atlantica and Pistacia terebinthus in the middle Atlas region of Morocco

Abstract The genus Pistacia, with its species having notable ecological, economic, and medicinal implications, demonstrates remarkable environmental adaptability. The central objective of the study is to analyze interspecific variations between Pistacia atlantica subsp. atlantica and Pistacia terebinthus across three distinct bioclimatic zones in the Middle Atlas region of Morocco. The methodology includes collecting dendrometric measurements and conducting macromorphological examinations on these two taxa, with a detailed analysis of 27 qualitative and quantitative variables. A micro-morphological analysis of leaves, using scanning electron microscopy (SEM), is employed to explore specific features such as size and stomatal density, as well as qualitative aspects like epidermal cell shape and trichomes. Dendrometric measurements have revealed that the canopy surface and the number of trunks per tree can serve as distinctive features between the two species. Regarding the sex ratio of Pistacia atlantica subsp. atlantica, 59% of the examined trees are males, primarily associated with the jujube tree in arid zones and the dwarf palm in humid areas. In contrast, female Pistacia terebinthus exhibit a similar percentage, predominantly associated with oak groves and cade juniper in their distribution areas. Principal component analysis of biometric measurements emphasized a significant disparity between the two species, representing 60.25% of the total variance. The use of SEM unveiled new features facilitating the identification of the two species. By leveraging the macromorphological and micromorphological variability of pistachio trees, we can qualify those best suited to diverse bioclimates. In this regard, we suggest incorporating them into reforestation and rehabilitation programs aimed at restoring our declining ecosystems.

Exploring mixture estimators in stratified random sampling.

Advancements in sensor technology have brought a revolution in data generation. Therefore, the study variable and several linearly related auxiliary variables are recorded due to cost-effectiveness and ease of recording. These auxiliary variables are commonly observed as quantitative and qualitative (attributes) variables and are jointly used to estimate the study variable's population mean using a mixture estimator. For this purpose, this work proposes a family of generalized mixture estimators under stratified sampling to increase efficiency under symmetrical and asymmetrical distributions and study the estimator's behavior for different sample sizes for its convergence to the Normal distribution. It is found that the proposed estimator estimates the population mean of the study variable with more precision than the competitor estimators under Normal, Uniform, Weibull, and Gamma distributions. It is also revealed that the proposed estimator follows the Cauchy distribution when the sample size is less than 35; otherwise, it converges to normality. Furthermore, the implementation of two real-life datasets related to the health and finance sectors is also presented to support the proposed estimator's significance.

Hospital-based study of cervical rib/ribs and its association with neurological complication

Introduction: Cervical rib is an additional rib which is the costal element of seventh cervical vertebra. Hence cervical ribs may first be revealed by nervous (particularly on eighth cervical and first thoracic spinal nerve) and vascular symptoms. Cervical rib is more often unilateral and somewhat more frequent on the right. In 90% of cases cervical rib probably causes no trouble. So the present study addresses the prevalence of cervical rib/ribs among both adult male and female and Radiological measurement of costal elements and its association with neurological involvements. Aim: the main aim of this study is to find out the prevalence and relation of length of costal process of 7th cervical rib with neurological symptoms among KPC Medical College outdoor patients. Methods: A prospective study has been carried out using technically adequate cervical spine AP and chest x rays PA view taken in department of Radiology, KPC Medical College and Hospital. Results: In this study all numerical and nonnumeric data of 495 patients has been plotted in Excel of Office 13. Observed 13 cases of cervical rib/ribs in which 7 were unilateral and 6 bilateral. Out of these 13 cases 12 cases had rudimentary cervical ribs.

Individual Psychological Profiles And Their Influence On Self-Regulatory Behaviour In Sport

The purpose of this paper is to study how individual psychological characteristics of an athlete affect his/her ability to effectively self-regulate during training. In the course of the study, the testing method was applied (Cognitive Strategies in Sport Scale and Psychological Stress Measurement). Methods of descriptive statistics for the analysis of the mean value and square deviation and Pearson's correlation coefficient (chi-square). The results of the study showed that there is a statistically significant difference between the mean values of all cognitive strategies. The most pronounced differences were observed between emotional control and other strategies, as well as between focusing attention and other strategies. This suggests that emotional control and attention focus are key cognitive strategies for achieving success in sport. In addition, psychological aspects and individual personality traits of athletes significantly affect their ability to self-regulate during training, which confirms the importance of taking these factors into account when developing training programmes. The study confirmed that emotional control and focus are key cognitive strategies for achieving success in sport. Individual psychological characteristics of athletes have a significant impact on their ability to effectively self-regulate during training. This highlights the importance of integrating psychological training into training programmes to improve athletes' performance. Further research should focus on developing methods to help improve these aspects.

Twenty-Four-Hour Post-Thrombolysis NIHSS Score As the Strongest Prognostic Predictor After Acute Ischemic Stroke: ENCHANTED Study.

This study was conducted to determine optimal predictive ability of National Institutes of Health Stroke Scale (NIHSS) measurements at baseline, 24 hours, and change from baseline to 24 hours after thrombolysis on functional recovery in patients with acute ischemic stroke who participated in the ENCHANTED (Enhanced Control of Hypertension and Thrombolysis Stroke Study). ENCHANTED was an international, multicenter, 2×2 quasifactorial, prospective, randomized open trial of low-dose versus standard-dose intravenous alteplase and intensive versus guideline-recommended blood pressure lowering in thrombolysis-eligible patients with acute ischemic stroke. Absolute (baseline minus 24 hours) and percentage (absolute change/baseline × 100) changes in NIHSS scores were calculated. Receiver operating characteristic curve analyses assessed performance of different NIHSS measurements on 90-day favorable functional recovery (modified Rankin Scale [mRS] score 0-2) and excellent functional recovery (mRS score 0-1). Youden index was used to identify optimal predictor cutoff points. A total of 4410 patients in the ENCHANTED trial were enrolled. The 24-hour NIHSS score had the highest discriminative ability for predicting favorable 90-day functional recovery (mRS score 0-2; area under the curve 0.866 versus 0.755, 0.689, 0.764; P<0.001) than baseline, absolute, and percentage change of NIHSS score, respectively. The optimal cutoff point of 24-hour NIHSS score for predicting favorable functional recovery was ≤4 (sensitivity 66.5%, specificity 87.1%, adjusted odds ratio, 9.44 [95% CI, 7.77-11.48]). The 24-hour NIHSS score (≤3) was the best predictor of 90-day excellent functional recovery (mRS score 0-1). Findings were consistent across subgroups, including sex, race, baseline NIHSS score, stroke subtype, and age. In thrombolysis-eligible patients with acute ischemic stroke, 24-hour NIHSS score (optimal cutpoint of 4) is the strongest predictor of 90-day functional recovery over baseline and early change of NIHSS score. URL: https://clinicaltrials.gov. Unique Identifier: NCT01422616.

Feasibility and usefulness of video-based markerless two-dimensional automated gait analysis, in providing objective quantification of gait and complementing the evaluation of gait in children with cerebral palsy

BackgroundGait analysis aids in evaluation, classification, and follow-up of gait pattern over time in children with cerebral palsy (CP). The analysis of sagittal plane joint kinematics is of special interest to assess flexed knee gait and ankle joint deviations that commonly progress with age and indicate deterioration of gait. Although most children with CP are ambulatory, no objective quantification of gait is currently included in any of the known international follow-up programs. Is video-based 2-dimensional markerless (2D ML) gait analysis with automated processing a feasible and useful tool to quantify deviations, evaluate and classify gait, in children with CP?MethodsTwenty children with bilateral CP with Gross Motor Function Classification Scale (GMFCS) levels I–III, from five regions in Sweden, were included from the national CP registry. A single RGB-Depth video camera, sensitive to depth and contrast, was positioned laterally to a green walkway and background, with four light sources. A previously validated markerless method was employed to estimate sagittal plane hip, knee, ankle kinematics, foot orientation and spatio-temporal parameters including gait speed and step length.ResultsMean age was 10.4 (range 6.8–16.1) years. Eight children were classified as GMFCS level I, eight as II and four as III. Setup of the measurement system took 15 min, acquisition 5–15 min and processing 50 min per child. Using the 2D ML method kinematic deviations from normal could be determined and used to implement the classification of gait pattern, proposed by Rodda et al. 2001.Conclusion2D ML assessment is feasible, since it is accessible, easy to perform and well tolerated by the children. The 2D ML adds consistency and quantifies objectively important gait variables. It is both relevant and reasonable to include 2D ML gait assessment in the evaluation of children with CP.

Simulation of Discovering Market Opportunities Using AI Methods

Due to the strong turbulence that exists in the business environment, the view is sometimes articulated that the time horizon of decisions is no longer a hallmark of strategic management. Opposing views argue that a turbulent environment changes the trajectory of reaching future objectives determined by strategic decisions. Such trajectories require the company to be agile, which is manifested by seizing opportunities. Opportunities arise because the environment is volatile. An example of market opportunity is the unsatisfied demand for products. Buyers are driven by different motives when choosing between products that are alternative to them. From the producer's point of view, the purchase is therefore random. As a consequence, the alignment of the offered product portfolio with the actual purchasing decisions of customers is undetermined. In this article, we address the problem of determining the product portfolio structure expected by customers, i.e. one that is consistent with their future purchasing decisions. We hypothesise that this problem can be represented by a model of flows between alternative resources. Problems of this type are solved using Markov Chain Monte Carlo (MCMC) methods. In order to discover the expected demand (opportunity), we simulated the flow model we developed using the Metropolis-Hastings algorithm, which is a special case of the MCMC method used in AI. Due to operating on numerical data, such simulations fall under quantitative research. In the article, we present a model of customer flows between alternative products and then the simulation result, which is the expected value of these flows (stationary point). We convert this value into the structure of the product portfolio using a model of customer purchasing decisions that we have developed. The obtained results confirm the effectiveness of our method and have significant practical value, especially for SMEs. It also enables the assessment of the company's product strategy based on real sales data. The application of our methodology is limited to discovering opportunities in situations where purchasing decisions are influenced by variable environmental factors, causing irregular purchases by individual customers. It is useful, for example, in discovering opportunities in markets such as Household Articles, Furniture, Vehicles, Equipment Repairs, Construction, and many others.

Development and preliminary validation of the South Wales Social Well-being Scale (SWSWBS)

BackgroundPhysical, mental and social components of well-being are known to be important to health. However, research on well-being often focuses on physical and mental well-being with little attention paid to social well-being. This research aims to develop and preliminarily validate the South Wales Social Well-being Scale (SWSWBS) to measure social well-being.MethodsA non-experimental and cross-sectional design was applied with two phases: scale development and preliminary validation. Initially, 24 items were drawn from a Group Concept Mapping study exploring the concept of social well-being. These items were reviewed and reduced to 14 for preliminary validation among 103 university students and staff in health and social care disciplines. Construct validity (exploratory factor analysis and convergent validity) were tested. Reliability was demonstrated by internal consistency. Floor and ceiling effects were also evaluated.ResultsA 3-factor structure was identified and explored, which highlight the most important features of the social world a person inhabits: “Safe and inclusive interaction with others” (6 items), “Learning, helping, and feeling useful” (4 items), and “Security, worthwhile activities, family and friends” (4 items). The SWSWBS was correlated to the Warwick Edinburgh Mental Well-being Scale and the Four measures of Personal Well-being Scale to some extent. The Cronbach alpha coefficient was 0.85 for the sum score and 0.83–0.86 for individual items. The item-total correlation coefficients ranged between 0.08 and 0.65. The split half reliability coefficient was 0.78. There is absence of a floor effect, but most items had a ceiling effect.ConclusionsPreliminary validation of the SWSWBS shows the scale has satisfactory psychometric properties with good validity, reliability, and reasonable variability. This study needs to be replicated with larger and representative populations to explore how the scale can be used alongside the Warwick Edinburgh Mental Well-being Scale to capture a holistic/multi-dimensional understanding of well-being.