Usage Of Models Research Articles

A novel model using leukocytes to differentiating mild autonomous cortisol secretion and non-functioning adrenal adenoma

BackgroundMild autonomous cortisol secretion (MACS) accounts for a significant proportion of adrenal incidentaloma. Current endocrinological screening tests for MACS are complex, particularly in areas with limited medical resources. This study aimed to develop a diagnostic tool based on leukocyte-related parameters to differentiate between MACS and non-functioning adrenal adenoma (NFA).MethodsInthis retrospective case-control study, propensity score-matching was used to select 567 patients from a cohort of 1108 patients (201 MACS, 907 NFA). External validation cohort included 52MACS and 48 NFA from two hospitals, which did not overlap with the modeling cohort patients. Leukocyte-related parameters were evaluated, and the diagnostic efficacy of each parameter was assessed by calculating Youden’s J index (J) and the area under the curve (AUC). The study population was divided into training and testing samples using a 10-fold cross-validation method. Machine learning (ML) and classification and regression tree (CART) model were established.ResultsAfter propensity score matching, 567 patients were enrolled, including 197 MACS and 370 NFA. With the exception of basophil percentage, all other parameters differed significantly between the two groups. Lymphocyte count, lymphocyte percentage, eosinophils count, eosinophils percentage, and basophil percentage were lower in the MACS group compared to the NFA group. Eosinophils percentage demonstrated the highest AUC (0.650), with a sensitivity of 51.3% and specificity of 73.2%. The ML model, based on multiple parameters,exhibited better performance in diagnosing MACS (sensitivity 76%, specificity 77.4%, and AUC 0.818). A clinically usable CART model achieved an AUC of 0.872, with a sensitivity of 95% and a specificity of 75.7%. In the validation cohort, the prediction accuracy of the ML model and the CART model were 0.784 and 0.798, respectively.ConclusionTheCART diagnostic model, constructed based on leukocyte-related parameters, could assist clinicians in distinguishing between MACS and NFA.

Establishment of a nomogram for potential prediction of lung metastasis in patients with primary limb bone tumors: a study based on the SEER database.

The prognosis of lung metastasis in primary limb bone tumors represents a pivotal yet challenging aspect of oncological management. Despite advancements in diagnostic modalities, the predictive accuracy for metastatic spread remains suboptimal. This study aims to bridge this gap by leveraging the Surveillance, Epidemiology, and End Results (SEER) database to construct a nomogram that forecasts the risk of lung metastasis, thereby enhancing clinical decision-making processes. A retrospective cohort, including 1,822 patients with primary limb bony tumors from 2010 to 2015 in the SEER database, was extracted. Using precise inclusion and exclusion criteria, variables essential for predicting lung metastasis were identified through univariate and multivariate analyses, along with least absolute shrinkage and selection operator (LASSO) regression. These variables provided a solid basis for creating the multivariable nomogram, of which the discriminating power and utility were verified using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis. The model incorporated seven key predicting variables, including age, histological type, surgery, radiation, chemotherapy, T stage, and N stage. The nomogram emerged as a cohesive whole with good discriminative power. The area under the curve (AUC) was 0.806 in the training cohort and 0.767 in the validation cohort. The calibration curves demonstrated the model's validity by showing a good match between the actual outcomes and the model-predicted probabilities of lung metastasis. This study showed for the first time the reliability of the predictive model in translating the hard-to-interpret demographic, clinical, and pathologic data into a very usable predictive model. Thus, it represents a significant step toward demystifying the risk of lung metastasis in primary limb bone tumors. It is an invitation for a paradigm shift of oncology, to evidence-based, person-based oncology that is taking a new metric for cancer prognosis.

Decision support for United States—Canada energy integration is impaired by fragmentary environmental and electricity system modeling capacity

Abstract The renewable energy transition is leading to increased electricity trade between the United States and Canada, with Canadian hydropower providing firm lower-carbon power and buffering variability of wind and solar generation in the U.S. However, long-term power purchase agreements and transborder transmission projects are controversial, with two of four proposed transmission lines between Quebec, Canada and the northeast U.S. cancelled since 2018. Here, we argue that controversies are exacerbated by a lack of open-source data and tools to understand tradeoffs of new hydropower generation and transmission infrastructure in comparison to alternatives. This gap includes impacts that incremental transmission and generation projects have on the economics of the entire system, for example, how new transmission projects affect exports to existing markets or incentivize new generation. We identify priority areas for data synthesis and model development, such as integrating linked hydropower and hydrologic interactions in energy system models and openly releasing (by utilities) or back-calculating (by researchers) hydropower generation and operational parameters. Publicly available environmental (e.g. streamflow, precipitation) and techno-economic (e.g. costs, reservoir size,) data can be used to parameterize freely usable and extensible models. Existing models have been calibrated with operational data from Canadian utilities that are not publicly available, limiting the range of scientific and commercial questions these tools have been used to answer and the range of parties that have been involved. Studies conducted using highly resolved, national-scale public data exist in other countries, notably, the United States, and demonstrate how greater transparency and extensibility can drive industry action. Improved data availability in Canada could facilitate approaches that (1) increase participation in decarbonization planning by a broader range of actors; (2) allow independent characterizations of environmental, health, and economic outcomes of interest to the public; and (3) identify decarbonization pathways consistent with community values.

Development of Land Suitability Assessment Applications for Sorghum, Sweet Potato and Sugarcane

Cultivation of food crops should be done on suitable land based on the results of the soil suitability assessment. The Soil Suitability Assessment Framework was initiated by the Food and Agriculture Organization (FAO). Today, smartphones have become a ubiquitous technology for solving problems in most environments, including: Assessment of soil suitability for food crops, plantations and many others. This white paper aims to address these benefits by transforming the framework into a mobile app. This solution aims to help land users conduct land valuations more effectively and efficiently. A rule-based system (RBS) algorithm is used to build the framework into a set of rules that are interconnected to draw land suitability conclusions. Regulations relate to annual rainfall, land topography, drainage, soil type, pH, flood risk, soil fertility, soil depth, etc. Only three of his food crops, sorghum, sweet potato, and sugarcane, will be evaluated in this study. Agroclimatic data governing crop suitability have evolved into generic and crop-specific criteria. An application that assesses land suitability for three food crops will be available on the Play Store for smartphones. Usability models were surveyed by 35 respondents who used the app. The user-friendliness of the app was evaluated as "very good."

Development of Land Suitability Assessment Applications for Sorghum, Sweet Potato and Sugarcane

Cultivation of food crops should be done on suitable land based on the results of the soil suitability assessment. The Soil Suitability Assessment Framework was initiated by the Food and Agriculture Organization (FAO). Today, smartphones have become a ubiquitous technology for solving problems in most environments, including: Assessment of soil suitability for food crops, plantations and many others. This white paper aims to address these benefits by transforming the framework into a mobile app. This solution aims to help land users conduct land valuations more effectively and efficiently. A rule-based system (RBS) algorithm is used to build the framework into a set of rules that are interconnected to draw land suitability conclusions. Regulations relate to annual rainfall, land topography, drainage, soil type, pH, flood risk, soil fertility, soil depth, etc. Only three of his food crops, sorghum, sweet potato, and sugarcane, will be evaluated in this study. Agroclimatic data governing crop suitability have evolved into generic and crop-specific criteria. An application that assesses land suitability for three food crops will be available on the Play Store for smartphones. Usability models were surveyed by 35 respondents who used the app. The user-friendliness of the app was evaluated as "very good."

Development of Imiquimod-induced HaCaT-THP-1 co-culture for modeling of psoriasis

Psoriasis is one of the most prevalent and chronic inflammatory disease of the skin, associated with disrupted barrier function. Currently, a widely accepted, generally usable cell culture model has not been developed yet. In the present work, we aimed to establish a co-culture model with human keratinocyte (HaCaT) and human monocyte cells (THP-1) induced by Imiquimod (IMQ), which acts on the TLR7 receptor. The role of TLR7 expressed on THP-1 cells was confirmed by immunofluorescence staining of NF-κB activation. Chloroquine (CH) was used as a receptor inhibitor, in the presence or absence of which the NF-κB pathway was activated. We determined the most effective proliferation-stimulating IMQ concentration by RTCA method and the hyperproliferative effect was investigated by wound-healing test. The effect of IMQ was compared with the effects of the anthocyanin (AC) components from the anti-inflammatory sour cherry extract that we have already studied. We found that IMQ significantly increased the migration rate however, the combined treatment resulted in a decreased migration rate compared to the IMQ treatment alone. Inflammatory cytokines were measured from the supernatant of co-culture by ELISA. During the development of the co-culture intended to model psoriasis, we confirmed the induction effect of IMQ and in the case of AC treatment, we supported the stabilizing effect of the barrier.

Improved Efficiency and Sensitivity Analysis of 3-D Agent-based Model for Pain-related Neural Activity in the Amygdala.

Neuropathic pain is caused by nerve injury and involves brain areas such as the central nucleus of the amygdala (CeA). We developed the first 3-D agent-based model (ABM) of neuropathic pain-related neurons in the CeA using NetLogo3D. The execution time of a single ABM simulation using realistic parameters (e.g., 13,000 neurons and 22,000+ neural connections) is an important factor in the model's usability. In this paper, we describe our efforts to improve the computational efficiency of our 3-D ABM, which resulted in a 28% reduction in execution time on average for a typical simulation. With this upgraded model, we performed one- and two-parameter sensitivity analyses to study the sensitivity of model output to variability in several key parameters along the anterior to posterior axis of the CeA. These results highlight the importance of computational modeling in exploring spatial and cell-type specific properties of brain regions to inform future wet lab experiments.

Investigating the Effect of m-Commerce Application’s Functional and Non-Functional Attributes on Usability and Continuance Intention

The development of mobile commerce (m-commerce) applications has indicated a shift in goals from ease-of-use to sustainable use in the future. This shift has prompted changes in the combination of attributes that constitute the usability of m-commerce applications. This study developed an m-commerce usability model that combines functional and non-functional attributes. The research data was collected using questionnaires distributed to users living in Jakarta and was processed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique. The results demonstrate that efficiency, satisfaction, and effectiveness can explain the usability of m-commerce applications. This study proved that three non-functional usability attributes – productivity, navigation, and memorability – have a positive effect on efficiency, while two functional attributes of usability – content and security – positively influence effectiveness. The study further proved that beside non-functional attributes, functional attributes also play an important role in increasing the user experience in the m-commerce context and thereby contribute to improving the usability of m-commerce systems and the continuance intention. The overall results of the study can be used in developing strategies for m-commerce application developers to design applications that can satisfy users and help them complete their tasks correctly and efficiently.

Development and evaluation of a usable blastocyst predictive model using the biomechanical properties of human oocytes.

The purposes of this study were to determine whether biomechanical properties of mature oocytes could predict usable blastocyst formation better than morphological information or maternal factors, and to demonstrate the safety of the aspiration measurement procedure used to determine the biomechanical properties of oocytes. A prospective split cohort study was conducted with patients from two IVF clinics who underwent in vitro fertilization. Each patient's oocytes were randomly divided into a measurement group and a control group. The aspiration depth into a micropipette was measured, and the biomechanical properties were derived. Oocyte fertilization, day 3 morphology, and blastocyst development were observed and compared between measured and unmeasured cohorts. A predictive classifier was trained to predict usable blastocyst formation and compared to the predictions of four experienced embryologists. 68 patients and their corresponding 1252 oocytes were included in the study. In the safety analyses, there was no significant difference between the cohorts for fertilization, while the day 3 and 5 embryo development were not negatively affected. Four embryologists predicted usable blastocyst development based on oocyte morphology with an average accuracy of 44% while the predictive classifier achieved an accuracy of 71%. Retaining the variables necessary for normal fertilization, only data from successfully fertilized oocytes were used, resulting in a classifier an accuracy of 81%. To date, there is no standard guideline or technique to aid in the selection of oocytes that have a higher likelihood of developing into usable blastocysts, which are chosen for transfer or vitrification. This study provides a comprehensive workflow of extracting biomechanical properties and building a predictive classifier using these properties to predict mature oocytes' developmental potential. The classifier has greater accuracy in predicting the formation of usable blastocysts than the predictions provided by morphological information or maternal factors. The measurement procedure did not negatively affect embryo culture outcomes. While further analysis is necessary, this study shows the potential of using biomechanical properties of oocytes to predict embryo developmental outcomes.

Conceptually Funded Usability Evaluation of an Application for Leveraging Descriptive Data Analysis Models for Cardiovascular Research.

The focus of this study, and the subject of this article, resides in the conceptually funded usability evaluation of an application of descriptive models to a specific dataset obtained from the East Slovak Institute of Heart and Vascular Diseases targeting cardiovascular patients. Delving into the current state-of-the-art practices, we examine the extent of cardiovascular diseases, descriptive data analysis models, and their practical applications. Most importantly, our inquiry focuses on exploration of usability, encompassing its application and evaluation methodologies, including Van Welie's layered model of usability and its inherent advantages and limitations. The primary objective of our research was to conceptualize, develop, and validate the usability of an application tailored to supporting cardiologists' research through descriptive modeling. Using the R programming language, we engineered a Shiny dashboard application named DESSFOCA (Decision Support System For Cardiologists) that is structured around three core functionalities: discovering association rules, applying clustering methods, and identifying association rules within predefined clusters. To assess the usability of DESSFOCA, we employed the System Usability Scale (SUS) and conducted a comprehensive evaluation. Additionally, we proposed an extension to Van Welie's layered model of usability, incorporating several crucial aspects deemed essential. Subsequently, we rigorously evaluated the proposed extension within the DESSFOCA application with respect to the extended usability model, drawing insightful conclusions from our findings.

IPhone-Based Cartilage Topography Scanning Yields Similar Results to Computed Tomography Scanning

To investigate the feasibility and accuracy of 3-dimensional (3D) iPhone scans using commercially available applications compared with computed tomography (CT) for mapping chondral surface topography of the knee. Ten cadaveric dysplastic trochleae, 16 patellae, and 24 distal femoral condyles (DFCs) underwent CT scans and 3D scans using 3 separate optical scanning applications on an iPhone X. The 3D surface models were compared by measuring surface-to-surface least distance distribution of overlapped models using a validated 3D-3D registration volume merge method. The absolute least mean square distances for the iPhone-generated models from each scanning application were calculated in comparison to CT models using a point-to-surface distance algorithm allowing regional "inside/outside" measurement of the absolute distance between models. Only 1 of the 3 scanning applications created models usable for quantitative analysis. Overall, there was a median absolute least mean square distance between the usable model and CT-generated models of 0.18 mm. The trochlea group had a significantly lower median absolute least mean square distance compared with the DFC group (0.14 mm [interquartile range, 0.13-0.17] vs 0.19 mm [0.17-0.25], P= .002). iPhone models were smaller compared with CT models (negative signed distances) for all trochleae, 83% of DFCs, and 69% of patellae. In this study, we found minimal differences between a 3D iPhone scanning application and conventional CT scanning when analyzing surface topography. Emerging 3D iPhone scanning technology can create accurate, inexpensive, real-time 3D models of the intended target. Surface topography evaluation may be useful in graft selection during surgical procedures such as osteochondral allograft transplantation.

Intensively Cultivated Roman Villae Estates: Case Study of Medulin Bay (Istria, Croatia)

ABSTRACTOur image of the Roman landscape of Istria is characterised by large‐scale centuriation and architectural remains of Roman villae. Detection and mapping of other, less dramatic landscape features require systematic large‐scale prospection, but this faces significant difficulties in the Mediterranean environment. However, the developments in the field of airborne laser scanning offer the possibility to create archaeologically usable digital terrain models under water and under very dense and low maquis vegetation. This paper reports on the use of terrain models created using a green laser and a sophisticated archaeologically driven ground point filtering strategy. Combined with archaeological aerial photo interpretation, this provides the means for landscape mapping and interpretation that has revealed a wealth of archaeological structures hinting at Roman agricultural practices and landscape. Our case study is based on a laser scan of about 24 km2 of land and underwater terrain in Medulin Bay. Processed, visualised and interpreted for archaeological purposes, the data reveal not only features ranging from prehistoric hilltop settlements to modern military installations but also features a complex picture of the Roman land use. Of particular interest is the large number of planting pits, which extend over a total length of 4 km. They were laid on a regular grid of approximately 35 × 35 m, sometimes combined in contiguous parcels. They can be interpreted as remains of orchards or tree nurseries of Roman date, and the paper examines also the question of whether they can be linked to the associated Roman estates. The case study area presented here shows that the potential of remote sensing methods goes far beyond the mere finding of traditional sites but can open up new landscape‐scale perspectives on regions that have been archaeologically little explored.

An In-Depth Analysis of SIMPKB: Revealing Performance Tests and Efficiency from a User Experience

This study comprehensively analyzes the performance and usability of the SIMPKB website in the context of teacher professional development. This research carries a qualitative descriptive approach with the aim of deeply understanding the performance and usability of the SIMPKB website. This research consists of two complementary stages, the first involves performance testing using GT Metrix software, and the second phase focuses on in-depth interviews with 5 driving teachers in Kabupaten Jember by applying the concept of the Five Dimensions of Usability (5E) model. Through performance testing using GT Metrix and 5E interviews with driving teachers, significant findings have been revealed. Although SIMPKB shows relatively good response speeds, there are areas of improvement that can be improved, especially in terms of loading times and Largest Contentful Paint (LCP). The 5E evaluation of the mobilizing teacher provides an in-depth perspective on the effectiveness, efficiency, engagement, errors, and ease of learning on the platform. The test and interview results complement each other, providing a holistic picture of SIMPKB's condition and potential improvement. Improvement recommendations, which involve improving response speed and improving usability, can be a foothold for improving the user experience. Further research is recommended to explore optimizing technical performance, implementing more intuitive interface designs, and evaluating the impact of implementing improvements on user effectiveness. By adopting these recommendations, SIMPKB can continue to develop as an effective, efficient, and user-friendly platform in supporting teacher professional development.

A visual-language foundation model for computational pathology.

The accelerated adoption of digital pathology and advances in deep learning have enabled the development of robust models for various pathology tasks across a diverse array of diseases and patient cohorts. However, model training is often difficult due to label scarcity in the medical domain, and a model's usage is limited by the specific task and disease for which it is trained. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text and, notably, over 1.17 million image-caption pairs through task-agnostic pretraining. Evaluated on a suite of 14 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving histopathology images and/or text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, and text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning.

A Mobile Applet for Assessing Medication Adherence and Managing Adverse Drug Reactions Among Patients With Cancer: Usability and Utility Study.

Medication adherence and the management of adverse drug reactions (ADRs) are crucial to the efficacy of antitumor drugs. A WeChat applet, also known as a "Mini Program," is similar to the app but has marked advantages. The development and use of a WeChat applet makes follow-up convenient for patients with cancer. This study aimed to assess the usability and utility of a newly developed WeChat applet, "DolphinCare," among patients with cancer in Shanghai. A qualitative methodology was used to obtain an in-depth understanding of the experiences of patients with cancer when using DolphinCare from the usability and utility aspects. The development phase consisted of 2 parts: alpha and beta testing. Alpha testing combined the theory of the Fogg Behavior Model and the usability model. Alpha testing also involved testing the design of DolphinCare using a conceptual framework, which included factors that could affect medication adherence and ADRs. Beta testing was conducted using in-depth interviews. In-depth interviews allowed us to assist the patients in using DolphinCare and understand whether they liked or disliked DolphinCare and found it useful. We included participants who had an eHealth Literacy Scale (eHEALS) score of ≥50%, and a total of 20 participants were interviewed consecutively. The key positive motivators described by interviewers were to be reminded to take their medications and to alleviate their ADRs. The majority of the patients were able to activate and use DolphinCare by themselves. Most patients indicated that their trigger to follow-up DolphinCare was the recommendation of their known and trusted health care professionals. All participants found that labels containing the generic names of their medication and the medication reminders were useful, including timed pop-up push notifications and text alerts. The applet presented the corresponding information collection forms of ADRs to the patient to fill out. The web-based consultation system enables patients to consult pharmacists or physicians in time when they have doubts about medications or have ADRs. The applet had usabilities and utilities that could improve medication adherence and the management of ADRs among patients with cancer. This study provides preliminary evidence regarding the usability and utility of this type of WeChat applet among patients with cancer, which is expected to be promoted for managing follow-up among other patients with other chronic disease.

Acceptability and usability of a WeChat-based intervention for depression in China: A mixed-methods study.

As research on the use of mobile technology to deliver mental health support grows, the research from China is still very limited. How to design an acceptable and usable mobile mental health service model suitable for China's social and cultural environment remains to be studied. To understand the acceptability and usability of a WeChat-based intervention among Chinese patients with depression, and to provide insights to promote future development of user-centered mobile mental health services design. The research team developed a multi-theoretical intervention that includes seven modules: recovery lessons, recovery journal, coaching sessions, mindfulness, personalized support, regular assessments and feedback collection. Forty-two patients diagnosed with depressive disorder were recruited, with a mixed sample of patients who were using an antidepressant medication (n = 29) and patients who were not using an antidepressant medication (n = 13). A single-arm mixed-methods study was conducted to understand engagement, satisfaction, usability and potential clinical effectiveness of the intervention. There was a retention rate of 83.33% - 22 participants who used an antidepressant medication and 13 participants who did not use an antidepressant medication completed the final assessments. The median (upper quartile-lower quartile) of the completed 60 recovery journals and 7 coaching sessions was 56 (59-46) and 6 (7-4) times, respectively. Participants' satisfaction regarding their recovery progress, and on perceived helpfulness on different modules were high. The overall score of the user version of the Mobile Application Rating Scale was 4.23 (SD 0.44, range 1-5), indicating high acceptability and usability. Qualitative feedback identified three key themes: an efficient access to professional help, a personalized source of social support, and a facilitator of cognitive and behavioral change. This study demonstrated that a WeChat-based intervention for depression was acceptable, and has the potential to promote personal recovery. More studies are needed to understand the efficacy and implementation of this model in real world.

Prognosticating Fetal Growth Restriction and Small for Gestational Age by Medical History.

This study aimed to develop and externally validate a prognostic prediction model for screening fetal growth restriction (FGR)/small for gestational age (SGA) using medical history. From a nationwide health insurance database (n=1,697,452), we retrospectively selected visits of 12-to-55-year-old females to healthcare providers. This study used machine learning (including deep learning) and 54 medical-history predictors. The best model was a deep-insight visible neural network (DI-VNN). It had area under the curve of receiver operating characteristics (AUROC) 0.742 (95% CI 0.734 to 0.750) and a sensitivity of 49.09% (95% CI 47.60% to 50.58% at with 95% specificity). Our model used medical history for screening FGR/SGA with moderate accuracy by DI-VNN. In future work, we will compare this model with those from systematically-reviewed, previous studies and evaluate if this model's usage impacts patient outcomes.

Analisa Kinerja Individu Berdasarkan Kepuasan Penggunaan E-Government di Indonesia (Studi Kasus DIKUPASI Diskopukmperindag Kota Mojokerto)

This study aims to determine the perceived usefulness of users of the DIKUPASI application at Diskopukmperindag in Mojokerto City by using the Usability model approach with 4 variables, namely Learnability, Flexibility, Effectiveness, and Attitude, and to analyze this usefulness on application user satisfaction and performance improvement. DIKUPASI is an institutional application that functions to monitor and update data on UKM in Mojokerto City. The research was conducted on surveyors in the City of Mojokerto using a sampling technique of 42 surveyors spread across the City of Mojokerto. The collection of research data was carried out by distributing questionnaires. Data processing is done with the SmartPLS application. The data processing that has been done shows that there are 2 hypotheses H4 and H5 obtained from the Usability model which have a significant and positive influence. Based on the results of hypothesis testing, the perception of satisfaction with the DIKUPASI application is positively and significantly influenced by user attitudes. Perceived performance improvement for DIKUPASI application users is significantly influenced positively by user satisfaction. Participation in research on the Department of Cooperatives, Small and Medium Enterprises, Industry and Trade in Mojokerto City which adopted the DIKUPASI application is necessary to pay attention to the perceptions of user attitudes so that users feel satisfied in using the application and can improve user performance. The perception of satisfaction can be created through encouragement of surveyors' willingness and how the DIKUPASI application has benefits that can help surveyors' work.

Prediction Models for Mechanical Properties of Cement-Bound Aggregate with Waste Rubber

The high stiffness of cement-bound aggregate (CBA) is recognized as its main drawback. The stiffness is described by the modulus of elasticity, which is difficult to determine precisely in CBA. Incorporating rubber in these mixtures reduces their stiffness, but mathematical models of the influence of rubber on the mechanical characteristics have not previously been defined. The scope of this research was to define a prediction model for the compressive strength (fc), dynamic modulus of elasticity (Edyn) and static modulus of elasticity (Est) based on the measured ultrasonic pulse velocity as a non-destructive test method. The difference between these two modules is based on the measurement method. Within this research, the cement and waste rubber content were varied, and the mechanical properties were determined for three curing periods. The Edyn was measured using the ultrasonic pulse velocity (UPV), while the Est was determined using three-dimensional digital image correlation (3D DIC). The influence of the amount of cement and rubber and the curing period on the UPV was determined. The development of prediction models for estimating the fc and Est of CBA modified with waste rubber based on the non-destructive test results is highlighted as the most significant contribution of this work. The curing period was statistically significant for the prediction of the Est, which points to the development of CBA elastic properties through different stages during the cement-hydration process. By contrast, the curing period was not statistically significant when estimating the fc, resulting in a simplified, practical and usable prediction model.

Usability and acceptance as facilitators of behavioral intention to use a mixed reality exercise program in older adults: A structural equation model

BackgroundStaying active has been one of the mainstays in promoting health in older adults. Despite the potential of innovative health technologies for fitness, there is limited understanding of the factors influencing older adults’ behavioral intention to engage in mixed reality physical exercise. PurposeTo develop a theoretical model of technology usability and acceptance influencing behavioral intention to use a mixed reality physical exercise program among older adults. DesignA cross-sectional study was conducted to develop a virtual exercise coach in a mixed-reality environment. Older adults (n = 64) were purposively selected from a community senior center in the Philippines and participated in the exercise program. Their responses were analyzed using a partial least square approach to structural equation modeling. ResultsThe generated measurement model demonstrated valid and reliable estimates. Its structural model (R2 = 0.64) had a good fit (GoF = 0.56) with consistent quality indices. It also indicated that performance expectancy, facilitating conditions, and information quality are significant (p < 0.05) factors explaining behavioral intention to use the virtual coach system among older adults. ConclusionThe belief in technology usefulness, trust, data quality, and the value of shared responsibility between the technology promoters and users significantly facilitates behavioral intention to use mixed reality virtual coach systems. The study results are valuable in developing technologies and improving older adult participation in technology-driven community health programs.