Practical Model Research Articles

Grey-Box Modeling and Decoupling Control of a Lab Setup of the Quadruple-Tank System

The quadruple-tank system (QTS) is a popular educational resource in universities for studying multivariable control systems. It enables the analysis of the interaction between variables and the limitations imposed by multivariable non-minimum phase zeros, as well as the evaluation of new multivariable control methodologies. The works utilizing this system present a theoretical model that may be too idealistic and based on erroneous assumptions in real-world implementations, such as the linear behavior of the actuators. In other cases, an identified linear model is directly provided. This study outlines the practical grey-box modeling procedure conducted for the QTS at the University of Cordoba and provides guidance for its implementation. A configurable nonlinear model was developed and controlled in a closed loop using different controllers. Specifically, decentralized control, static decoupling control, and simplified decoupling control were compared. The simulation designs were experimentally validated with high accuracy, demonstrating that the conclusions reached with the developed model can be extrapolated to the real system. The comparison of these three control designs illustrates the advantages and disadvantages of decoupling in certain situations, especially in the presence of non-minimum phase zeros.

A Machine Learning Approach to Predict Relative Residual Strengths of Recycled Aggregate Concrete after Exposure to High Temperatures

In recent years, there has been a heightened focus among researchers and policymakers on assessing the environmental impact and sustainability of human activities. In this context, the reutilization of construction materials, particularly recycled aggregate concrete, has emerged as an environmentally friendly choice in construction projects, gaining significant traction. This study addresses the critical need to investigate the mechanical properties of recycled aggregate concrete under diverse extreme scenarios. Conducting an extensive literature review, key findings were synthesized on the relative residual strength of recycled aggregate concrete following exposure to high temperatures. Leveraging these insights, innovative hybrid machine learning models were developed, offering practical equations and model trees for predicting the relative residual compressive strength, flexural strength, elasticity modulus, and splitting tensile strength of recycled aggregate concrete post high temperature exposure. Uncertainty analysis was performed on each model to assess the reliability, while sensitivity analysis was performed to find out the significance of each input variable for each predictive model. This paper presents interpretable models achieving high levels of performance, with R2 values of 0.91, 0.94, 0.9, and 0.96 for predicting the relative residual compressive strength, flexural strength, modulus of elasticity, and splitting tensile strength of RCA concrete exposed to high temperatures, respectively. The unique contribution of the paper lies in the provision of easily applicable equations and model trees, enhancing accessibility for practitioners seeking to estimate mechanical properties of recycled aggregate concrete. Notably, our hybrid machine learning models stand out for their user-friendly nature compared with conventional ML algorithms, without compromising on accuracy. This paper not only advances our understanding of sustainable construction practices but also equips industry professionals with efficient tools for practical implementation.

Effects of coaching on wellbeing, perception of inclusion, and study-interest

The current study aimed to investigate the influence of an evidence-based coaching intervention on students’ wellbeing, perception of inclusion and study-interest, among 93 first year social work students (aged­ 16–24) in the Netherlands. The coaching intervention that was implemented was based on the REGROW model. A pre-test-intervention-post-test design with two conditions was employed. The experimental condition (n = 47) received coaching from their mentor between pre-test and post-test, while the control condition (n = 46) received regular academic career counselling from their mentor. The results indicate that coaching has a positive effect on students’ emotional wellbeing, but not on psychological and social wellbeing, perception of inclusion and study-interest. In addition, it was found that students’ evaluation of the coaching trajectory is predictive of their emotional wellbeing, specifically in relation to relatedness and pressure. In vocational education, coaching seems to be a valuable addition to academic career counselling. A structured practical coach model, such as the REGROW model, potentially combined with a coach card, could help implementing coaching in vocational education.

Social Media App for Connecting Similar Interests People Using flutter

Understanding interest similarity in Online Social Networks (OSNs) is crucial for various applications. This study addresses the challenge of determining interest similarity on platforms like Facebook, where users may not explicitly disclose their interests. Utilizing a substantial dataset of 479,048 users and 5,263,351 user-generated interests, the research focuses on movies, music, and TV shows. Findings reveal homophily in interest similarity, demonstrating that individuals tend to share more similar tastes when they have comparable demographic information or are connected as friends. A practical prediction model is proposed, facilitating the selection of users with high-interest similarities and enhancing decision-making for OSN applications. Additionally, the paper introduces a novel method using a tag network to connect users with similar interests, outperforming traditional methods by providing a more efficient means of connecting like-minded individuals in social networks. Key Word:Face image synthesis, Generative adversarial network, Face Recognition.

Establishment of a chicken intestinal organoid culture system to assess deoxynivalenol-induced damage of the intestinal barrier function

BackgroundDeoxynivalenol (DON) is a mycotoxin that has received recognition worldwide because of its ability to cause growth delay, nutrient malabsorption, weight loss, emesis, and a reduction of feed intake in livestock. Since DON-contaminated feedstuff is absorbed in the gastrointestinal tract, we used chicken organoids to assess the DON-induced dysfunction of the small intestine.ResultsWe established a culture system using chicken organoids and characterized the organoids at passages 1 and 10. We confirmed the mRNA expression levels of various cell markers in the organoids, such as KI67, leucine-rich repeat containing G protein-coupled receptor 5 (Lgr5), mucin 2 (MUC2), chromogranin A (CHGA), cytokeratin 19 (CK19), lysozyme (LYZ), and microtubule-associated doublecortin-like kinase 1 (DCLK1), and compared the results to those of the small intestine. Our results showed that the organoids displayed functional similarities in permeability compared to the small intestine. DON damaged the tight junctions of the organoids, which resulted in increased permeability.ConclusionsOur organoid culture displayed topological, genetic, and functional similarities with the small intestine cells. Based on these similarities, we confirmed that DON causes small intestine dysfunction. Chicken organoids offer a practical model for the research of harmful substances.

Development of a physical education learning model football game materials based on cooperative learning to increase student motivation and cooperation

Aim. (1) This study aims to test the production of a physical education learning model based on cooperative learning, designed to increase motivation and cooperation among high school students (2) Producing the feasibility of implementing a cooperative learning-based physical education learning model to increase motivation and cooperation for high school students (3) Produce practical implementation of a practical physical education learning model based on cooperative learning to increase motivation and cooperation for high school students. Material dan methods. This research employs a Research and Development (R&D) model. This research procedure uses the ADDIE development model. The population in the study was class X SMA N 1 Seyegan with a sample size of 16 students. Results. (1) Based on research, the average learning outcome for the development of the first meeting was 44,125 and at the second meeting was 64,875. Sig value (2-tailed) is.000. Due to the sig value..000 < 0.05 then H0 is rejected. Therefore, it can be concluded that there was a significant improvement between the first and second meetings. (2) Based on research, it shows that the average learning outcome at the second meeting was 64.870 and at the third meeting was 81.125, Sig. (2-tailed) of.001. Due to the significance value 0.001 < 0.05 then H0 is rejected and Ha cannot be rejected. So it can be concluded that at the second and third meetings there was a significant improvement (3) Based on research, it shows that the average learning outcome for the development of the first meeting was 44.125 and the third meeting was 81.125, the value of significance (2-tailed) of 0.000. Due to the significance value. 000 < 0.05 then H0 is rejected and Ha cannot be rejected. So it can be concluded that from the first meeting to the third meeting there was a significant improvement. Conclusions. Research on the development of a model for the development of a physical education learning model with cooperative learning-based football game material to increase high school students' motivation and cooperation which has been prepared and is suitable for use. The development of a physical education learning model with cooperative learning-based soccer game material to increase high school students' motivation and cooperation has proven effective.

Multilevel approaches to address disparities in lung cancer screening: a study protocol

BackgroundLow-dose computed tomography (lung cancer screening) can reduce lung cancer-specific mortality by 20–24%. Based on this evidence, the United States Preventive Services Task Force recommends annual lung cancer screening for asymptomatic high-risk individuals. Despite this recommendation, utilization is low (3–20%). Lung cancer screening may be particularly beneficial for African American patients because they are more likely to have advanced disease, lower survival, and lower screening rates compared to White individuals. Evidence points to multilevel approaches that simultaneously address multiple determinants to increase screening rates and decrease lung cancer burden in minoritized populations. This study will test the effects of provider- and patient-level strategies for promoting equitable lung cancer screening utilization.MethodsGuided by the Health Disparities Research Framework and the Practical, Robust Implementation and Sustainability Model, we will conduct a quasi-experimental study with four primary care clinics within a large health system (MedStar Health). Individuals eligible for lung cancer screening, defined as 50–80 years old, ≥ 20 pack-years, currently smoking, or quit < 15 years, no history of lung cancer, who have an appointment scheduled with their provider, and who are non-adherent to screening will be identified via the EHR, contacted, and enrolled (N = 184 for implementation clinics, N = 184 for comparison clinics; total N = 368). Provider participants will include those practicing at the partner clinics (N = 26). To increase provider-prompted discussions about lung screening, an electronic health record (EHR) clinician reminder will be sent to providers prior to scheduled visits with the screening-eligible participants. To increase patient-level knowledge and patient activation about screening, an inreach specialist will conduct a pre-visit phone-based educational session with participants. Patient participants will be assessed at baseline and 1-week post-visit to measure provider-patient discussion, screening intentions, and knowledge. Screening referrals and screening completion rates will be assessed via the EHR at 6 months. We will use mixed methods and multilevel assessments of patients and providers to evaluate the implementation outcomes (adoption, feasibility, acceptability, and fidelity).DiscussionThe study will inform future work designed to measure the independent and overlapping contributions of the multilevel implementation strategies to advance equity in lung screening rates.Trial registrationClinicalTrials.gov, NCT04675476. Registered December 19, 2020.

Developing a practical neurodevelopmental prediction model for targeting high-risk very preterm infants during visit after NICU: a retrospective national longitudinal cohort study

BackgroundFollow-up visits for very preterm infants (VPI) after hospital discharge is crucial for their neurodevelopmental trajectories, but ensuring their attendance before 12 months corrected age (CA) remains a challenge. Current prediction models focus on future outcomes at discharge, but post-discharge data may enhance predictions of neurodevelopmental trajectories due to brain plasticity. Few studies in this field have utilized machine learning models to achieve this potential benefit with transparency, explainability, and transportability.MethodsWe developed four prediction models for cognitive or motor function at 24 months CA separately at each follow-up visits, two for the 6-month and two for the 12-month CA visits, using hospitalized and follow-up data of VPI from the Taiwan Premature Infant Follow-up Network from 2010 to 2017. Regression models were employed at 6 months CA, defined as a decline in The Bayley Scales of Infant Development 3rd edition (BSIDIII) composite score > 1 SD between 6- and 24-month CA. The delay models were developed at 12 months CA, defined as a BSIDIII composite score < 85 at 24 months CA. We used an evolutionary-derived machine learning method (EL-NDI) to develop models and compared them to those built by lasso regression, random forest, and support vector machine.ResultsOne thousand two hundred forty-four VPI were in the developmental set and the two validation cohorts had 763 and 1347 VPI, respectively. EL-NDI used only 4–10 variables, while the others required 29 or more variables to achieve similar performance. For models at 6 months CA, the area under the receiver operating curve (AUC) of EL-NDI were 0.76–0.81(95% CI, 0.73–0.83) for cognitive regress with 4 variables and 0.79–0.83 (95% CI, 0.76–0.86) for motor regress with 4 variables. For models at 12 months CA, the AUC of EL-NDI were 0.75–0.78 (95% CI, 0.72–0.82) for cognitive delay with 10 variables and 0.73–0.82 (95% CI, 0.72–0.85) for motor delay with 4 variables.ConclusionsOur EL-NDI demonstrated good performance using simpler, transparent, explainable models for clinical purpose. Implementing these models for VPI during follow-up visits may facilitate more informed discussions between parents and physicians and identify high-risk infants more effectively for early intervention.

A nomogram prognostic model for early hepatocellular carcinoma with diabetes mellitus after primary liver resection based on the admission characteristics.

Background: Patients diagnosed with early-stage hepatocellular carcinoma (HCC) and diabetes mellitus (DM) are at a higher risk of experiencing complications and facing increased mortality rates. Hence, it is crucial to develop personalized clinical strategies for this particular subgroup upon their admission. The objective of this study is to determine the key prognostic factors in early HCC patients who received liver resection combined with DM and develop a practical personalized model for precise prediction of overall survival in these individuals. Method: A total of 1496 patients diagnosed hepatitis B virus (HBV) - related liver cancer from Beijing You'an Hospital were retrospectively enrolled, spanning from 1 January 2014, to 31 December 2019, and ultimately, 622 eligible patients of hepatocellular carcinoma (HCC) patients with diabetes were included in this present investigation. A multivariate COX regression analysis was conducted to identify prognostic factors that are independent of each other and develop a nomogram. The performance of the nomogram was evaluated using various statistical measures such as the C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) in both the training and validation groups. Survival rates were estimated using the Kaplan-Meier method. Results: The study included a total of 622 early HCC patients who underwent liver resection combined with DM. Random Forrest model and Multivariate Cox regression analysis revealed that drinking, tumor number, monocyte-to-lymphocyte ratio, white blood cell count and international normalized ratio at admission were identified as independent prognostic factors for early HCC patients who underwent liver resection combined with DM. The nomogram demonstrated good predictive performance in the training and validation cohorts based on the C-index values of 0 .756 and 0 .739 respectively, as well as the area under the curve values for 3-, 5-, and 8-year overall survival (0.797, 0.807, 0.840, and 0.725, 0.791, 0.855). Calibration curves and decision curve analysis indicated high accuracy and net clinical benefit rates. Furthermore, the nomogram successfully stratified enrolled patients into low-risk and high-risk groups based on their risk of overall survival. The difference in overall survival between these two groups was statistically significant in both the training and validation cohorts (p < 0.0001 and p = 0.0064). Conclusion: Our results indicate that the admission characteristics demonstrate a highly effective ability to predict the overall survival of early HCC patients who have undergone liver resection in combination with DM. The developed model has the potential to support healthcare professionals in making more informed initial clinical judgments for this particular subgroup of patients.

An adaptive internal model control approach for unmanned surface vehicle based on bidirectional long short-term memory neural network: Implementation and field testing

This paper investigates a practical adaptive internal model control (IMC) for an unmanned surface vehicle (USV) with unknown time-varying nonlinear model parameters and environmental disturbances. Firstly, an internal model of the USV is presented using the Bidirectional Long Short-Term Memory (BiLSTM) neural network. Then, an adaptive neural IMC controller is designed for the trajectory tracking of USV by using the IMC method. The internal model and the controller are updated by an error threshold algorithm. The proposed control scheme comprises of a trajectory guidance module via the Line-of-Sight (LOS) guidance method and a tracking control module designed by IMC theory. Under the proposed control scheme, the development processes of the vehicle platform and the control algorithms are described, and accurate tracking control can be achieved. Finally, the results of simulation and field experiments are presented and discussed to validate the effectiveness of the proposed control scheme.

Practical Semianalytical Models for Pressure-Transient Analysis of Carbonate Reservoirs with Complex Fracture and Cave Distributions

Practical Semianalytical Models for Pressure-Transient Analysis of Carbonate Reservoirs with Complex Fracture and Cave Distributions

Adaptive stabilization of stochastic systems with polynomial nonlinear conditions, unknown parameters, and dead‐zone actuator

AbstractMany practical dynamic models always suffer from complex nonlinearities, parameter uncertainties and random noises. Such kind of models are always characterized by uncertain stochastic nonlinear systems. Besides, many factors will lead to dead‐zone actuator, which further adds more challenges to control engineering. This paper studies stabilization issue of stochastic systems with polynomial nonlinear conditions, unknown parameters and dead‐zone actuator. An adaptive control approach is proposed by exploring homogeneous domination control and using dynamic‐gain‐based adaptive design strategy. It shows that all the signals are bounded almost surely and the system states converge to the origin almost surely. The presented method is successfully applied to the mass spring mechanical system.

Intelligence-led accident prevention and its application in petrochemical enterprises

Accidents pose significant threats to environmental protection, social development, economic performance, and human health; accident prevention is crucial. In the era of intelligence, safety intelligence plays an increasingly important role in accident prevention. It is a valuable resource that can effectively support organizations. This study proposes a new approach, intelligence-led accident prevention (ILAP), to explore application of safety intelligence in accident prevention. As ILAP is a developing concept in accident prevention, no research has been conducted to clarify its fundamental theoretical issues. Thus, there is a limited theoretical basis and methodological guidance for research and practice. This study addresses the fundamental issues regarding ILAP, proposes a practical model, and discusses its application in accident prevention in petrochemical enterprises, providing valuable insight for accident-prevention researchers and practitioners and establishing a theoretical foundation for future research in this field.

Assessment of Landslide Susceptibility in the Intermontane Basin Area of Northern Thailand

In mountainous terrain, landslides are common, particularly in intermontane basin locations. Such regions can adversely affect both human beings and the environment. In the assessment of landslide susceptibility, machine learning (ML) algorithms are increasingly popular due to their compatibility with geospatial data and tools. Herein, this study evaluated the performance of four ML algorithms: namely, random forest (RF), gradient boost (GB), extreme gradient boost (XGB), and stacking ensemble (STK). These algorithms were implemented to create a practical model of landslide susceptibility. The site under investigation is in the province of Chiang Mai, an intermontane basin area in northern Thailand where populations are settled. To address issues of multicollinearity, the variance inflation factor (VIF) was used. Eight out of fourteen factors were selected for examination; hyperparameters of each model were tested to acquire the best combination. Results indicated that the STK model outperforms all other models, providing evaluation metrics (precision, recall, F1-score, and overall accuracy) of 82.92%, 81.18%, 82.04%, and 81.75%, respectively. The area under the receiver operating characteristic (ROC) curve also reveals the high efficiency of the model, achieving 0.8928. However, further analysis of the appropriate model or base learner is necessary for achieving even higher predictive results.

A Versatile Intestine-on-Chip System for Deciphering the Immunopathogenesis of Inflammatory Bowel Disease.

The multifactorial nature of inflammatory bowel disease (IBD) necessitates reliable and practical experimental models to elucidate its etiology and pathogenesis. To model the intestinal microenvironment at the onset of IBD in vitro, it is important to incorporate relevant cellular and noncellular components before inducing stepwise pathogenic developments. A novel intestine-on-chip system for investigating multiple aspects of IBD's immunopathogenesis is presented. The system includes an array of tight and polarized barrier models formed from intestinal epithelial cells on an in-vivo-like subepithelial matrix within one week. The dynamic remodeling of the subepithelial matrix by cells or their secretome demonstrates the physiological relevance of the on-chip barrier models. The system design enables introduction of various immune cell types and inflammatory stimuli at specific locations in the same barrier model, which facilitates investigations of the distinct roles of each cell type in intestinal inflammation development. It is showed that inflammatory behavior manifests in an upregulated expression of inflammatory markers and cytokines (TNF-α). The neutralizing effect of the anti-inflammatory antibody Infliximab on levels of TNF-α and its inducible cytokines could be explicitly shown. Overall, an innovative approach to systematically developing a microphysiological system to comprehend immune-system-mediated disorders of IBD and to identify new therapeutic strategies is presented.

A timely efficient and emissions-aware multiobjective truck-sharing integrated scheduling model in container terminals

In line with the development of green port terminals and the necessity of environmentally friendly efficient use of handling and transportation equipment, criteria such as emission reduction, energy minimization, and efficiency in a container terminal are prominent issues, which in recent years, have been the main concern of container terminal managers in developing terminal operating systems. Therefore, in this study, a bi-objective mixed integer mathematical programing model of integrated scheduling for ships’ loading and unloading operation sequencing in container terminals is proposed, which considers the minimization of containers’ flow time, trucks’ emission, and energy consumption as well as sharing trucks among quay cranes and decreasing their empty trips. In this model trucks’ technical specifications and polluting levels are input parameters for estimating emission and energy consumption of operation, which consequently leads to two uniform parallel machine scheduling models. To derive the exact solution of the model, the epsilon-constraint method is applied. Nevertheless, due to the problem’s NP-hardness attribute, two variants of non-dominated sorting genetic algorithm and multiobjective particle swarm optimization metaheuristic algorithms are proposed to identify the approximate Pareto front of solutions. In large-size problems, these algorithms outperform the epsilon-constraint method by finding acceptable results in a reasonable time. This model, which could be embedded in terminal operating software, not only yields a practical optimum operational sequence but also measures the amount of energy and emission from trucks during loading and unloading operations. The results, which utilized the data collected from Shahid Rajaee port in southern Iran, indicate that this practical model as compared to the current procedure in the terminal, results in a significant improvement in energy and emission reduction and terminal flow time.

Effect of joint stiffness on the load-carrying capacity of single-layer cylindrical reticulated shell with improved bolt-column (IBC) joint

To enlarge the application of single-layer reticulated shells, this study proposes an innovative semi-rigid joint based on the original bolt-column (OBC) joint, referred to as the improved bolt-column (IBC) joint. The mechanical behavior of the IBC joint under the pure load and the coupling effect of bending and axial force is investigated using numerical tests. Then, the practical analytical model of IBC joint is developed based on a three-parameters power model and component method. Subsequently, a mechanical model considering joint stiffness is developed to establish semi-rigidly connected single-layer cylindrical reticulated shell. A total of 456 numerical models of shells are conducted to examine the impact of different kinds of joint stiffness on the load-carrying capacity of shells systematically. The results demonstrate that (i) weak axis bending stiffness and axial stiffness significantly affect the load-carrying capacity of shells by approximately 19 % and 14 % on average respectively; (ii) strong axis bending stiffness and torsional stiffness have relatively minor effects on the critical load of shells, with average decline ratios of only 5 % and 6 % respectively; (iii) out-of-plane shear stiffness and in-plane shear stiffness exhibit negligible impacts on the load-carrying capacity of shells, with an average effect not exceeding 1 %. Finally, the findings of numerical simulation considering all stiffness of IBC joint reveal insights into both varying laws of load-carrying capacity and failure modes for shells. It is observed that the mean critical load can reach up to 59 % of the one of rigidly-connected shells, which is significantly higher than those observed in shells with pinned joints.

An improved state‐space average model of the ultra‐high voltage inverter based on wavelet decomposition and reconstruction

SummaryUltra‐high voltage inverters are widely used as grid‐connected devices in new energy grids, and the state‐space average model is the most practical modeling method for the inverter. However, the accuracy of this modeling method is significantly reduced because it ignores the high‐frequency switching process, which contains overshoot voltage and other factors that affect system stability. Moreover, the amplitude of the overshoot voltage in the ultra‐high voltage inverter will be huge, which may lead to the damage to switching devices, circuit overheating, and even the collapse of the system. Therefore, the wavelet decomposition and reconstruction methods are used in this paper to extract the transient characteristics of the inverter and integrate them into the state‐space average model. Compared with the traditional state‐space average model, the improved model method can obtain a more accurate overshoot voltage peak value with the same simulation step size. Then, it can provide a more precise reference for the withstand voltage in the device selection of a high voltage level system. The single‐phase full‐bridge inverter is taken as an example to illustrate the construction process based on the wavelet decomposition and reconstruction method. Finally, the simulation waveform is verified to be in good agreement with the experiment.

Validated Clinical Score to Predict Gastroesophageal Reflux in Patients With Chronic Laryngeal Symptoms: COuGH RefluX

Background & AimsDiscerning whether laryngeal symptoms result from gastroesophageal reflux is clinically challenging and a reliable tool to stratify patients is needed. We aimed to develop and validate a model to predict likelihood of gastroesophageal reflux disease (GERD) among patients with chronic laryngeal symptoms. MethodsThis multicenter international study collected data from adults with chronic laryngeal symptoms that underwent objective testing (upper GI endoscopy and/or ambulatory reflux monitoring) between 3/2018-5/2023. The training phase identified a model with optimal receiver operating characteristic curves, and beta coefficients informed a weighted model. The validation phase assessed performance characteristics of the weighted model. Results856 adults, 304 in training cohort and 552 in the validation cohort, were included. In the training phase the optimal predictive model [AUC 0.68 (95% CI 0.62, 0.74)] - the COuGH RefluX score - consisted of Cough, Overweight/obesity, Globus, Hiatal Hernia, Regurgitation, and male seX with a lower threshold of 2.5 and upper threshold of 5.0 to predict proven GERD. In the validation phase, the COuGH RefluX score had an AUC of 0.67 (0.62, 0.71) with 79% sensitivity and 81% specificity for proven GERD. ConclusionsThe externally validated COuGH RefluX score is a clinically practical model to predict likelihood of proven GERD. The score classifies most patients with chronic laryngeal symptoms as low/high likelihood of proven GERD, with only 38% remaining as indeterminate. Thus, the COuGH RefluX score can guide diagnostic strategies and reduce inappropriate proton pump inhibitor use or testing for patients referred for evaluation of chronic laryngeal symptoms.

Staff Perspectives on Dementia Care in Nursing Homes: A Pre-Implementation, Content Analysis Study Guided by the Practical, Robust Implementation and Sustainability Model.

To assess implementation context prior to the implementation of Dementia Collaborative Coaching (DCC), a person-centered, communication coaching intervention for speech-language pathologists, certified nursing assistants, and people living with dementia in nursing homes. This was a pre-implementation, content analysis of focus groups. Focus group questions and subsequent analysis were guided by the Practical, Robust Implementation and Sustainability Model. Employees (N = 27) from three nursing homes participated in five focus groups. Five themes from directed content analysis included (a) Dementia Care Needs Improvement; (b) Dementia Collaborative Coaching Appears Promising; (c) Awareness of Necessary Implementation and Sustainability Plans; (d) Regulations Need Accurate Interpretation and Local Dissemination; and (e) Appropriate Staffing Concerns. Key factors related to the implementation of DCC included a perceived tension for change from staff to improve dementia care, concerns about appropriate staffing, and the need for explicit implementation and sustainability plans. The importance of continuing education with specific attention to temporary staff was also found to be a necessary element to support better uptake of the person-centered intervention. [Journal of Gerontological Nursing, 50(2), 5-9.].