TM Model Research Articles

AN INTEGRATED SHIP RE-DESIGN/MODIFICATION STRATEGY

Herein, we present an integrated ship re-design/modification strategy that integrates the ‘Computer-Aided Design (CAD)’ and ‘Computational Fluid Dynamics (CFD)’ to modify the ship hull form for better performance in resistance. We assume a modular design and the ship hull form modification focuses on the forward module (e.g. bulbous bow) and aft module (e.g. stern bulb) only. The ship hull form CAD model is implemented with NAPA*TM and CFD model is implemented with Shipflow**TM. The basic ship hull form parameters are not changed and the modifications in some of the technical parameters because of re-designed bulbous bow and stern bulb are kept at very minimum. The bulbous bow is re-designed by extending an earlier method (Sharma and Sha (2005b)) and stern bulb parameters for re-design are computed from the experience gained from literature survey. The re-designed hull form is modeled in CAD and is integrated and analyzed with Shipflow**TM. The CAD and CFD integrated model is validated and verified with the ITTC approved recommendations and guidelines. The proposed numerical methodology is implemented on the ship hull form modification of a benchmark ship, i.e. KRISO container ship (KCS). The presented results show that the modified ship hull form of KCS - with only bow and stern modifications - using the present strategy, results into resistance and propulsive improvement.

The Triomathematics, Trithmetic, Trichometry, Triangulus & Ternary Algebra Expressions that Define the Triophysics and Triological Sciences Related to the Triscalar Mitigation of Non-Ionized Radiation via Bioharmonic Biobalancing Healthy Smart Solar Nanob

The narrative in this paper provides a detailed and descriptive mathematical model for operative Healthy Smart Solar Nanobiotechnology ™ © (Osler, 2021) designed to biobalance a non-ionized irradiated environment. The in-depth mathematics mentioned in this paper are the foundational elements for groundbreaking innovative terminologies and new pioneering scientific fields covered in detail to explicitly explain how Healthy Smart Solar Nanobiotechnology ™ © interacts with and works within the environment. These revolutionary fields of study are directly associated with the mitigating mathematical operation of Solar Smart Nanobiotechnology ™ ©. The mathematics defined in the paper also aid in the creation of new, novel, and original areas and arenas of knowledge that are designed to most accurately describe the trichotomous holistic solutions that can be used to create an all around healthy and safe environment for the well-being of all.

A Critical Analysis of Myths About Dissociative Identity Disorder

In a review of the literature in this journal, Piedfort-Marin et al. (1921) identified what they purported to be myths about dissociative identity disorder (DID) and dissociative amnesia. When responding to these beliefs, they supported the Trauma Model of dissociation and argued for a causal etiological link between trauma and dissociative conditions. In contrast, they challenged the Sociocognitive Model (SCM), which they claimed rejects the existence of DID and associated disorders (e.g., dissociative amnesia) and considers symptoms to be the byproduct of fantasy, suggestion, and the iatrogenic effect of psychotherapies. In this article, we critically evaluate the authors’ arguments and propose a more balanced, accurate, and comprehensive view of the sociocognitive model. We demonstrate that this model neither rejects the existence of DID, nor a link between trauma and dissociation potentially mediated by a variety of cognitive-affective-behavioral variables. We argue, contrary to Piedfort-Marin et al., that the tendency to confabulate and other cognitive and sociocultural variables may also contribute to the development of DID. We contend that a multifactorial integrative etiological perspective can move the field beyond a limited focus on controversies that divide the TM and SCM models of dissociation.

Spatio-Temporal Variations of Precipitable Water Vapor and Horizontal Tropospheric Gradients from GPS during Typhoon Lekima

GPS data during Typhoon Lekima at 700 stations in China were processed by the Precise Point Positioning (PPP) method. A refined regional Tm model was used to derive the precipitable water vapor (PWV) at these GPS stations. Spatio-temporal variations of PWV with the typhoon process were analyzed. As the typhoon approached, PWV at stations near the typhoon center increased sharply from about 50 mm to nearly 80 mm and then dropped back to about 40–50 mm as the typhoon left. Comparisons of GPS, radiosonde, the Global Data Assimilation System (GDAS) Global Forecast System (GFS) analysis products and ERA5 reanalysis products at four matched GPS-RS stations show overall overestimations of PWV from radiosonde, GFS and ERA5 compared with GPS in a statistical perspective. An empirical orthogonal functions (EOF) analysis of the PWV during the typhoon event revealed some different patterns of variability, with both the first EOF (~36.1% of variance) and second EOF (~30.3% of variance) showing distinctively large anomalies over the typhoon landing locations. The typhoon caused a large horizontal tropospheric gradient (HTG) with the magnitude reaching 5 mm and the direction pointing to the typhoon center when it made a landfall on mainland China. The magnitude and the consistency of the HTG direction decreased overall as the typhoon weakened.

Conceptual Data Modeling: Entity-Relationship Models as Thinging Machines

Data modeling is a process of developing a model to design and develop a data system that supports an organization's various business processes. A conceptual data model represents a technology-independent specification of structure of data to be stored within a database. The model aims at providing richer expressiveness and incorporating a set of semantics to (a) support the design, control, and integrity parts of the data stored in data management structures and (b) coordinate viewing of connections and ideas on a database. The described structure of the data is often represented in an entity-relationship (ER) model, which was one of the first data-modeling techniques and is likely to continue to be a popular way of characterizing entity classes, attributes and relationships. This paper is an attempt to examine the basic ER modeling notions to analyze the concepts to which they refer as well as ways to represent them. In such a mission, we apply a new modeling methodology (thinging machine; TM) to ER in terms of its fundamental building constructs, representation entities, relationships and attributes. The goal of this venture is to further the understanding of data models and enrich their semantics. Three specific contributions to modeling in this context are incorporated: (a) using the TM model's five generic actions to inject processing in the ER structure; (b) relating the single ontological element of TM modeling (i.e., a thing/machine or thimac) to ER entities and relationships; and (c) proposing a high-level integrated, extended ER model that includes structural and time-oriented notions (e.g., events or behavior).

A Weighted Mean Temperature Model with Nonlinear Elevation Correction Using China as an Example

The weighted mean temperature (Tm) is a crucial parameter for determining the tropospheric delay in transforming precipitable water vapor. We used the reanalysis data provided by European Centre for Medium-Range Weather Forecasts (ECMWF) to analyze the distribution characteristics of Tm in the vertical direction in China. To address the problem that the precision of the traditional linear function model is limited in fitting the Tm profile, a scheme using the linear and Fourier functions to fit the Tm profile was constructed. Based on the least squares principle (LSQ) to fit the change in its coefficients over time, a Tm model for China with nonlinear elevation correction (CTm-h) was constructed. The experimental results show that, using ECMWF and radiosonde data to evaluate the precision of the CTm-h model, the RMS is 3.43 K and 4.64 K, respectively. Compared to GPT2w, the precision of the CTm-h model in China is increased by about 26.8%. The CTm-h model provides a significant improvement in the correction effect of Tm in the vertical direction, and the Tm profile calculated by the model is closer to the reference value.

Spatiotemporal characteristics of GNSS-derived precipitable water vapor during heavy rainfall events in Guilin, China

Precipitable Water Vapor (PWV), as an important indicator of atmospheric water vapor, can be derived from Global Navigation Satellite System (GNSS) observations with the advantages of high precision and all-weather capacity. GNSS-derived PWV with a high spatiotemporal resolution has become an important source of observations in meteorology, particularly for severe weather conditions, for water vapor is not well sampled in the current meteorological observing systems. In this study, an empirical atmospheric weighted mean temperature (Tm) model for Guilin is established using the radiosonde data from 2012 to 2017. Then, the observations at 11 GNSS stations in Guilin are used to investigate the spatiotemporal features of GNSS-derived PWV under the heavy rainfalls from June to July 2017. The results show that the new Tm model in Guilin has better performance with the mean bias and Root Mean Square (RMS) of − 0.51 and 2.12 K, respectively, compared with other widely used models. Moreover, the GNSS PWV estimates are validated with the data at Guilin radiosonde station. Good agreements are found between GNSS-derived PWV and radiosonde-derived PWV with the mean bias and RMS of − 0.9 and 3.53 mm, respectively. Finally, an investigation on the spatiotemporal characteristics of GNSS PWV during heavy rainfalls in Guilin is performed. It is shown that variations of PWV retrieved from GNSS have a direct relationship with the in situ rainfall measurements, and the PWV increases sharply before the arrival of a heavy rainfall and decreases to a stable state after the cease of the rainfall. It also reveals the moisture variation in several regions of Guilin during a heavy rainfall, which is significant for the monitoring of rainfalls and weather forecast.

A Study of Talent Management and Employee Job-related Outcomes in the Indian Manufacturing Industry

This study examined the relationship between talent management and employee job-related outcomes in the Indian manufacturing setting. Talent management practices included talent acquisition, talent development, talent engagement, and talent retention. Employee job-related outcomes of this study included intent to stay, job engagement, affective commitment, job satisfaction, and employee competency. This study used the social exchange theory and a resource-based view as the theoretical underpinning for the proposed TM model. The role of organizational culture as a moderator was also examined in the study. The sample of the study consisted of 992 full-time employees working in one of the largest textiles and paper manufacturing organizations of India with facilities located in two Indian states. Data was collected online through a questionnaire composed of several existing survey instruments. The response rate for the survey was high (62.98 percent). The primary method of data analysis was structural equation modeling. The overall relationship between talent management and employee job-related outcome was found significant and positive. The findings showed that organizational culture is significantly related to talent management and employee job-related outcomes. However, the organizational culture does moderate the relationship between talent management and employee job-related outcomes.

Advanced Grid Model of Weighted Mean Temperature Based on Feedforward Neural Network Over China

AbstractWeighted mean temperature (Tm) is a key parameter in Global Navigation Satellite System meteorology. In this study, European Centre for Medium‐Range Weather Forecasts Re‐Analysis product with a spatial resolution of 0.5° × 0.5° from 1999 to 2018 was used to study the spatiotemporal behaviors of Tm in China. Decomposed by Fast Fourier Transformation, Tm and lapse rate (β) variations are highly latitude‐dependent and exhibit periodicities on annual, semi‐annual, and diurnal scales. Meanwhile, Tm keeps increasing at a rate of 0.25 K per decade across China. Based on these discoveries, this study build a new grid Tm model based on feedforward neural network (FNN) with a spatial resolution of 0.5° × 0.5°, known as Grid‐FNN model. FNN is applied to each grid point to compensate the residual error of the corresponding periodic functions. And the fitting accuracy at each grid is improved by the FNN algorithm. ERA‐Interim product with a spatial resolution of 0.4° × 0.4° and Radiosonde data in 2018 are used to validate the new model, and the accuracy of Grid‐FNN model is proved 8.6% and 10.9% better than GPT2w‐Tm model, respectively. The Grid‐FNN model also shows better performance than IGPT2w, GTm‐III, and GTrop model in autumn and winter and in high‐altitude regions.

A Novel Modeling Strategy of Weighted Mean Temperature in China Using RNN and LSTM

In the meteorology of Global Navigation Satellite System, the weighted mean temperature (Tm) is a key parameter in the process of converting the zenith wetness delay into precipitable water vapor, and it plays an important role in water vapor monitoring. In this research, two deep learning algorithms, namely, recurrent neural network (RNN) and long short-term memory neural network (LSTM), were used to build a high-precision weighted mean temperature model for China using their excellent time series memory capability. The model needs site location information and measured surface temperature to predict the weighted mean temperature. We used data from 118 stations in and around China provided by the Integrated Global Radiosonde Archive from 2010 to 2015 to train the model and data from 2016 for model testing. The root mean square error (RMSE) of the RNN_Tm and LSTM_Tm models were 3.01 K and 2.89 K, respectively. Compared with the values calculated by the empirical GPT3 model, the accuracy was improved by 31.1% (RNN_Tm) and 33.9% (LSTM_Tm). In addition, we selected another 10 evenly distributed stations in China and used the constructed model to test the prediction capability of the weighted mean temperature from 2010 to 2016. The RMSE values were 2.95 K and 2.86 K, which proved that the model also exhibits high generalization in non-modeling sites in China. In general, the RNN_Tm and LSTM_Tm models have a good performance in weighted mean temperature prediction.

Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

Precise modeling of weighted mean temperature (Tm) is critical for realizing real-time conversion from zenith wet delay (ZWD) to precipitation water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology applications. The empirical Tm models developed by neural network techniques have been proved to have better performances on the global scale; they also have fewer model parameters and are thus easy to operate. This paper aims to further deepen the research of Tm modeling with the neural network, and expand the application scope of Tm models and provide global users with more solutions for the real-time acquisition of Tm. An enhanced neural network Tm model (ENNTm) has been developed with the radiosonde data distributed globally. Compared with other empirical models, the ENNTm has some advanced features in both model design and model performance, Firstly, the data for modeling cover the whole troposphere rather than just near the Earth’s surface; secondly, the ensemble learning was employed to weaken the impact of sample disturbance on model performance and elaborate data preprocessing, including up-sampling and down-sampling, which was adopted to achieve better model performance on the global scale; furthermore, the ENNTm was designed to meet the requirements of three different application conditions by providing three sets of model parameters, i.e., Tm estimating without measured meteorological elements, Tm estimating with only measured temperature and Tm estimating with both measured temperature and water vapor pressure. The validation work is carried out by using the radiosonde data of global distribution, and results show that the ENNTm has better performance compared with other competing models from different perspectives under the same application conditions, the proposed model expanded the application scope of Tm estimation and provided the global users with more choices in the applications of real-time GNSS-PWV retrival.

Far-field characterization of wave oblique incidence on body channel for implant communication by using an analytical model

Characterization of human body channel is a necessity to pave way for the practical implementation of implant communication. Considering there are few investigations on the characterization of incident wave propagating at an arbitrary angle to human tissues, this paper applies an analytical model of wave oblique incidence onto multilayered planar lossy tissues to evaluate the far-field EMFs and PLs of body channel. With this model, EMFs and PLs of both TM and TE wave oblique incidence at different angles and frequencies in out-to-in and in-to-out propagations are calculated. A FEM model and a measurement on porcine tissues are also carried out. Comparing with simulation and experiment, the analytical results of PLs have the maximum differences of 0.03 and 1.0 dB, respectively, in out-to-in propagation and of 0.09 and 1.7 dB, respectively, in in-to-out propagation. Therefore, the analytical model is accurate and indicates the mechanism of EM wave propagation in body channel theoretically.

Evaluation of Hourly PWV Products Derived From ERA5 and MERRA‐2 Over the Tibetan Plateau Using Ground‐Based GNSS Observations by Two Enhanced Models

AbstractPrecipitable water vapor (PWV), despite being the key factor for assessing climate change over the Tibetan Plateau (TP), is undersampled in this region due to the complex terrain and climate conditions therein. The fifth‐generation European Center for Medium‐Range Weather Forecasts Reanalysis (ERA5) and Second Modern‐Era Retrospective Analysis for Research and Applications (MERRA‐2) data sets are the two newly released global reanalyzes that provide hourly PWV products with a high spatiotemporal resolution for water vapor research. The accuracies of these two PWV products on the TP require a comprehensive and cautious assessment. In this study, the hourly PWV values derived from ERA5 and MERRA‐2 are systematically assessed by a comparison with 33 Global Navigation Satellite System (GNSS) stations from 2017 to 2018 over the TP. To improve the reliability of this evaluation, an enhanced Tm model and a PWV vertical correction model, namely, TP‐Tm and TP‐PWVC, respectively, are developed, and both models show better performance when compared against other widely used models. The accuracies of the ERA5‐ and MERRA‐2‐derived PWV at the 33 GNSS stations are very high; ERA5 has the smallest mean bias and root mean square (RMS) error of −0.08 and 1.77 mm, respectively, compared to those of 0.53 and 2.12 mm for MERRA‐2. In addition, the biases and RMS errors show significant regional and seasonal differences. The performance of ERA5 is slightly better than MERRA‐2, mainly due to its higher spatial resolution. The spatial distributions of the PWV derived from the two reanalyzes are consistent with the GNSS‐derived PWV distribution. The diurnal PWV variations derived from ERA5 are in good agreement with the diurnal variation of the GNSS‐derived PWV, whereas significant differences are observed for MERRA‐2 at several stations. These results indicate that ERA5 can provide more reliable hourly PWV estimates over the TP, which can help users further understand the strengths and weaknesses of these two PWV products and promote climate change research.

Compact and Low-Coupled Tripolarized Microstrip MIMO Antenna Based on Parasitic Patch Loading

A miniaturized and low-coupled tripolarized microstrip multiple-input multiple-output (MIMO) antenna working at 3.5 GHz is proposed. This antenna includes a main circular radiating patch, which is shorted to the ground plane by an outer layer of vias, and a parasitic patch inside the cavity, which is connected to the main patch by an inner layer of vias. Through the selection of suitable number of the outer layer vias and the loading of the parasitic patch, the resonant frequencies of modes TM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sub> and TM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">02</sub> can be reduced, individually. The design procedure of this antenna is demonstrated and a transmission-line model for TM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">02</sub> mode is presented, which can give a good explanation for the frequency reducing by the capacitance introduced by parasitic patch loading. This design can not only downsize the diameter of the unloaded antenna from 0.7λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> to 0.485λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , with the same height of 0.035λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , but also can reduce the interport coupling from -13 to -18 dB. The measured and simulated S-parameters and radiation patterns are in good agreements. Fine MIMO performance of the proposed tripolarized antenna in terms of large capacity and low correlation coefficients has been verified by some propagation measurements in a real-world office.

Conceptual Modeling for Computer Organization and Architecture

Understanding computer system hardware, including how computers operate, is essential for undergraduate students in computer engineering and science. Literature shows students learning computer organization and assembly language often find fundamental concepts difficult to comprehend within the topic materials. Tools have been introduced to improve students comprehension of the interaction between computer architecture, assembly language, and the operating system. One such tool is the Little Man Computer (LMC) model that operates in a way similar to a computer but that is easier to understand. Even though LMC does not have modern CPUs with multiple cores nor executes multiple instructions, it nevertheless shows the basic principles of the von Neumann architecture. LMC aims to introduce students to such concepts as code and instruction sets. In this paper, LMC is used for an additional purpose: a tool with which to experiment using a new modeling language (i.e., a thinging machine; TM) in the area of computer organization and architecture without involving complexity in the subject. That is, the simplicity of LMC facilitates the application of TM without going deep into computer organization/architecture materials. Accordingly, the paper (a) provides a new way for using the LMC model for whatever purpose (e.g., education) and (b) demonstrates that TM can be used to build an abstract level of description in the organization/architect field. The resultant schematics from the TM model of LMC offer an initial case study that supports our thesis that TM is a viable method for hardware/software-independent descriptions in the computer organization and architect field of study.

Neural Network-Based Models for Estimating Weighted Mean Temperature in China and Adjacent Areas

The weighted mean temperature (Tm) is a key parameter when converting the zenith wet delay (ZWD) to precipitation water vapor (PWV) in ground-based Global Navigation Satellite System (GNSS) meteorology. Tm can be calculated via numerical integration with the atmospheric profile data measured along the zenith direction, but this method is not practical in most cases because it is not easy for general users to get real-time atmospheric profile data. An alternative method to obtain an accurate Tm value is to establish regional or global models on the basis of its relations with surface meteorological elements as well as the spatiotemporal variation characteristics of Tm. In this study, the complex relations between Tm and some of its essentially associated factors including the geographic position and terrain, surface temperature and surface water vapor pressure were considered to develop Tm models, and then a non-meteorological-factor Tm model (NMFTm), a single-meteorological-factor Tm model (SMFTm) and a multi-meteorological-factor Tm model (MMFTm) applicable to China and adjacent areas were established by adopting the artificial neural network technique. The generalization performance of new models was strengthened with the help of an ensemble learning method, and the model accuracies were compared with several representative published Tm models from different perspectives. The results show that the new models all exhibit consistently better performance than the competing models under the same application conditions tested by the data within the study area. The NMFTm model is superior to the latest non-meteorological model and has the advantages of simplicity and utility. Both the SMFTm model and MMFTm model show higher accuracy than all the published Tm models listed in this study; in particular, the MMFTm model is about 14.5% superior to the first-generation neural network-based Tm (NN-I) model, with the best accuracy so far in terms of the root-mean-square error.

International Students Library Usage Frequency Patterns in Academic Libraries: A User Survey at Jiangsu University Library

Academic libraries are supposed to provide quality services to its user including international students in order to increase its patronage. The main purpose of this paper is to assess the frequency patterns of how international students use academic libraries. The user survey was conducted in Jiangsu University Library using the library usage pattern with Five-Point Likert scale survey questions from the LibQUAL+TM model. The survey was administered to the international students, with 201 respondents based on simple random sampling. The researchers used analysis methods like descriptive statistics in the form of frequencies and percentages, inferential statistics and Structural Equation Model (SEM). The study revealed that majority of international students frequently use library resources both on library premises and through the library webpage on daily, weekly, monthly and quarterly basis while majority of them use non library resources like google on daily and weekly basis. Majority of these respondents who use the library resources are Asians (Non- Chinese) and Africans. The results also revealed that there is a negative relationship and effect of using non-library resources on library premises on using library resources and no effect on using library resources through the library webpage. This implies that and increase in the use of non-library resources will significantly decrease the use of library resources. The study implies that academic libraries should improve its services and resources to international students to increase their patronage.

Improved atmospheric weighted mean temperature modeling using sparse kernel learning

As a crucial parameter in the process of converting the zenith wet delay into precipitable water vapor at Global Navigation Satellite System (GNSS) stations, the weighted mean temperature (Tm) influences the accuracy of GNSS-based water vapor retrievals. We propose an improved atmospheric Tm modeling method by introducing sparse kernel learning to obtain high-accuracy and high-temporal-resolution Tm data. To establish the model, the temporal variation characteristics of Tm time series are first analyzed by spectral analysis using the Lomb–Scargle periodogram. Second, the sparse kernel learning method is introduced to model the residuals from spectral analysis, for which the Gauss radial basis function kernel, L1-norm regularization, and the highly efficient fast iterative shrinkage thresholding algorithm are employed. To verify the performance of the proposed method, we used ERA5 hourly data from China’s 9 International GNSS Service stations produced by the European Centre for Medium-Range Weather Forecasts. ERA5 Tm data with a temporal resolution of 12 h are used as the training data, and the ERA5 hourly Tm data (excluding the data employed for modeling) are used for testing. The results show that compared with the spectral analysis accuracy, the accuracy of the calculated Tm can be improved by approximately 48.3% when the residual sparse kernel learning method is used. Thus, this proposed method for GNSS-based water vapor retrievals can provide high-accuracy and high-temporal-resolution Tm data.

Global grid-based Tm model with vertical adjustment for GNSS precipitable water retrieval

Global grid-based Tm model with vertical adjustment for GNSS precipitable water retrieval

Meeting Patients Where They Are: Evaluating a Student-Run, Shelter-Based Model for Connecting Homeless Patients with a Medical Home

Background: Substantial barriers exist between persons experiencing homelessness and the health care system, which contributes to significant health disparities. Innovations to connect patients to long-term primary care via the ‘medical home’ model have had mixed results. This study evaluates the effectiveness of an innovative, student-run, shelter-based clinic model that leverages its onsite location to link patients to a medical home.&#x0D; Methods: A retrospective chart review of patient visits at two shelter-based, student-run clinics between October 1, 2016 and October 1, 2017 was performed. Data collected included demographics, health risk factors, medical histories, and visit chief concerns. Patients referred to a medical home were assessed for successful follow-up at that location within 90 days. Successful medical home follow-up attendance was modeled using multivariable logistic regression with backwards elimination and a significance threshold of p&lt;0.05.&#x0D; Results: Within 90 days of referral, 30% of all patients referred to the medical home successfully attended a follow-up appointment, with 57% of new patients attending more than one appointment in the same time interval. Among patients who received referrals, those with a mental health related chief complaint were significantly more likely to follow-up at a medical home than those with other chief complaints (p=0.024). Medicaid insurance status negatively predicted successful follow-up (p=0.041).&#x0D; Conclusions: Student-run free clinics in locations that access vulnerable populations can be effectively used to link patients to medical homes. A better understanding of these factors may provide insight into how to help patients access a medical home and establish longitudinal care.