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This study employs an inverse grey-box (IGB) modeling approach, which combines measured data and the physics of systems to predict the performance of shallow underground thermal energy storage (UTES) with top and side insulation. A simplified IGB model of the shallow UTES was developed using thermal network analysis. Experimental studies were conducted for shallow vertical and horizontal UTES configurations. Detailed models representing the field experiments were developed using the TRNSYS simulation tool and calibrated with the measured data from the experimental setup. The 4 Resistance 2 Capacitance (4R2C) IGB model was trained and tested in MATLAB using field experimental data and the data from the calibrated TRNSYS model. In comparing experimental data with the TRNSYS model for UTES, the prediction of outlet water temperature showed good agreement, with root mean square error (RMSE) and coefficient of variation of RMSE (CVRMSE) values of 0.94 °C and 3.16 % for vertical, and 0.99 °C and 2.97 % for horizontal configurations. The IGB model also aligned well with the experimental data, showing CVRMSE of 7.91 % and 3.17 % for vertical and horizontal systems, respectively. Sensitivity analysis revealed that model performance improves with longer training durations and closer testing times to training periods. However, a convergence point of 20 weeks of training data was achieved for making long-term performance predictions.

The decarbonization of buildings and communities has become a requirement for sustainability. In this regard, South Korea has set a target of net-zero-energy buildings, wherein all public buildings with a floor area exceeding 500 m2 must cover 80% of the primary energy consumption using renewable energy systems by 2030. This study investigated the improvement and maximization of energy from renewable energy systems through energy sharing between buildings by retrofitting an existing community. The feasibility of achieving a positive energy balance was investigated via simulations. The existing community is comprised of two residential houses and two office buildings. Through the retrofit design planning of the existing community, the total capacity of renewable energy could be applied at 75.4 kW after renovation. Through simulation, power generation rates of the building integrated photovoltaic (BIPV) and photovoltaic and thermal (PVT) systems were analyzed. The renewable energy generation rates were 2.48 times higher when using BIPV and PVT systems. Therefore, the analysis of the annual energy balance of the community indicated that 46.2% of the surplus energy can be generated via renewable energy systems, compared with that of conventional community. Thus, the retrofit community can be renovated into a positive energy community.

Granulomatous gastritis (GG) is a rare condition, with incidence between 0.08 and 0.35% in gastric biopsies. Various infectious and non-infectious aetiologies can be considered to cause granulomatous gastritis. Foreign bodies are a rare aetiology of GG and may result from foods, suture materials, or medications. We report a 59-year-old woman who had eaten large amounts of peanuts for more than 10 years and presented with epigastric discomfort. Esophagogastroduodenoscopy revealed multiple nodular lesions with ulcer scars at the stomach, which was diagnosed as GG probably caused by chronic peanut ingestion on endoscopic mucosal resection.

Objective This study aimed to develop and verify an endometriosis self-assessment tool (ESAT). Methods A non-experimental, descriptive, correlational study design was used. Candidate items were developed based on a conceptual framework constructed using the results of in-depth interviews and an integrative literature review. The construct validity of the developed tool was also examined. One-hundred and forty-two participants (117 patients with endometriosis and 25 patients without endometriosis) were included in the validity and reliability tests. The data were collected between August and December 2018. Nomological validity was verified based on significant correlations between the ESAT and the quality-of-life scores. Results A 21-item ESAT was developed, and its construct validity was supported. Exploratory factor analysis indicated that the tool consisted of four components (gastrointestinal symptoms, dysmenorrhea, usual symptoms, and the amount and characteristics of menstrual bleeding) with a variance of 61.6%. The variance in quality-of-life scores, as explained by the ESAT scores, was relatively high. Receiver operator characteristics curve analysis indicated that ESAT scores significantly differentiated endometriosis from non-endometriosis with fair discriminatory power at a cut-off score of 50 (sensitivity, 0.76; specificity, 0.72; area under the curve, >0.75; P50 points were more likely to have endometriosis. Thus, the reliability of the ESAT was confirmed. Conclusion The devised tool appears valid and reliable. This tool may allow women to determine their risk of endometriosis by distinguishing between normal and pathological menstruation-related symptoms.

Background: Endoscopic resection (ER) is a minimally invasive therapeutic approach for early gastric cancer (EGC), particularly for cases with a low risk of lymph node metastasis (LNM). Tumor budding (TB) has gained attention as a potential prognostic indicator for LNM in EGC. Case Presentation: We report two cases—a 73-year-old and an 81-year-old male patient—who presented with gastric adenocarcinoma. Both patients had small-sized, differentiated, and intramucosal adenocarcinomas. However, high-grade TBs per high-power field under ×200 magnification at the invasive front and LNMs were found in both cases. Conclusions: These cases conformed to the post-ER observation guidelines of the current treatment protocol, yet demonstrated LNMs. We found that TB could serve as an effective prognostic marker for LNM compared to traditional risk factors. The aim of this study is to re-examine the ability of TB to predict LNM in EGC, thereby providing an impetus for reconsideration and potential revision of the current treatment guidelines for EGC.

An inappropriate thermal environment negatively impacts workers, causing mental stress and safety accidents. Unskilled workers are more vulnerable to industrial accidents and thermal stress compared with skilled workers due to suboptimal and unfamiliar work. Previous studies have focused on individual characteristics (such as gender, age, and race), with limited emphasis on the thermal comfort sensation. This study identified the preferred thermal environment according to work experience and how mental stress differed between unskilled and skilled workers by examining their thermal comfort sensation. Predicted mean vote (PMV) was used as an indicator of the thermal environment, and five environments were constructed for PMV: −2, −1, 0, 1, and 2. Participants were recruited among current workers and the public. Mental stress and thermal comfort sensation were assessed using heart rate variability and thermal comfort vote, respectively. This study demonstrated that the skilled group experienced higher mental stress and a lower thermal comfort sensation. Contrastingly, in the sensitivity analysis, the unskilled group exhibited greater sensitivity to changes in the thermal environment. Through a comprehensive analysis, this study derived an optimal PMV range for each group. The findings can provide a reference for configuring the optimal thermal environment of the workplace.

A decrease in dynamic balance ability (DBA) in the elderly is closely associated with aging. Various studies have investigated different methods to quantify the DBA in the elderly through DBA evaluation methods such as the timed up and go test (TUG) and the six-minute walk test (6MWT), applying the G-Walk wearable system. However, these methods have generally been difficult for the elderly to intuitively understand. The goal of this study was thus to generate a regression model based on machine learning (ML) to predict the age of the elderly as a familiar indicator. The model was based on inertial measurement unit (IMU) data as part of the DBA evaluation, and the performance of the model was comparatively analyzed with respect to age prediction based on the IMU data of the TUG test and the 6MWT. The DBA evaluation used the TUG test and the 6MWT performed by 136 elderly participants. When performing the TUG test and the 6MWT, a single IMU was attached to the second lumbar spine of the participant, and the three-dimensional linear acceleration and gyroscope data were collected. The features used in the ML-based regression model included the gait symmetry parameters and the harmonic ratio applied in quantifying the DBA, in addition to the features of description statistics for IMU signals. The feature set was differentiated between the TUG test and the 6MWT, and the performance of the regression model was comparatively analyzed based on the feature sets. The XGBoost algorithm was used to train the regression model. Comparison of the regression model performance according to the TUG test and 6MWT feature sets showed that the performance was best for the model using all features of the TUG test and the 6MWT. This indicated that the evaluation of DBA in the elderly should apply the TUG test and the 6MWT concomitantly for more accurate predictions. The findings in this study provide basic data for the development of a DBA monitoring system for the elderly.

An inappropriate thermal environment negatively impacts workers, causing mental stress and safety accidents. Unskilled workers are more vulnerable to industrial accidents and thermal stress compared with skilled workers due to suboptimal and unfamiliar work. Previous studies have focused on individual characteristics (such as gender, age, and race), with limited emphasis on the thermal comfort sensation. This study identified the preferred thermal environment according to work experience and how mental stress differed between unskilled and skilled workers by examining their thermal comfort sensation. Predicted mean vote (PMV) was used as an indicator of the thermal environment, and five environments were constructed for PMV: −2, −1, 0, 1, and 2. Participants were recruited among current workers and the public. Mental stress and thermal comfort sensation were assessed using heart rate variability and thermal comfort vote, respectively. This study demonstrated that the skilled group experienced higher mental stress and a lower thermal comfort sensation. Contrastingly, in the sensitivity analysis, the unskilled group exhibited greater sensitivity to changes in the thermal environment. Through a comprehensive analysis, this study derived an optimal PMV range for each group. The findings can provide a reference for configuring the optimal thermal environment of the workplace. [ABSTRACT FROM AUTHOR]