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The purpose of this parametric study was to develop a numerical simulation model calibrated with experimental data to predict the flexural behavior of prestressed concrete (PSC) girders subjected to long-term prestress losses. The model is capable of accurately simulating the flexural behavior of PSC girders using commercial finite-element (FE) software in the ABAQUS/Explicit program. The accuracy of the model was validated by comparing its results with flexural response test data from three post-tensioned girders, with the tendons ultimately having tensile strength capacities of 1860 MPa, 2160 MPa, and 2400 MPa. The comparison demonstrated generally excellent agreement between numerical and experimental results in terms of the load–deflection response and crack propagation behavior, from the onset of first cracking through the maximum load and into the ductile response range. Subsequently, a parametric study was conducted to evaluate the effects of tendon ultimate strength, amount of long-term prestress loss, grouting defects, degradation-induced reductions in concrete strength, and reductions in tendon cross-sectional area on girder flexural behavior. Through this parametric investigation, the study identified key factors with respect to long-term prestress loss that may influence the flexural behavior of aging PSC structures.

Shotcrete is a versatile construction material, yet its performance limitations, such as high rebound rates and poor adhesion, demand technological improvements to ensure structural reliability. Silicon manganese (SiMn) slag, a by-product of SiMn alloy production, has gained attention as a potential sustainable alternative to natural aggregates in construction materials, addressing both resource depletion and carbon reduction challenges in the industry. This study is conducted to develop and evaluate a new mix design of mortar incorporating SiMn slag as fine aggregate, focusing on enhancing performance. Mixtures with varying percentages (0%, 30%, 50%, 70%, and 100%) of SiMn slag as a fine aggregate replacement were evaluated for fresh properties (air content, slump), mechanical performance (compressive strength, flexural strength, splitting tensile strength), durability (chloride ion penetration resistance, freeze–thaw resistance, carbonation resistance), and constructability (rebound rate, free shrinkage) to assess suitability as mortar for shotcrete. The experimental results demonstrated that the mixture with 50% SiMn slag replacement demonstrated the most balanced performance, showing an increase of 12.33% in compressive strength, 8.97% in splitting tensile strength, and 18.4% in flexural strength compared to the control. Durability properties also improved by an average of 11.93%, while rebound rate and shrinkage were significantly reduced. The findings confirm that SiMn slag is a technically viable and advantageous substitute for fine aggregates in shotcrete. Further research is needed to refine its economic feasibility and broaden its implementation in sustainable construction.

After the first COVID-19 patient was diagnosed, non-pharmaceutical interventions such as social distancing and behavior change campaigns were implemented in South Korea. The social distancing policy restricted unnecessary gatherings and activities to prevent local transmission. This study aims to evaluate the effect of social distancing, a strategy for COVID-19 prevention, on the number of acute respiratory infection inpatients. This study used the number of hospitalized patients with acute respiratory infection from the Infectious Disease Portal of the Korea Centers for Disease Control and Prevention (KCDC) between the first week of January 2018, to the last week of January 2021. Intervention 1t represents the first patient occurrence of COVID-19, Intervention 2t represents the relaxing of the social distancing policy. We used acute respiratory infection statistics from Korea and segmented regression analysis was used. The analysis showed that the trend of the number of acute respiratory infection inpatients decreased after the implementation of the first patient incidence of COVID-19 due to prevention activities. After the relaxing of the social distancing policy, the number of inpatients with acute respiratory infections significantly increased. This study verified the effect of social distancing on the reduction in hospital admissions for acute respiratory viral infections.

Ginkgo biloba is utilized as food, medicine, wood, and street trees among other things. The objective of this study was to develop a loop-mediated isothermal amplification (LAMP) assay for gender distinction of G. biloba. Male-specific SCAR gene can be utilized to identify G. biloba gender using LAMP. The optimized LAMP conditions, temperature 60 °C, 2-mM MgSO4, and [F3/B3]:[FIP/BIP] primer ratio of 1:4 were selected as final conditions. The G. biloba SCAR LAMP displayed a sensitivity of 10 ng when amplified by concentration under the optimum conditions. Additionally, it demonstrated a particular response in male with SYBR Green I in LAMP analysis that can be a more powerful tool for field and scale-up applications. Our work represents a first attempt to identify G. biloba gender using LAMP and offers an efficient and reliable tool for roadside landscaping.

We conducted an RNA sequencing study to identify novel gene fusions in 80 discovery dataset tumors collected from young patients with diffuse gastric cancer (DGC). Twenty-five in-frame fusions are associated with DGC, three of which ( CLDN18-ARHGAP26, CTNND1-ARHGAP26 , and ANXA2-MYO9A ) are recurrent in 384 DGCs based on RT-PCR. All three fusions contain a RhoGAP domain in their 3’ partner genes. Patients with one of these three fusions have a significantly worse prognosis than those without. Ectopic expression of CLDN18-ARHGAP26 promotes the migration and invasion capacities of DGC cells. Parallel targeted RNA sequencing analysis additionally identifies TACC2-PPAPDC1A as a recurrent and poor prognostic in-frame fusion. Overall, PPAPDC1A fusions and in-frame fusions containing a RhoGAP domain clearly define the aggressive subset (7.5%) of DGCs, and their prognostic impact is greater than, and independent of, chromosomal instability and CDH1 mutations. Our study may provide novel genomic insights guiding future strategies for managing DGCs. Diffuse Gastric Cancer (DGC) is increasingly being considered separate to intestinal type gastric cancer; several fusions events have been reported as drivers of the disease but few of those have been subsequently validated. Here the authors perform RNA-seq on early-onset DGC patients who had not been treated with chemotherapy or radiation and identify a previously unknown fusion.

Nasal endoscopy is routinely performed to distinguish the pathological types of masses. There is a lack of studies on deep learning algorithms for discriminating a wide range of endoscopic nasal cavity mass lesions. Therefore, we aimed to develop an endoscopic-examination-based deep learning model to detect and classify nasal cavity mass lesions, including nasal polyps (NPs), benign tumors, and malignant tumors. The clinical feasibility of the model was evaluated by comparing the results to those of manual assessment. Biopsy-confirmed nasal endoscopic images were obtained from 17 hospitals in South Korea. Here, 400 images were used for the test set. The training and validation datasets consisted of 149,043 normal nasal cavity, 311,043 NP, 9,271 benign tumor, and 5,323 malignant tumor lesion images. The proposed Xception architecture achieved an overall accuracy of 0.792 with the following class accuracies on the test set: normal = 0.978 ± 0.016, NP = 0.790 ± 0.016, benign = 0.708 ± 0.100, and malignant = 0.698 ± 0.116. With an average area under the receiver operating characteristic curve (AUC) of 0.947, the AUC values and F1 score were highest in the order of normal, NP, malignant tumor, and benign tumor classes. The classification performances of the proposed model were comparable with those of manual assessment in the normal and NP classes. The proposed model outperformed manual assessment in the benign and malignant tumor classes (sensitivities of 0.708 ± 0.100 vs. 0.549 ± 0.172, 0.698 ± 0.116 vs. 0.518 ± 0.153, respectively). In urgent (malignant) versus nonurgent binary predictions, the deep learning model achieved superior diagnostic accuracy. The developed model based on endoscopic images achieved satisfactory performance in classifying four classes of nasal cavity mass lesions, namely normal, NP, benign tumor, and malignant tumor. The developed model can therefore be used to screen nasal cavity lesions accurately and rapidly.

Introduction Previous studies suggested that obstructive sleep apnea (OSA) can affect the autonomic nervous system. Patients with OSA appear to have a higher sympathetic component, a lower parasympathetic component, and greater autonomic nervous system (ANS) imbalance. We compared heart rate variability (HRV) with existing studies and confirmed classification accuracy through deep learning analysis (DLA), using electrocardiogram (ECG) data extracted from polysomnography (PSG). Methods We retrospectively surveyed people who underwent PSG at our hospital from January 2015 to March 2023. The diagnosis of OSA was classified into normal, mild, moderate, and severe based on AHI, and whether arrhythmia was identified during the test was also investigated. HRV analysis performed by frequency domain analysis of the tachogram. For DLA, the tachogram was converted to a Mel-spectrogram and a Convolutional Neuronal Network (CNN) was used to confirm the confusion matrix. Results Of a total of 1,806 PSG, 1,554 cases were selected, excluding 252 cases of arrhythmia. OSA confirmed by PSG was normal in 282 patients, mild in 334, moderate in 293, and severe in 645. When comparing the results of HRV divided into AHI below 15 and above, VLF power (ms2/Hz) was 940.78 ± 763.72 vs 1132.75 ± 1104.50 (p < 0.001), LF power (ms2/Hz) was 719.26 ± 734.71 vs. 724.46 ± 945.26 (p = 0.908), HF power (ms2/Hz) was 763.61 ± 1058.92 vs 595.53 ± 1386.75 (p = 0.011), and LF/HF ratio was 1.27 ± 0.74 vs 1.63 ± 1.02 (p < 0.001). As a result of DLA, the ROC AUC Score was confirmed to be 0.7077 and the F1 Score was 0.67. Conclusion As a result of HRV using ECG from PSG, OSA patients were found to have low HF power and high LF/HF ratio, similar to previous studies. Additionally, if tachogram's DLA accuracy can be improved through preprocessing and deep learning model improvements, it is expected that it can be used as a screening tool in various place. Support (if any) This work was partly supported by Institute of Information & Communications Technology Planning & Evaluation grant funded by the Korea government No.RS_2023_00227552, Development of artificial intelligence video background removal SaaS service using domestic semiconductor 64 TOPS.