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Abstract Precisely forecasting how concrete reinforced with fiber-reinforced polymers (FRP) responds under compression is essential for fine-tuning structural designs, ensuring constructions fulfill safety criteria, avoiding overdesigning, and consequently minimizing material expenses and environmental impact. Therefore, this study explores the viability of gradient boosting regression tree (GBRT), random forest (RF), artificial neural network-multilayer perceptron (ANNMLP) and artificial neural network-radial basis function (ANNRBF) in predicting the compressive behavior of fiber-reinforced polymer (FRP)-confined concrete at ultimate. The accuracy of the proposed machine learning approaches was evaluated by comparing them with several empirical models concerning three different measures, including root mean square errors (RMSE), mean absolute errors (MAE), and determination coefficient (R2). In this study, the evaluations were conducted using a substantial collection of axial compression test data involving 765 circular specimens of FRP-confined concrete assembled from published sources. The results indicate that the proposed GBRT algorithm considerably enhances the performance of machine learning models and empirical approaches for predicting strength ratio of confinement (f′ cc /f′ co ) by an average improvement in RMSE as 17.3%, 0.65%, 66.81%, 46.12%, 46.31%, 46.87% and 69.94% compared to RF, ANNMLP, ANNRBF, and four applied empirical models, respectively. It is also found that the proposed ANNMLP algorithm exhibits notable superiority compared to other models in terms of reducing RMSE values as 9.67%, 11.29%, 75.11%, 68.83%, 73.64%, 69.49% and 83.74% compared to GBRT, RF, ANNRBF and four applied empirical models for predicting strain ratio of confinement (ε cc /ε co ), respectively. The superior performance of the GBRT and ANNMLP compared to other methods in predicting the strength and strain ratio confinements is important in evaluating structural integrity, guaranteeing secure functionality, and streamlining engineering plans for effective utilization of FRP confinement in building projects.

Abstract Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (NES), root mean square error (RMSE), Willmott’s index of agreement (WI), and Legates–McCabe’s index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Qt-n) and suspended sediment concentration (St-n) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (St-1, St-2, St-3, St-4) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest NES = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest NES = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.

Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these changes at regional scales. This assessment is particularly critical for Afghanistan, where agriculture serves as the primary livelihood for the population. New global climate model (GCM) simulations have recently been released for the recently established shared socioeconomic pathways (SSPs). This requires evaluating projected precipitation changes under these new scenarios and subsequent policy updates. This research employed six GCMs from the CMIP6 to project spatial and temporal precipitation changes across Afghanistan under all SSPs, including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The employed GCMs were bias-corrected using the Global Precipitation Climatological Center's (GPCC) monthly gridded precipitation data with a 1.0° spatial resolution. Subsequently, the climate change factor was calculated to assess precipitation changes for both the near future (2020–2059) and the distant future (2060–2099). The bias-corrected projections' multi-model ensemble (MME) revealed increased precipitation across most of Afghanistan for SSPs with higher emissions scenarios. The bias-corrected simulations showed a substantial increase in summer precipitation of around 50%, projected under SSP1-1.9 in the southwestern region, while a decline of over 50% is projected in the northwestern region until 2100. The annual precipitation in the northwest region was projected to increase up to 15% for SSP1-2.6. SSP2-4.5 showed a projected annual precipitation increase of around 20% in the southwestern and certain eastern regions in the far future. Furthermore, a substantial rise of approximately 50% in summer precipitation under SSP3-7.0 is expected in the central and western regions in the far future. However, it is crucial to note that the projected changes exhibit considerable uncertainty among different GCMs.

X-ray structural science is undergoing a revolution driven by the emergence of X-ray Free-electron Laser (XFEL) facilities. The structures of crystalline solids can now be studied on the picosecond time scale relevant to phonons, atomic vibrations which travel at acoustic velocities. In the work presented here, X-ray diffuse scattering is employed to characterize the time dependence of the liquid phase emerging from femtosecond laser-induced melting of polycrystalline gold thin films using an XFEL. In a previous analysis of Bragg peak profiles, we showed the supersonic disappearance of the solid phase and presented a model of pumped hot electrons carrying energy from the gold surface to scatter at internal grain boundaries. This generates melt fronts propagating relatively slowly into the crystal grains. By conversion of diffuse scattering to a partial X-ray pair distribution function, we demonstrate that it has the characteristic shape obtained by Fourier transformation of the measured F(Q). The diffuse signal fraction increases with a characteristic rise-time of 13 ps, roughly independent of the incident pump fluence and consequent final liquid fraction. This suggests the role of further melt-front nucleation processes beyond grain boundaries.

Abstract The structures, strain fields, and defect distributions in solid materials underlie the mechanical and physical properties across numerous applications. Many modern microstructural microscopy tools characterize crystal grains, domains and defects required to map lattice distortions or deformation, but are limited to studies of the (near) surface. Generally speaking, such tools cannot probe the structural dynamics in a way that is representative of bulk behavior. Synchrotron X-ray diffraction based imaging has long mapped the deeply embedded structural elements, and with enhanced resolution, dark field X-ray microscopy (DFXM) can now map those features with the requisite nm-resolution. However, these techniques still suffer from the required integration times due to limitations from the source and optics. This work extends DFXM to X-ray free electron lasers, showing how the $$10^{12}$$ 10 12 photons per pulse available at these sources offer structural characterization down to 100 fs resolution (orders of magnitude faster than current synchrotron images). We introduce the XFEL DFXM setup with simultaneous bright field microscopy to probe density changes within the same volume. This work presents a comprehensive guide to the multi-modal ultrafast high-resolution X-ray microscope that we constructed and tested at two XFELs, and shows initial data demonstrating two timing strategies to study associated reversible or irreversible lattice dynamics.

Purpose: To investigate the effects of intra-articular glenohumeral joint triamcinolone injection in treating secondary adhesive capsulitis after breast cancer surgery. Methods: This study prospectively enrolled 37 participants, including 22 in the breast cancer surgery group and 15 in the idiopathic group. All participants received intra-articular glenohumeral joint triamcinolone injection in the affected shoulder joint. The clinical outcomes included the Shoulder Pain and Disability Index (SPADI), passive range of motion (PROM), and pain intensity on the Numeric Rating Scale (NRS), which were evaluated before the intervention and 1, 3, and 6 months after. The primary outcome of this study was the mean difference in the total SPADI from baseline to 6 months after the intervention. Results: The mean differences in the total SPADI scores from baseline to 6 months after the intervention were 36.2 ± 16.4 and 47.9 ± 15.2 in the breast cancer surgery group and the idiopathic group, respectively. There was no significant difference between the two groups (p = 0.1495). However, the improvements in the SPADI pain subscale at the 3- and 6-month follow-up visits (−31.2 vs. −48.8, p = 0.042; −34.1 vs. −50.7, p = 0.0006) and the PROM of abduction at the 3-month follow-up (52.4 vs. 70.3, p = 0.0072) were inferior in the breast cancer surgery group compared to the idiopathic group. There were no adverse events in either group. Conclusion: Intra-articular triamcinolone injection is an effective and safe treatment option for adhesive capsulitis after breast cancer surgery; however, it has less effect than for idiopathic adhesive capsulitis.

Background and Objectives: This study evaluated the efficacy and safety of temporary ureteral occlusion combined with urinary diversion using a single-access route created by inserting a balloon catheter through a pigtail nephrostomy drainage catheter. With this approach, we aimed to offer an alternative for patients with ureteral leaks who are suboptimal surgical candidates. Materials and Methods: This retrospective study included nine patients (eight of which were bilateral cases and one was unilateral, totaling seventeen cases) who underwent the surgery between September 2023 and March 2024. The method involved gaining percutaneous access to the pelvicalyceal system, inserting a 4-French Fogarty balloon catheter through a pigtail nephrostomy catheter, and inflating the balloon at the proximal or mid-ureter. Results: All 17 cases achieved technical successful with no major complications. The procedure effectively relieved symptoms associated with urinary leakage in most patients. However, the significant deflation of the balloon catheter occurred in five cases (29.4%), with three (17.6%) experiencing complete deflation. In these five cases, the final balloon size was 5.81 mm (range: 0–8.9 mm), confirming a 25.0% decrease in size from pre- to post-procedure. Ureteral occlusion was 28.3 d long on average (range: 8–57 d). All patients experienced symptom relief during temporary ureteral occlusion. Except for two patients lost to follow-up, three patients showed symptom improvement with only PCN and four patients underwent surgical closure of the fistula tract before or after balloon catheter removal. Conclusions: This study confirms that this approach is safe and effective.

Abstract High-throughput virtual screening for crystals aims to discover new materials by evaluating the property of every virtual candidate in the database exhaustively. During this process, the major computational bottleneck is the costly structural relaxation of each hypothetical material on the large-scale dataset using density functional theory (DFT) calculations. Here, we present a generative domain translation framework that maps the unrelaxed structural domains to the relaxed domains, enabling data-driven structural translations. The model predicts the materials formation energy with a small mean absolute error without DFT relaxations, and furthermore can produce the atomic coordinates consistent with the DFT relaxed structures. The utility of the proposed concept is not restricted to the structural domains, and we expect that it can be extended to translate the domain of easy-to-compute properties into the domain of more difficult properties.

Abstract Identifying the three-dimensional (3D) crystal plane and strain-field distributions of nanocrystals is essential for optical, catalytic, and electronic applications. However, it remains a challenge to image concave surfaces of nanoparticles. Here, we develop a methodology for visualizing the 3D information of chiral gold nanoparticles ≈ 200 nm in size with concave gap structures by Bragg coherent X-ray diffraction imaging. The distribution of the high-Miller-index planes constituting the concave chiral gap is precisely determined. The highly strained region adjacent to the chiral gaps is resolved, which was correlated to the 432-symmetric morphology of the nanoparticles and its corresponding plasmonic properties are numerically predicted from the atomically defined structures. This approach can serve as a comprehensive characterization platform for visualizing the 3D crystallographic and strain distributions of nanoparticles with a few hundred nanometers, especially for applications where structural complexity and local heterogeneity are major determinants, as exemplified in plasmonics.

Trash mulches are remarkably effective in preventing soil erosion, reducing runoff-sediment transport-erosion, and increasing infiltration. The study was carried out to observe the sediment outflow from sugar cane leaf (trash) mulch treatments at selected land slopes under simulated rainfall conditions using a rainfall simulator of size 10 m × 1.2 m × 0.5 m with the locally available soil material collected from Pantnagar. In the present study, trash mulches with different quantities were selected to observe the effect of mulching on soil loss reduction. The number of mulches was taken as 6, 8 and 10 t/ha, three rainfall intensities viz. 11, 13 and 14.65 cm/h at 0, 2 and 4% land slopes were selected. The rainfall duration was fixed (10 minutes) for every mulch treatment. The total runoff volume varied with mulch rates for constant rainfall input and land slope. The average sediment concentration (SC) and sediment outflow rate (SOR) increased with the increasing land slope. However, SC and outflow decreased with the increasing mulch rate for a fixed land slope and rainfall intensity. The SOR for no mulch-treated land was higher than trash mulch-treated lands. Mathematical relationships were developed for relating SOR, SC, land slope, and rainfall intensity for a particular mulch treatment. It was observed that SOR and average SC values correlated with rainfall intensity and land slope for each mulch treatment. The developed models' correlation coefficients were more than 90%.

Wastewater quality modelling plays a vital role in planning and management of wastewater treatment plants (WWTP). This paper develops a new hybrid machine learning model based on extreme learning machine (ELM) optimized by Bat algorithm (ELM-Bat) for modelling five day effluent biochemical oxygen demand (BOD5). Specifically, this hybrid model combines the Bat algorithm for model parameters optimization and the standalone ELM. The proposed model was developed using historical measured effluents wastewater quality variables, i.e., the chemical oxygen demand (COD), temperature, pH, total suspended solid (TSS), specific conductance (SC) and the wastewater flow (Q). The performances of the hybrid ELM-Bat were compared with those of the multilayer perceptron neural network (MLPNN), the random forest regression (RFR), the Gaussian process regression (GPR), the random vector functional link network (RVFL), and the multiple linear regression (MLR) models. By comparing several input variables combination, the improvement achieved in the accuracy of prediction through the hybrid ELM-Bat was quantified. All models were first calibrated using training dataset and later tested using validation and based on four performances metrics namely, root mean square error (RMSE), mean absolute error (MAE), the correlation coefficient (R), and the Nash-Sutcliffe model efficiency (NSE). In all, it is concluded that the ELM-Bat is the most accurate model when all the six input were included as input variables, and it outperforms all other benchmark models in terms of predictive accuracy, exhibiting RMSE, MAE, R and NSE values of approximately, 0.885, 0.781, 2.621, and 1.989, respectively.

Total phosphorus (T-P) refers to the concentration of phosphorus in water and is one of the important parameters for eutrophication in lakes and rivers. In current research, neuroscience dependent (i.e., singular and double-platform synthetic) approaches were employed to predict the river T-P concentration. Singular techniques were developed utilizing machine learning based (support vector machines (SVMs), stochastic gradient boosting (SGB)), and deep learning based multilayer perceptron (DMLP) models. Besides, double-platform synthetic techniques were developed by integrating a prior data-processing (Discrete-wavelet) algorithm with the singular techniques. Six input scenarios conditional on different water quantity and quality parameters acquired from two stations (Hwangji and Toilchun), South Korea, were employed for appraising singular and double-platform synthetic techniques. The various promoted models were evaluated using four statistical standards viz., mean absolute error (MAE), root mean square error (RMSE), scatter index (SI), and Pearson correlation coefficient (CCp), and four visual references viz., scatter diagram, box-and-whisker plot, violin plot, and Taylor diagram. It can be indicated from the outcomes that the double-platform synthetic techniques did not always lead to more accurate predictions than the singular techniques. Further, results also supplied that the SGB with the 6th input (MAE = 0.012 mg/L, RMSE = 0.014 mg/L, and CCp = 0.650 for Hwangji; MAE = 0.011 mg/L, RMSE = 0.017 mg/L, and CCp = 0.963 for Toilchun) scenario model demonstrated the best accuracy for predicting river T-P concentration by the singular techniques for both stations, whereas the discrete-wavelet SVMs with the 4th input (MAE = 0.007 mg/L, RMSE = 0.011 mg/L, and CCp = 0.765 for Hwangji) and discrete-wavelet SGB with the 5th input (MAE = 0.012 mg/L, RMSE = 0.017 mg/L, and CCp = 0.953 for Toilchun) scenario models provided the best predictive accuracy among double-platform synthetic techniques, respectively.

Preventing surface defects of metal products during the production process is challenging due to manufacturing complexity, material properties and environmental factors. Relying on human inspectors for quality control can introduce human error, which increases the risk of delivering defective products to customers. To address these challenges, we propose an Inception-CNN model specifically designed for surface defect recognition in servo motor housings (SMHs). The model incorporates an inception module between convolutional layers to effectively capture multi-scale local information and extract complex and abstract features. Additionally, we introduce an image patching technique that enhances defect recognition for small defects by reducing image complexity while maintaining defect visibility. Moreover, we propose a surface defect generation GAN (SDG-GAN) method, a novel approach that addresses the data imbalance problem and improves the accuracy and robustness of the classification model through generating diverse and high-quality synthetic defect images. The original data was collected using a line scan camera installed in the SMHSI system. We ensured model generalization through 10-fold cross-validation using the SMHSD-P-GAN dataset. Evaluation results indicate that our classification model outperformed other CNN models and achieved strong generalization, with 99.40% accuracy in cross-validation and 99.23% on the original test data. This represents a substantial 32.31% improvement over a baseline CNN model trained on the SMHSD-O-TA dataset, underscoring the effectiveness of our proposed approaches in enhancing classification performance Our method efficiently processes 12 images per second, making it ideal for real-time defect inspection in SMHs. Its successful integration into the SMHSI system confirms its practicality and effectiveness in real-world industrial applications.

As an indicator measured by incubating organic material from water samples in rivers, the most typical characteristic of water quality items is biochemical oxygen demand (BOD5) concentration, which is a stream pollutant with an extreme circumstance of organic loading and controlling aquatic behavior in the eco-environment. Leading monitoring approaches including machine leaning and deep learning have been evolved for a correct, trustworthy, and low-cost prediction of BOD5 concentration. The addressed research investigated the efficiency of three standalone models including machine learning (extreme learning machine (ELM) and support vector regression (SVR)) and deep learning (deep echo state network (Deep ESN)). In addition, the novel double-stage synthesis models (wavelet-extreme learning machine (Wavelet-ELM), wavelet-support vector regression (Wavelet-SVR), and wavelet-deep echo state network (Wavelet-Deep ESN)) were developed by integrating wavelet transformation (WT) with the different standalone models. Five input associations were supplied for evaluating standalone and double-stage synthesis models by determining diverse water quantity and quality items. The proposed models were assessed using the coefficient of determination (R2), Nash-Sutcliffe (NS) efficiency, and root mean square error (RMSE). The significance of addressed research can be found from the overall outcomes that the predictive accuracy of double-stage synthesis models were not always superior to that of standalone models. Overall results showed that the SVR with 3th distribution (NS = 0.915) and the Wavelet-SVR with 4th distribution (NS = 0.915) demonstrated more correct outcomes for predicting BOD5 concentration compared to alternative models at Hwangji station, and the Wavelet-SVR with 4th distribution (NS = 0.917) was judged to be the most superior model at Toilchun station. In most cases for predicting BOD5 concentration, the novel double-stage synthesis models can be utilized for efficient and organized data administration and regulation of water pollutants on both stations, South Korea.

Abstract Because of its extremely rare incidence, the safety and efficacy of bronchial artery embolization (BAE) for the treatment of hemoptysis caused by pulmonary metastasis from HCC are not well known. We therefore evaluated the safety and efficacy of BAE in these patients. Data from 18 patients with hepatocellular carcinoma (HCC) and pulmonary metastasis who received BAE for the treatment of hemoptysis between 2003 and 2021 were retrospectively reviewed. Technical and clinical success were achieved in 100% and 94% of patients, respectively. Of the 18 embolization procedures, six were performed using polyvinyl alcohol (PVA) particles only, five were performed using gelfoam only, three were performed using gelfoam plus microcoils, one was performed using PVA plus microcoils, one was performed using embospheres, one was performed using lipiodol plus PVA and gelfoam, and one was performed using hystoacryl with microballoon protection. In eight patients for whom CT just before BAE and at follow-up were available, the mean size of the largest metastatic tumor decreased from 5.1 to 3.7 cm (P = 0.035). Hemoptysis recurred in three patients (17%) during follow-up. The median overall and hemoptysis-free survival periods were 149 days and 132 days, respectively. BAE is an effective and safe option for the treatment of hemoptysis in patients with pulmonary metastasis from HCC, with a favorable clinical success rate and a low rate of hemoptysis recurrence. In addition, we also observed BAE to have a positive antitumor effect on pulmonary metastases from HCC, but this requires confirmation in a future study.

This study investigated changes in the interfacial properties of epoxy-coated concrete exposed to various conditions, regarding the epoxy type, coating equipment, and exposure environment and period. The measured coating thickness and pull-off bond strength exhibited diverse trends, depending on the exposure period and conditions. In the real sea (RS) environment, the average bond strengths for bisphenol A (BPA) (E1), BPA with zinc powder (E2), and BPA with cresyl glycidyl ether (E3) were 1.26, 1.93, and 1.92 MPa, respectively. The coating method did not significantly affect the measured coating thickness and strength values. The conventional roller (D1) exhibited the highest thickness variation, with a value of 214.45 μm. The RS condition significantly increased the coating thickness (34% to 158%) compared to the tap water (TW) condition. The exposure conditions had little impact on bond strength except for E3, which showed an increased strength (2.71 MPa) over 7–91 days, especially under RS conditions, while E2 remained constant at approximately 1.82 MPa. This study offers insights into factors influencing marine concrete coating performance and discusses limitations and future work.

Purpose To assess experience with and perceptions of clinical application of artificial intelligence (AI) to chest radiographs among doctors in a single hospital. Materials and methods A hospital-wide online survey of the use of commercially available AI-based lesion detection software for chest radiographs was conducted with all clinicians and radiologists at our hospital in this prospective study. In our hospital, version 2 of the abovementioned software was utilized from March 2020 to February 2021 and could detect three types of lesions. Version 3 was utilized for chest radiographs by detecting nine types of lesions from March 2021. The participants of this survey answered questions on their own experience using AI-based software in daily practice. The questionnaires were composed of single choice, multiple choices, and scale bar questions. Answers were analyzed according to the clinicians and radiologists using paired t-test and the Wilcoxon rank-sum test. Results One hundred twenty-three doctors answered the survey, and 74% completed all questions. The proportion of individuals who utilized AI was higher among radiologists than clinicians (82.5% vs. 45.9%, p = 0.008). AI was perceived as being the most useful in the emergency room, and pneumothorax was considered the most valuable finding. Approximately 21% of clinicians and 16% of radiologists changed their own reading results after referring to AI, and trust levels for AI were 64.9% and 66.5%, respectively. Participants thought AI helped reduce reading times and reading requests. They answered that AI helped increase diagnostic accuracy and were more positive about AI after actual usage. Conclusion Actual adaptation of AI for daily chest radiographs received overall positive feedback from clinicians and radiologists in this hospital-wide survey. Participating doctors preferred to use AI and regarded it more favorably after actual working with the AI-based software in daily clinical practice.

This study presents rice husk silica-reinforced elastomer composites fabricated by wet compounding, exhibiting 10% higher dynamic friction coefficients than the reference composites made by conventional dry compounding. Depending on the fabrication method, the filler micro-dispersion was characterized to correlate the measured friction coefficients with the viscoelastic properties with respect to sliding velocity and substrate lubrication. The effect of viscoelastic properties of the elastomer composites on the adhesion and hysteresis friction coefficients was investigated based on the elastomer friction theory. It was found that the constructed friction master curves showed a reasonable correlation with the theory. This study demonstrates that the rice husk silica can be used as a reinforcing filler for sustainable tires. More importantly, the well-dispersed filler microstructures can ensure the higher friction coefficients of the elastomer composites on rough surfaces, improving the tire performances (e.g., wet traction, cornering, and acceleration) of the ever-growing electric vehicles and future mobility.

The likelihood of surface water and groundwater contamination is higher in regions close to landfills due to the possibility of leachate percolation, which is a potential source of pollution. Therefore, proposing a reliable framework for monitoring leachate and groundwater parameters is an essential task for the managers and authorities of water quality control. For this purpose, an efficient hybrid artificial intelligence model based on grey wolf metaheuristic optimization algorithm and extreme learning machine (ELM-GWO) is used for predicting landfill leachate quality (COD and BOD5) and groundwater quality (turbidity and EC) at the Saravan landfill, Rasht, Iran. In this study, leachate and groundwater samples were collected from the Saravan landfill and monitoring wells. Moreover, the concentration of different physico-chemical parameters and heavy metal concentration in leachate (Cd, Cr, Cu, Fe, Ni, Pb, Mn, Zn, turbidity, Ca, Na, NO3, Cl, K, COD, and BOD5) and in groundwater (Cd, Cr, Cu, Fe, Ni, Pb, Mn, Zn, turbidity, EC, TDS, pH, Cl, Na, NO3, and K). The results obtained from ELM-GWO were compared with four different artificial intelligence models: multivariate adaptive regression splines (MARS), extreme learning machine (ELM), multilayer perceptron artificial neural network (MLPANN), and multilayer perceptron artificial neural network integrated with grey wolf metaheuristic optimization algorithm (MLPANN-GWO). The results of this study confirm that ELM-GWO considerably enhanced the predictive performance of the MLPANN-GWO, ELM, MLPANN, and MARS models in terms of the root-mean-square error, respectively, by 43.07%, 73.88%, 74.5%, and 88.55% for COD; 23.91%, 59.31%, 62.85%, and 77.71% for BOD5; 14.08%, 47.86%, 53.43%, and 57.04% for turbidity; and 38.57%, 59.64%, 67.94%, and 74.76% for EC. Therefore, ELM-GWO can be applied as a robust approach for investigating leachate and groundwater quality parameters in different landfill sites.

Abstract We aimed to determine the proper modified thresholds for detecting and weighting CAC scores at 100 kV through histogram matching in comparison with 120 kV as a standard reference. From the training set (680 participants), modified thresholds at 100 kV were obtained through histogram matching of calcium pixels to 120 kV. From the validation set (213 participants), a standard CAC score at 120 kV, and modified CAC score at 100 kV using modified thresholds were compare through the paired t test and the Bland–Altman plot. Agreement for risk categories (no, minimal, mild, moderate, and severe) was evaluated using kappa statistics. Radiation doses were also compared. For the validation set, there was no significant difference between standard (median, 18.7; IQR, 0.0–207.0) and modified (median, 17.3; IQR, 0.0–220.9) CAC scores (P = 0.689). A small bias was achieved (0.74) with 95% limits of agreement from − 52.35 to 53.83. Agreements for risk categories were excellent (κ = 0.994). The mean dose-length-product of 100-kV scanning (30.1 ± 0.8 mGy * cm) was significantly decreased compared to 120-kV scanning (42.9 ± 0.6 mGy * cm) (P

Purpose The purpose of this study was to evaluate the predictive performance of ultrasonography (US)-based radiomics for axillary lymph node metastasis and to compare it with that of a clinicopathologic model. Methods A total of 496 patients (mean age, 52.5±10.9 years) who underwent breast cancer surgery between January 2014 and December 2014 were included in this study. Among them, 306 patients who underwent surgery between January 2014 and August 2014 were enrolled as a training cohort, and 190 patients who underwent surgery between September 2014 and December 2014 were enrolled as a validation cohort. To predict axillary lymph node metastasis in breast cancer, we developed a preoperative clinicopathologic model using multivariable logistic regression and constructed a radiomics model using 23 radiomic features selected via least absolute shrinkage and selection operator regression. Results In the training cohort, the areas under the curve (AUC) were 0.760, 0.812, and 0.858 for the clinicopathologic, radiomics, and combined models, respectively. In the validation cohort, the AUCs were 0.708, 0.831, and 0.810, respectively. The combined model showed significantly better diagnostic performance than the clinicopathologic model. Conclusion A radiomics model based on the US features of primary breast cancers showed additional value when combined with a clinicopathologic model to predict axillary lymph node metastasis.

Abstract This study aims to evaluate the performance of a radiomic signature-based model for predicting recurrence-free survival (RFS) of locally advanced gastric cancer (LAGC) using preoperative contrast-enhanced CT. This retrospective study included a training cohort (349 patients) and an external validation cohort (61 patients) who underwent curative resection for LAGC in 2010 without neoadjuvant therapies. Available preoperative clinical factors, including conventional CT staging and endoscopic data, and 438 radiomic features from the preoperative CT were obtained. To predict RFS, a radiomic model was developed using penalized Cox regression with the least absolute shrinkage and selection operator with ten-fold cross-validation. Internal and external validations were performed using a bootstrapping method. With the final 410 patients (58.2 ± 13.0 years-old; 268 female), the radiomic model consisted of seven selected features. In both of the internal and the external validation, the integrated area under the receiver operating characteristic curve values of both the radiomic model (0.714, P

Abstract We aimed to predict molecular subtypes of breast cancer using radiomics signatures extracted from synthetic mammography reconstructed from digital breast tomosynthesis (DBT). A total of 365 patients with invasive breast cancer with three different molecular subtypes (luminal A + B, luminal; HER2-positive, HER2; triple-negative, TN) were assigned to the training set and temporally independent validation cohort. A total of 129 radiomics features were extracted from synthetic mammograms. The radiomics signature was built using the elastic-net approach. Clinical features included patient age, lesion size and image features assessed by radiologists. In the validation cohort, the radiomics signature yielded an AUC of 0.838, 0.556, and 0.645 for the TN, HER2 and luminal subtypes, respectively. In a multivariate analysis, the radiomics signature was the only independent predictor of the molecular subtype. The combination of the radiomics signature and clinical features showed significantly higher AUC values than clinical features only for distinguishing the TN subtype. In conclusion, the radiomics signature showed high performance for distinguishing TN breast cancer. Radiomics signatures may serve as biomarkers for TN breast cancer and may help to determine the direction of treatment for these patients.

Abstract Background Poultry coccidiosis is a parasitic enteric disease with a highly negative impact on chicken production. In-feed chemoprophylaxis remains the primary method of control, but the increasing ineffectiveness of anticoccidial drugs, and potential future restrictions on their use has encouraged the use of commercial live vaccines. Availability of such formulations is constrained by their production, which relies on the use of live chickens. Several experimental approaches have been taken to explore ways to reduce the complexity and cost of current anticoccidial vaccines including the use of live vectors expressing relevant Eimeria proteins. We and others have shown that vaccination with transgenic Eimeria tenella parasites expressing Eimeria maxima Apical Membrane Antigen-1 or Immune Mapped Protein-1 (EmAMA1 and EmIMP1) partially reduces parasite replication after challenge with a low dose of E. maxima oocysts. In the present study, we have reassessed the efficacy of these experimental vaccines using commercial birds reared at high stocking densities and challenged with both low and high doses of E. maxima to evaluate how well they protect chickens against the negative impacts of disease on production parameters. Methods Populations of E. tenella parasites expressing EmAMA1 and EmIMP1 were obtained by nucleofection and propagated in chickens. Cobb500 broilers were immunised with increasing doses of transgenic oocysts and challenged two weeks later with E. maxima to quantify the effect of vaccination on parasite replication, local IFN-γ and IL-10 responses (300 oocysts), as well as impacts on intestinal lesions and body weight gain (10,000 oocysts). Results Vaccination of chickens with E. tenella expressing EmAMA1, or admixtures of E. tenella expressing EmAMA1 or EmIMP1, was safe and induced partial protection against challenge as measured by E. maxima replication and severity of pathology. Higher levels of protection were observed when both antigens were delivered and was associated with a partial modification of local immune responses against E. maxima, which we hypothesise resulted in more rapid immune recognition of the challenge parasites. Conclusions This study offers prospects for future development of multivalent anticoccidial vaccines for commercial chickens. Efforts should now be focused on the discovery of additional antigens for incorporation into such vaccines.

The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic programming (MGGP), and ‘M5Tree’ were assessed to simulate the pan evaporation in monthly scale (EPm) at two stations (e.g. Ranichauri and Pantnagar) in India. Monthly climatological information were used for simulating the pan evaporation. The utmost effective input-variables for the MM-ANN, MGGP, MARS, SVM, and M5Tree were determined using the Gamma test (GT). The predictive models were compared to each other using several statistical criteria (e.g. mean absolute percentage error (MAPE), Willmott's Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe’s Index (LM)) and visual inspection. The results showed that the MM-ANN-1 and MGGP-1 models (NSE, WI, LM, RMSE, MAPE are 0.954, 0.988, 0.801, 0.536 mm/month, 9.988% at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297% at Ranichauri station, respectively) with input variables equal to six were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations. Thus, the results of proposed MM-ANN-1 and MGGP-1 models will help to the local stakeholders in terms of water resources management.

The predominant Android operating system has captured enormous attention globally not only in smart phone industry but also for varied smart devices. The open architecture and application programming interfaces (APIs) while hosting third party applications has led to explosive growth of varied pervasive sophisticated Android malware production. In this study, we propose a robust, scalable and efficient Cuda-empowered multi-class malware detection technique leveraging Gated Recurrent Unit (GRU) to identify sophisticated Android malware. Experimentation of the proposed technique has been carried out using current state-of-the-art datasets of Android applications (i.e., Android Malware Dataset (AMD), Androzoo). Moreover, to rigorously evaluate the performance of the proposed technique, we have employed standard performance evaluation metrics (e.g., accuracy, precision, recall, F1-score etc.) and compared it with our constructed DL-driven architectures and benchmark algorithms. The GRU-based malware detection system outperforms with 98.99% detection accuracy for malware identification with a trivial trade off in speed efficiency.

Cheap, easy-to-produce oral vaccines are needed for control of coccidiosis in chickens to reduce the impact of this disease on welfare and economic performance. Saccharomyces cerevisiae yeast expressing three Eimeria tenella antigens were developed and delivered as heat-killed, freeze-dried whole yeast oral vaccines to chickens in four separate studies. After vaccination, E. tenella replication was reduced following low dose challenge (250 oocysts) in Hy-Line Brown layer chickens (p

The potential of the soil to hold plant nutrients is governed by the cation-exchange capacity (CEC) of any soil. Estimating soil CEC aids in conventional soil management practices to replenish the soil solution that supports plant growth. In this study, a multiple model integration scheme supervised with a hybrid genetic algorithm-neural network (MM-GANN) was developed and employed to predict the accuracy of soil CEC in Tabriz plain, an arid region of Iran. The standalone models (i.e., artificial neural network (ANN) and extreme learning machine (ELM)) were implemented for incorporation into the MM-GANN. In addition, it was tested to enhance the prediction accuracy of the standalone models. The soil parameters such as clay, silt, pH, carbonate calcium equivalent (CCE), and soil organic matter (OM) were used as model inputs to predict soil CEC. With the use of several evaluation criteria, the results showed that the MM-GANN model involving the predictions of ELM and ANN models calibrated by considering all the soil parameters (e.g., Clay, OM, pH, silt, and CCE) as inputs provided superior soil CEC estimates with a Nash Sutcliffe Efficiency (NSE) = 0.87, Root Mean Square Error (RMSE) = 2.885, Mean Absolute Error (MAE) = 2.249, Mean Absolute Percentage Error (MAPE) = 12.072, and coefficient of determination (R2) = 0.884. The proposed MM-GANN model is a reliable intelligence-based approach for the assessment of soil quality parameters intended for sustainability and management prospects.

To combat the COVID-19 pandemic, an assortment of vaccines has been developed. Nucleic acid vaccines have the advantage of rapid production, as they only require a viral antigen sequence and can readily be modified to detected viral mutations. Doggybone™ DNA vaccines targeting the spike protein of SARS-CoV-2 have been generated and compared with a traditionally manufactured, bacterially derived plasmid DNA vaccine that utilizes the same spike sequence. Administered to Syrian hamsters by jet injection at two dose levels, the immunogenicity of both DNA vaccines was compared following two vaccinations. Immunized hamsters were then immunosuppressed and exposed to SARS-CoV-2. Significant differences in body weight were observed during acute infection, and lungs collected at the time of euthanasia had significantly reduced viral RNA, infectious virus, and pathology compared with irrelevant DNA-vaccinated controls. Moreover, immune serum from vaccinated animals was capable of neutralizing SARS-CoV-2 variants of interest and importance in vitro. These data demonstrate the efficacy of a synthetic DNA vaccine approach to protect hamsters from SARS-CoV-2.

Eimeria maxima is a common cause of coccidiosis in chickens, a disease that has a huge economic impact on poultry production. Knowledge of immunity to E. maxima and the specific mechanisms that contribute to differing levels of resistance observed between chicken breeds and between congenic lines derived from a single breed of chickens is required. This study aimed to define differences in the kinetics of the immune response of two inbred lines of White Leghorn chickens that exhibit differential resistance (line C.B12) or susceptibility (line 15I) to infection by E. maxima. Line C.B12 and 15I chickens were infected with E. maxima and transcriptome analysis of jejunal tissue was performed at 2, 4, 6 and 8 days post-infection (dpi). RNA-Seq analysis revealed differences in the rapidity and magnitude of cytokine transcription responses post-infection between the two lines. In particular, IFN-γ and IL-10 transcript expression increased in the jejunum earlier in line C.B12 (at 4 dpi) compared to line 15I (at 6 dpi). Line C.B12 chickens exhibited increases of IFNG and IL10 mRNA in the jejunum at 4 dpi, whereas in line 15I transcription was delayed but increased to a greater extent. RT-qPCR and ELISAs confirmed the results of the transcriptomic study. Higher serum IL-10 correlated strongly with higher E. maxima replication in line 15I compared to line C.B12 chickens. Overall, the findings suggest early induction of the IFN-γ and IL-10 responses, as well as immune-related genes including IL21 at 4 dpi identified by RNA-Seq, may be key to resistance to E. maxima.

Eimeria species parasites infect the gastrointestinal tract of chickens, causing disease and impacting on production. The poultry industry relies on anticoccidial drugs and live vaccines to control Eimeria and there is a need for novel, scalable alternatives. Understanding the outcomes of experimental infection in commercial chickens is valuable for assessment of novel interventions. We examined the impact of different infectious doses of Eimeria tenella (one low dose, three high doses) in three commercial layer chicken lines, evaluating lesion score, parasite replication and cytokine response in the caeca. Groups of eight to ten chickens were housed together and infected with 250, 4,000, 8,000 or 12,000 sporulated oocysts at 21 days of age. Five days post-infection caeca were assessed for lesions and to quantify parasite replication by qPCR and cytokine transcription by RT-qPCR. Comparison of the three high doses revealed no significant variation between them in observed lesions or parasite replication with all being significantly higher than the low dose infection. Transcription of IFN-γ and IL-10 increased in all infected chickens relative to unchallenged controls, with no significant differences associated with dose magnitude (p > 0.05). No significant differences were detected in lesion score, parasite replication or caecal cytokine expression between the three lines of chickens. We therefore propose 4,000 E. tenella oocysts is a sufficient dose to reliably induce lesions in commercial layer chickens, and that estimates of parasite replication can be derived by qPCR from these same birds. However, more accurate quantification of Eimeria replication requires a separate low dose challenge group. Optimisation of challenge dose in an appropriate chicken line is essential to maximize the value of in vivo efficacy studies. For coccidiosis, this approach can reduce the numbers of chickens required for statistically significant studies and reduce experimental severity.

Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resources is very crucial as it might affect its applicability from agricultural, drinking, and industrial aspects. Although geo-statistics methods can provide insight about spatial distribution of quality factors, applications of advanced artificial intelligence (AI) models can contribute to produce more accurate results as robust alternative for such a complex geo-science problem. The present research investigates the capacity of several types of AI models for modeling four key water quality variables namely electrical conductivity (EC), sodium adsorption ratio (SAR), total dissolved solid (TDS) and Sulfate (SO4) using dataset obtained from 90 wells in Tabriz Plain, Iran; assessed by k-fold testing. Two different modeling scenarios were established to make simulations using other quality parameters and the geographical information. The obtained results confirmed the capabilities of the AI models for modeling the well groundwater quality variables. Among all the applied AI models, the developed hybrid support vector machine-firefly algorithm (SVM-FFA) model achieved the best predictability performance for both investigated scenarios. The introduced computer aid methodology provided a reliable technology for groundwater monitoring and assessment.

Product defect inspections are extremely important for industrial manufacturing processes. It is necessary to develop a special inspection system for each industrial product due to their complexity and diversity. Even though high-precision 3D cameras are usually used to acquire data to inspect 3D objects, it is hard to use them in real-time defect inspection systems due to their high price and long processing time. To address these problems, we propose a product inspection system that uses five 2D cameras to capture all inspection parts of the product and a deep learning-based 2D convolutional neural network (CNN) with spatial and channel attention (SCA) mechanisms to efficiently inspect 3D ball joint socket products. Channel attention (CA) in our model detects the most relevant feature maps while spatial attention (SA) finds the most important regions in the extracted feature map of the target. To build the final SCA feature vector, we concatenated the learned feature vectors of CA and SA because they complement each other. Thus, our proposed CNN with SCA provides high inspection accuracy as well as it having the potential to detect small defects of the product. Our proposed model achieved 98% classification accuracy in the experiments and proved its efficiency on product inspection in real-time.

Soil temperature has a vital importance in biological, physical and chemical processes of terrestrial ecosystem and its modeling at different depths is very important for land-atmosphere interactions. The study compares four machine learning techniques, extreme learning machine (ELM), artificial neural networks (ANN), classification and regression trees (CART) and group method of data handling (GMDH) in estimating monthly soil temperatures at four different depths. Various combinations of climatic variables are utilized as input to the developed models. The models' outcomes are also compared with multi-linear regression based on Nash-Sutcliffe efficiency, root mean square error, and coefficient of determination statistics. ELM is found to be generally performs better than the other four alternatives in estimating soil temperatures. A decrease in performance of the models is observed by an increase in soil depth. It is found that soil temperatures at three depths (5, 10 and 50 cm) could be mapped utilizing only air temperature data as input while solar radiation and wind speed information are also required for estimating soil temperature at the depth of 100 cm.

A new version of the fuzzy logic model, called the co-active neuro fuzzy inference system (CANFIS), is introduced for predicting standardized precipitation index (SPI). Multiple scales of drought information at six meteorological stations located in Uttarakhand State, India, are used. Different lead times of SPI were computed for prediction, including 1, 3, 6, 9, 12, and 24 months, with inputs abstracted by autocorrelation function (ACF) and partial-ACF (PACF) analysis at 5% significance level. The proposed CANFIS model was validated against two models: classical artificial intelligence model (e.g., multilayer perceptron neural network (MLPNN)) and regression model (e.g., multiple linear regression (MLR)). Several performance evaluation metrices (root mean square error, Nash-Sutcliffe efficiency, coefficient of correlation, and Willmott index), and graphical visualizations (scatter plot and Taylor diagram) were computed for the evaluation of model performance. Results indicated that the CANFIS model predicted the SPI better than the other models and prediction results were different for different meteorological stations. The proposed model can build a reliable expert intelligent system for predicting meteorological drought at multi-time scales and decision making for remedial schemes to cope with meteorological drought at the study stations and can help to maintain sustainable water resources management.

The structural changes at low-coordination sites of nanocatalysts such as edges, remain poorly understood. Here, the authors report observations of high-lattice distortion at edges of Pt nanocrystals during heterogeneous catalytic methane oxidation by using in situ 3D Bragg coherent X-ray diffraction imaging.

One of the characteristics of academic writing is the inclusion of citations and references. As the development of reference styles used for international scholarly communication has mostly been led by Western academic societies, the reference styles developed in Western nations do not reflect the characteristics of Korean names. As a result, it is hard to distinguish Korean authors through citations based on Western reference styles, which in turn decreases the retrieval efficiency of relevant authors and ultimately the efficiency of scholarly communication. This paper intends to analyze author name disambiguation of Korean authors indicated according to Western reference styles. It aims to suggest the necessity for enhancing name disambiguation of Korean authors and revision of reference styles. Its ultimate goal is to increase the efficiency of scholarly communication through the improvement of name disambiguation of Korean authors. For this purpose, this study collected and analyzed name data of Korean researchers and compared name disambiguation of authors by reference style. Based on research results, this study confirmed a necessity for revising reference styles to improve name disambiguation of authors and suggested a necessity for research into the improvement of plans for revision.

Abstract Campylobacter jejuni is the leading cause of bacterial food-borne gastroenteritis worldwide and human infections are frequently associated with handling and consumption of contaminated poultry. The polysaccharide capsule of C. jejuni plays important roles in colonisation of the chicken gut, invasion of epithelial cells and serum resistance and is subject to modification with O-methyl phosphoramidate (MeOPN) in most strains. In this study, the cytokine responses of mouse bone marrow-derived macrophages (mBMMs), chicken bone marrow-derived macrophages (chBMMs) and human monocyte-derived macrophages (hMDMs) were measured following infection with C. jejuni 11168H wild-type (WT) or isogenic mutants lacking either the capsule (Δcj1439) or its MeOPN modification (Δcj1417). Consistent with previous observations using murine bone marrow-derived dendritic cells, mutants lacking the capsule or MeOPN elicited enhanced transcription of IL-6 and IL-10 in mBMMs compared to wild-type C. jejuni. However, the lack of capsule and MeOPN did not alter IL-6 and IL-10 expression in chBMMs and hMDMs compared to C. jejuni WT. Phagocytosis assays showed the acapsular mutant was not impaired in uptake or net intracellular survival after phagocytosis in both chicken and human macrophages; however, the phagocytosis of the MeOPN mutant was significantly decreased in both chicken and human macrophages. In conclusion, differences in the response of macrophages of varying host origin to Campylobacter were detected. The absence of MeOPN modification on the capsule of C. jejuni did not alter the levels of innate cytokine expression in both chicken and human macrophages compared to the 11168H WT, but affected phagocytosis by host macrophages.

Assessment of magnetic resonance imaging (MRI) after neoadjuvant chemoradiation therapy (nCRT) is essential in rectal cancer staging and treatment planning. However, when predicting the pathologic complete response (pCR) after nCRT for rectal cancer, existing works either rely on simple quantitative evaluation based on radiomics features or partially analyze multi-parametric MRI. We propose an effective pCR prediction method based on novel multi-parametric MRI embedding. We first seek to extract volumetric features of tumors that can be found only by analyzing multiple MRI sequences jointly. Specifically, we encapsulate multiple MRI sequences into multi-sequence fusion images (MSFI) and generate MSFI embedding. We merge radiomics features, which capture important characteristics of tumors, with MSFI embedding to generate multi-parametric MRI embedding and then use it to predict pCR using a random forest classifier. Our extensive experiments demonstrate that using all given MRI sequences is the most effective regardless of the dimension reduction method. The proposed method outperformed any variants with different combinations of feature vectors and dimension reduction methods or different classification models. Comparative experiments demonstrate that it outperformed four competing baselines in terms of the AUC and F1-score. We use MRI sequences from 912 patients with rectal cancer, a much larger sample than in any existing work.

Three types of connectors were proposed and tested for a modular double-skinned composite tubular (DSCT) wind turbine tower, which is composed of two concentric steel tubes filled with concrete between them. The three proposed types were a socket type connector, an H-type connector, and a bolted–welded with shear key connector. Using the proposed connectors, three modular DSCT tower specimens and a single-body specimen were built. Then, quasi-static tests were conducted to evaluate the performance of the three types of connectors, and their behavioral characteristics and failure modes were analyzed. The test results showed that the bolted–welded with shear key connector specimen exerted an almost equal moment resisting capacity as the single-body specimen; however, the other modular specimens exerted only half the moment resisting capacity of the single-body specimen. Moreover, the results showed that the bolted–welded with shear key connector is applicable in a modular DSCT wind turbine tower as it has equal ductility, maximum lateral displacement, and energy dissipation as the single-body specimen.

Abstract Background A unique clade of the bacterium Mycoplasma gallisepticum (MG), which causes chronic respiratory disease in poultry, has resulted in annual epidemics of conjunctivitis in North American house finches since the 1990s. Currently, few immunological tools have been validated for this songbird species. Interleukin-1β (IL-1β) is a prototypic multifunctional cytokine and can affect almost every cell type during Mycoplasma infection. The overall goal of this study was to develop and validate a direct ELISA assay for house finch IL-1β (HfIL-1β) using a cross-reactive chicken antibody. Methods A direct ELISA approach was used to develop this system using two different coating methods, carbonate and dehydration. In both methods, antigens (recombinant HfIL-1b or house finch plasma) were serially diluted in carbonate-bicarbonate coating buffer and either incubated at 4 °C overnight or at 60 °C on a heating block for 2 hr. To generate the standard curve, rHfIL-1b protein was serially diluted at 0, 3, 6, 9, 12, 15, 18, 21, and 24 ng/mL. Following blocking and washing, anti-chicken IL-1b polyclonal antibody was added, plates were later incubated with detecting antibodies, and reactions developed with tetramethylbenzidine solution. Results A commercially available anti-chicken IL-1β (ChIL-1β) polyclonal antibody (pAb) cross-reacted with house finch plasma IL-1β as well as bacterially expressed recombinant house finch IL-1β (rHfIL-1β) in immunoblotting assays. In a direct ELISA system, rHfIL-1β could not be detected by an anti-ChIL-1β pAb when the antigen was coated with carbonate-bicarbonate buffer at 4°C overnight. However, rHfIL-1β was detected by the anti-ChIL-1β pAb when the antigen was coated using a dehydration method by heat (60°C). Using the developed direct ELISA for HfIL-1β with commercial anti-ChIL-1β pAb, we were able to measure plasma IL-1β levels from house finches. Conclusions Based on high amino acid sequence homology, we hypothesized and demonstrated cross-reactivity of anti-ChIL-1β pAb and HfIL-1β. Then, we developed and validated a direct ELISA system for HfIL-1β using a commercial anti-ChIL-1β pAb by measuring plasma HfIL-1β in house finches.

PURPOSE :We aimed to assess the usefulness and safety of the God’s Hand pneumatic compression device for hemostasis in patients undergoing percutaneous endovascular procedures via femoral artery.METHODS:Two hundred thirty-seven patients in whom hemostasis of femoral catheterization was achieved using a God’s Hand pneumatic compression device were enrolled. The patients were divided into group A, those in whom the device was applied for four hours, and group B, those in whom the device was applied for two hours, with an additional two hours of bed rest in both groups. Groups A and B were regrouped to groups A’ and B’ using the propensity score matching method (n=65, for both). Chi-squared test and logistic regression models were used to analyze the relationship between the complication rate and patient characteristics and procedure-related factors.RESULTS:Clinical success was achieved in 216 of 237 patients (91.1%): 63 in group A (84%) and 153 in group B (94.4%); in propensity score matched groups, clinical success was seen in 47 patients in group A’ (81.5%) and 62 patients in group B’ (95.4%). Group B’ showed a higher clinical success rate than group A’ (P = 0.028). There were no major complications. In logistic regression models, a negative association was noted between the complication rate and the duration of God’s Hand application; however, this association was not statistically significant.CONCLUSION:The God’s Hand pneumatic compression device is effective and safe for the hemostasis of femoral catheterization, and four hours of bed rest is sufficient for hemostasis in selected patients.

Artificial intelligence (AI) is expected to affect various fields of medicine substantially and has the potential to improve many aspects of healthcare. However, AI has been creating much hype, too. In applying AI technology to patients, medical professionals should be able to resolve any anxiety, confusion, and questions that patients and the public may have. Also, they are responsible for ensuring that AI becomes a technology beneficial for patient care. These make the acquisition of sound knowledge and experience about AI a task of high importance for medical students. Preparing for AI does not merely mean learning information technology such as computer programming. One should acquire sufficient knowledge of basic and clinical medicines, data science, biostatistics, and evidence-based medicine. As a medical student, one should not passively accept stories related to AI in medicine in the media and on the Internet. Medical students should try to develop abilities to distinguish correct information from hype and spin and even capabilities to create thoroughly validated, trustworthy information for patients and the public.

The new supporting structure, internally confined hollow reinforced concrete (ICH RC), was suggested by former researchers. It maintains the material saving effect, which is the advantage of the hollow concrete structure, and it solves the brittle fracture problem of the inner wall by the inner steel pipe to make it into the 3-axis confinement state. However, until now, its design and analysis model has been limited to a circular cross-section. In this study, to expand the applicability, research and development of an ICH RC structure with a polygonal cross-section were performed. The material model was developed by defining the constraint stress in the members of the concrete and deriving a reasonable stress-strain relationship. For the column model, it was developed to predict the behavior of the polygonal ICH RC columns by analyzing the axial force-moment correlation, moment-curvature, and lateral force-displacement relationship. Each model was verified not only by comparing with the results of previous experiments but also by analyzing the results according to parameters. The maximum load and ultimate displacement values through the developed model showed the difference with the experimental results within 6% of mean error. It was verified that the proposed analytical model reasonably reflects the behavior of actual columns.

Cretaceous volcano-sedimentary basins and successions in the Korean Peninsula are located along NE-SW- and NNE-SSW-trending sinistral strike–slip fault systems. Soft-sediment deformation structures (SSDS) of lacustrine sedimentary strata occur in the Wido, Buan, and Haenam areas of the southwestern Korean Peninsula. In this study, systematic geological, geochronological, and geochemical investigations of the volcanic-sedimentary successions were conducted to constrain the origin and timing of SSDS-bearing lacustrine strata. The SSDS-bearing strata is conformably underlain and overlain by volcanic rocks, and it contains much volcaniclastic sediment and is interbedded with tuffs. The studied SSDSs were interpreted to have formed by ground shaking during syndepositional earthquakes. U-Pb zircon ages of volcanic and volcaniclastic rocks within the studied volcano-sedimentary successions were ca. 87–84 Ma, indicating that active volcanism was concurrent with lacustrine sedimentation. Geochemical characteristics indicate that these mostly rhyolitic rocks are similar to subduction-related calc-alkaline volcanic rocks from an active continental margin. This suggests that the SSDSs in the study area were formed by earthquakes related to proximal volcanic activity due to the oblique subduction of the Paleo-Pacific Plate during the Late Cretaceous.

The purpose of this study is to develop and validate a faith scale for young children. Data were collected from 424 young children, who had not yet entered elementary school, with their parents rating their faith level. Sixty-five preliminary questions were formulated under three domains―knowing, loving, and living—that were based on existing studies related to faith. The questions were reduced to 40 through a content validity test conducted by a seven-member panel. These questions were subsequently refined through pilot study, main survey, and statistical analysis. After exploratory and confirmatory factor analysis, the scale was finalized, comprising 25 questions that can be categorized into three factors: confessional faith life, missional life, and distinctive life. This scale is expected to measure early childhood faith and prove the effectiveness of Christian education programs on a young child’s faith development.

Owing to the reduction of surface-water resources and frequent droughts, the exploitation of groundwater resources has faced critical challenges. For optimal management of these valuable resources, careful studies of groundwater potential status are essential. The main goal of this study was to determine the optimal network structure of a Bayesian network (BayesNet) machine-learning model using three metaheuristic optimization algorithms—a genetic algorithm (GA), a simulated annealing (SA) algorithm, and a Tabu search (TS) algorithm—to prepare groundwater-potential maps. The methodology was applied to the town of Baghmalek in the Khuzestan province of Iran. For modeling, the location of 187 springs in the study area and 13 parameters (altitude, slope angle, slope aspect, plan curvature, profile curvature, topography wetness index (TWI), distance to river, distance to fault, drainage density, rainfall, land use/cover, lithology, and soil) affecting the potential of groundwater were provided. In addition, the statistical method of certainty factor (CF) was utilized to determine the input weight of the hybrid models. The results of the OneR technique showed that the parameters of altitude, lithology, and drainage density were more important for the potential of groundwater compared to the other parameters. The results of groundwater-potential mapping (GPM) employing the receiver operating characteristic (ROC) area under the curve (AUC) showed an estimation accuracy of 0.830, 0.818, 0.810, and 0.792, for the BayesNet-GA, BayesNet-SA, BayesNet-TS, and BayesNet models, respectively. The BayesNet-GA model improved the GPM estimation accuracy of the BayesNet-SA (4.6% and 7.5%) and BayesNet-TS (21.8% and 17.5%) models with respect to the root mean square error (RMSE) and mean absolute error (MAE), respectively. Based on metric indices, the GA provides a higher capability than the SA and TS algorithms for optimizing the BayesNet model in determining the GPM.

The spine is the most common site of skeletal involvement in tuberculosis. The radiologic features are reportedly characterized by destruction of the vertebral body, subligamentous extension or subchondral penetration, frequent paravertebral abscess formation and late involvement of the disk space. We experienced a case of a 25-year-old male who was a human immunodeficiency virus carrier without antiretroviral therapy. Incidental findings on abdominal computed tomography included multiple well-demarcated and ovoid osteolytic lesions with hyperdense rims disseminated in the thoracic, lumbar, and sacrum vertebrae, as well as in both ilii. On the lumbar spine magnetic resonance imaging, multiple small round lesions of isointense signal intensity with peripheral hyperintense rims were found on both T1- and T2-weighted imaging. The lesions had peripheral rim enhancement on gadolinium-enhanced T1-weighted imaging. Based on our experience, this rare image finding is one of the manifestations of disseminated tuberculosis.