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In this study, the fatigue damage to a power takeoff (PTO) shaft was evaluated under various operating conditions in rotary-tillage operations, considering soil strength and texture. Pearson correlation analysis was conducted to identify the significant variables influencing PTO shaft fatigue damage, and a prediction formula was derived through regression analysis using these variables. The PTO shaft exhibited increased shear stress with higher transmission gear stages, PTO gear stages, or soil properties, including strength and texture. The fatigue damage increased with higher transmission gear stages and soil strength while decreasing with higher PTO gear stages. Notably, as the PTO gear stage increased, the mean stress increased; however, the stress amplitude and equivalent completely reversed stress significantly reduced fatigue damage. Statistical analyses revealed a strong correlation between PTO shaft fatigue damage and factors such as tractor travel speed, PTO shaft power consumption, PTO shaft rotational speed properties, including strength and texture. The developed prediction equation, incorporating all significant variables, demonstrated, with a coefficient of determination (R²) of 0.93 and a root mean square error (RMSE) of 2.94×10-9. This equation effectively identifies trends in PTO shaft fatigue damage based on key operational variables. Furthermore, the findings emphasize the critical role of soil texture in assessing PTO shaft fatigue damage.

Tractor fuel consumption has typically been predicted using indoor test results under specific conditions. This study analyzes the factors affecting fuel consumption during rotary tillage in actual fields and develops a prediction model. The test field was divided into sections using a grid method, and rotary tillage operations were performed to measure various parameters, including soil strength, tractor’s transmission and PTO gear stages, tillage pitch, travel speed, engine and PTO shaft torque and speed, and fuel consumption. Pearson correlation identified variables affecting specific fuel consumption, and regression analysis was used to develop a prediction model. The model’s accuracy was analyzed using the coefficient of determination (R2) and root mean square error (RMSE), and it was compared with the ASABE’s fuel consumption prediction model. The test results showed that higher transmission and PTO gear stages, and tillage pitch decreased specific fuel consumption, while soil strength had no significant effect. Thus, operating at higher gear and PTO stages within suitable conditions enhances energy efficiency in rotary tillage. Statistical analysis showed that specific fuel consumption significantly correlated with tractor travel speed, PTO shaft power, and PTO shaft speed. The prediction model, including these variables, had the highest accuracy with R2 of 0.91 and RMSE of 0.011 L/kW·h. The developed prediction model showed significantly improved accuracy compared to the ASABE model, indicating that it can predict specific fuel consumption based on key operational variables in actual rotary tillage operations.

In this work, a robust subspace clustering algorithm is developed to exploit the inherent union-of-subspaces structure in the data for reconstructing missing measurements and detecting anomalies. Our focus is on processing an incessant stream of large-scale data such as synchronized phasor measurements in the power grid, which is challenging due to computational complexity, memory requirement, and missing and corrupt observations. In order to mitigate these issues, a low-rank representation (LRR) model-based subspace clustering problem is formulated that can handle missing measurements and sparse outliers in the data. Then, an efficient online algorithm is derived based on stochastic approximation. The convergence property of the algorithm is established. Strategies to maintain a representative yet compact dictionary for capturing the subspace structure are also proposed. The developed method is tested on both simulated and real phasor measurement unit (PMU) data to verify the effectiveness and is shown to significantly outperform existing algorithms based on simple low-rank structure of data.

Background/Aims: There have been few studies regarding the prognosis of intestinal Behçet’s disease (iBD) patients according to consensus-based diagnostic categories, which reflects the typicality of intestinal ulcers, the presence of oral ulcers, and the accompanying systemic manifestations. Methods: The medical records of patients who had ileocolonic ulcers with a clinical impression of iBD were reviewed. The patients were categorized according to the diagnostic algorithm at the time of diagnosis. Adverse events were defined as major surgery or admission related to iBD deterioration. Results: A total of 163 patients were included in the study. The male-to-female ratio was 1:1.2, and the mean age at the time of diagnosis was 48.9±15.9 years. The numbers of patients who met the definite, probable, suspected, and nondiagnostic iBD criteria were 19 (11.7%), 61 (37.4%), 38 (23.3%), and 45 (27.6%), respectively. The event-free survival of patients with definite, probable, and suspected iBD was significantly shorter than that of patients with nondiagnostic iBD (p=0.026), while there was no significant difference among the definite iBD, probable iBD, and suspected iBD groups (p=0.596). After excluding patients with nondiagnostic iBD, multivariate analysis showed that anemia, fever, colonic involvement other than the ileocecum, and accompanying hematologic disorders at the time of diagnosis were significantly associated with the development of adverse events. Conclusions: The clinical course of patients with definite, probable, and suspected iBD is distinguished from that of patients with nondiagnostic iBD, but patients with definite, probable, and suspected iBD share similar clinical courses.

Tractor accidents caused by lateral overturning and backward rollover during agricultural activities and general driving are common. In this study, various research cases were analyzed to identify the factors influencing the lateral overturning and backward rollover of tractors and to examine their static and dynamic stability. Studies on the analysis of the major causes of these incidents and evaluation of tractor safety were compiled. Test methods, including actual tests and simulations, were categorized, and the characteristics of lateral overturning and backward rollover safety of tractors in different studies were examined. Additionally, safety improvement measures were proposed by identifying and summarizing the causes of accidents involving agricultural machinery. Tractor safety was evaluated primarily by conducting actual tractor and simulation tests. These tests were classified into field tests, tests on scale models, spreadsheet programs, and 3D simulation programs. The primary causes of lateral overturning and backward rollover were unstable center of gravity, extremely high driving speed, and ground conditions. Given the considerable number of studies dedicated to evaluating tractor safety, various technologies aimed at preventing lateral overturning and backward rollover incidents are expected to be applied to tractors in the future. The production and testing of safe agricultural machinery are expected to contribute to a reduction in accident rates.

In this study, we developed a monitoring system to accurately track the seeding rate and to identify the locations where the mechanical pot-seeding machine failed to sow seeds correctly. The monitoring system employs diverse image processing techniques, including the Hough transform, hue–saturation–value color space conversion, image morphology techniques, and Gaussian blur, to accurately pinpoint the seeding rate and the locations where seeds are missing. To determine the optimal operating conditions for the seeding rate monitoring system, a factorial experiment was conducted by varying the brightness and saturation values of the image data. When the derived optimal operating conditions were applied, the system consistently achieved a 100% seed recognition rate across various seeding conditions. The monitoring system developed in this study has the potential to significantly reduce the labor required for supplementary planting by enabling the real-time identification of locations where seeds were not sown during pot-seeding operations.

In this study, to evaluate the safety of the fastening device, which is a vulnerable part of the agricultural by-product collector, the stress in fastening devices was measured, and the operational and driving safety were analyzed by deriving the static safety factor and fatigue life. The position with the maximum stress in fastening devices was identified through structural analysis simulation, and a stress measurement system was constructed using strain gauges. Test conditions for stress measurement were classified into three operating conditions (collection operation, driving with the loading part lifted to the highest point, and driving with the loading part lifted to the lowest point) and three soil conditions (even pavement, sloped pavement, and farmland). A process for deriving the fatigue life based on the measured stress was constructed by applying the rain-flow counting method, Goodman equation, and Palmgren–Miner’s rule via commercial software. From the stress measurement results, the collection operation exhibited the highest maximum stress, followed by driving with the loading part lifted to the highest point and driving with the loading part lifted to the lowest point. Under all conditions, the static safety factor of the fastening devices was found to be higher than 1.0 (1.16–1.33). The fatigue life of the fastening devices was also found to be longer than the service life of Korean agricultural machinery under all operating conditions. Therefore, the fastening devices are expected to operate safely under generated static and dynamic loads. The agricultural by-product collector can perform agricultural work and drive stably and is expected to contribute to reducing unnecessary labor force for Korean farms.

A new class of highly efficient nonlinear optical organic salt crystals is reported. In nonlinear optics based on organic materials, it is well known that using two electron‐withdrawing groups (EWGs) onto cationic electron acceptors instead of conventional one EWG remarkably enhances microscopic optical nonlinearity for chromophores. However, the corresponding organic crystals possessing enhanced large macroscopic optical nonlinearity have not been reported yet. Herein, a design strategy is proposed for obtaining highly efficient nonlinear optical crystals based on two EWGs in cationic electron acceptors. Introducing a phenolic electron donor, promoting a head‐to‐tail interionic assembly, along with a two‐EWG N‐pyrimidinyl pyridinium electron acceptor in cationic chromophores results in a preferred non‐centrosymmetric, perfectly parallel alignment of chromophores in crystal. Newly designed OPR (4‐(4‐hydroxystyryl)‐1‐(pyrimidin‐2‐yl)pyridinium) crystals exhibit approximately two times larger effective first hyperpolarizability than that of analogous N‐alkyl OHP (4‐(4‐hydroxystyryl)‐1‐methylpyridinium) crystals based on only one EWG. OPR crystals exhibit comparable second‐order optical nonlinearity to benchmark red‐colored DAST (4‐(4‐(dimethylamino)styryl)‐1‐methylpyridinium 4‐methylbenzenesulfonate) crystals, but a significant blue‐shifted absorption resulting in orange‐color crystals. Therefore, phenolic organic salt crystals using two EWGs are highly promising materials for various nonlinear optical applications.

RF signal recognition is an important element toward RF situational awareness and dynamic spectrum management. In this work, machine learning-based signal recognition algorithms are proposed. Our key contribution is to engineer feature learning such that the classifiers can perform robustly even when a mixture of heterogeneous signal classes is observed, although the training is still done using non-mixture single-label samples. To achieve this, discriminative dictionary learning algorithms are developed with various feature-shaping constraints. The signal detection can then be done in a way reminiscent of the multi-user detection in wireless communication, employing linear equalizers. The algorithms are tested using real wideband RF measurement data. It is verified that the proposed algorithms can robustly classify the component signals even when their powers are widely different and their number is not known a priori.

Demand response (DR) aims at improving the reliability and efficiency of the power grids by shaping the power demand over time. Given that building energy consumption constitutes a significant portion of the overall grid load, building energy management is a critical component for the DR portfolio. In this study, DR control policies for lighting and air-conditioner systems for the individual spaces in buildings are proposed. The policies are designed to achieve the energy reduction amount specified in the DR request while minimizing the user discomfort. A significant challenge is to cope with the uncertainty of various environmental factors such as the solar illuminance and ambient temperature, as well as the psycho-economic factors such as the energy usage preferences of the occupants. We employ a data-driven machine learning approach to tackle this challenge. Our novel idea is to take advantage of the structural similarity of the control policies across the spaces in a lifelong multi-task learning framework. To accommodate significant nonlinearity in efficient policies, a kernel-based learning approach is pursued. The dual decomposition method is employed to relax the constraint coupled across the spaces, which allows solving the overall learning problem via a series of unconstrained subproblems. The efficacy of the proposed method is verified by numerical experiments based on semi-real data sets.

Small heterodimer partner interacting leucine zipper protein (SMILE) is an orphan nuclear receptor and a member of the bZIP family of proteins. We investigated the mechanism by which SMILE suppressed the development of inflammatory bowel disease (IBD) using a DSS-induced colitis mouse model and peripheral blood mononuclear cells (PBMCs) from patients with ulcerative colitis (UC). Metformin, an antidiabetic drug and an inducer of AMPK, upregulated the level of SMILE in human intestinal epithelial cells and the number of SMILE-expressing cells in colon tissues from DSS-induced colitis mice compared to control mice. Overexpression of SMILE using a DNA vector reduced the severity of DSS-induced colitis and colitis-associated intestinal fibrosis compared to mock vector. Furthermore, SMILE transgenic mice showed ameliorated DSS-induced colitis compared with wild-type mice. The mRNA levels of SMILE and Foxp3 were downregulated and SMILE expression was positively correlated with Foxp3 in PBMCs from patients with UC and an inflamed mucosa. Metformin increased the levels of SMILE, AMPK, and Foxp3 but decreased the number of interleukin (IL)-17–producing T cells among PBMCs from patients with UC. These data suggest that SMILE exerts a therapeutic effect on IBD by modulating IL-17 production.

BackgroundFecal calprotectin (FC) is widely used for the diagnosis and monitoring disease activity of inflammatory bowel disease (IBD). Quantitative rapid assays can be a reliable alternative to the time-consuming assay. This study aimed to evaluate and compare the diagnostic performance of two quantitative rapid FC assays (Ichroma calprotectin, and Buhlmann Quantum blue).MethodsA total of 192 patients were included in this study; 84 patients with IBD (67 ulcerative colitis and 17 Crohn's disease) and 108 patients with non-IBD. We compared quantitative FC levels in different disease statuses and evaluated the correlation between the FC results of the two FC kits. Diagnostic performances in predicting active IBD were evaluated in reference to different cut-off levels.ResultsThe FC levels in 45 patients with active IBD as defined by endoscopic score were significantly higher compared to the inactive IBD and other diseases (P 250 μg/g confirmed 85.7% (iChroma) and 64.1% (Buhlmann) of active IBD patients.ConclusionThe results of the two rapid FC assays iChroma and Buhlmann showed a significant correlation, but the two test results were not interchangeable. With optimized cut-off values, rapid FC tests could be helpful in the diagnosis of IBD and differentiating active IBD from inactive or organic bowel disease.

Abstract New organic THz generators are designed herein by molecular engineering of the refractive index, phonon mode, and spatial asymmetry. These benzothiazolium crystals simultaneously satisfy the crucial requirements for efficient THz wave generation, including having nonlinear optical chromophores with parallel alignment that provide large optical nonlinearity; good phase matching for enhancing the THz generation efficiency in the near‐infrared region; strong intermolecular interactions that provide restraining THz self‐absorption; high solubility that promotes good crystal growth ability; and a plate‐like crystal morphology with excellent optical quality. Consequently, the as‐grown benzothiazolium crystals exhibit excellent characteristics for THz wave generation, particularly at near‐infrared pump wavelengths around 1100 nm, which is very promising given the availability of femtosecond laser sources at this wavelength, where current conventional THz generators deliver relatively low optical‐to‐THz conversion efficiencies. Compared to a 1.0‐mm‐thick ZnTe crystal as an inorganic benchmark, the 0.28‐mm‐thick benzothiazolium crystal yields a 19 times higher peak‐to‐peak THz electric field with a broader spectral bandwidth (>6.5 THz) when pumped at 1140 nm. The present work provides a valuable approach toward realizing organic crystals that can be pumped by near‐infrared sources for efficient THz wave generation.

S-shaped characteristics in turbine mode are prone to instabilities in associated transient processes. A single value of the speed factor corresponds to multiple values of the discharge factor, having the possibility of changing the operating point among the turbine, turbine brake, and reverse pump modes. Because of this characteristic, the S-shaped curves induce instability in transient processes. Understanding the hydraulic behavior of a turbine on the four-quadrant characteristic is important since it provides detailed performance information through the whole discharge range of the turbine. This study was numerically and experimentally investigated the scale effect on the S-shaped characteristics in the turbine transition region. The four-quadrant characteristic curves (full- and laboratory-scale) in the turbine mode were predicted by numerical simulations. To verify the predicted results, a laboratory-scale experiment was performed in the turbine, turbine brake, and reverse pump modes. Although the full-scale experiment was performed in the normal operating head range, the scale effect can be validated by comparing steady operating points between the two models. Based on the verified results, the internal flow and pressure pulsation characteristics were determined at the operating point in a specific transition region.

Undesirable flow phenomena in Francis turbines are caused by pressure fluctuations induced under conditions of low flow rate; the resulting vortex ropes with precession in the draft tube (DT) can degrade performance and increase the instability of turbine operations. To suppress these DT flow instabilities, flow deflectors, grooves, or other structures are often added to the DT into which air or water is injected. This preliminary study investigates the effects of anti-cavity fins on the suppression of vortex ropes in DTs without air injection. Unsteady-state Reynolds-averaged Navier–Stokes analyses were conducted using a scale-adaptive simulation shear stress transport turbulence model to observe the unsteady internal flow and pressure characteristics by applying anti-cavity fins in the DT of a Francis turbine model. A vortex rope with precession was observed in the DT under conditions of low flow rate, and the anti-cavity fins were confirmed to affect the mitigation of the vortex rope. Moreover, at the low flow rate conditions under which the vortex rope developed, the application of anti-cavity fins was confirmed to reduce the maximum unsteady pressure.

Sequential sensing algorithms are developed for OFDM-based hierarchical cognitive radio (CR) systems. Secondary users sense multiple subbands simultaneously for possible spectrum availabilities under hard misdetection constraints to prevent interference to the primary users. Accounting for the fact that the sensing time overhead can often be significant, a novel performance metric is introduced based on the effective achievable data rate. An optimization problem is formulated in the framework of optimal stopping problems to maximize the average effective data rate by determining the best time to stop taking samples and proceed to data transmission. A basis expansion-based suboptimal algorithm is developed to reduce the prohibitive complexity of the optimal solution. The numerical results presented verify the efficacy of the proposed approach.