Cell–cell interaction accounts for one of the most influential factors affecting the viability and functionality of cell‐based tissue models. In this respect, various methods capable of producing ...micro‐patterns with cell spheroids are introduced to simultaneously improve contact‐dependent and ‐independent cell‐cell interactions. However, no method has yet been designed to effectively generate precise 3D patterns with multiple spheroid types. In this study, a new high‐precision and convenient 3D spheroid printing technology is developed, designated as 3D bio‐dot printing. This new technique is designed to produce cell‐laden, non‐adhesive micro‐pores within 3D structures to allow cell spheroids to be induced at printed sites. Experimental results show that various cell types, including hepatocytes, pancreatic β‐cells, and breast cancer cells, can be employed for the in situ formation of cell spheroids, and 3D freeform structures with multiple spheroid types can be printed. Moreover, this novel technology can also be used for performing 3D invasion assays. More importantly, it ensures that the precise control of spheroid size and position is achieved at micrometer scale. Finally, the usefulness of this novel technology is demonstrated by producing multicellular micro‐patterns with primary hepatocyte spheroids and endothelial cells, that exhibit significantly improved long‐term hepatic function and drug metabolism.
A new, high‐precision 3D cell spheroid printing technique, named 3D bio‐dot printing, is presented. The new technique exhibits convenient, precise, and 3D producible results with multiple cell spheroids, and can be applied to 3D spheroid invasion assays. Its application to hepatic models demonstrates that the technique can significantly improve long‐term hepatic function and drug metabolism through precise patterning with cell spheroids.
Artificial neural networks (ANNs) have been extensively used to forecast monthly precipitation for water resources management over the past few decades. Efforts to produce more accurate and stable ...forecasts face ongoing challenges as the so‐called single‐ANN (S‐ANN) approach has several limitations, particularly regarding uncertainty. Many attempts have been made to deal with different types of uncertainties by applying ensemble approaches. Here, we propose a new ANN ensemble model (ANN‐ENS) dealing with uncertainty in model structure and input variable selection to provide a more accurate and stable forecasting performance. This model is structured by generating various input layers, considering all the candidate input variables (i.e.,large‐scale climate indices and lagged precipitation). We developed a modified backward elimination method to select the preliminary input variables from all the candidate input variables. Then, we tested and validated the proposed ANN‐ENS using observed monthly precipitation from 10 meteorological stations in the Han River basin, South Korea. Our results demonstrated that the ANN‐ENS enhanced the forecasting performance in terms of both accuracy and stability. Although a significant uncertainty was introduced by using all the candidate input variables, the forecasting result outperformed S‐ANNs for all employed stations. Additionally, the ANN‐ENS provided a more stable forecasting performance in comparison with S‐ANNs, which are highly sensitive. Moreover, the generated ensemble members were slightly biased at some stations but were generally reliable.
Key Points
We develop a new artificial neural network (ANN) ensemble model (ANN‐ENS) to deal with uncertainty in model structure and input variable selection
This is based on a modified backward elimination method that improves preliminary input variables for the ANN‐ENS
The resulting ANN‐ENS demonstrated remarkable performance in terms of forecasting accuracy, stability, and ensemble reliability
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•A new bio-inspired design for PDMS sponge has been proposed to improve the absorption capacity for oil-water separation.•The concept of the water absorption and storage functions of ...cacti plant was applied in designing the PDMS sponge.•The sponge with microscale architecture was fabricated by 3D printing technology.•The new design showed an absorption capacity increased by about 3.7 times in the test of oil-water separation.
Oil spills from disasters such as the sinking of ships and the discharge of oily wastes cause serious environmental problems. Polydimethylsiloxane(PDMS) sponges are valuable tools for isolating spilled oil. Here, we propose new PDMS sponges with bio-inspired design and enhanced absorption capacities. 3D printing was used to produce templates having negative designs, and after being filled with PDMS, the templates were selectively dissolved. Through this, PDMS sponges with well-interconnected and controlled porosities were produced within 10% error. The wettability of sponges with various pore sizes and line widths was investigated. The surfaces were found to be highly hydrophobic, with water contact angles of 100-143°, and oleophilic, with oil contact angles of ∼0°. The sponge fabricated with line width of 200 μm and pore size of 400 μm showed the highest hydrophobicity and oleophilicity. These parameters were used to produce the surfaces of hollow sponges having bio-inspired design that mimics the water absorption and storage functions of cactus. Repeated oil-water separation testing was conducted, and the absorption capacities were compared with those of non-hollow and conventional sponges. The new design showed absorption capacity up to 3.7 times that of the sponges. The bio-inspired PDMS sponge provides a significant advance in oil–water separation ability.
•Nine factors that characterize data-based value creation were identified.•The nine factors were identified and defined based on six action research projects.•The projects used specific datasets to ...design new information-intensive services.•Data usage in 149 information-intensive services was also analyzed.•The nine factors are useful in analyzing and designing data-based value creation.
Service is a key context for the application of IT, as IT digitizes information interactions in service and facilitates value creation, thereby contributing to service innovation. The recent proliferation of big data provides numerous opportunities for information-intensive services (IISs), in which information interactions exert the greatest effect on value creation. In the modern data-rich economy, understanding mechanisms and related factors of data-based value creation in IISs is essential for using IT to improve such services. This study identified nine key factors that characterize this data-based value creation: (1) data source, (2) data collection, (3) data, (4) data analysis, (5) information on the data source, (6) information delivery, (7) customer (information user), (8) value in information use, and (9) provider network. These factors were identified and defined through six action research projects with industry and government that used specific datasets to design new IISs and by analyzing data usage in 149 IIS cases. This paper demonstrates the usefulness of these factors for describing, analyzing, and designing the entire value creation chain, from data collection to value creation, in IISs. The main contribution of this study is to provide a simple yet comprehensive and empirically tested basis for the use and management of data to facilitate service value creation.
Although infected pancreatic necrosis can develop as a result of rare conditions involving trauma, surgery, and systemic infection with an uncommon pathogen, it usually occurs as a complication of ...pancreatitis. Early phase of acute pancreatitis can be either edematous interstitial pancreatitis or necrotizing pancreatitis. The late complications of pancreatitis can be divided into pancreatic pseudocyst due to edematous interstitial pancreatitis or walled-off necrosis due to necrotizing pancreatitis. During any time course of pancreatitis, bacteremia can provoke infection inside or outside the pancreas. The patients with infected pancreatic necrosis may have fever, chills, and abdominal pain as inflammatory symptoms. These specific clinical presentations can differentiate infected pancreatic necrosis from other pancreatic diseases. Herein, I report an atypical case of infected pancreatic necrosis in which abdominal pain, elevation of white blood cell, and fever were not found at the time of admission. Rather, a 10-kg weight loss (from 81 to 71 kg) over 2 months nearly led to a misdiagnosis of pancreatic cancer. The patient was finally diagnosed based on endoscopic ultrasound-guided fine-needle aspiration. This case highlights that awareness of the natural course of pancreatitis and infected pancreatic necrosis is important. In addition, endoscopic ultrasound-guided fine-needle aspiration should be recommended for the diagnosis and treatment of indeterminate pancreatic lesions in selected patients.
Permanent magnet synchronous motors are widely employed in various fields due to their excellent efficiency and output characteristics. However, magnets are susceptible to irreversible ...demagnetization faults (IDF), where their magnetic properties degrade due to stresses such as external magnetic fields and high temperature condition. IDF can deteriorate motor performance and even lead to catastrophic failures like control failures. Therefore, there is a need to study diagnostic techniques for IDF. Various diagnostic techniques have been proposed. However, due to the uneven distribution of stresses, such as heat and magnetic flux, on the motor magnets, IDF exhibit non-uniformity along the z-axis direction. Most of fault detection method cannot provide diagnostics for individual magnet segments. In this paper, a fault diagnosis technique using planar search coils attached to the surface of the motor's stator is introduced. The planar search coils can be fabricated in various shapes, allowing for the arrangement of multiple coils along the z-axis of the motor to acquire signals based on the flux linkage on each coil. This technique is validated through finite element method and experiments. As a result, it is confirmed that the proposed technique allows for fault diagnosis in both the non-excited and excited states using the acquired signals.
Considering the lack of antiviral drugs worldwide, we investigated the antiviral potential of fucoxanthin, an edible carotenoid purified from Sargassum siliquastrum, against zika virus (ZIKV) ...infection. The antiviral activity of fucoxanthin was assessed in ZIKV-infected Vero E6 cells, and the relevant structural characteristics were confirmed using molecular docking and molecular dynamics (MD) simulation. Fucoxanthin decreased the infectious viral particles and nonstructural protein (NS)1 mRNA expression levels at concentrations of 12.5, 25, and 50 µM in ZIKV-infected cells. Fucoxanthin also decreased the increased mRNA levels of interferon-induced proteins with tetratricopeptide repeat 1 and 2 in ZIKV-infected cells. Molecular docking simulations revealed that fucoxanthin binds to three main ZIKV proteins, including the envelope protein, NS3, and RNA-dependent RNA polymerase (RdRp), with binding energies of −151.449, −303.478, and −290.919 kcal/mol, respectively. The complex of fucoxanthin with RdRp was more stable than RdRp protein alone based on MD simulation. Further, fucoxanthin bonded to the three proteins via repeated formation and disappearance of hydrogen bonds. Overall, fucoxanthin exerts antiviral potential against ZIKV by affecting its three main proteins in a concentration-dependent manner. Thus, fucoxanthin isolated from S. siliquastrum is a potential candidate for treating zika virus infections.
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•Ti element was embedded into LN92 cathode using nano-TiO2 precursor.•Incorporating the Ti enhanced the mechanical strength of LN92 cathode materials.•Ti-incorporated LN92 cathodes ...exhibited improved retention after 100 cycles.•Ti-incorporated LN92 cathodes offered a stable interface by inhibiting microcracks.
Given the exponential rising demand for high-performance lithium-ion batteries (LIBs), Ni-rich layered LiNixCoyMn(1-x-y)O2 (LN) cathode materials have gained significant attention. Nevertheless, poor structural stability of LN cathode materials is a critical hurdle to their use in LIBs. In this regard, Ti-doped LN cathode materials are designed to enrich the structural reliability of LN cathode materials, and their underlying behaviors are elucidated. Once Ti is replaced in the layered sites of the LN cathode materials, it strongly binds to lattice oxygen owing to its outstanding binding affinity. Therefore, Ti-doped Ni-rich LN cathode materials exhibit increased thermal stability and mechanical rigidity compared with bare LN cathode materials. In terms of cycling behavior, the Ti-doped Ni-rich LN cathode materials deliver remarkably improved cycling retention; the 2.0% Ti-doped Ni-rich LN cathode material yields an 88.0% retention, while the bare Ni-rich LN cathode material yield a 52.2% retention after 100 cycles. Further analyses of the recovered Ni-rich LN cathodes indicate that structural deformations, such as microcracks and phase transitions, are evidently suppressed in the Ti-doped LN cathode, and additional parasitic reactions, such as electrolyte decomposition, are also inhibited after cycling. This means that incorporating Ti in Ni-rich LN cathode materials not only suppresses structural deformation but also simultaneously increases the interfacial stability upon cycling, thus leading to a comprehensively improved cycling behavior.
•We compared the AIC, AICc, BIC, and LRT for nonstationary GEV models.•Monte Carlo simulation was conducted for evaluating the performances of all tests.•Under stationary conditions, the BIC shows ...the best performance (N>40).•Under nonstationary conditions, regression lines for model selection were proposed.•The results of simulations were verified through the application of observed data.
Several evidences of hydrologic data series being nonstationary in nature have been found to date. This has resulted in the conduct of many studies in the area of nonstationary frequency analysis. Nonstationary probability distribution models involve parameters that vary over time. Therefore, it is not a straightforward process to apply conventional goodness-of-fit tests to the selection of an appropriate nonstationary probability distribution model. Tests that are generally recommended for such a selection include the Akaike’s information criterion (AIC), corrected Akaike’s information criterion (AICc), Bayesian information criterion (BIC), and likelihood ratio test (LRT). In this study, the Monte Carlo simulation was performed to compare the performances of these four tests, with regard to nonstationary as well as stationary generalized extreme value (GEV) distributions. Proper model selection ratios and sample sizes were taken into account to evaluate the performances of all the four tests. The BIC demonstrated the best performance with regard to stationary GEV models. In case of nonstationary GEV models, the AIC proved to be better than the other three methods, when relatively small sample sizes were considered. With larger sample sizes, the AIC, BIC, and LRT presented the best performances for GEV models which have nonstationary location and/or scale parameters, respectively. Simulation results were then evaluated by applying all four tests to annual maximum rainfall data of selected sites, as observed by the Korea Meteorological Administration.
•Inherent climate variability in monthly precipitation are decomposed by EEMD.•Four- and ten-month lagged NINO12 indices represent the periodic oscillations.•Behavior of AMO index indicates inherent ...long-term trend of monthly precipitation.•Climate indices as predictive indicators to predict precipitation are suggested.•Statistical relationships are examined by a multiple linear regression model.
Climate indices characterize climate systems and may identify important indicators for long-term precipitation, which are driven by climate interactions in atmosphere-ocean circulation. In this study, we investigated the climate indices that are effective indicators of long-term precipitation in South Korea, and examined their relationships based on statistical methods. Monthly total precipitation was collected from a total of 60 meteorological stations, and they were decomposed by ensemble empirical mode decomposition (EEMD) to identify the inherent oscillating patterns or cycles. Cross-correlation analysis and stepwise variable selection were employed to select the significant climate indices at each station. The climate indices that affect the monthly precipitation in South Korea were identified based on the selection frequencies of the selected indices at all stations. The NINO12 indices with four- and ten-month lags and AMO index with no lag were identified as indicators of monthly precipitation in South Korea. Moreover, they indicate meaningful physical information (e.g. periodic oscillations and long-term trend) inherent in the monthly precipitation. The NINO12 indices with four- and ten- month lags was a strong indicator representing periodic oscillations in monthly precipitation. In addition, the long-term trend of the monthly precipitation could be explained by the AMO index. A multiple linear regression model was constructed to investigate the influences of the identified climate indices on the prediction of monthly precipitation. Three identified climate indices successfully explained the monthly precipitation in the winter dry season. Compared to the monthly precipitation in coastal areas, the monthly precipitation in inland areas showed stronger correlation to the identified climate indices.
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