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Besides antiviral functions, type I IFN expresses potent anti-inflammatory properties and is being widely used to treat certain autoimmune conditions, such as multiple sclerosis. In a murine model of multiple sclerosis, experimental autoimmune encephalomyelitis, administration of IFN-β effectively attenuates the disease development. However, the precise mechanisms underlying IFN-β–mediated treatment remain elusive. In this study, we report that IFN-induced protein with tetratricopeptide repeats 2 (Ifit2), a type I and type III IFN-stimulated gene, plays a previously unrecognized immune-regulatory role during autoimmune neuroinflammation. Mice deficient in Ifit2 displayed greater susceptibility to experimental autoimmune encephalomyelitis and escalated immune cell infiltration in the CNS. Ifit2 deficiency was also associated with microglial activation and increased myeloid cell infiltration. We also observed that myelin debris clearance and the subsequent remyelination were substantially impaired in Ifit2−/− CNS tissues. Clearing myelin debris is an important function of the reparative-type myeloid cell subset to promote remyelination. Indeed, we observed that bone marrow–derived macrophages, CNS-infiltrating myeloid cells, and microglia from Ifit2−/− mice express cytokine and metabolic genes associated with proinflammatory-type myeloid cell subsets. Taken together, our findings uncover a novel regulatory function of Ifit2 in autoimmune inflammation in part by modulating myeloid cell function and metabolic activity.

In this study, a method for extracting the waterbody of small- to medium-sized streams is verified using SAR (Synthetic Aperture Radar) images based on the shortest plane distance (DST). The areas investigated are the Hantangang River and Jungnangcheon stream. To verify the waterbody extraction method, SAR satellite image data (Sentinel-1) and PlanetScope, which is a high-resolution optical satellite, are used simultaneously. After preprocessing the Sentinel-1 images, their brightness distribution is equalized via histogram matching. To realize an efficient stream extraction, a weight, which is the DST from the stream centerline, is added. After combining this weight value and Sentinel-1's VH and VV polarizations, the optimal parameter value is obtained using the k-means method. This value allows the waterbody to be extracted from Sentinel-1 images with maximum accuracy, depending on the resolution limit. Based on the correlation among the VV, VH, and DST, the waterbody extraction can be calculated using an elliptic equation. Results show that the average accuracy is 0.45-0.75, and the average Kappa coefficient is 0.60-0.85. This study shows that the waterbody area can be estimated using the DST. Additionally, the proposed method extracts the waterbody more simply and rapidly than the existing waterbody extraction method.

Simulation of fast reacting hydrological systems often requires sub-hourly precipitation data to develop appropriate climate adaptation strategies and tools, i.e. upgrading drainage systems and reducing flood risks. However, this sub-hourly data is typically not provided by measurements and atmospheric models, and many statistical disaggregation tools are applicable only up to an hourly resolution. Here two different models for disaggregation of precipitation data from daily to sub-hourly scale are presented. The first one is a stochastic disaggregation model based on first-order Markov Chains and Copulas (WayDown), while the second one is a stochastic precipitation generator based on a double Poisson process (LetItRain). Both approaches aim to reproduce observed precipitation statistics over different time scales. The developed models were validated using 10-min radar data representing 10 climate stations in Germany and South Korea, thus covering various climate zones and precipitation systems. Various statistics were compared including mean, variance, autocorrelation, transition probabilities, and proportion of wet period. Additionally, extremes were examined, including the frequencies of different thresholds, extreme quantiles and annual maxima. While both models successfully reproduced the observed statistics, WayDown was better than LetItRain at reproducing the ensemble median showing strength in precisely refining the coarse input data. In the meantime, LetItRain produced rainfall with greater ensemble variability capturing a variety of scenarios that may happen in reality. Both methods reproduced extremes in a similar manner: overestimation until a certain threshold of rainfall thereafter underestimation. Finally, the models were applied to climate projection data. The change factors for various statistics and extremes were computed and compared between historical (radar) and the climate projections on a daily and 10-min scale. Both methods showed similar results for the respective stations and RCP scenarios. Several consistent trends jointly confirmed by disaggregated and daily data were found for mean, variance, autocorrelation and proportion of wet periods. Further, they presented similar behavior of annual maxima for the majority of the stations for both RCP scenarios in comparison to the daily scale, namely a similar systematic underestimation.

In this study, SAR (Synthetic Aperture Radar) images are used to validate a method for extracting the waterbody from small and medium-sized streams based on the shortest plane distance (DST). The research area is the Hantangang River and Jungnangcheon in the Hangang River Basin of South Korea. To validate the water body extraction method, SAR satellite image data (Sentinel-1) and high-resolution optical satellite PlanetScope are employed concurrently. After preprocessing, the brightness distribution of the Sentinel-1 photos is equalized using histogram matching. To achieve an efficient stream extraction, a weight is applied that is the DST from the stream centerline. The optimal parameter value is obtained using the k-means method after combining this weight value with Sentinel-1's VH and VV polarizations. Depending on the resolution limit, this value allows the waterbody to be extracted with maximum accuracy from Sentinel-1 images. The waterbody extraction can be calculated using an elliptic equation based on the correlation between the VV, VH, and DST. Results show that the average accuracy is 0.45-0.75, and the average Kappa coefficient is 0.60-0.85. This study demonstrates that the DST can be used to estimate the area of a waterbody. Furthermore, the proposed method extracts the waterbody more easily and quickly than the existing method.This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2021R1A2C2003471).

This study developed a distributed hydrologic model based on Long Short-Term Memory (LSTM) to predict flow discharge of Joongrang stream located in a highly urbanized area in Seoul, Korea. The model inputs are the time series of 10-minute radar-gauge composite precipitation data at 239 grid cells (1km2) in the watershed and the Normalized Difference Vegetation Index (NDVI) data derived from Landsat 8 images and the model output is the 10-minute flow discharge at the watershed outlet as output. The model was trained for the calibration period of 2013-2016 and was validated for the period of 2017-2019. The NSE value over the validation period corresponding to the optimal model architecture (256 LSTM hidden layers) with and without NDVI input data was 0.68 and 0.52, respectively, which suggests that the machine can learn dynamic processes of soil infiltration and plant interception from the remotely sensed information provided by satellite.This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1A4A3032838).

We propose an approach for stochastic simulation of realistic continuous snow depth time series using a snow depth estimation model and a stochastic weather generation model. The snow depth estimation model consists of three steps: (1) determination of the precipitation type, (2) estimation of the snow ratio, and (3) estimation of the decreased snow depth. In the first step, air temperature and relative humidity are used as indicators to determine the type of precipitation when precipitation occurs. In the second step, when the type is determined as snow, the snow ratio is estimated, converting the depth of precipitation into depth of fresh snow. Here, the air temperature is used as an indicator to estimate the snow ratio using sigomidal relationship with the snow ratio. In the last step, the amount of decreased snow depth was estimated using a novel temperature index snowmelt equation considering a trend of depth-dependent decreasing snow depth. The snow depth estimation model was applied to the four snowiest meteorological stations of Korea and yielded high Nash Sutcliffe efficiency values which ranged between 0.745 and 0.875 for calibration, and ranged between 0.432 and 0.753 for validation. This calibrated snow depth estimation model was then applied to the simulated weather time series (precipitation, temperature, and relative humidity) from the stochastic weather generation model to simulate continuous snow depth time series. The simulated snow depth data accurately reproduced standard and extreme value statistics of the observed data, the latter of which were consistent with the estimates provided in Korean Building Code. Then, the model was extended to investigate the influence of climate change on the future snow depth. For this, future weather statistics were obtained by applying factor of change to the current weather statistics and then were used to calibrate the weather generation model. Lastly, the future snow depth time series for three future time windows (2021-2040, 2041-2070, and 2071-2100) were simulated using future weather time series and snow depth estimation model. This research was supported by a grant(2022-MOIS61-003) of Development Risk Prediction Technology of Storm and Flood for Climate Change based on Artificial Intelligence funded by Ministry of Interior and Safety(MOIS, Korea).

An LSTM-based distributed hydrologic model for an urban watershed of Korea was developed. The input of the model is the time series of the 10-minute radar-gauge composite rainfall data and 10-minute temperature data at the 239 model grid cells, and the output of the model is the 10-minute flow discharge at the watershed outlet. The Nash-Sutcliffe Efficiency (NSE) coefficients of the calibration period (2013-2016) and validation period (2017-2019) were 0.99 and 0.67, respectively. Normal events were better predicted than the extreme ones. Further in-depth analyses revealed that: (1) the model composes the watershed outlet flow discharge by linearly superimposing multiple time series created by each of the LSTM units. Unlike conventional hydrologic models, most of these time series greatly fluctuated in both positive and negative domain; (2) the runoff to rainfall ratio of each of the model grid cells does not reflect its counterpart parameters of the conceptual hydrologic models revealing that the model simulates the watershed responses in a unique manner; (3) the model successfully reproduced the soil-moisture dependent runoff processes, which is an essential prerequisite of continuous hydrologic models; (4) Each of the LSTM units have different temporal sensitivity to a unit rainfall stimulus, and the LSTM units that is sensitive to rainfall input have greater output weight factors nearby the watershed outlet, and vice versa. This means that the model learned a mechanism to separately consider the hydrologic components with distinct response time such as direct runoff and the low frequency baseflow. AcknowledgementThis research was supported by the Basic Science Research Program (Grant Number: 2021R1A2C2003471) and the Basic Research Laboratory Program (Grant Number: 2022R1A4A3032838) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT.

Physically-based hydrologic models can accurately simulate streamflow in natural environment, but they cannot precisely consider the anthropogenic disturbance caused by the operation of large-scale dams. We tried to overcome this issue by developing a hybrid modeling framework, consisting of physically-based models for simulating upstream natural watersheds and deep-learning-based models for simulating dam operation. The model was developed for the Paldang Dam watershed, a major water source for Seoul metropolitan area, where the importance of stable water supply has increased due to the increase of population and water use per capita. The prediction performance of the hybrid model was compared with that of models built based only on the physically-based hydrologic model, namely the Variable Infiltration Capacity model, with single and cascaded structure. For the validation period, Nash-Sutcliffe Efficiency from the developed hybrid model, the single model, and the cascaded model were 0.6410, -0.1054, and 0.2564, respectively, suggesting that the consideration of dam operation aided by the machine learning algorithm is essential for accurate assessment of streamflow.This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1A4A3032838).

Precipitation forecast models based on meteorological radar data using machine learning architectures accurately predict spatio-temporal progress of precipitation. However, these data-driven forecasting models tend to underestimate magnitude of extreme precipitation events because the training of them is based on the observed precipitation data in which the normal precipitation events are included significantly more than the rare extreme events. This study proposes a ConvLSTM-based precipitation nowcasting model that can accurately predict space-time field of extreme precipitation. First, precipitation events were classified into 5 subsets using the k-means clustering algorithm based their statistical properties such as mean, standard deviation, skewness, duration, and the calendar month at which the precipitation event occurred. Then, a ConvLSTM-based neural network was trained based on the subset containing extreme precipitation events (events with large mean, variance, and duration occurred in summer months). The model was trained and tested based on the 4km-10minute resolution radar-gauge composite precipitation field of central part of South Korea (200km x 200km) for the period of 2009-2015 and 2016-2020, respectively. The NSE of the model that was trained based on the whole precipitation data was 0.55 while the one trained based on the subset of extreme precipitation was 0.78 showing a significant improvement.This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2021R1A2C2003471).

The advancement of wireless sensor networks (WSNs) improves various smart home automation services and home users' living standards. However, efficiently collecting data and automating smart home services require the extensive deployment of the sensors. Thus, one of the crucial and challenging tasks is to minimize the sensors' energy consumption for monitoring and automating various activities in a smart home. In this article, we present a solution to control the excessive energy consumption of sensors used to detect various activities of daily living (ADL) of a smart home resident. The sensors within a smart home network are divided into various groups employing the recurrent neural network (RNN) and dynamic time warping (DTW) techniques to predict the activities with high accuracy and less energy consumption. The smart home users' future activities are forecast with bidirectional long short-term memory (BLSTM) RNN model to select those sensors that are likely to predict the upcoming activities. Similarly, to predict the home users' unusual activities, a guard sensor is elected among sensors with high similarities with each other using DTW. The sensor's role is evenly switched between different modes to maintain a fair tradeoff between energy and accuracy. An extensive set of simulations is performed to validate the proposed scheme's work integrating datasets from authentic sources. Finally, the proposed system significantly reduces the sensors' energy consumption and prolongs the battery lifetime to approximately 137 days.

In this work, a highly stable Ca2+ ion-selective sensor was successfully developed by depositing a poly(3,4-ethylenedioxythiophene): poly(styrene-sulfonate) (PEDOT:PSS) layer on top of a carbon paste electrode as an ion-electron transducing layer. The PEDOT:PSS was treated by a co-solvent of ethylene glycol (EG) using the bath-sonication technique to enhance electrical and electrochemical characteristics, and finally coated with Ca2+ ion-selective membrane cocktail. The developed Ca2+ ion-selective electrode (Ca2+-ISE) showed excellent electrochemical properties by much-improved charge transfer kinetics with remarkable electric conductivity. The fabricated Ca2+-ISE exhibited an excellent sensitivity of 37.7 mV/decade (n = 4) in the range from 10-4 to 10-1 M with a rapid response (< 20 seconds). Moreover, the EG-treated PEDOT:PSS (PEDOT:PSS-EG) based ISE showed negligible responses for primary interfering ions of human biofluid samples, proving significant potential selectivity. Furthermore, the negligible drift of open circuit potential (2.78 μV/s) proved the stability of the PEDOT:PSS-EG compared to pristine PEDOT:PSS(10.63 μV/s) based ISE. Based on these analyses, it can be expected that the organic solvent treatment on PEDOT:PSS will pave the way for long-term monitoring of other biochemical compounds.

IL-27 is an IL-12 family cytokine with immune regulatory properties, capable of modulating inflammatory responses, including autoimmunity. While extensive studies investigated the major target cells of IL-27 mediating its functions, the source of IL-27 especially during tissue specific autoimmune inflammation has not formally been examined. IL-27p28 subunit, also known as IL-30, was initially discovered as an IL-27-specific subunit, and it has thus been deemed as a surrogate marker to denote IL-27 expression. However, IL-30 can be secreted independently of Ebi3, a subunit that forms bioactive IL-27 with IL-30. Moreover, IL-30 itself may act as a negative regulator antagonizing IL-27. In this study, we exploited various cell type specific IL-30-deficient mouse models and examined the source of IL-30 in a T cell mediated autoimmune neuroinflammation. We found that IL-30 expressed by infiltrating and CNS resident APC subsets, infiltrating myeloid cells and microglia, is central in limiting the inflammation. However, dendritic cell-derived IL-30 was dispensable for the disease development. Unexpectedly, in cell type specific IL-30 deficient mice that develop severe EAE, IL-30 expression in the remaining wild-type APC subsets is disproportionately increased, suggesting that increased endogenous IL-30 production may be involved in the severe pathogenesis. In support, systemic recombinant IL-30 administration exacerbates EAE severity. Our results demonstrate that dysregulated endogenous IL-30 expression may interfere with immune regulatory functions of IL-27, promoting encephalitogenic inflammation in vivo.

Recently, a variety of the integrated service platforms demand the innovative network technologies such as software-defined networking (SDN). However, it is problematic and complicated to provide interconnectivity and reachability between inter-domain SDNs. Thus, this paper proposes a scheme for the inter-SDN federation and communication based on the abstracted network information exchange and automated peer network configuration for the integrated network services. The proposed scheme is implemented and verified over the de-facto wide-area SDN infrastructure (KREONET-S) which is deployed on Korea Research Environment Open Network (KREONET), a representative national research network in Korea. The proposed method is implemented and experimented based on the distributed and layer-2 oriented network architecture by using the abstracted message exchanges between local SDN controllers in order to provide low message overheads and high federation scalability. Our research work contributes to more efficient inter-SDN communications by means of the data modeling abstraction of the local SDN network information and the simplified interactions between individual controllers by the automated configurations through the specific application interfaces. The proposed scheme also contributes to the development of the integrated service platforms which are demanded to provide IoT, Big Data, AI, and cloud services in the tightly coupled manner over the inter-SDN domains.

Acupuncture is a representative treatment field in oriental medicine that is used widely in various medical conditions. Although acupuncture is considered a safe procedure, it can cause complications. This paper reports the case of a 44-year-old quadriplegic woman who received a percutaneous gastrostomy (PEG) tube and was referred for the endoscopic removal of swallowed acupuncture needles. The needles were removed successfully by endoscopy through the PEG tube using the rendezvous technique without significant complications.

The development of highly sensitive, reliable, and cost-effective strain sensors is a big challenge for wearable smart electronics and healthcare applications, such as soft robotics, point-of-care systems, and electronic skins. In this study, we newly fabricated a highly sensitive and reliable piezoresistive strain sensor based on polyhedral cobalt nanoporous carbon (Co-NPC)-incorporated laser-induced graphene (LIG) for wearable smart healthcare applications. The synergistic integration of Co-NPC and LIG enables the performance improvement of the strain sensor by providing an additional conductive pathway and robust mechanical properties with a high surface area of Co-NPC nanoparticles. The proposed porous graphene nanosheets exploited with Co-NPC nanoparticles demonstrated an outstanding sensitivity of 1,177 up to a strain of 18%, which increased to 39,548 beyond 18%. Additionally, the fabricated sensor exhibited an ultralow limit of detection (0.02%) and excellent stability over 20,000 cycles even under high strain conditions (10%). Finally, we successfully demonstrated and evaluated the sensor performance for practical use in healthcare wearables by monitoring wrist pulse, neck pulse, and joint flexion movement. Owing to the outstanding performance of the sensor, the fabricated sensor has great potential in electronic skins, human-machine interactions, and soft robotics applications.

Internet of things (IoT) application in e-health can play a vital role in countering rapidly spreading diseases that can effectively manage health emergency scenarios like pandemics. Efficient disease control also requires monitoring of Standard operating procedure (SOP) follow-up of the population in the disease-prone area with a cost-effective reporting and responding mechanism to register any violation. However, the IoT devices have limited resources and the application requires delay-sensitive data transmission. Named Data Networking (NDN) can significantly reduce content retrieval delays but inherits cache overflow and network congestion challenges. Therefore, we are motivated to present a novel smart COVID-19 pandemic-controlled eradication over NDN-IoT (SPICE-IT) mechanism. SPICE-IT introduces autonomous monitoring in indoor environments with efficient pull-based reporting mechanism that records violations at local servers and cloud server. Intelligent face mask detection and temperature monitoring mechanism examines every person. Cloud server controls the response action from the centre with an adaptive decision-making mechanism. Long short-term memory (LSTM) based caching mechanism reduces the cache overflow and overall network congestion problem.

Cellular vehicle-to-everything (C-V2X) is one of the key enabling vehicular communication technologies endorsed by the vehicular industry, scientists, and researchers. Introduced by the 3GPP, LTE device-to-device in Release 12 evolved into C-V2X with mode 3 and mode 4. In mode 4, semi-persistent -scheduling-based sensing is performed by vehicles to select resources. However, when the density of vehicles increases, the performance of C-V2X mode 4 degrades. To address this challenge, 3GPP Release 16 introduced New Radio (NR) for V2X services, also known as NR-V2X. Modes 1 and 2 along with four additional sub-modes were considered in NR-V2X to achieve high reliability and higher throughput with low latency. This article discusses and evaluates the advancements introduced in NR-V2X by comparative analysis with C-V2X. To compare the performance, an NR-V2X simulator based on a network simulator (ns-3) for sub-6 GHz band is also introduced. In addition, feasible practical solutions to resolve resource selection problems are also discussed.

Flood warning systems can provide an effective and low-cost solution to protect urban residents especially in developing countries suffering from severe flood damages caused by weak capacity of flood defense structures. Rainfall Threshold for Flood Warning (RTFW) is an essential key of accurate flood warning system. This study proposed an easy-to-use mathematical equation for RTFW for small urban watersheds based on computer simulations. First, a coupled 1D–2D dual-drainage model was used to simulate the flooded area in nine watersheds of Seoul, Korea corresponding to 540 scenarios of various synthetic rainfall events and watershed imperviousness. Then, the results of the 101 simulations that caused the critical flooded depth (0.25 m–0.35 m) were used to develop the equation that relates the value of RTFW to the rainfall event temporal variability (represented as coefficient of variation or CV) and the watershed imperviousness (represented as NRCS Curve Number or CN). The results suggest that (1) RTFW exponentially decreases as the rainfall CV increases; (2) RTFW linearly decreases as the watershed CN increases; and that (3) RTFW is dominated by CV when the rainfall has low temporal variability (e.g., CV 0.4). For validation, the proposed equation was applied for the flood warning system with two storm events occurred in 2010 and 2011 over 239 watersheds in Seoul. The system showed the hit, false and missed alarm rates at 69% (48%), 31% (52%) and 6.7% (4.5%), respectively for the 2010 (2011) event.

Accurate prediction of reservoir sediment inflows (Min) and adaptation of feasible sediment management strategies pose challenges in water engineering. This study proposed a two-stage complementary modeling approach for comprehensive reservoir sediment management. In the first stage, artificial neural network-based models provide real-time Min predictions using water inflow, water head, and outflow as input parameters. In the second stage, the parameter estimation method of the RESCON model is applied to hydraulic flushing in a reservoir. This approach was applied to the Sangju Weir and Nakdong River Estuary Barrage (NREB) in South Korea. Results from the RESCON model revealed that hydraulic flushing was effective for sediment management at both the Sangju Weir reservoir and the NREB approach channel. Efficient flushing at the Sangju Weir required a flushing discharge of 100 m3/s for 6 days and 40 m of water head. Efficient flushing at the NREB required a flushing discharge of 25 m3/s for 6 days with 1.8 m of water-level drawdown. The proposed approach is expected to prove useful in reservoir sediment management.

Over the years, interleukin (IL)-27 has received much attention because of its highly divergent, sometimes even opposing, functions in immunity. IL-30, the p28 subunit that forms IL-27 together with Ebi3 and is also known as IL-27p28 or IL-27A, has been considered a surrogate to represent IL-27. However, it was later discovered that IL-30 can form complexes with other protein subunits, potentially leading to overlapping or discrete functions. Furthermore, there is emerging evidence that IL-30 itself may perform immunomodulatory functions independent of Ebi3 or other binding partners and that IL-30 production is strongly associated with certain cancers in humans. In this review, we will discuss the biology of IL-30 and other IL-30-associated cytokines and their functions in inflammation and cancer., Immunity and cancer: A fresh look at a well-known signaling protein Studying the ways that interleukin IL-30 regulates immune responses may provide novel insights into tumor development and inflammatory conditions. Interleukins are a diverse family of proteins involved in intercellular communications and immunity, where they can exert divergent and even opposing functions. Booki Min at Northwestern University in Chicago, USA, and co-workers reviewed the current understanding of IL-30 and its links to inflammation and cancer. IL-30 forms the IL-27 complex with the Ebi3 protein and was thought to be a surrogate for IL-27 in terms of activity. However, recent insights suggest that IL-30 may perform discrete immune modulation functions. Elevated IL-30 secretion is linked to prostate and breast cancer development. Extensive research is needed into the formation of IL-30, its associated protein interactions, and the development of a suitable animal model.

Suspended sediment load (SSL) flowing into a reservoir contributes to the overall safety of dam. Owing to the complexity and stochastic nature of sedimentation, accurate prediction of reservoir SSL inflow is still challenging. Moreover, research and application of machine learning (ML) techniques for reservoir sedimentation are still deficient. A comprehensive evaluation of six ML models for a reservoir SSL inflow prediction was performed in this study. ML techniques including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), radial basis function neural network (RBFNN), support vector machine (SVM), genetic programming (GP), and deep learning (DL) were applied to develop predictive models of daily SSL inflow at Sangju Weir, South Korea. Significant input vectors for each model were selected with streamflow, water temperature, water stage, reservoir outflow for different time lags. Model performances were evaluated using various statistical indices including the coefficient of determination (R2), mean absolute error (MAE), percentage of bias (PBIAS), Willmott index (WI), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and Pearson correlation coefficient (PCC). The best input combinations were found to be unique for each ML model, but all six models performed reasonably well for SSL inflow predictions. ANN model outperformed other models with R2 = 0.821, MAE = 4.244 tons/day, PBIAS = 0.055, WI = 0.891, NSE = 0.991, RMSE = 11.692 tons/day, PCC = 0.826. The models were ranked based on their SSL prediction capabilities as ANN > ANFIS > DL > RBFNN > SVM > GP from best to worst. The findings are expected to be useful for future dam safety and risk assessment, and for achieving sustainability of reservoir operation through comprehensive sediment management.

Hydrogel-based electronics have attracted substantial attention in the field of biological engineering, energy storage devices, and soft actuators due to their resemblance to living tissues, biocompatibility, tunable softness, and consolidated structures. However, combining the properties of quick resilience, hysteresis-free, and robust mechanical properties in physically cross-linked hydrogels is still a great challenge. Herein, we present a vinyl hybrid silica nanoparticle (VSNPs)/polyacrylamide (PAAm)/alginate double-network hydrogel-based strain sensor with the characteristics of quick resilience, hysteresis-free, and a low limit of detection (LOD). The physical cross-linking among PAAm chains and covalent cross-linking between PAAm, alginate, and

Length–weight relations were estimated for 14 fish species sampled from the coastal waters off the Gwangyang Bay in South Korea. The following species were studied: Okamejei kenojei (Müller et Henle, 1841); Muraenesox cinereus (Forsskål, 1775); Thryssa adelae (Rutter, 1897); Thryssa kammalensis (Bleeker, 1849); Tribolodon hakonensis (Günther, 1877); Inimicus japonicus (Cuvier, 1829); Chelidonichthys spinosus (McClelland, 1844); Jaydia lineata (Temminck et Schlegel, 1842); Sillago japonica Temminck et Schlegel, 1843; Pholis nebulosa (Temminck et Schlegel, 1845); Favonigobius gymnauchen (Bleeker, 1860); Pampus echinogaster (Basilewsky, 1855); Cynoglossus joyneri Günther, 1878; Takifugu niphobles (Jordan et Snyder, 1901).The length–weight relation of Thryssa adelae (Rutter, 1897), (Engraulidae) has not been previously reported. The new maximum total length of Thryssa kammalensis (18.0 cm) is now provided. The values of coefficient a ranged from 0.0007 to 0.0218, and the values of exponent b ranged from 2.82 to 3.52.

This study proposes two variants of the traditional conditional merging (CM) method that merges the next-generation radar (NEXRAD) ground gauge precipitation data. The first method, named CM considering simple optimal estimation (SOE), employs a novel algorithm of simultaneously considering rainfall spatial intermittency and inner variability to replace the conventional semivariogram algorithms of the CM method. The second variant, called CM-GR-SOE, employs additional Ground-Radar rainfall ratio (so called the G/R ratio) to the CM-SOE method. Model performance was evaluated using the hourly rainfall data collected between 2004 and 2007 in the regions of Houston and Dallas in Texas. The leave-one-out cross-validation was conducted, and the relative mean error (RME) and coefficient of determination (R2) were calculated for each of the methods. In areas where the rainfall intensity was low (

A unique empirical approach of estimating the urban flood warning threshold is presented. First, the rainfall depth-duration relationship was estimated based on radar-gauge merged rainfall data with 1 km–10 min space-time resolution for 319 highly urbanized watersheds of the study area, for the extreme rainfall event occurred on the September 11, 2017 in Busan, Korea. The rainfall depth-duration relationship at the watersheds were further categorized by the nine different domains of the rainfall temporal variability and the watershed flooded area proportion that is estimated from the Sentinel-1 Synthetic Aperture Radar data analysis. The minimum possible slope of the depth-duration relationship in each of the domain was determined as the flood warning rainfall intensity threshold for this urban area. The results revealed that 55 mm/hr of rainfall intensity was the universal threshold. When this threshold was applied to issue the flood warning, the number of missed warning was minimized at the expense of the increased frequencies of false warnings. The highest identified threshold was 73 mm/hr. It was also found that the temporally variable rainfall is more likely to cause floods than the one with less temporal variability. The identified rainfall threshold values were validated against the 2010 and 2011 extreme events occurred in Seoul, Korea. The validation revealed that the balance between the accuracy and the reliability of the flood warning system highly depends on the choice of the tolerable flooded area proportions. The study reveals the great potential of operational urban flood warning system composed of a weather radar and a network of ground rainfall gauges that can be simply established using satellite-based flooded area data, which may be especially useful for developing countries.

Disasters associated with heavy rainfall such as urban floods, riverine floods, and landslides often simultaneously occur while each of them sensitively reacts to rainfall variabilities at distinct ranges of time scales. Therefore, a stochastic rainfall model suitable for modeling compounding of disasters must be good at reproducing the rainfall variability across all timescales relevant to all types of disasters. This study proposes a point stochastic rainfall generator that can reproduce various rainfall characteristics at timescales between 5 minutes and one decade. The model generates the fine-scale rainfall time series using a randomized Bartlett-Lewis Rectangular Pulse (RBLRP) model. Then the rainstorms are shuffled such that the correlation structure between the consecutive storms is preserved. Finally, the time series is rearranged again at the monthly timescale based on the result of the separate coarse-scale monthly rainfall model. The method was tested using the 69 years of 5-minute rainfall data recorded at Bochum, Germany. The mean, variance, covariance, skewness, and rainfall intermittency were well reproduced at the timescales from 5 minutes to a decade without any systematic bias. The extreme values were also well reproduced at timescales from 5 minutes to 3 days. The past-7-day rainfall before an extreme rainfall event, which is highly associated with the extreme riverine flow and landslide was reproduced well too. Then, the model was extended to integrate the influence of climate change. For this, the model was re-parameterised in terms of parameters representing average magnitude and temporal structure of the rainfall time series. Then, the relationship between these new parameters and the covariates (e.g. monthly, weekly, daily temperature) were investigated. Lastly, the derived regression relationships were applied to adjust the duration and the magnitude of rain storms and cells that were generated by the stationary RBLRP model.

Besides anti-viral functions, Type I IFN expresses potent anti-inflammatory properties and is being widely used to treat certain autoimmune conditions, such as multiple sclerosis (MS). In murine model of MS, experimental autoimmune encephalomyelitis (EAE), administration of IFNβ effectively attenuates the disease development. However, the precise mechanisms underlying the treatment remain elusive. In this study, we report that IFN-induced protein with tetratricopeptide repeats 2 (Ifit2), a type I and type III IFN-stimulated gene, plays a previously unrecognized immune regulatory role during autoimmune neuroinflammation. Mice deficient in Ifit2 display greater susceptibility to EAE and escalated immune cell infiltration in the central nervous system. Ifit2 deficiency is also associated with microglial activation and increased myeloid cell infiltration. Unexpectedly, myelin debris clearance and the subsequent remyelination is impaired in Ifit2-/- CNS tissues. Clearing myelin debris is an important property of reparative M2 type myeloid cells to promote remyelination. Indeed, we observed that bone marrow derived macrophages, CNS infiltrating myeloid cells, and microglia from Ifit2-/- mice express cytokine and metabolic genes associated with proinflammatory M1 type subsets. Taken together, our findings uncover a novel regulatory function of Ifit2 in autoimmune inflammation in part by modulating myeloid cell function and metabolic activity.

Vehicle ad hoc networks (VANETs) is an emerging ad hoc network technology with a promising future but significant limitations, particularly in communication networks. An effective routing system can significantly enhance the performance of VANETs. However, creating an effective routing protocol in an urban context to transport the data packet to the destination is still challenging due to complex road conditions, sporadic connectivity among vehicles, frequent disconnections, and quick changes in network topology. To enhance the accuracy of routing decisions in an urban setting, we propose a novel “multiple attributes decision-making junction selection routing (MADMJSR)” routing protocol based on vehicle position for VANETs. Formerly, most of the research was focused on static weight-based attribute selection for communication rather than optimal values, which led to a drop in junction decision-making accuracy. Our proposed scheme considers the multiple attribute selection and the optimal weight value assigned to each attribute, which significantly improves the routing decision accuracy. The results show a high packet delivery ratio (PDR) gain within minimum latency and less network load. Furthermore, a thorough discussion of the functioning of our proposed protocol is presented along with a detailed performance evaluation in comparison to other protocols.

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