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The damage caused by floods is increasing worldwide, and if floods can be predicted, the economic and human losses from floods can be reduced. A key parameter of flooding is water level data, and this paper proposes a water level prediction model using long short-term memory (LSTM) and a gated recurrent unit (GRU). As variables used as input data, meteorological data, including upstream and downstream water level, temperature, humidity, and precipitation, were used. The best results were obtained when the LSTM–GRU-based model and the Automated Synoptic Observing System (ASOS) meteorological data were included in the input data when experiments were performed with various model structures and different input data formats. As a result of the experiment, the mean squared error (MSE) value was 3.92, the Nash–Sutcliffe coefficient of efficiency (NSE) value was 0.942, and the mean absolute error (MAE) value was 2.22, the highest result in all cases. In addition, the test data included the historical maximum water level of 3552.38 cm in the study area, and the maximum water level error was also recorded as 55.49, the lowest result. Through this paper, it was possible to confirm the performance difference according to the composition of the input data and the time series prediction model. In a future study, we plan to implement a flood risk management system that can use the predicted water level to determine the risk of flooding, and evacuate in advance.

Accurate prediction of wind power is crucial for grid scheduling and the integration of renewable energy, given its significant temporal variability and nonlinear characteristics. This study proposed a multi-module integrated model for wind power forecasting based on time–frequency domain analysis, aiming to enhance prediction accuracy and reliability. The mode9l combined several advanced techniques, including Wavelet Convolutions (WTC), Long Short-Term Memory Networks (LSTM), Time Series Lightweight Adaptive Network (TSLANet), Frequency Enhanced Channel Attention Mechanism (FECAM), and Fast Kolmogorov-Arnold Networks (FastKAN). Each module was designed to capture distinct characteristics in wind power data, such as local frequency features, temporal dependencies, global contextual information, frequency-domain features, and complex nonlinear relationships. Through the integration of these modules, the model achieved high-precision predictions in multi-scale and dynamic environments. Additionally, the Least Squares Support Vector Machine (LSSVM) was employed for error correction, further reducing prediction errors. Experimental results showed that the model delivered exceptional performance across various test scenarios, significantly improving the handling of multi-scale, complex nonlinear, and global dependency issues in wind power forecasting, demonstrating considerable application potential.

Social networking services (SNS) are platforms to form and manage personal connections and create a foundation for human relationships. Intending to identify why, how, and for what outcome users use SNS, this study contributes to the body of knowledge on SNS by analyzing how motivation, participation, and performance are related to each other in the SNS context. Drawing on a theoretical perspective of the motivation-participation-performance framework, we identify four significant why motivations (i.e., vertical social, horizontal social, hedonic, and utilitarian motivations), two main ways (how) of participation (i.e., sharing and collaboration), and two ultimate benefits (for what outcome) of SNS use (i.e., personal and job performance). The analyzed results of empirical data collected from SNS users indicate that the identified motivations significantly influence participation in sharing and collaboration activities on SNS and that SNS participation significantly affects personal and professional/job-related performance. This study contributes to theory by providing a multidimensional view of SNS use, its predictors, and its consequences.

Photovoltaic (PV) power generation is significantly impacted by environmental factors that exhibit substantial uncertainty and volatility, posing a critical challenge for accurate PV power prediction in power system management. To address this, a parallel model is proposed for PV short-term prediction utilizing a multi-level attention mechanism. Firstly, gray relation analysis (GRA) and an improved ISODATA algorithm are used to select a dataset of similar days with comparable meteorological characteristics to the forecast day. A transformer encoder layer with multi-head attention is then used to extract long-term dependency features. Concurrently, BiGRU, optimized with a Global Attention network, is used to capture global temporal features. Feature fusion is performed using Cross Attention, calculating attention weights to emphasize significant features and enhancing feature integration. Finally, high-precision predictions are achieved through a fully connected layer. Utilizing historical PV power generation data to predict power output under various weather conditions, the proposed model demonstrates superior performance across all three climate types compared to other models, achieving more reliable predictions.

World-wide studies on multi-junction (tandem) solar cells have led to record-breaking improvements in conversion efficiencies year after year. To obtain detailed and proper feedback for solar-cell design and fabrication, it is necessary to establish standard methods for diagnosing subcells in fabricated tandem devices. Here, we propose a potential standard method to quantify the detailed subcell properties of multi-junction solar cells based on absolute measurements of electroluminescence (EL) external quantum efficiency in addition to the conventional solar-cell external-quantum-efficiency measurements. We demonstrate that the absolute-EL-quantum-efficiency measurements provide I–V relations of individual subcells without the need for referencing measured I–V data, which is in stark contrast to previous works. Moreover, our measurements quantify the absolute rates of junction loss, non-radiative loss, radiative loss and luminescence coupling in the subcells, which constitute the “balance sheets” of tandem solar cells.

Representative marine materials such as biopolymers and bioceramics contain bioactive properties and are applied in regenerative medicine and tissue engineering. The marine organism-derived extracellular matrix (ECM), which consists of structural and functional molecules, has been studied as a biomaterial. It has been used to reconstruct tissues and improve biological functions. However, research on marine-derived extracellular vesicles (EVs) among marine functional materials is limited. Recent studies on marine-derived EVs were limited to eco-system studies using bacteria-released EVs. We aimed to expand the range of representative marine organisms such as fish, crustaceans, and echinoderms; establish the extraction process; and study the bioactivity capability of marine EVs. Results confirmed that marine organism ECM-anchored EVs (mEVs) have a similar morphology and cargos to those of EVs in land animals. To investigate physiological effects, lipopolysaccharide (LPS)-infected macrophages were treated with EVs derived from sea cucumber, fish, and shrimp. A comparison of the expression levels of inflammatory cytokine genes revealed that all types of mEVs alleviated pro-inflammatory cytokines, although to different degrees. Among them, the sea cucumber-derived EVs showed the strongest suppression ability. This study showed that research on EVs derived from various types of marine animals can lead to the development of high value-added therapeutics from discarded marine wastes.

We demonstrate that the magneto-optical Kerr effect at normal incidence in the terahertz (THz) frequency range is useful for evaluating carrier transport properties of particular layers of a p - n junction. Since a single p -type thin film only exhibits a small magneto-optical Kerr effect, magneto-optical Kerr spectroscopy cannot be used to determine the carrier densities of such a film with high sensitivity. However, because the electric field is enhanced at the p -layer in a p - n junction due to the interference between the THz waves that are reflected at the highly doped substrate and the p -layer at the surface, it is possible to conduct magneto-optical Kerr spectroscopy with a higher sensitivity. We numerically calculate and experimentally determine the spectra of the ellipticity and polarization rotation angles for single n - and p -GaAs epitaxial layers and GaAs-based photovoltaic devices with a p-i-n structure and evaluate the carrier densities of the n - and p -layers. At normal incidence, this method has a high spatial resolution, which is beneficial for imaging of large-area devices.

Femtosecond semiconductor lasers are ideal devices to provide the ultrashort pulses for industrial and biomedical use because of their robustness, stability, compactness and potential low cost. In particular, gain-switched semiconductor lasers have significant advantages of flexible pulse shaping and repetition rate with the robustness. Here we first demonstrate our laser, which is initiated by very strong pumping of 100 times the lasing threshold density, can surpass the photon lifetime limit that has restricted the pulse width to picoseconds for the past four decades and produce an unprecedented ultrashort pulse of 670 fs with a peak power of 7.5 W on autocorrelation measurement. The measured phenomena are reproduced effectively by our numerical calculation based on rate equations including the non-equilibrium intraband carrier distribution, which reveal that the pulse width is limited by the carrier–carrier scattering time, instead of the photon lifetime. Femtosecond lasers are used for a vast variety of applications where super resolution is required. The authors present gain-switched semiconductor-laser operations using an extreme optical pump allowing them to generate ultrashort, high power pulses.

Robotics is continuously being integrated in animal behaviour studies to create customizable, controllable, and repeatable stimuli. However, few systems have capitalized on recent breakthroughs in computer vision and real-time control to enable a two-way interaction between the animal and the robot. Here, we present a “closed-loop control” system to investigate the behaviour of zebrafish, a popular animal model in preclinical studies. The system allows for actuating a biologically-inspired 3D-printed replica in a 3D workspace, in response to the behaviour of a zebrafish. We demonstrate the role of closed-loop control in modulating the response of zebrafish, across a range of behavioural and information-theoretic measures. Our results suggest that closed-loop control could enhance the degree of biomimicry of the replica, by increasing the attraction of live subjects and their interaction with the stimulus. Interactive experiments hold promise to advance our understanding of zebrafish, offering new means for high throughput behavioural phenotyping.

As the automotive industry accelerates its virtual engineering capabilities, there is a growing requirement for increased accuracy across a broad range of vehicle simulations. Regarding control system development, utilizing vehicle simulations to conduct ‘pre-tuning’ activities can significantly reduce time and costs. However, achieving an accurate prediction of, e.g., stopping distance, requires accurate tire modeling. The Magic Formula tire model is often used to effectively model the tire response within vehicle dynamics simulations. However, such models often: i) represent the tire driving on sandpaper; and ii) do not accurately capture the transient response over a wide slip range. In this paper, a novel methodology is developed using the MF-Tyre/MF-Swift tire model to enhance the accuracy of ABS braking simulations. The methodology – developed between Hyundai Motor Company and Siemens Digital Industries Software – is validated on a full-vehicle level by comparing ABS braking simulations of ‘sandpaper’, ‘asphalt’, and ‘translated asphalt’ tire models against full-vehicle measurements, where friction modeling provided a way to translate sandpaper tire models to represent tires on asphalt. Results show a much-improved correlation of the asphalt-based simulations with vehicle measurements compared with flat-trac-based simulations. Thus, the methodology provides a suitable tire model for ABS braking simulations in the early stages of vehicle development without requiring physical vehicle tests.