Tamkang University

Fifth-generation (5G) mobile technology has firmly established itself as a critical infrastructure for the modern digital economy, attracting extensive research interest from both industry and academia, with a specific focus on its opportunities and challenges [...]

ABSTRACT Developing efficient and robust catalysts for the acidic oxygen evolution reaction (OER) is crucial for advancing proton exchange membrane water electrolyzer (PEMWE). However, the benchmark iridium oxide (IrO 2 ) catalysts remain limited by their high cost and low activity under practical conditions. Herein, we report an iridium (Ir)‐based heterostructured catalyst co‐doped with high‐valent tungsten (W) and molybdenum (Mo) atoms on a strongly coupled IrCuO x /MnO 2 framework (W/Mo–IrCuO x /MnO 2 ). Using a modified molten‐salt method, W/Mo dopants are incorporated into IrCuO x /MnO 2 nanosheets, leading to optimized electronic structure and enhanced active‐site exposure. The resulting W/Mo–IrCuO x /MnO 2 exhibits a low overpotential of 186 mV at 10 mA cm −2 in 0.5 m H 2 SO 4 and delivers a high mass activity of 1083 A g Ir −1 . Remarkably, the catalyst exhibits exceptional durability, maintaining for 400 h at 50 mA cm −2 . Additionally, the nanosheet catalyst maintains long‐term PEMWE performance (>500 h @ 1 A cm −2 at 80°C). Operando spectroscopy and theoretical calculations reveal that co‐doping high‐valent W and Mo significantly enhances the Ir oxidation state and modulates Ir─O coordination at the W/Mo–IrCuO x /MnO 2 interface, which generates optimized active sites and accelerates OER kinetics. These findings offer a promising strategy for designing high‐performance, cost‐effective OER catalysts for acidic water electrolysis.

Providing reliable information on tropical cyclone (TC) track forecast uncertainty is essential for effective disaster preparedness. Conventionally, the radius of the Cone of Uncertainty (CoU) is derived from historical distance errors, often resulting in a static, climatological value that fails to account for the characteristics of individual storms. While traditional approaches attempt to categorize scenarios based on factors like translation speed, they are often limited by sample sparsity and struggle to objectively incorporate complex environmental influences.To address these limitations, this study proposes an autoregressive encoder-decoder Long Short-Term Memory (LSTM) framework to generate situation-dependent CoU estimates. We utilize a multi-source dataset comprising official forecasts from the Central Weather Administration (CWA) and global models (ECMWF and NCEP) from the past five years. By employing an autoregressive architecture, the model can also iteratively generate a large ensemble of potential track realizations to characterize the forecast error distribution while preserving serial correlations across lead times.In this presentation, we compare traditional methods with the proposed LSTM approach to highlight the advantages of situation-dependent estimation. Our results also show that the LSTM-based CoU provides a robust representation of observed tracks, covering approximately 68% and 95% of observations within one and two standard deviations, respectively. Furthermore, integrating global numerical model information significantly reduces the uncertainty radius while maintaining reliable coverage. Overall, this work demonstrates how deep learning can offer context-aware uncertainty quantification, serving as a promising advancement for TC forecasting.

The potential damages of low-rise building foundations on liquefiable sites were studied using the Midas GTS-NX analysis. Both free-field and foundation responses for single raft and piled raft foundations were monitored on layered sands using the UBCSand model. It was found that a single raft can be easily uplifted by surrounding groundwater and soil liquefaction occurred. The piled raft foundation has better seismic resistance compared to the single raft. The foundation load will enlarge the ground settlements but reduce the horizontal ground displacements underneath the foundation. The 2D analysis can provide rational predictions of the piled raft responses despite the foundation geometry not being fully simulated. Using a thicker raft can eliminate the angle variable of the foundation. Considerable amounts of foundation settlements were found for single rafts and piled rafts according to the design specification. Longer piles will help to reduce the foundation settlement.

Efficiently reducing nitrate across wide-range concentrations in wastewater remains a major challenge for electrochemical nitrate reduction (NO3RR) to ammonia (NH3), where the dynamic control of active proton is critical. Here we proposed a dynamic proton allocator strategy featuring adaptive control of active proton availability in response to local nitrate levels. Applied to high-entropy alloy aerogels, this approach achieved >90% Faradaic efficiencies (FE) over a wide nitrate concentration range from 0.01∼1.0M, nearly an order-of-magnitude increase in NH3 yield rate compared to other catalysts at identical nitrate concentrations. In situ spectroscopic investigations revealed that the high-entropy element distribution modulated molecular structure of interfacial water, enhancing active proton availability for nitrate hydrogenation. Theoretical calculations demonstrated that the unique high-entropy electronic configuration optimized intermediate adsorption, shifted the rate-determining step and lowered the reaction energy, promoting NH3 formation. These results highlight the pivotal role of proton management across wide-ranging nitrate concentrations in NO3RR, demonstrating the potential for integrating sustainable chemical synthesis with environmental restoration.

In this paper, an anisotropic object electromagnetic image reconstruction system based on a two-stage multi-frequency extended network is developed by deep learning techniques. We obtain the scattered field information by irradiating the TM different polarization waves to uniaxial objects located in free space. We input the measured single-frequency scattered field into the Deep Residual Convolutional Neural Network (DRCNN) for training and to be further extended to multi-frequency data by the trained model. In the second stage, we feed the multi-frequency data into the Deep Convolutional Encoder–Decoder (DCED) architecture to reconstruct an accurate distribution of the dielectric constants. We focus on EMIS applications using Transverse Magnetic (TM) and Transverse Electric (TE) waves in 2D scenes. Numerical findings confirm that our method can effectively reconstruct high-contrast uniaxial objects under limited information. In addition, the TM/TE scattering from uniaxial anisotropic objects is governed by polarization-dependent Lippmann–Schwinger integral equations, yielding a nonlinear and severely ill-posed inverse operator that couples the dielectric tensor components with multi-frequency field responses. Within this mathematical framework, the proposed two-stage DRCNN–DCED architecture serves as a data-driven approximation to the anisotropic inverse scattering operator, providing improved stability and representational fidelity under limited-aperture measurement constraints.

Lithium- and manganese-rich layered oxides (LMR) are promising high-capacity cathodes for next-generation lithium-ion batteries, yet their practical use is hindered by severe surface degradation and irreversible oxygen release under high-voltage operation. These surface instabilities not only accelerate voltage decay but also limit the structural integrity and cycle life. Here, we employ synchrotron-based scanning transmission X-ray microscopy (STXM), complemented by X-ray absorption fine structure (XAFS), to directly visualize shell-core-specific (surface-bulk) electronic reconstruction in LMR cathodes and establish its correlation with local atomic structures. We show that surface Al incorporation generates a controlled density of oxygen vacancies in the shell, which weakens 3d transition metal (TM)-O covalency while activating Ni t2g-O 2p-Mn t2g π-type superexchange (SE) interactions. This orbital reconfiguration stabilizes Ni in a high-spin Ni4+ configuration even at 4.6 V, allowing Ni t2g orbitals to buffer oxygen oxidation charges and thereby mitigate excessive oxygen anionic redox (OAR). As a result, irreversible O2 evolution and lattice collapse are noticeably suppressed, while XAFS analysis confirms shortened Ni-O bonds and reduced coordination loss in Al-modified LMR. Importantly, the Mn valence and 3d configuration remain unchanged, highlighting the fact that the stabilization is dominantly Ni-O driven. These findings demonstrate how nanoscale orbital engineering at the particle shell can suppress the level of OAR driven surface degradation, offering a practical pathway to improve the cycling durability and high-voltage stability of advanced LMR cathodes.

The quick response codes (QR codes) are commonly seen in our daily lives. They are mainly composed of black or white square blocks, containing the URL or other form of information. We may compose several QR codes together by using interlacing for layered QR codes. With the concept of visual secret sharing (VSS), we may separate the interlaced QR code into several shares, and once received shares is more than the threshold, decoding for layered QR codes can be performed. Even the separated QR codes are not 100% recoverable, they can still be scanned and the information therein can be extracted. Our experiments have presented the applicability of the composition of two QR codes with the concept of visual secret sharing in the proposed method.

In this study, we addressed the critical challenge of digital transformation (DT) strategy evaluation in the tourism industry. While the literature extensively discusses DT's importance, it lacks quantitative frameworks for systematic strategy prioritization under varying market conditions, leaving practitioners without actionable decision-support tools. Therefore, we presented a comprehensive hierarchical decision support system that integrates the Analytic Hierarchy Process (AHP) with robust sensitivity analysis. The analysis identified 'Digitalized Customer Preferences' as the paramount success factor and 'Business Model Transformation' (BMT) as the optimal strategy. Critically, sensitivity analysis revealed a 78.68% threshold for 'Digital Consumer Demand', where strategic priorities shifted from BMT to product-service optimization. These findings advance DT theory through dynamic, threshold-based strategy frameworks while providing tourism managers with quantitative guidance for adaptive strategic decision-making based on market dynamics.