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Abstract Near-surface air temperature (Tair) is crucial for assessing urban thermal conditions and their impact on human health. Traditional Tair estimation methods, reliant on sparse weather stations, often miss spatial variability. This study proposes a novel framework using a federated learning artificial neural network (FLANN) for fine-scale Tair prediction. Leveraging spatially complete thermal data from Landsat 8/9, Sentinel 3, and Himawari 8/9 (105 acquisition days, 2013–2023), and data from automatic weather stations, 23 predictor variables were extracted. After rigorous selection processes, nine variables significantly correlated with Tair were identified. Comparative analysis against established machine learning and linear models, using cross-validation data, showed FLANN’s superior performance with a Pearson correlation coefficient (r) of 0.98 and a root mean square error (RMSE) of 0.97 K, compared to r and RMSE of 0.85 and 1.09, respectively, for the linear model. FLANN showed greater improvements for urban stations with r and RMSE differences of 0.19 and − 2.03 K. Application of FLANN to predict Tair in Hong Kong in July 2023 enabled detailed urban heat island (UHI) analysis, revealing dynamic spatial and temporal UHI patterns. This study highlights FLANN’s potential for accurate Tair prediction and UHI analysis, enhancing urban thermal environment management.

Dialogue journals are written conversations in which two partners communicate regularly. When practiced between a teacher and student, dialogue journaling has the potential to engage students in writing, and can lead to improved teacher-student rapport. In this "Innovations in Practice" article, we evaluate the use of structured dialogue journals between a student teacher and his English as a Second Language (ESL) students during a seven-week teaching practicum at a local secondary school in Hong Kong. The aim of this practice was for the teacher to engage with students on a personal level, thereby enhancing teacher--student rapport -- an otherwise challenging goal during short-term school placements. The dialogue journals were designed to elicit students' learning experiences, other out-of-class life experiences, and personal interests, which, when reciprocated with teachers' comments, created a dialogic context for improving open and honest teacher-student communication in English. In this practice, a total of 11 rounds of dialogue journals were administered and completed throughout the teaching practicum period. An evaluation of the practice was based on an analysis of the dialogue journal entries, semi-structured interviews with students, and the student teacher's own reflection. Implications of this practice are discussed in terms of the possibilities for ESL/English as a Foreign Language (EFL) teachers to use dialogue journals for enhancing teacher-student rapport.

Abstract The identification of aviation hazardous winds is crucial and challenging in air traffic management for assuring flight safety, particularly during the take-off and landing phases. Existing criteria are typically tailored for special wind types, and whether there exists a universal feature that can effectively detect diverse types of hazardous winds from radar/lidar observations remains as an open question. Here we propose an interpretable semi-supervised clustering paradigm to solve this problem, where the prior knowledge and probabilistic models of winds are integrated to overcome the bottleneck of scarce labels (pilot reports). Based on this paradigm, a set of high-dimensional hazard features is constructed to effectively identify the occurrence of diverse hazardous winds and assess the intensity metrics. Verification of the paradigm across various scenarios has highlighted its high adaptability to diverse input data and good generalizability to diverse geographical and climate zones.

Objective: To assess the predictive accuracy of the EULAR definition of clinically suspected arthralgia (CSA) for progression to rheumatoid arthritis (RA) in patients presenting with arthralgia without arthritis. Methods: This prospective study recruited 100 patients with arthralgia but no clinical arthritis. Patients were evaluated at baseline and followed up every 6 months for 2 years. The association of the presence of [Formula: see text] and [Formula: see text] out of seven CSA parameters at baseline with RA development was investigated. The association of individual CSA parameters with RA development was also evaluated. Results: Twenty-one of 100 patients (21%) developed RA. The proportion of patients meeting [Formula: see text] CSA criteria was higher in those who developed RA (66.7%) compared to those who did not (39.2%, [Formula: see text]). Similarly, a higher proportion of patients who developed RA met [Formula: see text] CSA criteria (47.6%) compared to those who did not (20.3%, [Formula: see text]). The sensitivity and specificity of [Formula: see text] CSA parameters were 71.4% and 69.6%, and for [Formula: see text] CSA parameters were 42.9% and 87.3%, respectively. “Symptom duration [Formula: see text] year” and “difficulty holding a fist” were significantly associated with RA development. A simplified criteria using these two items provided a similar area under receiver operating characteristic (ROC) curve (0.747 vs. 0.676) compared to the original criteria. Conclusion: Meeting EULAR CSA criteria was significantly associated with progression to RA in patients with arthralgia. Symptom duration [Formula: see text] year and difficulty holding a fist were the most predictive individual parameters. Continuous monitoring using the CSA criteria, and probably a simplified version, could help identify high-risk individuals for RA development.

Abstract In recent years, there has been a rise in human activities in oceanic areas, making the land–atmosphere interactions over islands a major scientific concern on a global scale. Examining the observation data from offshore areas enables a more comprehensive understanding of the turbulent fluxes in offshore atmospheric environments, patterns of momentum, energy and material exchange between the atmosphere and underlying surface in an oceanic boundary layer, and development of a heterogeneous atmospheric boundary layer. The related findings will assist in developing theoretical models and parameterization schemes to simulate the influence of heterogeneous surfaces on land–atmosphere interactions on the South China Sea Islands. Existing studies on the turbulent fluxes over the South China Sea Islands were mainly conducted on the Nansha Islands, whereas studies on the waters of the South China Sea are scarce. In this study, we used 10 Hz high‐frequency turbulence measurements to calculate the latent and sensible heat fluxes over the South China Sea Islands using the eddy correlation method. These findings were then compared with data from the Dunhuang Gobi, Ordos desert, and Xilingol grassland regions in inland China, along with the observed net radiation and surface heat fluxes. The findings indicate that the energy fluxes over the South China Sea in summer exhibit prominent diurnal variations. The magnitude of either latent or soil heat flux is low, and the net radiation is predominantly transformed into sensible heat flux, which warms the atmosphere. Furthermore, the daily variation curves of sensible and latent heat fluxes are influenced by intermittent turbulence on the islands and reefs, resulting in a less smooth pattern compared with soil heat flux. Although the South China Sea Islands have small land areas and are surrounded by the sea, the land–atmosphere interactions over the underlying surface of this region are similar to those over the underlying surface of grasslands in inland China during summer. The daily mean sensible heat flux on the islands is higher than that in an inland area, and the time lag in its response to sunrise is longer than that in inland areas by approximately 1 h. The overall energy balance ratio is approximately 0.75, c which is in line with the average level, but an energy balance residual of approximately 25% still exists. Furthermore, extreme weather conditions, such as typhoons, can disrupt the diurnal variations of sensible and latent heat fluxes, and the cyclical patterns are subsequently restored.

ABSTRACTSevere wind shear events near airport runways pose serious safety risks and are a growing concern in civil aviation. Identification of severe wind shear risk factors may enhance aviation safety. Although rare, severe wind shear impacts safety by affecting the airspeed, lift, and maneuverability of aircraft. This study presents TabNet, a novel deep learning technique coupled with Bayesian optimization (BO) to predict wind shear severity in the runway vicinity using Doppler LiDAR data from Hong Kong International Airport. To address imbalanced wind shear data, it was first processed by resampling techniques and then used as input to TabNet. The analysis demonstrated that Bayesian-tuned TabNet (BO-TabNet) with SVM-SMOTE-processed data led to better performance compared to other strategies. The TabNet architecture employs the attention mechanism to enable model-specific interpretability. Analysis showed that the most important contributing factor was the summer season, followed by the wind shear encounter location (1 nautical miles from the runway at the departure end). Additionally, a more comprehensive model-agnostic LIME method was used to elucidate the model from a local perspective. By predicting severe wind shear and assessing contributing factors, aviation stakeholders can proactively manage and mitigate the associated risks, leading to safer and more efficient operations.

Existing Land Surface Temperature (LST) fusion models encounter some challenges due to missing data, complex weather areas, and rapid land cover changes. To overcome these limitations, we proposed the Integrated SpatioTemporal Fusion Algorithm (ISFAT). ISFAT is developed based on contemporary fusion models but in addition incorporates data from partially contaminated LSTs using the masked weight function. This helps to predict fine-scale LST on prediction date while considering error resulting from landcover changes between the base and prediction date. This algorithm also factors in the calculation of model residuals, which are distributed back to the predicted fine-scale LST using the thin-plate spline function. The fine-scale LST on prediction can thereafter employed for predicting hourly fine-scale LST images by integrating a coarse resolution LST with hourly temporal resolution. Compared to contemporary LST fusion models, ISFAT demonstrates superior performance, with mean average differences of 0.1 K and 0.27 K over SADFAT and STITFM, respectively. Additionally, diurnal LST predictions from ISFAT compare well with air temperatures from automatic weather stations. Notably, on February 18, 2020, ISFAT effectively optimized fine-scale LST for Hong Kong, achieving an RMSE of 3.33 K, despite the limitation of cloud cover in the base date. The newly developed ISFAT could facilitate better LST retrieval over a large spatial coverage under different degrees of cloud contamination.

In this research, we present an advanced predictive framework designed to assess the turbulence induced by low-level wind shear near the runways at Hong Kong International Airport (HKIA), utilizing data from Pilot Reports (PIREP). This framework integrates the TabNet architecture with SHapley Additive exPlanations (SHAP), thereby enhancing both predictive accuracy and interpretability. Given the imbalance in the PIREP data, we implement various data augmentation techniques and employ Bayesian optimization to fine-tune the hyperparameters of the TabNet model. Our analysis revealed that the TabNet model, when applied to data balanced with the Support Vector Machine - Synthetic Minority Over-sampling Technique (SVM-SMOTE), demonstrated superior performance, evidenced by a Geometric Mean (G-Mean) of 0.74, a Matthews Correlation Coefficient (MCC) of 0.37, a Balanced Accuracy (BA) of 0.74, and an Area Under the Receiver Operating Characteristic (AU-ROC) of 0.739. SHAP analysis further enhanced the interpretability of the TabNet model by identifying key contributing factors, including the magnitude of wind shear and altitude, which significantly influence the likelihood of significant turbulence occurrence. Specifically, SHAP insights demonstrated the critical impact of wind shears between 15 and 25 knots and the pronounced effects of complex terrain and sea breezes on significant turbulence occurrence, predominantly at altitudes below 1200 feet. These findings not only demonstrate the efficacy of the TabNet-SHAP model in enhancing aviation safety through improved turbulence prediction but also provide actionable insights for operational and safety protocols at airports prone to low-level wind shear.

This paper presents a low-voltage CMOS four-stage amplifier operating in the subthreshold region. The first design technique includes the cross-feedforward positive feedback frequency compensation (CFPFC) for obtaining better bandwidth efficiency in a low-voltage multi-stage amplifier. The second design technique incorporates both the bulk-drain-driven input stage topology in conjunction with a low-voltage attenuator to permit operation at a low voltage, and improves the input common-mode range (ICMR). The proposed circuit is implemented using TSMC-40 nm process technology. It consumes 0.866 μW at a supply voltage of 0.5 V. With a capacitive load of 50 pF, this four-stage amplifier can achieve 84.59 dB in gain, 161.00 kHz in unity-gain bandwidth, 96 deg in phase margin, and 5.7 dB in gain margin whilst offering an input-referred noise of 213.63 nV/Hz @1 kHz, small-signal power-bandwidth FoMss of 9.31 (MHz∙pF/μW), and noise-power per bandwidth-based FoMnpb of 1.15 × 10−6 ((µV/Hz)·µW/Hz). Compared to the conventional bulk-driven input stage design technique, it offers improved multi-parameter performance metrics in terms of noise, power, and bandwidth at a compromising tradeoff on ICMR with respect to bulk-driven amplifier design. Compared with conventional gate-source input stage design, it offers improved ICMR. The amplifier is useful for low-voltage analog signal-processing applications.

The Hong Kong Observatory has been using a parametric storm surge model to forecast the rise of sea level due to the passage of tropical cyclones. This model includes an offset parameter to account for the rise in sea level due to other meteorological factors. By adding the sea level rise forecast to the astronomical tide prediction using the harmonic analysis method, coastal sea level prediction can be produced for the sites with tidal observations, which supports the high water level forecast operation and alert service for risk assessment of sea flooding in Hong Kong. The Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) Modelling System, which comprises the Weather Research and Forecasting (WRF) Model and Regional Ocean Modelling System (ROMS), which in itself is coupled with wave model WaveWatch III and nearshore wave model SWAN, was tested with tropical cyclone cases where there was significant water level rise in Hong Kong. This case study includes two super typhoons, namely Hato in 2017 and Mangkhut in 2018, three cases of the combined effect of tropical cyclone and northeast monsoon, including Typhoon Kompasu in 2021, Typhoon Nesat and Severe Tropical Storm Nalgae in 2022, as well as two cases of monsoon-induced sea level anomalies in February 2022 and February 2023. This study aims to evaluate the ability of the WRF-ROMS-SWAN model to downscale the meteorological fields and the performance of the coupled models in capturing the maximum sea levels under the influence of significant weather events. The results suggested that both configurations could reproduce the sea level variations with a high coefficient of determination (R2) of around 0.9. However, the WRF-ROMS-SWAN model gave better results with a reduced RMSE in the surface wind and sea level anomaly predictions. Except for some cases where the atmospheric model has introduced errors during the downscaling of the ERA5 dataset, bias in the peak sea levels could be reduced by the WRF-ROMS-SWAN coupled model. The study result serves as one of the bases for the implementation of the three-way coupled atmosphere–ocean–wave modelling system for producing an integrated forecast of storm surge or sea level anomalies due to meteorological factors, as well as meteorological and oceanographic parameters as an upgrade to the two-way coupled Operational Marine Forecasting System in the Hong Kong Observatory.

In the study of aircraft wake target detection, as the wake evolves and develops, the detection area of the LiDAR often shows the presence of two distinct vortices, one on each side. Sometimes, only a single wake vortex may be present. This can lead to a reduction in the accuracy of wake detection and an increased likelihood of missed detections, which may have a significant impact on the flight safety. Hence, we propose an algorithm based on the YOLOv8n–CBAM–EfficientNetV2 model for wake detection. The algorithm incorporates the lightweight network of EfficientNetV2 and the Convolutional Block Attention Module (CBAM) based on the YOLOv8n model, which achieves the lightweight improvement in the YOLOv8n algorithm and the improvement in detection accuracy. First, this study classifies the wake vortices in the wake greyscale images obtained at Hong Kong International Airport, based on the Range–Height Indicator (RHI) scanning characteristics of the LiDAR and the symmetry of the wake vortex pairs. The classification is used to detect left and right vortices for more accurate wake detection in wind field images, which thereby improves the precision rate of target detection. Subsequently, experiments are conducted using a YOLOv8n–CBAM–EfficientNetV2 model for aircraft wake detection. Finally, the performance of the YOLOv8n–CBAM–EfficientNetV2 model is analysed. The results show that the algorithm proposed in this study can achieve a 96.35% precision rate, 93.58% recall rate, 95.06% F1-score, and 250 frames/s. The results show that the method proposed in this study can be effectively applied in aircraft wake detection.

The impact of tropical cyclones on the operation of bridges and railways are mostly dependent on the forecast wind speed trend (upward, downward or staying steady) at present in Hong Kong. There are requests to forecast the exceedance of wind speed thresholds for such operations with a lead time of many hours ahead. This study considers the technical feasibility of forecasting the wind speed along a recently built bridge in Hong Kong with a coupled mesoscale numerical weather prediction (NWP)–computational fluid dynamics (CFD) model. This bridge features numerous anemometers where the coupled model can be verified. It is found that these two tropical cyclone cases are very challenging, especially in representing the wind structure of the cyclone because of its relatively small circulation. As such, the timing of the maximum wind could be 2 to 4 h earlier than the actual observation. The maximum wind speed from CFD modeling could be higher than that from the NWP model alone by 5 to 10 m/s, which shows that CFD modeling could add value in the forecasting of wind speed exceedance, although the maximum simulated value is still lower than the actual observation by as much as 10 m/s. As a result, while the general wind speed trend may be forecast, the exceedance of definite values of wind speed limits is still practically rather challenging, given the present two cases of tropical cyclones.

The occurrence of wind shear and severe thunderstorms during the final approach phase contributes to nearly half of all aviation accidents. Pilots usually employ the go-around procedure in order to lower the likelihood of an unsafe landing. However, multiple factors influence the go-arounds induced by wind shear. In order to predict the wind shear-induced go-around, this study utilized a cutting-edge AI-based Combined Kernel and Tree Boosting (KTBoost) framework with various data augmentation strategies. First, the KTBoost model was trained, tested, and compared to other Machine Learning models using the data extracted from Hong Kong International Airport (HKIA)-based Pilot Reports for the years 2017–2021. The performance evaluation revealed that the KTBoost model with Synthetic Minority Oversampling Technique - Edited Nearest Neighbor (SMOTE-ENN)- augmented data demonstrated superior performance as measured by the F1-Score (94.37%) and G-Mean (94.87%). Subsequently, the SHapley Additive exPlanations (SHAP) approach was employed to elucidate the interpretation of the KTBoost model using data that had been treated with the SMOTE-ENN technique. According to the findings, flight type, wind shear magnitude, and approach runway contributed the most to the wind shear-induced go-around. Compared to international flights, Hong Kong-based airlines endured the highest number of wind shear-induced go-arounds. Shear due to the tailwind contributed more to the go-around than the headwinds. The runways with the most wind shear-induced Go-arounds were 07C and 07R.

Abstract Atmospheric wet delay caused by Precipitable Water Vapor (PWV) significantly impacts the performance of many geodetic surveying systems such as Global Navigation Satellite System (GNSS). In this study, we use wet delay corrections forecast by the Weather Research and Forecasting (WRF) model to enhance GNSS Precise Point Positioning (PPP) during two observation periods with two different weather conditions, that is, period 1: March 01 to 14, 2020 (average PWV: 23.5 kg/m2) and period 2: June 02 to 15, 2020 (flooding weather with average PWV: 55.6 kg/m2), over the South China. PWV data from 277 to 263 GNSS stations are assimilated into WRF model to enhance the WRF water vapor forecasting capability for period 1 and period 2, respectively. Wet delay corrections from two different WRF configurations, that is, WRF no data assimilation and WRF with assimilation of GNSS PWV, are used to augment the PPP. Totally, eight WRF‐enhanced PPP schemes are tested. The results show that WRF‐enhanced PPP schemes generally have a better positioning performance in the up component than traditional PPP. After using WRF wet delay corrections, for static mode, the vertical positioning accuracy is improved by 14.6% and 33.7% for period 1 and period 2, respectively. The corresponding convergence time are reduced by 41.8% and 25.0% for period 1 and period 2, respectively. For kinematic mode, the positioning accuracy improvements in the up component reach 13.8% and 19.0% for period 1 and period 2, respectively. The kinematic PPP convergence time is reduced by up to 8.2% for period 1.

Background Taiwan is well-known for its quality healthcare system. The country's medical licensing exams offer a way to evaluate ChatGPT's medical proficiency. Methods We analyzed exam data from February 2022, July 2022, February 2023, and July 2033. Each exam included four papers with 80 single-choice questions, grouped as descriptive or picture-based. We used ChatGPT-4 for evaluation. Incorrect answers prompted a “chain of thought” approach. Accuracy rates were calculated as percentages. Results ChatGPT-4's accuracy in medical exams ranged from 63.75% to 93.75% (February 2022–July 2023). The highest accuracy (93.75%) was in February 2022's Medicine Exam (3). Subjects with the highest misanswered rates were ophthalmology (28.95%), breast surgery (27.27%), plastic surgery (26.67%), orthopedics (25.00%), and general surgery (24.59%). While using “chain of thought,” the “Accuracy of (CoT) prompting” ranged from 0.00% to 88.89%, and the final overall accuracy rate ranged from 90% to 98%. Conclusion ChatGPT-4 succeeded in Taiwan's medical licensing exams. With the “chain of thought” prompt, it improved accuracy to over 90%.

Abstract Accurate and reliable assessment of wind energy potential has important implication to the wind energy industry. Most previous studies on wind energy assessment focused solely on wind speed, whereas the dependence of wind energy on wind direction was much less considered and documented. In this paper, a copula‐based method is proposed to better characterize the direction‐related wind energy potential at six typical sites in Hong Kong. The joint probability density function (JPDF) of wind speed and wind direction is constructed by a series of copula models. It shows that Frank copula has the best performance to fit the JPDF at hilltop and offshore sites while Gumbel copula outperforms other models at urban sites. The derived JPDFs are applied to estimate the direction‐related wind power density at the considered sites. The obtained maximum direction‐related wind energy density varies from 41.3 W/m2 at an urban site to 507.9 W/m2 at a hilltop site. These outcomes are expected to facilitate accurate micro‐site selection of wind turbines, thereby improving the economic benefits of wind farms in Hong Kong. Meanwhile, the developed copula‐based method provides useful references for further investigations regarding direction‐related wind energy assessments at various terrain regions. Notably, the proposed copula‐based method can also be applied to characterize the direction‐related wind energy potential somewhere other than Hong Kong.

The atmosphere exhibits variability across different time scales. Currently, in the field of atmospheric science, statistical filtering is one of the most widely used methods for extracting signals on certain time scales. However, signal extraction based on traditional statistical filters may be sensitive to missing data points, which are particularly common in meteorological data. To address this issue, this study applies a new type of temporal filters based on a one-dimensional convolution neural network (1D-CNN) and examines its performance on reducing such uncertainties. As an example, we investigate the advantages of a 1D-CNN bandpass filter in extracting quasi-biweekly-to-intraseasonal signals (10–60 days) from temperature data provided by the Hong Kong Observatory. The results show that the 1D-CNN achieves accuracies similar to a 121-point Lanczos filter. In addition, the 1D-CNN filter allows a maximum of 10 missing data points within the 60-point window length, while keeping its accuracy higher than 80% (R2 > 0.8). This indicates that the 1D-CNN model works well even when missing data points exist in the time series. This study highlights another potential for applying machine learning algorithms in atmospheric and climate research, which will be useful for future research involving incomplete time series and real-time filtering.

Abstract Aircraft landings are especially perilous when the wind is gusty near airport runways. For this reason, an aircraft may deviate from its glide slope, miss its approach, or even crash in the worst cases. In the study, we used the state-of-the-art glass-box model, the Explainable Boosting Machine (EBM), to estimate the variation in headwind speed and turbulence intensity along the airport runway glide slope and to interpret the various contributing factors. To begin, the wind field characteristics were examined by developing a scaled-down model of Hong Kong International Airport (HKIA) runway as well as and the surrounding buildings and complex terrain in the TJ-3 atmospheric boundary layer wind tunnel. The placement of probes along the glide slope of the model runway aided in the measurement of wind field characteristics at different locations in the presence and absence of surrounding buildings. Next, the experimental data was used to train the EBM model in conjunction with Bayesian optimization approach. The counterpart black box models (extreme gradient boosting, random forest, extra tree and adaptive boosting) as well as other glass box models (linear regression and decision tree) were compared with the outcomes of the EBM model. Based on the holdout testing data, the EBM model revealed superior performance for both variation in headwind speed and turbulence intensity in terms of mean absolute error, mean squared error, root mean squared error and R-square values. To further evaluate the impact of different factors on the wind field characteristics along the airport runway glide slope, the EBM model allows for a full interpretation of the contribution of individual and pairwise interactions of factors to the prediction results from both a global and a local perspective.

Objective This research explores the performance of ChatGPT, compared to human doctors, in bilingual, Mandarin Chinese and English, medical specialty exam in Nuclear Medicine in Taiwan. Methods The study employed generative pre-trained transformer (GPT-4) and integrated chain-of-thoughts (COT) method to enhance performance by triggering and explaining the thinking process to answer the question in a coherent and logical manner. Questions from the Taiwanese Nuclear Medicine Specialty Exam served as the basis for testing. The research analyzed the correctness of AI responses in different sections of the exam and explored the influence of question length and language proportion on accuracy. Results AI, especially ChatGPT with COT, exhibited exceptional capabilities in theoretical knowledge, clinical medicine, and handling integrated questions, often surpassing, or matching human doctor performance. However, AI struggled with questions related to medical regulations. The analysis of question length showed that questions within the 109–163 words range yielded the highest accuracy. Moreover, an increase in the proportion of English words in questions improved both AI and human accuracy. Conclusions This research highlights the potential and challenges of AI in the medical field. ChatGPT demonstrates significant competence in various aspects of medical knowledge. However, areas like medical regulations require improvement. The study also suggests that AI may help in evaluating exam question difficulty and maintaining fairness in examinations. These findings shed light on AI role in the medical field, with potential applications in healthcare education, exam preparation, and multilingual environments. Ongoing AI advancements are expected to further enhance AI utility in the medical domain.

Abstract Thin polymer coatings are used to improve the interface between biological species and functional materials. Their interaction is significantly influenced by the functional groups and roughness of the polymer film and prediction of the interaction is thus of great interest. However, for conventional polymer films, this cannot be examined independently because of the interplay of defects, residual solvent molecules, roughness, and functional groups. Solvent‐free polymer films prepared by initiated chemical vapor deposition (iCVD) exhibit conformal, defect‐free characteristics and enable precise tailoring of the functional groups. This facilitates to isolate the contribution of functional groups on the bio‐interface performance. Consequently, in silico studies can enable a prediction of ligand interaction in anti‐viral activity for SARS‐CoV‐2 based on defined polymer and key protein structures. Furthermore, the cell viability of human fibroblasts can be traced back to the functional groups of the repeating units. For human liver cancer cell culture, it turns out that more sophisticated models are needed. The insilico‐iCVD approach can enable precise tailoring of complex polymer films optimized for the respective interfaces. In addition, this first big scan of the bio‐interface performance of iCVD films enables a solid starting point in areas like anticancer, antiviral, and biocompatibility for future studies.

With increasing air and sea temperatures, the thermodynamic environments over the oceans are becoming more favourable for the development of intense tropical cyclones (TCs) with rapid intensification (RI). The South China coastal region consists of highly densely populated cities, especially over the Pearl River Delta (PRD) region. Intense TCs maintaining their strength or the RI of TCs close to the coastal region can present substantial forecasting challenges and have significant potential impacts on the coastal population. This study investigates the effect of sea-surface and sub-surface temperatures and salinity on the intensification of five TCs, namely Super Typhoon Hato in 2017, Super Typhoon Mangkhut in 2018, and Typhoon Talim, Super Typhoon Saola, and Severe Typhoon Koinu in 2023, which have significantly affected the South China coastal region and triggered high TC warning signals in Hong Kong in the past few years. This analysis utilised the Hong Kong Observatory’s TC best-track and intensity data, along with sea temperature and salinity profiles generated using the China Ocean ReAnalysis version 2 (CORA2) product from the National Marine Data and Information Service of China. It was found that high sea-surface temperatures (SST) of 30 °C or above for a depth of about 20 m, low sea-surface salinity (SSS) levels of 33.8 psu or below for a depth of at least 20 m, and strong salinity stratification of at least 0.6 psu per 100 m depth might offer useful hints for predicting the RI of TCs over the western North Pacific and the South China Sea (SCS) in operational forecasting, while noting other contributing environmental factors and synoptic flow patterns conducive to RI. This study represents the first documentation of sub-surface salinity’s impact on some intense TCs traversing the SCS during 2017–2023 based on an observational study. Our aim is to supplement operational techniques for forecasting RI with some quantitative guidance based on upper-level ocean observations of temperatures and salinity, on top of well-known but more rapidly changing dynamical factors like low-level convergence, weak vertical wind shear, and upper-level divergent outflow, as forecasted with numerical weather prediction models. This study will also encourage further research to refine the analysis of quantitative contributions from different RI factors and the identification of essential features for developing AI models as one way to improve the forecasting of TC RI before the TC makes landfall near the PRD, with due consideration given to the effect of freshwater river discharge from the Pearl River.

Turbulence is a significant operational aviation safety hazard during all phases of flight. There is an urgent need for a method of airline turbulence identification in aviation systems to avoid turbulence hazards to aircraft during flight. Integrating flight data and machine learning significantly enhances the efficacy of turbulence identification. Nevertheless, present studies encounter issues including unstable model performance, challenges in data feature extraction, and parameter optimization. Hence, it is imperative to propose a superior approach to enhance the accuracy of turbulence identification along airline. The paper presents a combined swarm intelligence and machine learning model based on data mining for identifying airline turbulence. Based on the theory of swarm-intelligence-based optimization algorithm, the optimal parameters of Categorical Boosting (CatBoost) are obtained by introducing the whale optimization algorithm (WOA), and the corresponding WOA-CatBoost fusion model is established. Then, the Recursive Feature Elimination algorithm (RFE) is used to eliminate the data with lower feature weights, extract the effective features of the data, and the combination with the WOA brings robust optimization effects, whereby the accuracy of CatBoost increased by 11%. The WOA-CatBoost model can perform accurate turbulence identification from QAR data, comparable to that with established EDR approaches and outperforms traditional machine learning models. This discovery highlights the effectiveness of combining swarm intelligence and machine learning algorithms in turbulence monitoring systems to improve aviation safety.

In civil aviation, severe weather conditions such as strong wind shear, crosswinds, and thunderstorms near airport runways often compel pilots to abort landings to ensure flight safety. While aborted landings due to wind shear are not common, they occur under specific environmental and situational circumstances. This research aims to accurately predict aircraft aborted landings using three advanced deep learning techniques: the conventional deep neural network (DNN), the deep and cross network (DCN), and the wide and deep network (WDN). These models are supplemented by various data augmentation methods, including the Synthetic Minority Over-Sampling Technique (SMOTE), KMeans-SMOTE, and Borderline-SMOTE, to correct the imbalance in pilot report data. Bayesian optimization was utilized to fine-tune the models for optimal predictive accuracy. The effectiveness of these models was assessed through metrics including sensitivity, precision, F1-score, and the Matthew Correlation Coefficient. The Shapley Additive Explanations (SHAP) algorithm was then applied to the most effective models to interpret their results and identify key factors, revealing that the intensity of wind shear, specific runways like 07R, and the vertical distance of wind shear from the runway (within 700 feet above runway level) were significant factors. The results of this research provide valuable insights to civil aviation experts, potentially revolutionizing safety protocols for managing aborted landings under adverse weather conditions, thereby improving overall airport efficiency and safety.

Aircraft wake is a pair of counter-rotating vortices generated behind the aircraft, which can greatly impact the safety of fast takeoff and landing of aircraft and limit the improvement of airport capacity. The current wake parameter retrieval methods cannot locate the wake vortex's position and estimate its strength level in real time. To deal with this issue, a novel algorithm based on the YOLOv5s deep learning network is proposed. The new algorithm establishes a single vortex locating concept to adapt the wake vortex's evolution at complicate background wind field conditions, and proposes strength-based classification standard which can represent the real-time hazard of wake vortex to shorten the takeoff and landing intervals. Meanwhile, the EIOU loss function is introduced to improve the precision of YOLOv5s network. Compared with the state-of-the-art object detection approaches, such as Cascade R-CNN, FCOS, and YOLOv5l, the superiority of new method is demonstrated in terms of accuracy and robustness by using the field detection data from Hong Kong International Airport.

Abstract Background and Aim Thiopurines are recommended for maintenance of steroid‐free remission (SFR) in inflammatory bowel disease (IBD). Thiopurine metabolite monitoring (MM) is increasingly used in the West but remains novel in Singapore, with limited information on its therapeutic and economic benefits. Hence, this study aims to investigate MM's clinical utility and its impact on healthcare resource utilization in Singaporean IBD patients. Methods A retrospective observational study was conducted at Singapore General Hospital outpatient IBD Centre. Patients with IBD, baseline MM during 2014–2017, and weight‐based thiopurine doses for ≥4 weeks were followed up for 1 year. Actions were taken to optimize therapy, and metabolite levels before and after the first action were documented. Outcomes assessed included SFR, no therapy escalation or surgery, healthcare resource utilization, and direct healthcare costs. Results Ninety IBD patients (50 Crohn's disease, 40 ulcerative colitis) were included. Among them, 40% had baseline metabolite levels within therapeutic range, 31.1% sub‐therapeutic, 21.1% supra‐therapeutic, and 7.8% shunters. Repeated MM with subsequent dose optimization helped 67.2% of patients achieve therapeutic levels after 1 year. Overall, 87.8% of patients achieved SFR and 90% had no therapy escalation or surgery. Despite greater outpatient visits and laboratory investigations with MM, the median total healthcare costs at 1 year only increased marginally (S$6407.66 [shunters] vs S$5215.20 [supra‐therapeutic] vs S$4970.80 [sub‐therapeutic] vs S$4370.48 [control (within therapeutic range)], P = 0.592). Conclusion MM guided timely therapy escalation for non‐responders, identification of non‐adherence, and reversal of shunting. Therefore, it is a useful clinical tool to optimize thiopurines without significantly increasing healthcare costs.

Abstract Extra-tropical continental summer heatwaves often occur under persistent anticyclones or blocking. Here we partition heatwave changes into contributions from blocking changes, heatwave–blocking relation change and mean temperature increase, under global warming in climate models. We employ an optimized blocking index that best correlates with heatwaves (Pearson correlation of 0.7) and find heatwave-driving blocking decreases but the change in heatwave–blocking relation likely dominates. Over Europe, with a historical heatwave frequency of 2.5%, less blocking will cause 0.6% fewer heatwaves, steepened heatwave–blocking relation will cause 1.4% more heatwaves, and the mean temperature increase will cause 60% more heatwaves. Over Greenland, flattened heatwave–blocking relation will dominate over the insignificant decrease in blocking. The future increase in heatwave frequency is not caused by changes in blocking frequency, but by factors such as thermodynamics, that enhance the capacity of blocking to drive heatwaves.

Abstract This study analyzes microphysical signatures relevant to the short‐cycle lightning activity in the inner core of Typhoon Hato (2017) before landfall in China. Observations reveal that the lightning bursts were accompanied by enhanced inner‐core convection with a behavior cycle of about 3 hr. The coupling between wavenumber‐2 vortex Rossby waves (VRWs) and shear‐forced convective asymmetries resulted in a local updraft enhancement. Furthermore, supercooled liquid water droplets invigorated a striking enhancement of graupel via riming immediately above the freezing level, further enhancing charge separation and lightning generation outside the eyewall. Furthermore, when similar phase‐locking was associated with other propagating VRWs, graupel and updraft volumes were significantly boosted, leading to short‐cycle lightning outbreaks in the inner core.

Based on the simulation of the fluid-structure interaction response, the cause of an overturning of a gantry crane induced by a downburst in Shenzhen is studied in this paper. According to the results, (1) Vicroy's downburst model could establish the steady-state wind field of the downburst more reasonably when there was only low-level wind speed observation data, and its simulation results were close to the two-dimensional downburst numerical simulation results; (2) Compared with the normal exponential vertical profile of wind speed, the disturbance caused by the front girder of the double-girder gantry crane structure under the downburst wind field was more severe, which increases the probability of the gantry crane overturning. (3) The downwind displacement of the main girder of the gantry crane under the condition of downburst is far greater than that under the normal condition. At the same time, under the condition of downburst, the pressure difference on the surface of the gantry crane was greater, and the distribution of the support reaction force was more uneven, resulting in a stronger overturning tendency of the gantry crane. (4) Under the condition of downburst, the overturning moment and the shearing force borne by the foundation of gantry crane exceeded the critical value to maintain the stability of the gantry crane by the gravity, resulting in the overturning of the gantry crane.

The accurate prediction of small-scale three-dimensional wind fields is of great practical significance for aviation safety, wind power generation, and related fields. This study proposes a novel method for predicting small-scale three-dimensional wind fields by combining the mesoscale Weather Research and Forecasting (WRF) model with Computational Fluid Dynamics (CFD). The method consists of three components: the WRF module, the hybrid neural network prediction module, and the CFD module. First, mesoscale meteorological fields are simulated using the WRF module to establish a historical inflow boundary dataset for the CFD domain. Next, deep separable convolutions are incorporated, and convolutional long short-term memory (ConvLSTM) is combined with a deep separable convolution-gated recurrent unit (DSConvGRU) to construct a hybrid neural network prediction module named ConvLSTM-DSConvGRU. This module is employed for predicting inflow boundary data. Finally, the predicted inflow boundary conditions drive the CFD module to predict small-scale three-dimensional wind fields. The effectiveness of the WRF and CFD downscaling coupling method was validated using observed data from meteorological stations within the simulated domain, along with statistical indicators of errors. Additionally, a comparative evaluation was conducted between the proposed hybrid network model and the four commonly used spatiotemporal prediction models to assess its prediction performance. The results demonstrate that our proposed wind field prediction method achieves accurate simulation and short-term prediction of small-scale three-dimensional wind fields, and the hybrid network model exhibits comprehensive advantages in terms of model complexity and prediction accuracy.

This study focuses on optimizing precipitation forecast induced by tropical cyclones (TCs) in the Northwest Pacific region, with lead times ranging from 6 to 72 h. The research employs deep learning models, such as U-Net, UNet3+, SE-Net, and SE-UNet3+, which utilize precipitation forecast data from the Global Forecast System (GFS) and real-time GFS environmental background data using a U-Net structure. To comprehensively make use of the precipitation forecasts from these models, we additionally use probabilistic matching (PM) and simple averaging (AVR) in rainfall prediction. The precipitation data from the Global Precipitation Measurement (GPM) Mission serves as the rainfall observation. The results demonstrate that the root mean squared errors (RMSEs) of U-Net, UNet3+, SE-UNet, SE-UNet3+, AVR, and PM are lowered by 8.7%, 10.1%, 9.7%, 10.0%, 11.4%, and 11.5%, respectively, when compared with the RMSE of the GFS TC precipitation forecasts, while the mean absolute errors are reduced by 9.6%, 11.3%, 9.0%, 12.0%, 12.8%, and 13.0%, respectively. Furthermore, the neural network model improves the precipitation threat scores (TSs). On average, the TSs of U-Net, UNet3+, SE-UNet, SE-UNet3+, AVR, and PM are raised by 12.8%, 21.3%, 19.3%, 20.7%, 22.5%, and 22.9%, respectively, compared with the GFS model. Notably, AVR and PM outperform all other individual models, with PM’s performance slightly better than AVR’s. The most important feature variables in optimizing TC precipitation forecast in the Northwest Pacific region based on the UNet-based neural network include GFS precipitation forecast data, land and sea masks, latitudinal winds at 500 hPa, and vertical winds at 500 hPa.

This paper presents a low-voltage low-power chopper-stabilized differential difference amplifier (DDA) realized using 40 nm CMOS technology. Operating with a supply voltage of 0.5 V, a three-stage DDA has been employed to achieve an open-loop gain of 89 dB, while consuming just 0.74 μW of power. The proposed DDA incorporates feed-forward frequency compensation and a Type II compensator to achieve pole-zero cancellation and damping factor control. The DDA has a unity-gain bandwidth (UGB) of 170 kHz, a phase margin (PM) of 63.98°, and a common-mode rejection ratio (CMRR) of up to 100 dB. This circuit can effectively drive a 50 pF capacitor in parallel with a 300 kΩ resistor. The use of the chopper stabilization technique effectively mitigates the offset and 1/f noise. The chopping frequency of the chopper modulator is 5 kHz. The input noise is 245 nV/sqrt (Hz) at 1 kHz, and the input-referred offset under Monte Carlo cases is only 0.26 mV. Such a low-voltage chopper-stabilized DDA will be very useful for analog signal processing applications. Compared to the reported chopper DDA counterparts, the proposed DDA is regarded as that with one of the lowest supply voltages. The proposed DDA has demonstrated its effectiveness in tradeoff design when dealing with multiple parameters pertaining to power consumption, noise, and bandwidth.