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In future 6G networks, real-time and accurate vehicular data are key requirements for enhancing the data-driven multi-access edge computing (MEC) applications. Existing estimation techniques to forecast vehicle position aim to meet the real-time data needs but compromise accuracy due to a lack of context awareness. While algorithms such as the Kalman filter improve estimation accuracy by considering certainty-grading and current-state estimate of measurements, they do not include the road context, which is vital for more accurate predictions. Unfortunately, current implementations of linear Kalman filters are not road-aware and struggle to predict a two-dimensional movement accurately. To this end, we propose a significant road-aware rectification-assisted prediction mechanism that enhances the modified Kalman filter predictions by incorporating road awareness. The parameters used for the Kalman filter include vehicle location, angle, speed, and time. In contrast, road-aware location rectification incorporates predicted location and lane shape, increasing the accuracy and precision of vehicle location predictions, reaching up to 99.9%. Performance is evaluated by comparing actual, predicted, and rectified vehicular traces at different speeds. The results demonstrate that the prediction error is approximately 0.005, while the proposed rectification process further reduces the error to 0.001, highlighting the effectiveness of the proposed approach. Overall, results support the idea of provisioning accurate, proactive, and real-time vehicular location data at the edge using a road-aware approach, thereby revolutionizing 6G vehicle location provisioning in MEC. [ABSTRACT FROM AUTHOR]

Wind shear presents a considerable hazard to aviation safety, especially during the critical phases of takeoff and landing. Accurate forecasting of wind shear events is essential to mitigate these risks and improve both flight safety and operational efficiency. This paper introduces a hybrid Temporal Convolutional Networks and Tree-Based Models (TCNs-TBMs) framework specifically designed for time series modeling and the prediction of wind shear intensity. The framework utilizes the ability of TCNs to capture intricate temporal patterns and integrates it with the predictive strengths of TBMs, such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Categorical Boosting (CatBoost), resulting in robust forecast. To ensure optimal performance, hyperparameter tuning was performed using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), enhancing predictive accuracy. The effectiveness of the framework is validated through comparative analyses with standalone machine learning models such as XGBoost, RF, and CatBoost. The proposed TCN-XGBoost model outperformed these alternatives, achieving a lower Root Mean Squared Error (RMSE: 1.95 for training, 1.97 for testing), Mean Absolute Error (MAE: 1.41 for training, 1.39 for testing), and Mean Absolute Percentage Error (MAPE: 7.90% for training, 7.89% for testing). Furthermore, the uncertainty analysis demonstrated the model's reliability, with a lower mean uncertainty (7.14 × 10−8) and standard deviation of uncertainty (6.48 × 10−8) compared to other models. These results highlight the potential of the TCNs-TBMs framework to significantly enhance the accuracy of wind shear intensity predictions, emphasizing the value of advanced time series modeling techniques for risk management and decision-making in the aviation industry. This study highlights the framework's broader applicability to other meteorological forecasting tasks, contributing to aviation safety worldwide. [ABSTRACT FROM AUTHOR]

In developing countries, vehicle emissions are a major source of atmospheric pollution, worsened by aging vehicle fleets and less stringent emissions regulations. This results in elevated levels of particulate matter, contributing to the degradation of urban air quality and increasing concerns over the broader effects of atmospheric emissions on human health. This study proposes a Hybrid Explainable Boosting Machine (EBM) framework, optimized using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), to predict vehicle-related PM2.5 concentrations and analyze contributing factors. Air quality data were collected from Open-Seneca sensors installed along the Nairobi Expressway, alongside meteorological and traffic data. The CMA-ES-tuned EBM model achieved a Mean Absolute Error (MAE) of 2.033 and an R2 of 0.843, outperforming other models. A key strength of the EBM is its interpretability, revealing that the location was the most critical factor influencing PM2.5 concentrations, followed by humidity and temperature. Elevated PM2.5 levels were observed near the Westlands roundabout, and medium to high humidity correlated with higher PM2.5 levels. Furthermore, the interaction between humidity and traffic volume played a significant role in determining PM2.5 concentrations. By combining CMA-ES for hyperparameter optimization and EBM for prediction and interpretation, this study provides both high predictive accuracy and valuable insights into the environmental drivers of urban air pollution, providing practical guidance for air quality management. [ABSTRACT FROM AUTHOR]

Ensuring passenger safety in public transportation systems is a critical challenge, especially under pandemic conditions that necessitate adherence to social distancing measures, such as maintaining a two-meter distance between individuals. This research focuses on evaluating the performance of subway station walkways when subjected to these distancing requirements. To conduct this analysis, a discrete-event simulation (DES) approach was implemented. This technique models the movement and interaction of passengers within station walkways as discrete events, allowing for a detailed assessment of system performance under various conditions. Key performance indicators, including the average area occupied by each passenger, the time spent on the walkway (dwell time), and the likelihood of congestion (blocking probability), were evaluated. The DES considered varying passenger arrival patterns by utilizing phase-type (PH) distribution and walkway dimensions to reflect a range of real-world scenarios. The operational outcomes under normal conditions were compared with those observed under pandemic-induced social distancing protocols. Through this comparison, insights were provided into how enforced distancing impacts walkway efficiency, and we identified potential bottlenecks. [ABSTRACT FROM AUTHOR]

This study reports a novel CuSe-TiO2-GO composite, synthesized by a facile hydrothermal method at a controlled temperature, and investigates its electrochemical performance for supercapacitors (SCs) and photocatalytic behavior for degrading methylene blue (MB) dye. The compositional phase structure and chemical bond interaction were thoroughly investigated. The as-fabricated pristine, binary, and ternary composites underwent comprehensive characterization employing spectroscopic techniques and electrochemical analysis. Compared with pure and binary compounds (CuSe, TiO2, and binary CuSe-TiO2 composites), the ternary CuSe-TiO2-GO composites demonstrated a high degradation efficiency while degrading MB in less than just 80 min (240 min, 100 min, and 140 min, respectively). The photocatalytic activity of the ternary CuSe-TiO2-GO composites is enhanced due to the highly positive conduction band of CuSe, leading to the quick excitation of electrons to the conduction band of CuSe. Subsequently, graphene oxide (GO) left holes on the photocatalyst surface for MB, as GO assisted the photoexcited electron–hole pairs, resulting in enhanced photocatalytic performance. The CuSe-TiO2-GO electrode for the supercapacitor indicates a 310.6 F/g and 135.2 F/g capacitance when the discharge current upsurges from 1 to 12 A/g. The good photocatalytic and energy storage performance is due to the smaller charge transfer resistance, which promotes efficient separation of electron–hole pairs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Prefabricated temporary buildings are a promising solution for post-disaster scenarios for their modularity, sustainability and transportation advantages. However, their low thermal mass building envelope shows a fast response to heat flux excitations. This leads to the risk of not meeting the occupant comfort and HVAC energy-saving requirements. The literature shows different measures implementable in opaque surfaces, like vacuum insulation panels (VIPs), phase change materials (PCMs) and switchable coatings, and in transparent surfaces (switchable glazing) to mitigate thermal issues, like overheating, while preserving the limited available internal space. This paper investigates the energy and overheating performance of the mentioned interventions by using building performance simulation tools to assess their effectiveness. The optimization also looks at the transportation flexibility of each intervention to better support the decision maker for manufacturing innovative temporary units. The most energy-efficient measures turn to be VIPs as a better energy solution for winter and PCMs as a better thermal comfort solution for summer. [ABSTRACT FROM AUTHOR]

In smart education, adaptive e-learning systems personalize the educational process by tailoring it to individual learning styles. Traditionally, identifying these styles relies on learners completing surveys and questionnaires, which can be tedious and may not reflect their true preferences. Additionally, this approach assumes that learning styles are fixed, leading to a cold-start problem when automatically identifying styles based on e-learning platform behaviors. To address these challenges, we propose a novel approach that annotates unlabeled student feedback using multi-layer topic modeling and implements the Felder–Silverman Learning Style Model (FSLSM) to identify learning styles automatically. Our method involves learners answering four FSLSM-based questions upon logging into the e-learning platform and providing personal information like age, gender, and cognitive characteristics, which are weighted using fuzzy logic. We then analyze learners' behaviors and activities using web usage mining techniques, classifying their learning sequences into specific styles with an advanced deep learning model. Additionally, we analyze textual feedback using latent Dirichlet allocation (LDA) for sentiment analysis to enhance the learning experience further. The experimental results demonstrate that our approach outperforms existing models in accurately detecting learning styles and improves the overall quality of personalized content delivery. [ABSTRACT FROM AUTHOR]

The manipulation of trap states plays a crucial role in the development of efficient photocatalysts. An ultrathin-shelled Zn-AgIn5S8/ZnS quantum dots (QDs) photocatalyst was synthesized via in situ growth using a low-temperature hydrothermal method. The optical properties of the samples coated with ZnS shell were studied vis UV-vis absorption and fluorescence spectra. The ultrathin ZnS shell plays an important role in the Zn-AgIn5S8/ZnS core–shell heterostructure photocatalytic water splitting system, which could reduce surface defects, prolong the carrier lifetime and improve the photo-generated electron–hole pair separation effectively, resulting in the improved photocatalytic efficiency and enhanced stability of the catalyst. The results provide an effective guideline for shell thickness design in future constructions of the core–shell heterostructure photocatalyst. [ABSTRACT FROM AUTHOR]

IoT has seen remarkable growth, particularly in healthcare, leading to the rise of IoMT. IoMT integrates medical devices for real-time data analysis and transmission but faces challenges in data security and interoperability. This research identifies a significant gap in the existing literature regarding a comprehensive ontology for vulnerabilities in medical IoT devices. This paper proposes a fundamental domain ontology named MIoT (Medical Internet of Things) ontology, focusing on cybersecurity in IoMT (Internet of Medical Things), particularly in remote patient monitoring settings. This research will refer to similar-looking acronyms, IoMT and MIoT ontology. It is important to distinguish between the two. IoMT is a collection of various medical devices and their applications within the research domain. On the other hand, MIoT ontology refers to the proposed ontology that defines various concepts, roles, and individuals. MIoT ontology utilizes the knowledge engineering methodology outlined in Ontology Development 101, along with the structured life cycle, and establishes semantic interoperability among medical devices to secure IoMT assets from vulnerabilities and cyberattacks. By defining key concepts and relationships, it becomes easier to understand and analyze the complex network of information within the IoMT. The MIoT ontology captures essential key terms and security-related entities for future extensions. A conceptual model is derived from the MIoT ontology and validated through a case study. Furthermore, this paper outlines a roadmap for future research, highlighting potential impacts on security automation in healthcare applications. [ABSTRACT FROM AUTHOR]

Natural disasters, notably landslides, pose significant threats to communities and infrastructure. Landslide susceptibility mapping (LSM) has been globally deemed as an effective tool to mitigate such threats. In this regard, this study considers the northern region of Pakistan, which is primarily susceptible to landslides amid rugged topography, frequent seismic events, and seasonal rainfall, to carry out LSM. To achieve this goal, this study pioneered the fusion of baseline models (logistic regression (LR), K-nearest neighbors (KNN), and support vector machine (SVM)) with ensembled algorithms (Cascade Generalization (CG), random forest (RF), Light Gradient-Boosting Machine (LightGBM), AdaBoost, Dagging, and XGBoost). With a dataset comprising 228 landslide inventory maps, this study employed a random forest classifier and a correlation-based feature selection (CFS) approach to identify the twelve most significant parameters instigating landslides. The evaluated parameters included slope angle, elevation, aspect, geological features, and proximity to faults, roads, and streams, and slope was revealed as the primary factor influencing landslide distribution, followed by aspect and rainfall with a minute margin. The models, validated with an AUC of 0.784, ACC of 0.912, and K of 0.394 for logistic regression (LR), as well as an AUC of 0.907, ACC of 0.927, and K of 0.620 for XGBoost, highlight the practical effectiveness and potency of LSM. The results revealed the superior performance of LR among the baseline models and XGBoost among the ensembles, which contributed to the development of precise LSM for the study area. LSM may serve as a valuable tool for guiding precise risk-mitigation strategies and policies in geohazard-prone regions at national and global scales. [ABSTRACT FROM AUTHOR]

This study researches environmental factors, vehicle dynamics, and loading conditions on pavement structures, aiming to comprehend and predict their impact. The susceptibility of asphalt pavement to temperature variations, vehicle speed, and loading cycles is explored, with a particular focus on the lateral distribution of wheel tracks in driving and passing lanes. Utilizing video analysis and finite element modelling (FEM) through ABAQUS 2022 software, multiple input factors, such as speed (60, 80 and 100 km/h), loading cycles (100,000 to 500,000), and temperature range (0 °C to 50 °C), are applied to observe the maximum rutting (17.89 mm to 24.7 mm). It is observed that the rut depth exhibited is directly proportional to the loading cycles and temperature, but the opposite is true in the case of vehicle speed. Moreover, interpretable machine learning models, particularly the Bayesian-optimized light gradient boosting machine (LGBM) model, demonstrate superior predictive performance in rut depth. Insights from SHAP interpretation highlight the significant roles of temperature and loading frequency in pavement deformation. This study concludes with a comprehensive understanding of how these factors impact road structures in Pakistan. Its implications extend to valuable insights for optimizing road design, offering a significant contribution to enhancing the durability and sustainability of road infrastructure in the region. [ABSTRACT FROM AUTHOR]

The purpose of this study is to evaluate whether early initiation of catheter-directed thrombolysis (CDT) in patients presenting with acute pulmonary embolism is associated with improved in-hospital outcomes. A retrospective cohort was extracted from the 2016–2019 National Inpatient Sample database, consisting of 21,730 weighted admissions undergoing CDT acute PE. From the time of admission, the sample was divided into early (<48 h) and late interventions (>48 h). Outcomes were measured using regression analysis and propensity score matching. No significant differences in mortality, cardiac arrest, cardiogenic shock, or intracranial hemorrhage (p > 0.05) were found between the early and late CDT groups. Late CDT patients had a higher likelihood of receiving systemic thrombolysis (3.21 [2.18–4.74], p < 0.01), blood transfusion (1.84 [1.41–2.40], p < 0.01), intubation (1.33 [1.05–1.70], p = 0.02), discharge disposition to care facilities (1.32 [1.14–1.53], p < 0.01). and having acute kidney injury (1.42 [1.25–1.61], p < 0.01). Predictors of late intervention were older age, female sex, non-white ethnicity, non-teaching hospital admission, hospitals with higher bed sizes, and weekend admission (p < 0.01). This study represents a comprehensive evaluation of outcomes associated with the time interval for initiating CDT, revealing reduced morbidity with early intervention. Additionally, it identifies predictors associated with delayed CDT initiation. The broader ramifications of these findings, particularly in relation to hospital resource utilization and health disparities, warrant further exploration. [ABSTRACT FROM AUTHOR]

Vortex beams carrying orbital angular momentum (OAM) provide a new degree of freedom for light waves in addition to the traditional degrees of freedom, such as intensity, phase, frequency, time, and polarization. Due to the theoretically unlimited orthogonal states, the physical dimension of OAM is capable of addressing the problem of low information capacity. With the advancement of the OAM optical communication technology, OAM router devices (OAM-RDs) have played a key role in significantly improving the flexibility and practicability of communication systems. In this review, major breakthroughs in the OAM-RDs are summarized, and the latest technological standing is examined. Additionally, a detailed account of the recent works published on techniques related to the OAM-RDs has been categorized into five areas: channel multicasting, channel switching, channel filtering, channel hopping, and channel adding/extracting. Meanwhile, the principles, research methods, advantages, and disadvantages are discussed and summarized in depth while analyzing the future development trends and prospects of the OAM-RDs. [ABSTRACT FROM AUTHOR]

Pilots commonly undergo training to effectively manage instances of wind shear (WS) during both the landing and takeoff stages. Nevertheless, in exceptional circumstances, there may be instances of severe wind shear (SWS) surpassing a magnitude of 30 knots, leading to adverse effects on the operation of taking off and landing aircraft. This phenomenon can lead to the execution of aborted landing maneuvers and deviations from the intended glide path. This study utilized the explainable boosting machine (EBM), an advanced machine learning (ML) model known for its transparency, to predict the severity of WS occurrences and analyze the underlying factors. The dataset consisted of 21,392 data points from 2018 to 2022 acquired from two Doppler light detection and ranging (LiDAR) systems installed at Hong Kong International Airport (HKIA). Initially, the Doppler LiDAR data received data treatment in order to address the issue of data imbalance. Subsequently, utilizing the processed data, the hyperparameters of EBM were optimized using the Bayesian optimization technique. The EBM model underwent subsequent training and evaluation, wherein its performance metrics were computed and compared with those of an alternative glass-box model including decision tree (DT) and counterpart black-box models, namely, random forest (RF) and extreme gradient boosting (XGBoost). The EBM model trained on synthetic minority oversampling technique (SMOTE)-treated data demonstrated superior performance in comparison with the alternative models, as indicated by its higher geometric mean (0.77), balanced accuracy (0.78), and Matthews' correlation coefficient (0.169). Furthermore, the EBM exhibited enhanced predictive performance and facilitated a comprehensive analysis of individual and pairwise factor interactions in the prediction of WS severity. This enabled the assessment of the factors that contributed to the instances of SWS in the proximity of airport runways. [ABSTRACT FROM AUTHOR]

This paper focuses on the seismic vulnerabilities of multi-storey buildings in hilly regions like Sikkim and Uttarakhand, where rapid construction is driven by population growth and tourism. The study particularly evaluates step-back buildings on hilly slopes, comparing their vulnerability to standard buildings on flat terrain. Using non-linear analysis to assess structural aspects like displacement and storey drift ratio, the research examines the performance of these buildings in both uphill and downhill orientations against typical three-storey and six-storey structures, respectively. The findings indicate that step-back buildings, especially those without infill walls, are more susceptible to seismic damage. For instance, on the uphill side, a step-back building shows a mean drift ratio 15.11% greater in the X direction and 4.57% greater in the Y direction compared to a three-storey regular building (3SR). This vulnerability is exacerbated when infill walls are absent, with mean drift ratios in step-back buildings being 74.75% and 33% higher in the X and Y directions, respectively. Moreover, at a seismic acceleration of 0.36 g, the mean displacement of a step-back building is 83% greater in the X direction and 51% greater in the Y direction than those with infill walls (SBIN), underscoring the significant role of infill walls in enhancing earthquake resilience. The study also highlights that short columns in step-back buildings are particularly prone to severe damage, especially just above the uppermost foundation level. While infill walls offer substantial mitigation of damage at the Design Basis Earthquake (DBE) level, at the Maximum Considered Earthquake (MCE) level, step-back buildings still endure severe damage compared to regular buildings with infill walls. Consequently, the research establishes that step-back buildings demonstrate greater vulnerability at DBE levels without infill walls and are more susceptible to damage than flat terrain buildings at MCE levels, emphasizing the need for careful design and reinforcement strategies in earthquake-prone hilly areas. [ABSTRACT FROM AUTHOR]

Driving simulators serve as valuable instruments for traffic safety research because they enable the creation of various scenarios that are hard to replicate in the real world. Eye tracker devices have proven to be immensely beneficial in studying eye movements. In this particular study, the objective was to examine potential variations in the adaptability of young male Chinese and Pakistani student drivers to left-hand traffic (LHT) and right-hand traffic (RHT) infrastructures when navigating under unfamiliar driving rules and environments. To achieve this, twenty-one Pakistani and twenty Chinese young male drivers were recruited to participate in different simulated driving scenarios (LHT and RHT). The factors tested were: (1) hazard perception; (2) time to collision (TTC); and (3) intersectional and lane-changing behavior. Using data collected from the driving simulator and eye tracker, differences in adaptability between both pools of drivers were compared using the ANOVA technique. The results showed that young male Chinese drivers were more vigilant and had a higher adaptability to unfamiliar infrastructure (3), they also had a better hazard perception (1) and time to collision (1 and 2). Young male Pakistani drivers had poorer hazard perception (2) and consequently had the shortest brake response time in the RHT scenario (2). [ABSTRACT FROM AUTHOR]

The creation of junctions between 0D and 2D materials can be an efficient strategy to enhance charge separation for solar hydrogen production. In this study, a simple in situ growth method has been used to synthesize a series of 0D/2D Zn-Ag-In-S quantum dots/reduced graphene oxide (ZAIS QDs/RGO) heterojunctions. The developed heterojunctions were characterized for structural characteristics, morphology, and photocatalytic performance, while varying the content of RGO. We observed that photocatalytic hydrogen production reached a maximum at an RGO content of 30 μL (342.34 µmol g−1 h−1), surpassing that of pure ZAIS QDs (110.38 µmol g−1 h−1) by 3.1 times, while maintaining excellent stability. To understand this enhancement, we performed time-resolved fluorescence and electrochemical impedance spectroscopy. The fluorescence lifetime of RGO loaded at 30 μL (417.76 ns) was significantly higher than that of pure ZAIS QDs (294.10 ns) and had the fastest charge transfer, which can be attributed to the charge transfer and storage capacity of RGO to extend the lifetime of photogenerated carriers and improve the charge separation efficiency. This study offers a simple synthesis method for constructing 0D/2D QDs/RGO heterojunction structures and provides a valuable reference for further enhancing the activity and stability of I-III-VI sulfide QDs. [ABSTRACT FROM AUTHOR]

Surface coatings are widely used for preventive maintenance, as they prolong the durability of concrete structures. Partial short cover depth may result due to poor construction quality and uneven formwork. In this study, lithium nitrite-based gel was used as an anodic corrosion inhibitor coating material to investigate its influence on the corrosion of rebar embedded in a partial short cover depth in the presence of chloride. The specimens were cured at a temperature of 20 °C and a relative humidity of higher than 95%. (a) Macrocell corrosion current density, (b) microcell corrosion current, and (c) polarization curves were measured, and the results were compared with those of uncoated specimens after aging for 28 days. The results revealed that the corrosion rate was reduced considerably due to the application of the coating with the gel type. [ABSTRACT FROM AUTHOR]

Karakoram Highway (KKH) is an international route connecting South Asia with Central Asia and China that holds socio-economic and strategic significance. However, KKH has extreme geological conditions that make it prone and vulnerable to natural disasters, primarily landslides, posing a threat to its routine activities. In this context, the study provides an updated inventory of landslides in the area with precisely measured slope deformation (Vslope), utilizing the SBAS-InSAR (small baseline subset interferometric synthetic aperture radar) and PS-InSAR (persistent scatterer interferometric synthetic aperture radar) technology. By processing Sentinel-1 data from June 2021 to June 2023, utilizing the InSAR technique, a total of 571 landslides were identified and classified based on government reports and field investigations. A total of 24 new prospective landslides were identified, and some existing landslides were redefined. This updated landslide inventory was then utilized to create a landslide susceptibility model, which investigated the link between landslide occurrences and the causal variables. Deep learning (DL) and machine learning (ML) models, including convolutional neural networks (CNN 2D), recurrent neural networks (RNNs), random forest (RF), and extreme gradient boosting (XGBoost), are employed. The inventory was split into 70% for training and 30% for testing the models, and fifteen landslide causative factors were used for the susceptibility mapping. To compare the accuracy of the models, the area under the curve (AUC) of the receiver operating characteristic (ROC) was used. The CNN 2D technique demonstrated superior performance in creating the landslide susceptibility map (LSM) for KKH. The enhanced LSM provides a prospective modeling approach for hazard prevention and serves as a conceptual reference for routine management of the KKH for risk assessment and mitigation. [ABSTRACT FROM AUTHOR]

The close proximity of crosswinds to airport runways presents great hazards to landing operations. As a result, an aircraft is susceptible to encountering a loss of control. Elevated levels of turbulence are commonly linked with strong crosswind speeds over the runway glide path. Therefore, it is imperative to evaluate the factors that impact crosswind speeds. The susceptibility of the runways at Hong Kong International Airport (HKIA) to severe crosswinds is well established. This study aimed to build a scaled model of HKIA, along with its surrounding terrain/buildings, within a TJ-3 ABL wind tunnel to compute the crosswind speeds under different wind directions over the runway glide path. Subsequently, utilizing the outcomes of the experiment, a cutting-edge local cascade ensemble (LCE) model was employed in conjunction with a tree-structured Parzen estimator (TPE) to evaluate the crosswind speed over the north runway glide path. The comparative analysis of the TPE-LCE model was also conducted with other machine learning models. The TPE-LCE model demonstrated superior predictive capabilities in comparison to alternative models, as assessed by MAE (0.490), MSE (0.381), RMSE (0.617), and R2 (0.855). The SHAP analysis, which utilized TPE-LCE predictions, revealed that two factors, specifically "Effect of Terrain/Buildings" and "Distance from Runway," exhibiting noteworthy influence over the probability of encountering elevated crosswind speeds over the runway glide path. The optimal conditions for high-crosswind speeds were found to be characterized by the absence of nearby terrain features or structures, a smaller distance from HKIA's north runway threshold, and with a wind direction ranging from 125 to 180 degrees. [ABSTRACT FROM AUTHOR]

Autonomous docking and recharging are among the critical tasks for autonomous mobile robots that work continuously in manufacturing environments. This requires robots to demonstrate the following abilities: (i) detecting the charging station, typically in an unstructured environment and (ii) autonomously docking to the charging station. However, the existing research, such as that on infrared range (IR) sensor-based, vision-based, and laser-based methods, identifies many difficulties and challenges, including lighting conditions, severe weather, and the need for time-consuming computation. With the development of deep learning techniques, real-time object detection methods have been widely applied in the manufacturing field for the recognition and localization of target objects. Nevertheless, those methods require a large amount of proper and high-quality data to achieve a good performance. In this study, a Hikvision camera was used to collect data from a charging station in a manufacturing environment; then, a dataset for the wireless charger was built. In addition, the authors of this paper propose an autonomous docking and recharging method based on the deep learning model and the Lidar sensor for a mobile robot operating in a manufacturing environment. In the proposed method, a YOLOv7-based object detection method was developed, trained, and evaluated to enable the robot to quickly and accurately recognize the charging station. Mobile robots can achieve autonomous docking to the charging station using the proposed Lidar-based approach. Compared to other methods, the proposed method has the potential to improve recognition accuracy and efficiency and reduce the computation costs for the mobile robot system in various manufacturing environments. The developed method was tested in real-world scenarios and achieved an average accuracy of 95% in recognizing the target charging station. This vision-based charger detection method, if fused with the proposed Lidar-based docking method, can improve the overall accuracy of the docking alignment process. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance (AMR) is a well-recognized global threat. The World Health Organization (WHO) issued a report ranking the critical types of bacterial resistance that need to be monitored. Several studies from individual institutions in Saudi Arabia have reported rates of antimicrobial resistance using automated methods. However, no national surveillance study has been conducted to date using gold standard methods for antimicrobial susceptibility testing. This review summarizes AMR rates for major bacterial pathogens in Saudi Arabia and provides a justification for the need for a national surveillance project. In Saudi Arabia, AMR rates for both Gram-positive and Gram-negative bacteria are on the rise. Surveillance studies help identify AMR trends and emergence of outbreaks. The WHO has started a program, the Global Antimicrobial Resistance Surveillance System (GLASS), encouraging its member states, including Saudi Arabia, to conduct antimicrobial surveillance studies to estimate AMR rates worldwide. Of the microbiological methods used to test antimicrobial susceptibility, only broth microdilution (BMD) is considered the "gold standard." As AMR studies in Saudi Arabia are sparse, mostly limited to single centers and were conducted using automated methods, a national AMR surveillance project is needed to evaluate the current status and to inform stewardship decisions. [ABSTRACT FROM AUTHOR]

Oversaturation of highways occurs due to their inadequate assessment and design. In this paper, we propose both a mathematical queuing model and a Discrete-Event Simulation (DES) framework based on Newell's triangular flow-density relationship for the performance analysis of a multi-lane highway section. The proposed framework is a finite capacity queuing system, which captures an increase in the flow with the vehicle density up to the capacity of the section in an unsaturated condition and a decrease in the flow in the case of a saturated condition, depicting the actual traffic conditions on the highway section. First, the Birth–Death Process is used to build the mathematical queuing model (BDP), and the average number of vehicles (average queue length) and blocking probability on the highway section are estimated. Then, the accuracy of the mathematical queuing model is verified by the proposed DES framework. The "significance and effects" of different design factors are evaluated using the two-level full factorial design technique. The analysis of the experimental results reveals that the length of the highway section and the number of lanes are the most significant factors affecting the average queue length and blocking probability, while the jam density only has a significant effect on the average queue length and does not affect the blocking probability. In case of a two-way interaction, the combined effect of the "length-lanes" significantly affects the average queue length. In the end, a multiple-factor linear regression model is also developed for the prediction of the average number of vehicles on the highway section based on the design factors. [ABSTRACT FROM AUTHOR]

The Cavity Simulation Model (CAVSIM) is a 3D, parameterisable simulator of the Underground Coal Gasification Process (UCG) that serves as a benchmark for UCG prediction. Despite yielding accurate outputs, CAVSIM has some limitations, which chiefly include inadequate graphical capabilities to visualise cavity geometry and gas production, time-ineffectiveness in terms of parametrisation, i.e., it involves editing, compiling multiple files and checking for errors, and lack of tools to synthesise a controller. Therefore, to compensate for these shortcomings, the services of third-party software, such as MATLAB, must be procured. CAVSIM was integrated with MATLAB to utilise its functionalities and toolboxes such as System Identification, Neural Network, and Optimization Toolbox etc. The integration was accomplished by designing C-mex files, and furthermore, the simulation results in both environments exhibit the same behaviour, demonstrating successful integration. Consequently, CAVSIM has also acquired a controllable structure, wherein parametrisation is now a single-click process; this is demonstrated by a case study outlining the implementation of Model Predictive Control (MPC) on a UCG plant. Moreover, the performance metrics, i.e., Mean Average Error (MAE) and Root Mean Square Error (RMSE) of 0.13, 0.23 for syngas heating value, and 0.012, 0.02 for flowrate quantitatively establishes the efficacy of CAVLAB in designing MPC for the UCG system. The novelty of this work lies in making the software package open-source with the aim of streamlining the research of multiple aspects of the UCG process. [ABSTRACT FROM AUTHOR]

Aircraft landings can be dangerous near airport runways due to wind variability. As a result, an aircraft could potentially miss an approach or divert off its flight path. In this study, turbulence intensity along the runway glide path was investigated using a scaled-down model of Hong Kong International Airport (HKIA) and the complex terrain nearby built in a TJ-3 atmospheric boundary layer wind tunnel. Different factors, including the effect of terrain, distance from the runway threshold, assigned approach runway, wind direction, and wind speed, were taken into consideration. Next, based on the experimental results, we trained and tested a novel tree-structured Parzen estimator (TPE)-optimized kernel and tree-boosting (KTBoost) model. The results obtained by the TPE-optimized KTBoost model outperformed other advanced machine learning models in terms of MAE (0.83), MSE (1.44), RMSE (1.20), and R2 (0.89). The permutation-based importance analysis using the TPE-optimized KTBoost model also revealed that the top three factors that contributed to the high turbulence intensity were the effect of terrain, distance from the runway threshold, and wind direction. The presence of terrain, the shorter distance from the runway, and the wind direction from 90 degrees to 165 degrees all contributed to high turbulence intensity. [ABSTRACT FROM AUTHOR]

In recent years, well-being has become an increasingly important consideration integrated with functional aspects in the design of educational spaces due to its significant impact on the built environment. Although the features of educational spaces have been explored in a myriad of studies, the potential of in-between spaces has not been well explored. This paper reveals the significance of in-between spaces in educational buildings beyond their functional use as the fundamental circulation within a building, indicating that they also play a dynamic role through their design as elastic gathering spaces that encourage informal interactions and link users psychologically and visually, which affects their well-being. However, the potential of these spaces is often overlooked compared to the main educational spaces. Additionally, during the design process, there seems to be a tendency to reduce these spaces to a bare minimum, in favor of exploiting larger spaces, which shows that they are regarded as merely connecting points or transitional spaces. The research problem addressed here is the lack of a clear, adequate understanding of the effect of designing in-between spaces on the productivity and well-being of users, as there are no criteria regarding in-between space designs to enhance well-being. The current paper aims to evaluate the reality of the in-between space design within the art and architecture faculties of Jordanian universities according to the perception of faculty members and students based on well-being requirements. Accordingly, a survey was designed to address the 11 key criteria identified in the literature, including (1) physical features and visual appearance; (2) size and design of in-between spaces; (3) circulation and movement space zoning; (4) ergonomics and furnishing; (5) lighting; (6) colors and finishing; (7) acoustics; (8) heating, ventilation, and air conditioning; (9) visual communication and instructional tools; (10) social and cultural spaces; and (11) accessories. The paper utilizes a mixed approach through survey and observation, where the findings are analyzed from three case studies to reveal the dimensions that need to be taken into account and developed to meet the needs of users. The results of the study demonstrate that the specific dimensions of circulation and movement, ergonomics and furnishings, colors and finishing, and accessories must be taken into account to create an interactive environment that advances the educational process and therefore enhances productivity. [ABSTRACT FROM AUTHOR]

The identification of causative factors and implementation of measures to mitigate work zone crashes can significantly improve overall road safety. This study introduces a Self-Paced Ensemble (SPE) framework, which is utilized in conjunction with the Shapley additive explanations (SHAP) interpretation system, to predict and interpret the severity of work-zone-related crashes. The proposed methodology is an ensemble learning approach that aims to mitigate the issue of imbalanced classification in datasets of significant magnitude. The proposed solution provides an intuitive way to tackle issues related to imbalanced classes, demonstrating remarkable computational efficacy, praiseworthy accuracy, and extensive adaptability to various machine learning models. The study employed work zone crash data from the state of New Jersey spanning a period of two years (2017 and 2018) to train and evaluate the model. The study compared the prediction outcomes of the SPE model with various tree-based machine learning models, such as Light Gradient Boosting Machine, adaptive boosting, and classification and regression tree, along with binary logistic regression. The performance of the SPE model was superior to that of tree-based machine learning models and binary logistic regression. According to the SHAP interpretation, the variables that exhibited the highest degree of influence were crash type, road system, and road median type. According to the model, on highways with barrier-type medians, it is expected that crashes that happen in the same direction and those that happen at a right angle will be the most severe crashes. Additionally, this study found that severe injuries were more likely to result from work zone crashes that happened at night on state highways with localized street lighting. [ABSTRACT FROM AUTHOR]

The UAE's thriving real estate market has resulted in a highly diversified built environment, attracting architects from around the world. However, this has also led to a misconception within the architectural practice and its regularization, particularly concerning acceptable professional qualifications. Therefore, this study aims to address these issues and provide a blueprint for policymakers and stakeholders. This study provided a comprehensive overview of architectural practice in the UAE by identifying the services offered by professionals in the built environment and categorizing them based on their qualifications and professional titles. The study adopts a classical research method, which includes a literature review, unstructured interviews, a statistical survey, and analysis. The study identifies 13 building designs and allied services commonly provided by professionals in the built environment in the UAE. The professionals providing these services are categorized based on their qualifications and professional titles, which include registered architects, architect engineers, architectural consultants, engineering consultants, and project managers. Furthermore, the study clarifies the definition of an acceptable qualification for a principal architect, which can be used as a reference by young professionals to understand the trends in architectural qualification and practice. The results of the study can also be used by policymakers and stakeholders to address the issues in professional practice and improve the quality of architectural services in the UAE. [ABSTRACT FROM AUTHOR]

Noble metal catalysts such as Pt/C and RuO2 are the most efficient oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) catalysts and show excellent activity. However, their high costs, scarcity, single function, and weak durability impede their large-scale practical application. Therefore, it is paramount to design bifunctional electrocatalysts with high activity, long durability, and low cost. In this work, we used the functionalized modification and hierarchical porous structure of MOFs and adjusted the ratio of Co/Mo atoms to prepare uniformly dispersed nanospheres. The uniform porous nitrogen-doped carbon nanotubes coated with Co/Mo2N composites were obtained by trapping the volatile CNx during high-temperature pyrolysis via a vapor deposition strategy. The physical and chemical properties of the materials were analyzed by various characterization methods such as XRD, XPS, TEM, SEM, Raman, and BET. Notably, CoMoN@NCNTs-700 exhibited excellent ORR/OER bifunctional electrocatalytic activity in alkaline conditions due to the synergistic effect of porous nitrogen-doped carbon nanotubes and the unique heterostructure of Co/Mo2N. In 0.1 M KOH, its ORR half-wave potential was E1/2 = 0.78 V, with a limiting current density even reached to 5.3 mA cm−2 and its operating potential was 1.60 V at a current density of 10 mA cm−2. At the same time, CoMoN@NCNTs-700 also showed better stability and methanol resistance than the commercial catalysts. This work provides a valuable reference for the design and construction of inexpensive non-noble metal bifunctional electrocatalysts. [ABSTRACT FROM AUTHOR]

Many studies have shown that β-glucan induces a trained immune phenotype in innate immune cells to defend against bacterial and fungal infections. The specific mechanism involves cellular metabolism and epigenetic reprogramming. However, it is unclear whether β-glucan plays a role in antiviral infection. Therefore, this study investigated the role of trained immunity induced by Candida albicans and β-glucan in antiviral innate immunity. It showed that C. albicans and β-glucan promoted the expression of interferon-β (IFN-β) and interleukin-6 (IL-6) in mouse macrophages triggered by viral infection. In addition, β-glucan pretreatment attenuated the pathological damage induced by the virus in mouse lungs and promoted the expression of IFN-β. Mechanistically, β-glucan could promote the phosphorylation and ubiquitination of TANK Binding Kinase 1 (TBK1), a key protein of the innate immune pathway. These results suggest that β-glucan can promote innate antiviral immunity, and this bioactive material may be a potential therapeutic target for antiviral treatment. [ABSTRACT FROM AUTHOR]

The vast applicability of spinel cobalt ferrite due to its unique characteristics implies the need for further exploration of its properties. In this regard, structural modification at the O-site of spinel with Li1+/Zn2+ was studied in detail for exploration of the correlation between structural, magnetic, and dielectric properties of the doped derivatives. The CTAB-assisted coprecipitation method was adopted for the synthesis of the desired compositions owing to its cost effectiveness and size controlling ability. Redistribution of cations at T- and O-sites resulted in the expansion of the crystal lattice, but no distortion of the cubic structure was observed, which further supports the flexible crystal structure of spinel for accommodating larger Li1+/Zn2+ cations. Moreover, an XPS analysis confirmed the co-existence of the most stable oxidation states of Zn2+, Li1+, Co2+, and Fe3+ ions with unstable Co3+ and Fe2+ ions as well, which induces the probability of hopping mechanisms to a certain extent and is a well-established behavior of cobalt ferrite nanoparticles. The experimental results showed that Li1+/Zn2+ co-doped samples exhibit the best magnetic properties at dopant concentration x = 0.3. However, increasing the dopant content causes disturbance at both sites, resulting in decreasing magnetic parameters. It is quite evident from the results that dielectric parameters are closely associated with each other. Therefore, dopant content at x = 0.1 is considered the threshold value exhibiting the highest dielectric parameters, whereas any further increase would result in decreasing the dielectric parameters. The reduced dielectric properties and enhanced magnetic properties make the investigated samples a potential candidate for magnetic recording devices. [ABSTRACT FROM AUTHOR]

In this work, a manganese selenide/graphene oxide (MnSe/GO)-based composite was prepared for wet-chemical assisted method against organic dye; herein, methylene blue (MB) dye removal from the water was employed as a metal selenide-based photocatalyst. The synthesized MnSe/GO composite was systematically characterized by X-ray diffraction (XRD), Fourier transform electron microscopy (FTIR), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), and UV-visible diffuse reflectance spectroscopy (UV-vis. DRS). The structural characteristic revealed the adequate synthesis of the sample with good crystallinity and purity of the obtained products. The morphological analysis indicates the formation of MnSe nanoflakes composed of tiny particles on their surface. At the same time, the GO nanosheets with high aggregation were formed, which may be due to the van der Waals forces. The bond interaction and compositional analysis studies confirmed and supported the structural findings with high purity. The optical analysis showed the bandgap energies of MnSe and their composites MnSe (1.7 eV), 7% GO-MnSe (2.42 eV), 14% GO-MnSe (2.6 eV), 21% GO-MnSe (3.02 eV), and 28% GO-MnSe (3.24 eV) respectively, which increase the bandgap energy after GO and MnSe recombination. Among different contents, the optimized 21% GO-MnSe composite displayed enhanced photocatalytic properties. For instance, a short time of 90 min was taken compared with other concentrations due to the narrow bandgap of MnSe and the highly conductive charge carrier's support, making the process to remove MB from water faster. These results show that the selenide-based photocatalyst can be an attractive candidate for future advanced photocatalysis applications. [ABSTRACT FROM AUTHOR]

With the increasing demand for wastewater treatment and multidrug resistance among pathogens, it was necessary to develop an efficient catalyst with enhanced photocatalytic and antibacterial applications. The present study proposes a facile and green strategy for synthesizing zinc oxide (ZnO) decorated nickel (Ni) nanomaterials. The synthesized Ni/ZnO nanocomposite displays a high crystallinity and spherical morphology, which was systematically characterized by XRD, SEM, FT-IR, UV-visible spectroscopy, EDX, HRTEM, and XPS techniques. In addition, the bacteriological tests indicated that Ni/ZnO nanocomposite exhibits potent antibacterial activity against human pathogens, i.e., Pseudomonas aeruginosa (P. aeruginosa), Staphylococcus aureus (S. aureus), and Escherichia coli (E. coli). The inhibition zone observed in light and dark conditions for E. coli was 16 (±0.3) mm and 8 (±0.4) mm, respectively, which confirms the high efficacy of the nanocomposite in the presence of light compared to dark conditions. The detailed inhibition mechanism of said bacterium and damage were also studied through fluorescence spectroscopy and SEM analysis, respectively. Evaluation of antioxidant activity based on free radical scavenging activity revealed that the Ni/ZnO nanocomposite effectively scavenges DPPH. In the photocatalytic performance, the Ni/ZnO nanocomposite exhibited a remarkable degradation ability under the optimized condition, which was attributed to their controllable size, high surface area, and exceptional morphology. Good selectivity, high photodegradation, and antibacterial activities and satisfactory hemolytic behavior of the as-prepared nanocomposite make them able to become a potential candidate for superior biological performance and environmental remediation. [ABSTRACT FROM AUTHOR]

Machine learning algorithms are applied to predict intense wind shear from the Doppler LiDAR data located at the Hong Kong International Airport. Forecasting intense wind shear in the vicinity of airport runways is vital in order to make intelligent management and timely flight operation decisions. To predict the time series of intense wind shear, Bayesian optimized machine learning models such as adaptive boosting, light gradient boosting machine, categorical boosting, extreme gradient boosting, random forest, and natural gradient boosting are developed in this study. The time-series prediction describes a model that predicts future values based on past values. Based on the testing set, the Bayesian optimized-Extreme Gradient Boosting (XGBoost) model outperformed the other models in terms of mean absolute error (1.764), mean squared error (5.611), root mean squared error (2.368), and R-Square (0.859). Afterwards, the XGBoost model is interpreted using the SHapley Additive exPlanations (SHAP) method. The XGBoost-based importance and SHAP method reveal that the month of the year and the encounter location of the most intense wind shear were the most influential features. August is more likely to have a high number of intense wind-shear events. The majority of the intense wind-shear events occurred on the runway and within one nautical mile of the departure end of the runway. [ABSTRACT FROM AUTHOR]

Severe low-level wind shear (S-LLWS) in the vicinity of airport runways (25 knots or more) is a growing concern for the safety of civil aviation. By comprehending the causes of S-LLWS events, aviation safety can be enhanced. S-LLWS is a rare occurrence, but it is hazardous for approaching and departing aircraft. This study introduced the self-paced ensemble (SPE) framework and Shapley additive explanations (SHAP) interpretation system for the classification, prediction, and interpretation of LLWS severity. Doppler LiDAR- and PIREPs-based LLWS data from Hong Kong International Airport were obtained, trained, and evaluated to predict LLWS severity. The SPE framework was also compared to state-of-the-art tree-based models, including light gradient boosting machine, adaptive boosting, and classification and regression tree models. The SPE does not require prior data treatment; however, SMOTE-ENN was utilized to treat highly imbalanced LLWS training data for tree-based models. In terms of prediction performance, the SPE framework outperforms all tree-based models. Using SHAP analysis, the SPE was interpreted. It was determined that "runway 25LD", "mean hourly temperature", and "mean wind speed" were the most significant contributors to the occurrence of S-LLWS. The most optimistic projections for the occurrence of S-LLWS events at runway 25LD were during periods of low-to-moderate temperatures and relatively medium-to-high wind speeds. Similarly, the majority of S-LLWS events took place on the runway. Without the need for data augmentation during preprocessing, the SPE framework coupled with the SHAP interpretation system could be utilized effectively for the prediction and interpretation of LLWS severity. This study is an invaluable resource for aviation policymakers and air traffic safety analysts. [ABSTRACT FROM AUTHOR]

The rise of the intelligent, local charging facilitation and environmentally friendly aspects of electric vehicles (EVs) has grabbed the attention of many end-users. However, there are still numerous challenges faced by researchers trying to put EVs into competition with internal combustion engine vehicles (ICEVs). The major challenge in EVs is quick recharging and the selection of an optimal charging station. In this paper, we present the most recent research on EV charging management systems and their role in smart cities. EV charging can be done either in parking mode or on-the-move mode. This review work is novel due to many factors, such as that it focuses on discussing centralized and distributed charging management techniques supported by a communication framework for the selection of an appropriate charging station (CS). Similarly, the selection of CS is evaluated on the basis of battery charging as well as battery swapping services. This review also covered plug-in charging technologies including residential, public and ultra-fast charging technologies and also discusses the major components and architecture of EVs involved in charging. In a comprehensive and detailed manner, the applications and challenges in different charging modes, CS selection, and future work have been discussed. This is the first attempt of its kind, we did not find a survey on the charging hierarchy of EVs, their architecture, or their applications in smart cities. [ABSTRACT FROM AUTHOR]

NeuroAid II, a folk Chinese Medicine, is currently used in Asia for the treatment of stroke. An experimental study demonstrated that NeuroAid enables neuronal cells to be more resistant to glutamate toxicity. This research was constructed to evaluate the efficacy of NeuroAid in the prevention of epilepsy (EP). Forty healthy adult male mice were used and divided into four groups (10 mice/group): normal control group; positive control group; NeuroAid-treated group (10 mg/kg); topiramate-treated group (10 mg/kg). The treatment continued for 7 days, and on the last day, EP was induced using strychnine at a dose of 2 mg/kg via intraperitoneal (ip) administration. Seizure severity, latency to the seizure onset, the number of seizures, and the duration of each seizure episode were observed for one hour. The death and protection rates over the next twenty-four hours were recorded. Brain specimens from surviving animals were extracted and examined pathologically for quantification of glutamate receptor (GluR) gene expression in the isolated hippocampus employing real-time PCR analysis. Treatment with NeuroAid resulted in a significant reduction in seizure severity, prolonged the onset of seizures, decreased the number and duration of episodes, reduced brain insult, and decreased mortality rate. Reductions in the gene expression of GluRs in the hippocampus with minor histopathological changes were observed in the NeruoAid- and topiramate-treated groups. It is concluded that NeuroAid has a potential antiepileptic effect (EP) with the ability to prevent convulsion through its effect on the glutamate receptor. [ABSTRACT FROM AUTHOR]

Pilots typically implement the go-around protocol to avoid landings that are hazardous due to wind shear, runway excursions, or unstable approaches. Despite its rarity, it is essential for safety. First, in this study, we present three Dynamic Ensemble Selection (DES) frameworks: Meta-Learning for Dynamic Ensemble Selection (META-DES), Dynamic Ensemble Selection Performance (DES-P), and K-Nearest Oracle Elimination (KNORAE), with homogeneous and heterogeneous pools of machine learning classifiers as base estimators for the prediction of aircraft go-around in wind shear (WS) events. When generating a prediction, the DES approach automatically selects the subset of machine learning classifiers which is most probable to perform well for each new test instance to be classified, thereby making it more effective and adaptable. In terms of Precision (86%), Recall (83%), and F1-Score (84%), the META-DES model employing a pool of Random Forest (RF) classifiers outperforms other models. Environmental and situational factors are subsequently assessed using SHapley Additive exPlanations (SHAP). The wind shear magnitude, corridor, time of day, and WS altitude had the greatest effect on SHAP estimation. When a strong tailwind was present at low altitude, runways 07R and 07C were highly susceptible to go-arounds. The proposed META-DES with a pool of RF classifiers and SHAP for predicting aircraft go-around in WS events may be of interest to researchers in the field of air traffic safety. [ABSTRACT FROM AUTHOR]

Low-level wind shear (LLWS) is a rare occurrence and yet poses a major hazard to the safety of aircraft. LLWS event occurrence within 800 feet of the runway level are dangerous to approaching and departing aircraft and must be accurately predicted. In this study, first the Bayesian Optimization–Ensemble Learning Classifiers (BO-ELCs) including Adaptive Boosting, Light Gradient Boosting Machine, Categorical Boosting, Extreme Gradient Boosting, and Random Forest were trained and tested using a dataset of 234 LLWS events extracted from pilot flight reports (PIREPS) and weather reports at Hong Kong International Airport. Afterward, the SHapley Additive exPlanations (SHAP) algorithm was utilized to interpret the best BO-ELC. Based on the testing set, the results revealed that the Bayesian Optimization–Random Forest Classifier outperformed the other BO-ELCs in accuracy (0.714), F1-score (0.713), AUC-ROC (0.76), and AUR-PRC (0.75). The SHAP analysis found that the hourly temperature, wind speed, and runway 07LA were the top three crucial factors. A high hourly temperature and a moderate-to-high wind speed made Runway 07LA vulnerable to the occurrence of critical LLWS events. This research was a first attempt to forecast the criticality of LLWS in airport runway vicinities and will assist civil aviation airport authorities in making timely flight operation decisions. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Comparison of In-Hospital Outcomes between Early and Late Catheter-Directed Thrombolysis in Acute Pulmonary Embolism: A Retrospective Observational Study." The correction addresses an error in the original publication regarding the analysis of predictors of late intervention. The authors conducted a multivariate regression analysis and found that the predictors of late intervention were female sex, non-white race, and weekend admission. The primary scientific conclusions of the study remain unaffected. The correction has been made to the abstract, results section, and discussion section of the article. [Extracted from the article]

Arsenic (III) is a toxic contaminant in water bodies, especially in drinking water reservoirs, and it is a great challenge to remove it from wastewater. For the successful extraction of arsenic (III), a nanocomposite material (ZnO–CuO/g–C3N4) has been synthesized by using the solution method. The large surface area and plenty of hydroxyl groups on the nanocomposite surface offer an ideal platform for the adsorption of arsenic (III) from water. Specifically, the reduction process involves a transformation from arsenic (III) to arsenic (V), which is favorable for the attachment to the –OH group. The modified surface and purity of the nanocomposite were characterized by SEM, EDX, XRD, FT–IR, HRTEM, and BET models. Furthermore, the impact of various aspects (temperatures, pH of the medium, the concentration of adsorbing materials) on adsorption capacity has been studied. The prepared sample displays the maximum adsorption capacity of arsenic (III) to be 98% at pH ~ 3 of the medium. Notably, the adsorption mechanism of arsenic species on the surface of ZnO–CuO/g–C3N4 nanocomposite at different pH values was explained by surface complexation and structural variations. Moreover, the recycling experiment and reusability of the adsorbent indicate that a synthesized nanocomposite has much better adsorption efficiency than other adsorbents. It is concluded that the ZnO–CuO/g–C3N4 nanocomposite can be a potential candidate for the enhanced removal of arsenic from water reservoirs. [ABSTRACT FROM AUTHOR]