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We conducted a policy situation analysis in three Mekong region countries, focused on how the animal and human health systems interact to control avian influenza (AI). The study used scoping literature reviews aimed at establishing existing knowledge concerning the regulatory context. We then conducted a series of key informant interviews with national and sub-national government officials and representatives of producers and poultry farmers to understand their realities in managing the complex interface of the two sectors to control AI. We found signs of formal progress in establishing the policy and legislative frameworks needed to enable cooperation of the two sectors but a series of constraints that impede their effective operation. These included the competitive relationships involved, especially with budgetary allocations and mandates that can conflict with each other. Many local actors also view development partners (e.g., bilateral and multilateral donors) as having a dominant role in establishing these collaborations, limiting the extent to which there is local ownership of the agenda. The animal and human health sectors are not equally resourced, with the animal health sector disadvantaged in terms of surveillance and laboratory systems, human resources and financial allocations. Contrasting strategies for achieving objectives have also characterised the two sectors in recent decades, seeing a major shift towards the use of incentive-based approaches in the human health sector but very little parallel development in the animal health sector, largely dependent on command and control approaches. Successful future collaborations between the two sectors are likely to depend on better resourcing in the animal health sector, increasing local ownership of the agenda, and ensuring that both sectors can use the full range of regulatory strategies available to achieve objectives., Highlights • One health is impeded by low-level trust between animal and human health sectors. • Bureaucratic red tape makes information sharing difficult between the two sectors. • Both sectors are establishing legislative and policy frameworks zoonotic outbreaks • Animal health is less resourced for service delivery, surveillance, etc. • Human health sector incentivizes the market, animal health uses command and control.

Recently, deep learning (DL) models, especially those based on long short-term memory (LSTM), have demonstrated their superior ability in resolving sequential data problems. This study investigated the performance of six models that belong to the supervised learning category to evaluate the performance of DL models in terms of streamflow forecasting. They include a feed-forward neural network (FFNN), a convolutional neural network (CNN), and four LSTM-based models. Two standard models with just one hidden layer—LSTM and gated recurrent unit (GRU)—are used against two more complex models—the stacked LSTM (StackedLSTM) model and the Bidirectional LSTM (BiLSTM) model. The Red River basin—the largest river basin in the north of Vietnam—was adopted as a case study because of its geographic relevance since Hanoi city—the capital of Vietnam—is located downstream of the Red River. Besides, the input data of these models are the observed data at seven hydrological stations on the three main river branches of the Red River system. This study indicates that the four LSTM-based models exhibited considerably better performance and maintained stability than the FFNN and CNN models. However, the complexity of the StackedLSTM and BiLSTM models is not accompanied by performance improvement because the results of the comparison illustrate that their respective performance is not higher than the two standard models—LSTM and GRU. The findings of this study present that LSTM-based models can reach impressive forecasts even in the presence of upstream dams and reservoirs. For the streamflow-forecasting problem, the LSTM and GRU models with a simple architecture (one hidden layer) are sufficient to produce highly reliable forecasts while minimizing the computation time.

The establishment of reliable water level prediction models is vital for urban flood control and planning. In this paper, we develop hybrid models (GA-XGBoost and DE-XGBoost) that couple two evolutionary models, a genetic algorithm (GA) and a differential evolution (DE) algorithm, with the extreme gradient boosting (XGBoost) model for hourly water level prediction. The Jungrang urban basin located on the Han River, South Korea, was selected as a case study for the proposed models. Hourly rainfall and water level data were collected between 2003 and 2020 to construct and evaluate the performance of the selected models. To compare the prediction efficiency, two other tree-based models were chosen: classification and registration tree (CART) and random forest (RF) models. A comparison of the results showed that two hybrid models, GA-XGBoost and DE-XGBoost, outperformed RF and CART in the multistep-ahead prediction of water level, and the relative errors of the hybrid model ranged from [2.18%-9.21%], compared to [3.76%-10.41%] and [2.99%-11.88%] for the RF and CART, respectively. Reliable performance was also supported by other measures. In general, the GA-XGBoost and DE-XGBoost models displayed relatively similar performance despite their small differences. The CART model was not preferable for multistep-ahead water level predictions, even though it yielded the lowest Akaike information criterion (AIC) value. This study verifies that despite having some drawbacks when considering long step-ahead prediction and model complexity, hybrid XGBoost models might be superior to many existing models for hourly water level prediction.

Cable-driven parallel robots (CDPRs) are vulnerable to vibration due to the inevitable flexible properties of the cables. Thus, vibration analysis is critical for CDPR’s operation in which highly accurate motion is required. However, most of the current methods related to vibration analysis of CDPRs rely on simple spring models which have limitations in their performance and complexity that are not general to analyze the vibration of various CDPRs. Hence, accurate, simple and general approaches for vibration analysis in CDPRs are need. To solve this problem, this paper presents the finite element method (FEM) and the modified analytical method to analyze the vibration of CDPRs. To validate these methods, free vibration analysis was conducted using the proposed methods for the planar and spatial cable-driven parallel robots. The natural frequencies of these two CDPRs were computed by the proposed two methods and compared with those of the commercial software, SAP2000. The solutions obtained by the FEM and the modified analytical models turned out to be close to SAP2000’s results, thereby verifying the validity of the proposed methods.

Purpose The purpose of this paper is to investigate the mechanical properties’ behaviors of woven composite cut-out structures with specific parameters. Because of the complexity of micro-scale and meso-scale structure, it is difficult to accurately predict the mechanical material behavior of woven composites. Numerical simulations are increasingly necessary for the design and optimization of test procedures for composite structures made by the woven composite. The results of the proposed method are well satisfied with the results obtained from the experiment and other studies. Moreover, parametric studies on different plates based on the stacking sequences are investigated. Design/methodology/approach A multi-scale modeling approach is suggested. Back-propagation neural networks (BPNN), radial basis function (RBF) and least square support vector regression are integrated with efficient global optimization (EGO) to reduce the weight of assigned structure. Optimization results are verified by finite element analysis. Findings Compared with other similar studies, the advantage of the suggested strategy uses homogenized properties behaviors with more complex analysis of woven composite structures. According to investigation results, it can be found that 450/−450 ply-orientation is the best buckling load value for all the cut-out shape requirements. According to the optimal results, the BPNN-EGO is the best candidate for the EGO to optimize the woven composite structures. Originality/value A multi-scale approach is used to investigate the mechanical properties of a complex woven composite material architecture. Buckling of different cut-out shapes with the same area is surveyed. According to investigation, 45°/−45° ply-orientation is the best for all cut-out shapes. Different surrogate models are integrated in EGO for optimization. The BPNN surrogate model is the best choice for EGO to optimization difficult problems of woven composite materials.

We present results from the Munich NO2 imaging campaign (MuNIC) where nitrogen dioxide (NO2) near-surface concentrations (NSC) and vertical column densities (VCD) were measured with stationary, mobile and airborne in situ and remote sensing instruments. The most intensive day of the campaign was 7 July 2016, when the NO2 VCD field was mapped with the Airborne Prism Experiment (APEX) imaging spectrometer. The spatial distribution of APEX VCDs was rather smooth with a horizontal gradient between lower values upwind and higher values downwind of the city center. The NO2 map had no pronounced source signatures except for the plumes of two combined heat and power plants (CHP). The APEX VCDs agree well with mobile MAX-DOAS observations from two vehicles conducted in the same afternoon (r = 0.55). In contrast to the VCDs, mobile NSC measurements revealed high spatial and temporal variability along the roads with highest values in congested areas and tunnels. The NOx emissions of the two CHP plants were estimated from the APEX observations using a mass-balance approach. The estimates are higher than reported emissions, but uncertainties are high because the campaign day was unstable and convective, resulting in low and highly variable wind speeds. The NOx emission estimates are consistent with CO2 emissions determined from two ground-based FTIR instruments operated near one CHP plant. We conclude that airborne imaging spectrometers are well suited to map the spatial distribution of NO2 VCDs over large areas. The emission plumes of point sources can be detected in the APEX observations, but accurate flow fields are essential to estimate emissions with sufficient accuracy. The application of airborne imaging spectrometers for studying NSCs, for example as input for epidemiological studies, is less straight forward and requires to account for the non-trivial relationship between VCDs and NSCs.

Recently, machine learning (ML) is being applied to various fields, including hydrology and hydraulics. The numerical models based on ML algorithms have been widely used for forecasting water levels or flowrate in different timescales. Especially in estuary areas where the hydrodynamic regime becomes complicated, the water level forecast information in this area plays an essential role in the operation of tidal sluices. This study proposes an efficient approach using an ML model, long short-term memory (LSTM), to predict short-term water levels in tidal sluice gates from 6 to 48 hours ahead. The An Tho culvert located in the Bac Hung Hai irrigation system, the most extensive irrigation system in Vietnam, was selected as a case study station. The high accuracy of predictive results reveals LSTM models' effectiveness in different forecasting scenarios. In the first scenario using just water level data at the prediction station, the Kling-Gupta efficiency (KGE) coefficient ranges from nearly 0.89 to 0.96. Meanwhile, in the second scenario, the combination of observed data of three gauge stations exhibited better performance with KGE coefficients ranging from just under 0.93 to 0.98 for eight forecasted cases. The findings of this study highlight the performance of LSTM models in providing high-accuracy short-period water level forecasts for areas near estuaries. These obtained results can play a vital role in the management and operation of tidal sluices in the Bac Hung Hai irrigation system, as well as a reference for the operation of other irrigation systems around the world.

Radar-based rainfall forecasts are widely used extrapolation algorithms that are popular in systems of precipitation for predicting up to six hours in lead time. Nevertheless, the reliability of rainfall forecasts gradually declines for heavy rain events with lead time due to the lack of predictability. Recently, data-driven approaches were commonly implemented in hydrological problems. In this research, the data-driven models were developed based on the data obtained from a radar forecasting system named McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation (MAPLE) and ground rain gauges. The data included thirteen urban stations in the five metropolitan cities located in South Korea. The twenty-five data points of MAPLE surrounding each rain station were utilized as the model input, and the observed rainfall at the corresponding gauges were used as the model output. The results showed superior capabilities of long short-term memory (LSTM) network in improving 180-min rainfall forecasts at the stations based on a comparison of five different data-driven models, including multiple linear regression (MLR), multivariate adaptive regression splines (MARS), multi-layer perceptron (MLP), basic recurrent neural network (RNN), and LSTM. Although the model still produced an underestimation of extreme rainfall values at some examined stations, this study proved that the LSTM could provide reliable performance. This model can be an optional method for improving rainfall forecasts at the stations for urban basins.

Recommender Systems are recently applied in many various areas such as entertainment, education, science, and especially e-commerce. In this paper, we propose an approach for building a recommender system in telecommunications for the telecom data about the historical behavior of consumers in six months. Our collected data spans over 6 months and contains how many times each offer has been purchased by a customer in each month and revenue generated by him/her in that month. Our recommender system consists of three main modules. In the segmentation module, we segment our customers into homogeneous groups, consumption wise. In the recommendation module, we utilize the Alternative Least Squares method to determine candidate offers. In the business rule module, we apply business rules to finalize the best telecom offers out of these candidate offers to recommend to each customer. Thanks to big data platforms, our recommender system attains a running time of approximately 10 seconds to 15 seconds to handle one million customers.

A composite material are made by combining two or more constituent materials to obtain the desired material properties of each product type. The matrix material which can be polymer and fiber is used as reinforcing material. Currently, the polymer matrix is widely used in many different fields with differently designed structures such as automotive structures and aviation, aerospace, marine, etc. because of their excellent mechanical properties; in addition, they possess the high level of hardness and durability together with a significant reduction in weight compared to traditional materials. However, during design process of structure, there will be many interruptions created for the purpose of assembling the structures together or for many other design purposes. Therefore, when this structure is subject to load-bearing, its failure occurs at these interruptions due to stress concentration. This paper proposes multi-scale modeling and optimization strategies in evaluation of the effectiveness of fiber orientation in an E-glass/Epoxy woven composite 3D shell with circular holes at the center investigated by FEA results. A multi-scale model approach was developed to predict the mechanical behavior of woven composite 3D shell with circular holes at the center with different designs of material and structural parameters. Based on the analysis result of laminae, we have found that the 3D shell with fiber direction of 450 shows the best stress and strain bearing capacity. Thus combining several layers of 450 fiber direction in a multi-layer composite 3D shell reduces the stresses concentrated on the cuts of the structures.

Urban flooding is a critical challenge in metropolitan cities around the world; thus, urban flood forecasting is required to support water-related managers in mitigating damage. Nevertheless, the accuracy of rainfall forecasting systems remains limited; for example, the predictions of radar-based systems are often inaccurate for heavy rainfall events. This study proposes a framework that couples a forecasting system and a developed 1D/2D urban hydrological model to predict water levels and inundation phenomena in an urban catchment. In the framework, a long short-term memory (LSTM) network uses the quantitative precipitation forecasts (QPFs) of the McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation (MAPLE) system to reproduce three-hour mean areal precipitation (MAP) forecasts. A coupled 1D/2D urban hydrological model was also developed in this study. The Gangnam urban catchment located in Seoul, South Korea, was selected as a case study for the proposed framework. To train and test the LSTM model, a database was established based on 24 heavy rainfall events, 22 grid points from the MAPLE system and the observed MAP values estimated from five ground rain gauges. The corrected MAP forecasts were input into the developed coupled model to predict water levels and relevant inundation areas. The results indicate the viability of the proposed framework for generating three-hour MAP forecasts and urban flooding predictions. This study demonstrates that despite slightly underestimating extreme values of rainfall and peak water levels for certain events, the framework has high practicability and can be used to improve MAP forecasts and urban inundation forecasts.

Fog computing is one of the promising technologies for realizing global-scale Internet of Things (IoT) applications as it allows moving compute and storage resources closer to IoT devices, where data is generated, in order to solve the limitations in cloud-based technologies such as communication delay, network load, energy consumption, and operational cost. However, this technology is still in its infancy stage containing essential research challenges. For instance, what is a suitable fog computing scheme where effective service provision models can be deployed is still an open question. This paper proposes a novel multitier fog computing architecture that supports IoT service provisioning. Concretely, a solid service placement mechanism that optimizes service decentralization on fog landscape leveraging context-aware information such as location, response time, and resource consumption of services has been devised. The proposed approach optimally utilizes virtual resources available on the network edges to improve the performance of IoT services in terms of response time, energy, and cost reduction. The experimental results from both simulated data and use cases from service deployments in real-world applications, namely, the intelligent transportation system (ITS) in Ho Chi Minh City, show the effectiveness of the proposed solution in terms of maximizing fog device utilization while reducing latency, energy consumption, network load, and operational cost. The results confirm the robustness of the proposed scheme revealing its capability to maximize the IoT potential.