esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Q-Learning Method for Smart Grid Price Forecasting
### Q-Learning-Based Electricity Price Forecasting
#### 4.2.2 Formulating System Model to Markov Decision Process  
As the dynamic electricity market has a stochastic environment, MDP is beneficial for reforming the system model. Only the current time slot will be considered for reward and energy consumption, and therefore, no historical data will have any impact on maintaining the stochastic feature of the environment. The MDP includes these major four components:  
1. \(t\) defines the time interval for the actions that represent retail price. It has to be discrete.
2. \(\lambda_{t,c}\) is the retail price chosen at time \(t\) for CU \(c\).
3. \(ed_{t,c}\) represents a CUs energy demand before being notified of the retail price from SP. \(ec_{t}\), is the consumption that occurs after the price signal.
4. \(r(ec_{t,c}|ed_{t,c},\lambda_{t,c})\) is the reward that defines a minimal cost of CU \(c\) and SP's maximum profit at time \(t\).  
Thus, for one episode, the reward will be  
\[\begin{array}{l}R=r(ec_{t,n}|ed_{1,c},\lambda_{1,c})+r(ec_{2,c}|ed_{2,n}, \lambda_{2,c})+\cdots+r(ec_{T,c}|ed_{T,c},\lambda_{T,c})r(ec_{t,c}|ed_{t,c}, \lambda_{t,c})\\ =\sum_{c=1}^{C}[\rho\cdot(\lambda_{t,c}-\pi_{t})\cdot ec_{t,c}-(1-\rho)\cdot (\lambda_{t,c}\cdot ec_{t,c}+\varphi_{t,c})]\end{array} \tag{7}\]  
The total future reward will be  
\[R_{t}=r(ec_{t,c}|ed_{t,c},\lambda_{t,c})+r(ec_{t+1,c}|ed_{t+1,c},\lambda_{t+1, c})+\cdots+r(ec_{T,c}|ed_{T,c},\lambda_{T,c}) \tag{8}\]  
As the environment is stochastic, the rewards for the same actions can also diverge significantly. Therefore, a discounted future reward is used.  
\[\begin{array}{l}R_{t}= r(ec_{t,c}|ed_{t,c}\lambda_{t,c})+\gamma\cdot r(ec_{t+1,c}|ed_{t +1,c},\lambda_{t+1,c})+\quad\gamma^{2}\cdot r(ec_{t+2,c}|ed_{t+2,c}|\lambda_{t +2,c})+\cdots+\\ \gamma^{T-t}\cdot r(ec_{T,c}|ed_{T,c},\lambda_{T,c})\end{array} \tag{9}\]  
where \(\lambda\in[0,\,1]\) is a discount factor that compares future reward with current reward system. A value of 1 for \(\gamma\) means that the same action implemented on the environment will result in the same reward each time, resulting in a deterministic environment. The pricing strategy that maps current states to action will be \(v:\lambda_{t,n}=v(ed_{t,c})\). With the help of \(Q\)-learning, the optimal policy \(v\) will be determined to maximize reward.
## 4 Proposed Sentiment Analysis Methodology
In addition, the Arabic sentiment analysis has not been studied at a level as high as other languages, such as English, Chinese, and French. Furthermore, the user reviews that use the Arabic language to express their views and orientations can use multi-dialect in the same comment, which causes an additional overhead during the data preprocessing and analysis. As presented in Figure 2, the proposed mechanism depends on interactive multi-level processes for splitting the dataset into training and testing and then performing a 10-fold cross-validation for interchanging the \(k\) folds. The next step is data preprocessing, where several stages are applied to clean the polarity sentiments from unwanted terms and particles to increase the analysis efficiency. The next feature generation stage transforms the tweets into a set of feature vectors with an encoder that will be applied to the training and testing dataset. The vectorization of features is executed using the Term Frequency--Inverse Document Frequency (TF-IDF) mechanism that generates the features from the overall corpus of documents and sentiment analysis documents with their word and polarity terms. The generated features are selected using a filter method that divides the input features into different feature vectors related to the target feature. The correlation is executed using three methods: Person, Chi\({}^{2}\), and Random Forest (RF). The correlated features are sorted according to their relevance to the target features where the best \(k\) features are ranked and selected. The next \(k\) features are added until all the features are processed. The final stage is based on modeling the selected features using differing ML algorithms, where the best accuracy for each correlation method with the ML modeling algorithm is recorded. Figure 2: Proposed methodology of multi-stage feature generation and selection mechanism.
###### Abstract  
In recent years, although deep neural networks have yielded immense success in solving various recognition and classification problems, the exploration of deep neural networks in recommender systems has received relatively less attention. Meanwhile, the inherent sparsity of data is still a challenging problem for deep neural networks. In this paper, firstly, we propose a new CIDAE (Continuous Imputation Denoising Autoencoder) model based on the Denoising Autoencoder to alleviate the problem of data sparsity. CIDAE performs regular continuous imputation on the missing parts of the original data and trains the imputed data as the desired output. Then, we optimize the existing advanced NeuMF (Neural Matrix Factorization) model, which combines matrix factorization and a multi-layer perceptron. By optimizing the training process of NeuMF, we improve the accuracy and robustness of NeuMF. Finally, this paper fuses CIDAE and optimized NeuMF with reference to the idea of ensemble learning. We name the fused model the I-NMF (Imputation-Neural Matrix Factorization) model. I-NMF can not only alleviate the problem of data sparsity, but also fully exploit the ability of deep neural networks to learn potential features. Our experimental results prove that I-NMF performs better than the state-of-the-art methods for the public MovieLens datasets.  
recommender systems; denoising autoencoder; deep neural networks; data imputation; matrix factorization; implicit feedback Article
## 2 Relevant Research Works  
In previous work, researchers proposed many unmixing algorithms, such as fully constrained least-squares unmixing (FCLSU) [44], graph-regularized \(1_{1/2}\)-NMF (GLNMF) [45], unmixing based on the graph Laplacian (GraphL) [46], the Merriman-Bence-Osher (MBO) scheme for solving a graph's total variation subproblem (gtvMBO) [47], deep autoencoder unmixing (DAEU) [27] and the endmember-guided unmixing network with pixelwise (EGU-pw) [48]. Their advantages and disadvantages are shown in Table 1.  
Although the FCLSU algorithm performs unmixing well, its final solution tends toward a local optimum solution, which is unfavorable for HSIs containing large amounts of data. The GLNMF approach hinges on converting a non-negative matrix with higher dimensions into two non-negative matrices with lower dimensions. However, when it is directly applied to the estimation of abundance, it often falls into the local minimum problem. The GraphL and gtvMBO methods improve the efficiency using the Laplacian graph operation, but their optimizations are aimed at the endmembers only, with no consideration of the abundance.  
With the development of technology, researchers put forward the DAEU, which uses a neural network, with its strong ability to fit nonlinear problems and process a large amount of data. It extracts the hidden input features through its encoder and reconstructs the input through a decoder, which can achieve good results. However, during dimensionality reduction, it will lose rich HSI feature information, which greatly reduces its unmixing performance. The EGU-pw is an end-to-end, two-stream deep unmixing network that simulates the physical properties of endmembers in the real world through self-supervision technology. Although it produces excellent results, it also ignores the rich HSI feature information.
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Unit** \\ \hline V\({}^{2+}\) diffusion coefficient & \(D_{V^{2+}}\) & \(2.4\times 10^{-10}\) & m\({}^{2}\) s\({}^{-1}\) \\ V\({}^{3+}\) diffusion coefficient & \(D_{V^{3+}}\) & \(2.4\times 10^{-10}\) & m\({}^{2}\) s\({}^{-1}\) \\ VO\({}^{2+}\) diffusion coefficient & \(D_{V^{2+}}\) & \(3.9\times 10^{-10}\) & m\({}^{2}\) s\({}^{-1}\) \\ VO\({}_{2}\) diffusion coefficient & \(D_{V^{2+}}\) & \(3.9\times 10^{-10}\) & m\({}^{2}\) s\({}^{-1}\) \\ Proton diffusion coefficient & \(D_{H^{+}}\) & \(9.312\times 10^{-9}\) & m\({}^{2}\) s\({}^{-1}\) \\ Initial vanadium concentration & \(c^{0}\) & 1500 & mol m\({}^{-3}\) \\ Initial proton concentration (negative) & \(c^{0}_{H_{+}}\) & 4500 & mol m\({}^{-3}\) \\ Initial proton concentration (positive) & \(c^{0}_{H_{+}}\) & 6000 & mol m\({}^{-3}\) \\ Standard reaction rate constant (negative) & \(k_{c}\) & \(1.7\times 10^{-7}\) & m s\({}^{-1}\) \\ Standard reaction rate constant (positive) & \(k_{c}\) & \(6.8\times 10^{-7}\) & m s\({}^{-1}\) \\ Anodic transfer coefficient & \(\alpha_{a}\) & 0.5 & – \\ Cathodic transfer coefficient & \(\alpha_{c}\) & 0.5 & – \\ Equilibrium potential: V\({}^{2+}\)/V\({}^{3+}\) & \(E^{0}_{c}\) & \(-0.255\) & V \\ Equilibrium potential: VO\({}^{2+}\)/VO\({}_{2}\)\({}^{+}\) & \(E^{0}_{a}\) & 1.004 & V \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Electrochemical parameters from [PERSON] et al. [12]. Figure 3: Experimental [12] and simulated charge curves for a 5 cm\({}^{2}\) cell at 40 mA cm\({}^{-2}\) from 10% to 90% SOC for model validation.
## 3 Aggregation Model Based on Multi-Scenario and Multi-Objective
### Objective Functions
\begin{array}{l}\min f_{3}=V_{L}=\frac{\alpha}{\mu}=\frac{\sqrt{\frac {1}{t}\sum\limits_{t=1}^{T}\left(L_{t}-L\right)^{2}}}{\sqrt{\prod\limits_{t=1 }^{T}\prod\limits_{t=1}^{T}L_{t}}}\\ L=\sum\limits_{k=1}^{K}p_{k}\sum\limits_{n=1}^{N}P_{n,k}^{DG}X_{n}^{DG}-\sum \limits_{n=1}^{N}P_{n}^{ES}X_{n}^{ES}+\sum\limits_{n=1}^{N}P_{n}^{DR}X_{n}^{DR }-P_{0}\end{array} \right. \tag{8}\]  
where \(L\) is the curve to measure volatility, \(V_{L}\) is the volatility of curve \(L\), \(\alpha\) is the standard deviation of the curve \(L\), \(\mu\) is the geometric mean of the curve \(L\). (3) The highest aggregation degree of RA  
When choosing DERs in each region, the geographical location of the region and the resource characteristics need to be considered. The distance between the \(n\)-th region and the center is defined as follows:  
\[d(n)=\sqrt{\left(x_{n}-x_{0}\right)^{2}+\left(y_{n}-y_{0}\right)^{2}} \tag{9}\]  
where \((x_{0},y_{0})\) is the location of the geometric center of the whole region, \((x_{n},y_{n})\) is the location of the \(n\)-th region. The average resource density in the \(n\)-th region is defined as follows;  
\[\rho(n)=\frac{\sum\limits_{k=1}^{K}p_{k}\sum\limits_{t=1}^{T}P_{t,n,k}^{DG}X_{ n}^{DG}+\sum\limits_{t=1}^{T}P_{t,n}^{ES}X_{n}^{ES}+\sum\limits_{t=1}^{T}P_{t,n}^{ DR}X_{n}^{DR}}{S_{n}} \tag{10}\]  
where \(S_{n}\) is the area of the \(n\)-th region. When constructing a RA, the more centralized the location of DERs is, the easier it is to regulate and control. In this paper, the aggregation degree of RA is calculated by combining the geographic location and average resource density of each region. The objective function of the highest aggregation degree is as follows;  
\[\min f_{4}=\sum\limits_{n=1}^{N}d(n)\rho(n) \tag{11}\]  
In summary, the optimal objective function set of the DERs aggregation model is as follows;  
\[F=(\min f_{1},\max f_{2},\min f_{3},\min f_{4}) \tag{12}\]\(F\) represents the minimum daily average cost, maximum daily output, minimum curve volatility and the highest aggregation degree of the RA. The decision variable \(X_{n}\) of the \(n\)-th region representing the presence or absence of various resources is as follows;  
\[X_{n}=\left[X_{n}^{\text{DG}},X_{n}^{ES},X_{n}^{\text{DR}}\right]^{T} \tag{13}\]  
The DERs aggregation model established in this paper is a multi-objective 0-1 planning model. The decision variable of all regions is a matrix composed of 0 and 1 elements;  
\[\left(X_{1},X_{2},\ldots,X_{n-1},X_{n}\right)=\left[ \begin{array}{cccc}X_{1 }^{\text{DG}}&X_{2}^{\text{DG}}&\ldots&X_{n-1}^{\text{DG}}&X_{n}^{\text{DG}}\\ X_{1}^{ES}&X_{2}^{ES}&\ldots&X_{n-1}^{ES}&X_{n}^{ES}\\ X_{1}^{\text{DR}}&X_{2}^{\text{DR}}&\ldots&X_{n-1}^{\text{DR}}&X_{n}^{\text{DR} }\end{array} \right] \tag{14}\]
# Predicting the [PERSON]'s Modulus of Concrete Using a Particle-Based Movable Cellular Automata Method
## 4 Comparison the MCA Results with Experiments
### Comparison with Lightweight Expanded Clay Aggregate Concrete
This standard also states that the value should be reduced by 10% for limestone aggregates. Similar to concrete with basaltic aggregates, the values of the elastic modulus established according to [82] are overestimated in the case of concretes using lightweight expanded clay aggregate. Structures that rely on overestimated data can be dangerous, especially over a long period of use, as improper assessment of the concrete's stiffness can lead to uncontrolled deformations and cracks. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Mixtures** & **w/c** & **Limestone** & **Coarse LECA** & **Fine LECA** & \(E_{s}\)[83] & \(E_{s}\)**[This Study]** \\  & **Ratio** & **V [\%]** & **V [\%]** & **V [\%]** & **GPa** & **GPa** \\ \hline Mix 7 & 0.3 & 30.85 & 27 & - & 19.6 & 19.59 \(\pm\) 0.07 \\ Mix 8 & 0.3 & 22.52 & 24 & 12 & 16.3 & 16.04 \(\pm\) 0.06 \\ Mix 4 & 0.4 & 24.518 & 24 & 12 & 12.9 & 13.74 \(\pm\) 0.05 \\ Mix 10 & 0.4 & 18.111 & 24 & 12 & 13.0 & 13.03 \(\pm\) 0.04 \\ Mix 9 & 0.5 & 20.963 & 27 & - & 11.8 & 13.30 \(\pm\) 0.04 \\ Mix 6 & 0.5 & 20.185 & 24 & 12 & 12.6 & 11.53 \(\pm\) 0.06 \\ Mix 1 & 0.6 & 32.78 & 27 & - & 14.9 & 14.24 \(\pm\) 0.08 \\ Mix 2 & 0.6 & 24.185 & 24 & 12 & 12.8 & 11.19 \(\pm\) 0.07 \\ \hline \end{tabular}
\end{table} 
Table 6: Composition of concretes with lightweight expanded clay aggregate based on data from [83] and results of numerical simulations, showing the mean static elastic moduli \(E_{s}\) with standard deviation evaluated for twenty samples.
## 4 Our Findings
### Significant Keyword Frequencies  
In our analysis, we found that the keyword frequencies provide significant insights into the research trends and priorities within the field. The term \"Blockchain\" emerged as the most frequently mentioned keyword, appearing 16,400 times, which underscores its central role in the studies. This was closely followed by \"Security\" (11,518) and \"Smart\" (13,760), indicating a strong emphasis on security aspects and smart technologies, particularly in the context of blockchain systems. We also observed that keywords such as \"Vulnerability/Vulnerabilities\" (8422), \"Attack\" (5450), and \"Detection\" (7043) were highly prevalent, reflecting a substantial focus on identifying and addressing security threats and weaknesses within blockchain and related technologies. Additionally, terms like \"Privacy\" (4454) and \"Encryption\" (1633) highlight the importance of data protection and cryptographic methods in ensuring secure transactions and network integrity. The prominence of \"Ethereum\" (4672) and \"Bitcoin\" (1971) further emphasize the dominance of these platforms in blockchain research. Overall, our findings illustrate a research landscape deeply engaged with blockchain technology, its security challenges, and the development of strategies to mitigate vulnerabilities and enhance system robustness. Table 4 illustrates the significant keywords from our included studies.
# Modelica-Based Energy Management of PEMFC Hybrid Power System of Vehicle
## 2 Mathematic Model of Hybrid Power System  
The proposed hybrid power system mainly consists of a water-cooled PEMFC stack and a lithium-ion battery. The PEMFC is primarily used to provide stable high-power output, while the lithium-ion battery is used to deliver instantaneous and rapid power response. The balance of plant (BOP) system of the PEMFC includes a cooling water circulation subsystem and an air compressor subsystem. The associated auxiliary components are also considered, as shown in Figure 1 below:  
The dashed lines in Figure 1 represent the control signal transmission path, while the solid lines represent the direction of electrical energy transmission. In the proposed configuration, the PEMFC and lithium-ion battery jointly supply the vehicle's required power. The PEMFC is connected in parallel to the DC bus through a unidirectional DC/DC converter, providing continuous power to the vehicle. The lithium-ion battery is connected to the DC bus via a bidirectional DC/DC converter, supplying supplementary power for the stack startup. The advantage of this configuration lies in the ability of the lithium-ion battery to assist the fuel cell system in providing supplementary power, thereby improving power output performance. Moreover, the lithium-ion battery can provide or absorb transient power demands or responses, extending the service life of the stack.
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## Appendix B Genetic Algorithm Implementation
### Operators  
The following operators have been used:  
* **Evaluation and ranking:** the quality of the individuals must be evaluated to distinguish between good and bad candidates. First, each individual is scored by evaluating the objective function in Equation (9), resulting in a raw fitness score. Then, solutions are ranked according to their raw fitness score. Those individuals that do not meet the problem constraints are penalized so that they are placed at the tail of the ranking according to the ratio of users that meet the constraints. Finally, if an individual is in the \(n\) position in the ranking, it is assigned a new scored termed expectation value of \(1/\sqrt{n}\).
* **Parent selection:** parents are selected through a stochastic uniform selection process among individuals, according to their expectation value. For example, for \(p=200\) individuals, 342 parents (\(152\times 2\) for CM operations plus 38 for SRM) are selected. By applying the proposed selection method, best individuals may be chosen multiple times as parents.
* **Mutation:** this operator consists of replacing genes of an individual for random coordinates within \(\mathcal{P}\) with probability \(p_{m}^{SRM}=(3\,D)^{-1}\) or \(p_{m}^{CM}=p_{m}^{SRM}/2\) for SRM and CM, respectively.
* **Crossover:** this operator randomly combines the genes of two parents to create a new individual.
## Appendix B Beamwidth Calculation  
The beamwidth can be calculated by the rotation matrix and the method of specular reflection, in Figure 11, we set \(\mathbf{u_{OG}}\) as the unit vector of incident laser center line \(OG\), whose reflected beamline is the \(GS^{\prime}\), and set the unit vector of reflected laser center line as \(\mathbf{u_{GS^{\prime}}}\). We set the initial normal unit vector of the RIS surface as \(\mathbf{u_{R}}=(x_{R},y_{R},z_{R})=(0,0,-1)\), due to the RIS being parallel to the ground surface at the beginning. Then, we set the normal vector after the first rotation as \(\mathbf{u_{R^{\prime}}}=(x_{R^{\prime}},y_{R^{\prime}},z_{R^{\prime}})\), and the normal vector after the second rotation as \(\mathbf{u_{R^{\prime\prime}}}=(x_{R^{\prime\prime}},y_{R^{\prime\prime}},z_{R^{ \prime\prime}})\). These two transfer angles \(\theta_{1}\) and \(\theta_{2}\) describe the RIS rotations around the x-axis and y-axis. The angles are set as anti-clockwise and positive when looking toward the origin. The rotations of the RIS can be denoted as the rotations of the normal unit vector of the RIS surface, according to the rotation matrix, as shown below \[\begin{split} x_{R^{\prime}}&=x_{R}\\ y_{R^{\prime}}&=y_{R}\cos\theta_{1}-z_{R}\sin\theta_{1} \\ z_{R^{\prime}}&=y_{R}\sin\theta_{1}+z_{R}\cos\theta_{1}, \\ x_{R^{\prime\prime}}&=z_{R^{\prime}}\sin\left( \theta_{2}\right)+x_{R^{\prime}}\cos\left(\theta_{2}\right)\\ y_{R^{\prime\prime}}&=y_{R^{\prime}}\\ z_{R^{\prime\prime}}&=z_{R^{\prime}}\cos\left( \theta_{2}\right)-x_{R^{\prime}}\sin\left(\theta_{2}\right).\end{split} \tag{10}\]  
After confirming the normal vector of the RIS after rotations, according to the law of specular reflection [53], the unit vector \(\mathbf{u}_{\mathbf{G}\mathbf{\prime}}\) of the reflected beam centre line can be denoted as  
\[\mathbf{u}_{\mathbf{G}\mathbf{\prime}}=2(\mathbf{u}_{\mathbf{R}^{\prime\prime}}\cdot\mathbf{u}_{\mathbf{O} \mathbf{G}})\mathbf{u}_{\mathbf{R}^{\prime\prime}}-\mathbf{u}_{\mathbf{O}\mathbf{G}}, \tag{11}\]  
then the problem is transformed to obtain the beam footprint center \(S^{\prime}(x_{S^{\prime}}[n],y_{S^{\prime}}[n],0)\) which is a line-plane intersection problem between reflected beam line \(O^{\prime}\mathbf{G}S^{\prime}\) and ground plane.  
Then, according to the geometrical analysis with line \(O^{\prime}\mathbf{G}S^{\prime}\) along with reflected beam, the beamwidth can be calculated by the visual LS coordinate \(O^{\prime}(x_{O^{\prime}}[n],y_{O^{\prime}}[n],z_{O^{\prime}}[n])\) and beam footprint center \(S^{\prime}(x_{S^{\prime}}[n],y_{S^{\prime}}[n],0)\), expressed at (11), where the divergence angle \(\delta\) of the Gaussian laser beam can be denoted as [49],  
\[\delta=\frac{2\lambda}{\pi W_{0}}, \tag{12}\]  
where \(\lambda\) is the wavelength of the laser used for communication and \(W_{0}\) is the initial beam waist and can be seen as equal to the radius of LS.
# Image Quality Improvement and Memory-Saving in a Permanent-Magnet-Array-Based MRI System
## 5 Conclusions  
This study investigates the approaches for image quality improvement in a PMA-based MRI system, and proposes an effective approach to obtain both a memory-saving reconstruction and a significant improvement in image quality simultaneously in such a system. An IO-ring PMA was used as an example to supply a non-linear SEM with non-linear gradients. Local k-spaces were used to identify the problem caused by non-linear gradients to the quality of reconstructed images which is the uneven population of signal points in the local k-spaces. To tackle this problem, increasing sampling points and increasing rotation angles are both investigated and their effectiveness was examined and demonstrated through numerical examples. The downside of this remedy was then clearly identified which is an significant increase in the memory consumption due to a resultant large and dense encoding matrix. To cut down the memory consumption after the size of the encoding matrix is increased through increasing the sampling points or rotation angles, the resultant large and dense matrix is transformed to the Fourier domain (frequency domain) where the matrix becomes sparse and the memory consumption is significantly reduced. The efficacy of the memory reduction was successfully shown. A truncation was proposed on the sparse encoding matrix in the frequency domain to further reduce the memory consumption. The effects of truncation with different percentage on the total memory consumption, the calculation speed, and the quality of reconstructed images were investigated where optimal truncation was identified with a good balance on the trade-offs.  
According to the investigation, increasing the sampling points leads to a stronger tissue contrast while increasing the number of rotation angles leads to clearer tissue boundaries. An increase in the memory consumption caused by an increase in the sampling points and rotation angles is successfully and effectively lowered down by a transformation of the system to frequency domain. Consequently, a system with a large matrix for a pursue of an improved quality of the image construction can be handled by a laptop. The proposed study is one step forward for a PMA-based MRI system towards its targeted point-of-care application scenario.  
[PERSON] contributed to the method and the paper writing, [PERSON] contributed to the idea polishing and manuscript polishing, [PERSON] to the method, the structure of the paper, and the paper writing. All authors have read and agreed to the published version of the manuscript.  
Singapore Ministry Of Education Academic Research Fund (AcRF) Tier 1.  
The authors declare no conflict of interest.