Nur Arina Bazilah Kamisan, Pow Jing Huei, Muhammad Hisyam Lee
Forecasting wind direction is inherently challenging due to its circular nature, where conventional numerical models often encounter discontinuities at the 0°/360° boundary. This study compares two modelling strategies for daily wind direction prediction in Alor Setar, Malaysia, using data from 2013–2017. A transformation-based approach and a direct numerical approach are compared for forecasting wind direction to assess their differences. In the transformation-based method, wind direction values are converted into sine and cosine components to preserve circularity, with predictions later reconstructed using inverse trigonometric functions. The direct approach predicts wind direction values without transformation. Three models, Prophet, Random Forest, and Holt-Winters, are applied under both strategies. Model performance is evaluated using time series plots, wind rose diagrams, and angular error metrics, including Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Results indicate that the Random Forest model is the best model for forecasting the wind direction in Alor Setar, and the transformation-based approach produces more accurate and stable predictions, effectively capturing directional continuity, while the direct approach yields higher angular errors and fails to replicate the observed wind direction distribution. To our knowledge, this is one of the first studies in Malaysia to systematically apply transformation-based approaches for wind direction forecasting. The findings highlight the practical importance of improved wind direction prediction for renewable energy optimization, aviation safety, and environmental monitoring.
In recent years, as machine learning models have become larger and more complex, it has become both more difficult and more important to be able to explain and interpret the results of those models, both to prevent model errors and to inspire confidence for end users of the model. As such, there has been a significant and growing interest in explainability in recent years as a highly desirable trait for a model to have. Similarly, there has been much recent attention on ensemble methods, which aim to aggregate results from multiple (often simple) models or metrics in order to outperform models that optimize for only a single metric. We argue that this latter issue can actually assist with the former: a model that optimizes for several metrics has some base level of explainability baked into the model, and this explainability can be leveraged not only for user confidence but to fine-tune the weights between the metrics themselves in an intuitive way. We demonstrate a case study of such a benefit, in which we obtain clear, explainable results based on an aggregate of five simple metrics of relevance, using Wikipedia data as a proxy for some large text-based recommendation problem. We demonstrate that not only can these metrics’ simplicity and multiplicity be leveraged for explainability, but in fact, that very explainability can lead to an intuitive fine-tuning process that improves the model itself.
Mussa S Abubakari, Gamal Abdul Nasir Zakaria, Priyanto Priyanto
et al.
Whether in general education or Islamic education, information and communication technologies (ICT) have shown to be effective in boosting learning and teaching processes. However, the optimal utilization of ICT in Islamic education is hardly observed, and its acceptance is rarely analysed. Therefore, the present study evaluated factors influencing individuals in Islamic education to adopt ICT based on the modified unified theory of acceptance and use of technology (UTAUT) model by incorporating two external factors: personal innovativeness and a religious perspective on the ICT aspect. The study employed a survey method to collect data from 225 valid respondents from Indonesia and applied a partial least squares-structural equation modelling (PLS-SEM) approach for analysis purposes. The study’s findings suggest that three factors significantly influenced behavioural intention to utilize ICT in Islamic education. These factors are the religious perspective on ICT, personal innovativeness, and social influence, with the religious perspective being more effective factor on behavioural intention than other factors. Moreover, religious perspective, facilitating conditions, and behavioural intention significantly affected actual ICT usage. Both effort and performance expectancies did not significantly affect behavioural intention to use ICT. Besides that, personal innovativeness was found insignificant in influencing usage behaviour, however, it significantly affected effort expectancy, while religious perspective significantly influenced performance expectancy. Finally, the study’s model explained 55.1% of behavioural intention and 54.0% of usage behaviour. The implications of the findings for practical and theoretical contributions are discussed in this paper.
In this paper, we develop the cell-average based neural network (CANN) method to solve third order and fifth order Korteweg-de Vries (KdV) type equations. The CANN method is based on the weak or integral formulation of the partial differential equations. A simple feedforward network is forced to learn the cell average difference between two consecutive time steps. One solution trajectory corresponding to a generic initial value is used to generate the data set to train the network parameters, which uniquely determine a one-step explicit finite volume based network method. Once well-trained, the CANN method can be generalized to a suitable family of initial value problems. Comparing with conventional explicit methods, where the time step size is restricted as Δt = O(Δx3) or Δt = O(Δx5), the CANN method is able to evolve the solution forward accurately with a much larger time step size of Δt = O(Δx). A large group of numerical tests are carried out to verify the effectiveness, stability and accuracy of the CANN method. Wave propagation is well resolved with indistinguishable dispersion and dissipation errors. The CANN approximations agree well with the exact solution for long time simulation.
For square contingency tables with ordered categories, Tomizawa, Biometrica J. 28 (1986), 387–393, considered the conditional point symmetry model. Kurakami et al., J. Stat. Adv. Theory Appl. 17 (2017), 33–42, considered the another point symmetry and the reverse global symmetry model. The present paper proposes Kullback–Leibler information type measures to represent the degree of departure from each of the models. Also this paper shows a theorem that the measure for the another point symmetry model is equal to the sum of the measures for the reverse global symmetry model and for the conditional point symmetry model.
In this paper we study the spatial spread of the COVID-19 infection in Lebanon. We inspect the spreading of the daily new infections across the 26 administrative districts of the country, and implement the univariate Moran’s I statistics in order to analyze the tempo-spatial clustering of the infection in relation to various variables parameterized by adjacency, proximity, population, population density, poverty rate and poverty density. We find out that except for the poverty rate, the spread of the infection is clustered and associated to those parameters with varying magnitude for the time span between July (geographic adjacency and proximity) or August (population, population density and poverty density) through October. We also determine the temporal dynamics of geographic location of the mean center of new and cumulative infections since late March. The understanding of the spatial, demographic and geographic aspects of the disease spread over time allows for regionally and locally adjusted health policies and measures that would provide higher levels of social and health safety in the fight against the pandemic in Lebanon.
In this paper, the systems with solutions in the form of degenerate hypergeometric Humbert functions of two variables reduced to Bessel functions of two variables are established and studied. The connections between the Humbert and Bessel functions of two variables are revealed, their differential properties are investigated. The addition and multiplication theorems are proved. In future, these proven properties allow us to establish recurrent relations between degenerate hypergeometric functions of two variables, similarly to extend these properties to the case of many variables. The connection between type systems of Bessel and Whittaker is shown. Using the Frobenius - Latysheva method, the singularities of constructing normalregular solutions of the newly established Bessel - type system are studied.
In this paper, some characterization results for exponential distribution are established. The results are concluded in terms of number of observations near of order statistics. It is shown that its probability mass function and its first moment can characterize the exponential distribution. Also, an estimator based on near-order statistics is introduced for tail thickness of exponential distribution.
Grouped data, such as repeated measures and longitudinal data, are increasingly collected in different areas of application, as varied as clinical trials, epidemiological studies, and educational testing. It is often of interest, for these data, to explore possible relationships between one or more response variables and available covariates. Because of the within-group correlation typically present with this type of data, special regression models that allow the joint estimation of mean and covariance parameters need to be used. Two main approaches have been proposed to represent the covariance structure of the data with these models: (i) via the use of random effects, the so-called conditional model and (ii) through direct representation of the covariance structure of the responses, known as the marginal approach. Here we discuss and compare these two approaches in the context of linear and non-linear regression models with additive Gaussian errors, using a real data example to motivate and illustrate the discussion.
<p>Let H be a real Hilbert space. Let F,K : H → H be Lipschitz monotone mappings with Lipschtiz constants L1and L2, respectively. Suppose that the Hammerstein type equation u + KFu = 0 has a solution in H. It is our purpose in this paper to construct a new explicit iterative sequence and prove strong convergence of the sequence to a solution of the generalized Hammerstein type equation. The results obtained in this paper improve and extend known results in the literature.</p>
Abstract. Use of auxiliary variables for generating proposal variables within a Metropolis–Hastings setting has been suggested in many different settings. This has in particular been of interest for simulation from complex distributions such as multimodal distributions or in transdimensional approaches. For many of these approaches, the acceptance probabilities that are used turn up somewhat magic and different proofs for their validity have been given in each case. In this article, we will present a general framework for construction of acceptance probabilities in auxiliary variable proposal generation. In addition to showing the similarities between many of the proposed algorithms in the literature, the framework also demonstrates that there is a great flexibility in how to construct acceptance probabilities. With this flexibility, alternative acceptance probabilities are suggested. Some numerical experiments are also reported.
Athanasios A. Pantelous, Nicholas E. Frangos, Alexandros A. Zimbidis
The paper revisits the classical problem of premium rating within a heterogeneous portfolio of insurance risks using a continuous stochastic control framework. The portfolio is divided into several classes where each class interacts with the others. The risks are modelled dynamically by the means of a Brownian motion. This dynamic approach is also transferred to the design of the premium process. The premium is not constant but equals the drift of the Brownian motion plus a controlled percentage of the respective volatility. The optimal controller for the premium is obtained using advanced optimization techniques, and it is finally shown that the respective pricing strategy follows a more balanced development compared with the traditional premium approaches.