Droughts are periods of inadequate precipitation that have severe and diverse impacts on human societies and ecosystems. However, a reliable forecasting technique can improve drought monitoring and mitigate impacts. This research aimed to assess the predictive accuracy of a hybrid forecasting model, Wavelet-ARIMA (W-ARIMA), for drought forecasting, using the Standardized Precipitation Index (SPI) with a traditional ARIMA model as a benchmark. Monthly rainfall data from the Badeggi district in the north-central part of Nigeria, covering the period from January 1968 to December 2018, was utilized for the analysis. Subsequently, SPI values for various time scales (3, 6, 9, and 12) were computed. The Wavelet Transform was then employed to decompose the data series into L components, encompassing details and approximations from A to D (AnD1D2 DL 1) of the SPIs, respectively. Furthermore, an ARIMA model was fitted to each of these details and approximations, and their sum constituted the forecasted W-ARIMA values. Evaluation based on the performance metric, the RMSE of W-ARIMA (0.4889, 0.4326, 0.3566, and 0.2177) while that of ARIMA (0.7771, 0.6667, 0.4648, and 0.3212), revealed that the hybrid model (W-ARIMA) consistently outperformed the ARIMA model among the metrics for all the SPI 3, 6, 9, and 12, respectively.

In the field of research on Deoxyribonucleic acid (DNA), graph theory can be applied to model the structure of a DNA molecule. In particular, the shortest path problem in graph theory can be used to identify the shortest path between vertices of a graph. Hence, it is possible to apply the shortest path problem for minimization of a DNA string so that the time taken for computation of genome assembly can be reduced. This paper presents a method to represent a DNA string graphically where the shortest path is calculated for the graph generated from the DNA string, and the shortest path is then used to minimize the DNA string. In this research, a DNA string is presented in graphical form by using base pairs of length two as the vertices where the initial bases used are Adenine (A) and Guanine (G) which are the main bases in purine. The number of base pairs between adjacent vertices in the DNA string is represented by the edges. The graph is then reduced by following a given set of rules where the shortest path is calculated for all start and end vertices of the reduced graph. Next, the simplification of the graph is done based on the shortest paths obtained by removing all the untraversed paths where the Euler path for the simplified graph is used to form a minimized DNA string. The result shows that a minimized DNA string can be obtained by simplifying the graph of the DNA string using the shortest path problem

Petroleum industries play a vital role in the global economy, and research institutions need to focus on optimizing their chemical processes. Achieving the ideal operating conditions for these processes is crucial for effective management decision-making. In the alkylation reaction, isobutane reacts with light olefins, like butylenes, in the presence of a strong acid catalyst to produce high-octane compounds. To optimize the profit function without relying on complex computer programming codes, we utilized a novel logarithmic penalty function (LPF) approach. This approach was specifically designed to handle nonlinear programming problems with irregular features. The approach was implemented using the fminunc routine function, employing quasi-Newton methods, and compared it with existing conventional methods (using nonlinear programming system optimization laboratory (NPSOL) code and Hock-Schittkowsky (HS)). The results demonstrated higher efficiency compared to NPSOL and HS approaches as the LPF method yielded a higher objective value, implying a more favorable outcome in terms of maximizing the desired outcome (e.g. profit).

Surfaces and their descriptions are significant in design, physical science, geology, and other natural phenomena. This study introduces a neutrosophic B´ezier surface approximation with a four-by-four control net for the bicubic situation. The neutrosophic notion defines the neutrosophic control net relation. The control net is mixed with the Bernstein basis function to generate a surface blending function and a neutrosophic bicubic B´ezier surface. Finally, the neutrosophic bicubic B´ezier surface is shown using an approximation approach and data points having neutrosophic properties.

Supplier selection is perceived as important decision-making process in any supply chain management. In this study, the best supplier for a company is being determined based on five main criteria chosen which are delivery, capacity, warranty, cost, and quality. The Analytic Hierarchy Process (AHP) and Entropy Measurement (EM) methods are integrated with Data Envelopment Analysis (DEA) was applied to set ranking and choose the best supplier as conventional DEA is not able to provide complete ranking among inefficient units. The mathematical modelling is executed using LINGO software. Supplier 3 has obtained efficient result of score 1 for both hybrid method and in Super Efficiency method as the most efficient supplier. Then, the results are validated using Spearman’s Rank Correlation Coefficient (SRCC) which shows positive correlation between both integrated methods. Finally, findings of this study indicate the feasibility of integrated AHP-DEA and EM-DEA for supplier selection with multiple criteria.

The integration of economic and social objectives is essential for Islamic bank to stimulate economic growth through the effective management and allocation of wealth and resources. This article proposes a hybrid mathematical optimization between the analytic hierarchy process (AHP) and multi-choice goal programming (MCGP) methods. The hybrid methods make the amalgamation of economic and social objectives possible in the multi-objective model of Islamic banking. The AHP method is used to determine the priorities or weights on each of the economic and social objectives. MCGP on the other hand, serves as an optimizer to the Multi-objective Decision Making (MODM) model that fits the priorities to the objectives and possible target goals. Results confirmed on the practicality and flexibility of the hybrid AHP-MCGP model which enable Islamic banks to better utilize their financial resources based on the priorities and multiple target goals. The feasible achievement of multi-objective with an interval target goal has offered the avenue for the decision makers at Islamic bank to satisfy economic and social goals simultaneously.

Currency exposure is defined as the risk where firm value is sensitive to changes in foreign exchange movement. Several studies have been devoted to measure the extent of foreign exchange exposure of Malaysian corporations. However, previous efforts have ignored the potential of Shariah compliance and time scale effects on the measurement. Motivated from these specification problems, the study investigated the influence of Shariah compliance on the level of multi-scale exchange rate exposure using the Maximal Overlap Discrete Wavelet Transformation (MODWT) approach. The study focused on a sample of 12 Shariah compliant firms (SCF) and 12 non-Shariah compliant firms (NSCF) listed under the Food and Beverage industry in Malaysia from July 2005 till August 2018. As a result, the study found that the level of Shariah compliance had a negligible impact on the level of exchange exposure. The study also found that the level of currency exposure increased from low to high time scale, indicating that widened investment intervals entail greater exchange risk for both SCF and NSCF. In terms of policy implication, the study infers that the SCF and NSCF share almost identical exchange risk profiles which uphold the law of one price. In fact, there is no basis to expect different risk management routines in dealing with exchange risk between the two groups. However, the study stresses the importance of incorporating the time scale factor in managing exchange risk for both groups.

Social media is used as a tool by many people to express their opinions. Sentiment analysis for social media is very important, as it allows information to be obtained about public opinion on government performance. The goal of this research is to learn about the opinions of Surabaya citizens, using deep learning methods. The data are extracted from the official Twitter accounts of the Surabaya government and a private radio station in Surabaya. The data are grouped into two categories: positive and negative sentiments. This research is conducted in three steps: data pre-processing, sentiment classification, and visualization. Data pre-processing is required before modelling approaches are applied. It is used to transform the unstructured text data into structured data. The data pre-processing consists of case folding, tokenizing, and the removal of stop words. Deep learning methods are then applied to the data. A Backpropagation Neural Network (BNN) and a Convolutional Neural Network (CNN) are used to perform the sentiment classification. The BNN and CNN are compared using various metrics, such as precision, sensitivity, and area under the receiver operating characteristic curve (AUC). A word cloud is then used to visualize the data and find the most frequent words in each class. The results show that the sentiment classification with CNN is better than that with the BNN because the values for the precision, sensitivity and AUC are higher.

In this paper, the problem of forced convection flow of micropolar fluid of lighter density impinging orthogonally on another heavier density of micropolar fluid on a stretching surface is investigated. The boundary layer governing equations are transformed from partial differential equations into a system of nonlinear ordinary differential equations using similarity transformation and solved numerically using dsolve function in Maple software version 2016. The velocity, microrotation and temperature of micropolar fluid are analyzed. It is found that both upper fluid and lower fluid display opposite behaviour when micropolar parameter K various with strong concentration n = 0, Pr = 7 and stretching parameter lambda = 0.5. The results also show that stretching surface exert the force that increasing the velocity of micropolar fluid.

DNA computing, or more generally, molecular computing, is a recent development on computations using biological molecules, instead of the traditional silicon-chips. Some computational models which are based on different operations of DNA molecules have been developed by using the concept of formal language theory. The operations of DNA molecules inspire various types of formal language tools which include sticker systems, grammars and automata. Recently, the grammar counterparts of Watson-Crick automata known as Watson-Crick grammars which consist of regular, linear and context-free grammars, are defined as grammar models that generate double-stranded strings using the important feature of Watson-Crick complementarity rule. In this research, a new variant of static Watson-Crick linear grammar is introduced as an extension of static Watson-Crick regular grammar. A static Watson-Crick linear grammar is a grammar counterpart of sticker system that generates the double-stranded strings and uses rule as in linear grammar. The main result of the paper is to determine some computational properties of static Watson-Crick linear grammars. Next, the hierarchy between static Watson-Crick languages, Watson-Crick languages, Chomsky languages and families of languages generated by sticker systems are presented.