Coupled Ostrovsky equations for internal waves in a shear flow

In the context of fluid flows, the coupled Ostrovsky equations arise when two distinct linear long wave modes have nearly coincident phase speeds in the presence of background rotation. In this paper, nonlinear waves in a stratified fluid in the presence of shear flow are investigated both analytically, using techniques from asymptotic perturbation theory, and through numerical simulations. The dispersion relation of the system, based on a three-layer model of a stratified shear flow, reveals various dynamical behaviours, including the existence of unsteady and steady envelope wave packets.Comment: 47 pages, 39 figures, accepted to Physics of Fluid

Asimetri Informasi Pada Pembiayaan Pemilikan Rumah Di Bank Syari'ah Mandiri

Development Bank of Shari'ah is currently experiencing rapid growth. However this has not been accompanied by an understanding of the community of the operational system and the Shari'ah banking products. So also with the implementation of the product, it still has things that are Shari'ah can be debated. Particularly on financing products, is seen still there applying the asymmetry of information legally muamalah Islam is not permitted. Therefore, the need to study in-depth research for the sake of improvement of the system and the governance of Shari'ah banking that comply with Islamic jurisprudence. This article examines about things related to the asymmetry of information on financing products possession with the Akad Murabaha

Peran Perpustakaan Sekolah terhadap Proses Belajar Mengajar di Sekolah

Dalam dunia pendidikan, buku terbukti berdaya guna dan bertepat guna sebagai salah satu saranapendidikan dan sarana komunikasi. Dalam kaitan inilah perpustakaan dan pelayanan perpustakaanharus dikembangkan sebagai salah satu institusi untuk mewujudkan tujuan mencerdaskankehidupan bangsa. Perpustakaan merupakan bagian yang vital dan besar pengaruhnya terhadapmutu pendidika

Performance of modified non-linear shooting method for simulation of 2nd order two-point BVPS

In this research article, numerical solution of nonlinear 2nd order two-point boundary value problems (TPBVPs) is discussed by the help of nonlinear shooting method (NLSM), and through the modified nonlinear shooting method (MNLSM). In MNLSM, fourth order Runge-Kutta method for systems is replaced by Adams Bashforth Moulton method which is a predictor-corrector scheme. Results acquired numerically through NLSM and MNLSM of TPBVPs are discussed and analyzed. Results of the tested problems obtained numerically indicate that the performance of MNLSM is rapid and provided desirable results of TPBVPs, meanwhile MNLSM required less time to implement as comparable to the NLSM for the solution of TPBVPs

Integration of a big data emerging on large sparse simulation and its application on green computing platform

The process of analyzing large data and verifying a big data set are a challenge for understanding the fundamental concept behind it. Many big data analysis techniques suffer from the poor scalability, variation inequality, instability, lower convergence, and weak accuracy of the large-scale numerical algorithms. Due to these limitations, a wider opportunity for numerical analysts to develop the efficiency and novel parallel algorithms has emerged. Big data analytics plays an important role in the field of sciences and engineering for extracting patterns, trends, actionable information from large sets of data and improving strategies for making a decision. A large data set consists of a large-scale data collection via sensor network, transformation from signal to digital images, high resolution of a sensing system, industry forecasts, existing customer records to predict trends and prepare for new demand. This paper proposes three types of big data analytics in accordance to the analytics requirement involving a large-scale numerical simulation and mathematical modeling for solving a complex problem. First is a big data analytics for theory and fundamental of nanotechnology numerical simulation. Second, big data analytics for enhancing the digital images in 3D visualization, performance analysis of embedded system based on the large sparse data sets generated by the device. Lastly, extraction of patterns from the electroencephalogram (EEG) data set for detecting the horizontal-vertical eye movements. Thus, the process of examining a big data analytics is to investigate the behavior of hidden patterns, unknown correlations, identify anomalies, and discover structure inside unstructured data and extracting the essence, trend prediction, multi-dimensional visualization and real-time observation using the mathematical model. Parallel algorithms, mesh generation, domain-function decomposition approaches, inter-node communication design, mapping the subdomain, numerical analysis and parallel performance evaluations (PPE) are the processes of the big data analytics implementation. The superior of parallel numerical methods such as AGE, Brian and IADE were proven for solving a large sparse model on green computing by utilizing the obsolete computers, the old generation servers and outdated hardware, a distributed virtual memory and multi-processors. The integration of low-cost communication of message passing software and green computing platform is capable of increasing the PPE up to 60% when compared to the limited memory of a single processor. As a conclusion, large-scale numerical algorithms with great performance in scalability, equality, stability, convergence, and accuracy are important features in analyzing big data simulation

Association of interest, attitude and learning habit in mathematics learning towards enhancing students’ achievement

Mathematics is fundamentally important for Science and Technology, as well as in engineering. Mathematics is compulsory for students since all engineering subjects were Mathematically oriented. However, the preliminary study found that students’ achievement in Mathematics courses have been associated with three main factors, namely interest, attitude and learning habit, as in the KASH Model (Knowledge, Attitude, Skills and Habits). This Model stipulated that poor performance is not just lacking in knowledge and skills but also including poor attitude and habits. Therefore, this study aims to investigate the students’ level and relationship between interest, attitude and learning habit based on KASH Model. A total of 58 students were selected as a sample of the study, who enrolled in the Thermodynamics, Fluid Mechanics and Solid Mechanics subjects. A set of questionnaires with 21 items was used to collect data; a descriptively analysis was used to find the mean and percentage, as well as correlation index using Pearson. The results; high level of factor of interest, attitude and learning habit, and high correlation between interest, attitude and habit. The implication is that teaching and learning process must equally fostering all these variables to achieve a high level of students’ achievement, especially in Mathematics subjects

Bio-polishing sludge adsorbents for dye removal

The objective of this work is to evaluate the removal of methylene blue dye by bio-polishing sludge-based adsorbents. The adsorbents were characterized according to the specific surface area, pH upon the treatment and surface functional groups. The adsorption of dye was carried out at room temperature, and the adsorption data were analyzed using the isotherm and kinetics models. The bio-polishing sludge is rich in ash content, and the presence of surface functional groups varied with the treatment strategies. The specific surface area of adsorbents is between 7.25 and 20.8 m2/g. Results show that the maximum removal of methylene blue by sludge adsorbents was observed to have the following order: untreated sludge (SR) > zinc chloride-treated (SZ) > microwave-dried (SW) = potassium carbonate-treated (SK) > acid-washed (SH). The maximum adsorption capacities for SR and SZ as predicted by the Langmuir model are 170 and 135 mg/g, respectively. Although SR demonstrates a higher maximum removal than SZ, the latter exhibits greater removal intensity and rate constant even at high dye concentration. The bio-polishing sludge is a promising adsorbent for dye wastewater treatment

Integration of a big data emerging on large sparse simulation and its application on green computing platform

The process of analyzing large data and verifying a big data set are a challenge for understanding the fundamental concept behind it. Many big data analysis techniques suffer from the poor scalability, variation inequality, instability, lower convergence, and weak accuracy of the large-scale numerical algorithms. Due to these limitations, a wider opportunity for numerical analysts to develop the efficiency and novel parallel algorithms has emerged. Big data analytics plays an important role in the field of sciences and engineering for extracting patterns, trends, actionable information from large sets of data and improving strategies for making a decision. A large data set consists of a large-scale data collection via sensor network, transformation from signal to digital images, high resolution of a sensing system, industry forecasts, existing customer records to predict trends and prepare for new demand. This paper proposes three types of big data analytics in accordance to the analytics requirement involving a large-scale numerical simulation and mathematical modeling for solving a complex problem. First is a big data analytics for theory and fundamental of nanotechnology numerical simulation. Second, big data analytics for enhancing the digital images in 3D visualization, performance analysis of embedded system based on the large sparse data sets generated by the device. Lastly, extraction of patterns from the electroencephalogram (EEG) data set for detecting the horizontal-vertical eye movements. Thus, the process of examining a big data analytics is to investigate the behavior of hidden patterns, unknown correlations, identify anomalies, and discover structure inside unstructured data and extracting the essence, trend prediction, multi-dimensional visualization and real-time observation using the mathematical model. Parallel algorithms, mesh generation, domain-function decomposition approaches, inter-node communication design, mapping the subdomain, numerical analysis and parallel performance evaluations (PPE) are the processes of the big data analytics implementation. The superior of parallel numerical methods such as AGE, Brian and IADE were proven for solving a large sparse model on green computing by utilizing the obsolete computers, the old generation servers and outdated hardware, a distributed virtual memory and multi-processors. The integration of low-cost communication of message passing software and green computing platform is capable of increasing the PPE up to 60% when compared to the limited memory of a single processor. As a conclusion, large-scale numerical algorithms with great performance in scalability, equality, stability, convergence, and accuracy are important features in analyzing big data simulation

Efficient Algorithms for Asymptotic Bounds on Termination Time in VASS

Vector Addition Systems with States (VASS) provide a well-known and fundamental model for the analysis of concurrent processes, parameterized systems, and are also used as abstract models of programs in resource bound analysis. In this paper we study the problem of obtaining asymptotic bounds on the termination time of a given VASS. In particular, we focus on the practically important case of obtaining polynomial bounds on termination time. Our main contributions are as follows: First, we present a polynomial-time algorithm for deciding whether a given VASS has a linear asymptotic complexity. We also show that if the complexity of a VASS is not linear, it is at least quadratic. Second, we classify VASS according to quantitative properties of their cycles. We show that certain singularities in these properties are the key reason for non-polynomial asymptotic complexity of VASS. In absence of singularities, we show that the asymptotic complexity is always polynomial and of the form $\Theta(n^k)$, for some integer $k\leq d$, where $d$ is the dimension of the VASS. We present a polynomial-time algorithm computing the optimal $k$. For general VASS, the same algorithm, which is based on a complete technique for the construction of ranking functions in VASS, produces a valid lower bound, i.e., a $k$ such that the termination complexity is $\Omega(n^k)$. Our results are based on new insights into the geometry of VASS dynamics, which hold the potential for further applicability to VASS analysis.Comment: arXiv admin note: text overlap with arXiv:1708.0925