Modeling Simulation of COVID-19 in Indonesia based on Early Endemic Data

The COVID-19 pandemic has recently caused so much anxiety and speculation around the world. This phenomenon was mainly driven by the drastic increase in the number of infected people with the COVID-19 virus worldwide. Here we propose a simple model to predict the endemic in Indonesia. The model is based on the Richard's Curve that represents a modified logistic equation. Based on the similar trends of initial data between Indonesia and South Korea, we use parameter values that are obtained through parameter estimation of the model to the data in South Korea. Further, we use a strict assumption that the implemented strategy in Indonesia is as effective as in South Korea. The results show that endemic will end in April 2020 with the total number of cases more than 8000

Reducing Numerical Dispersion with High-Order Finite Difference to Increase Seismic Wave Energy: -

The numerical dispersion of 2D acoustic wave modeling has become an interesting subject in wave modeling in producing better subsurface images. Numerical dispersion is often caused by error accumulation with increased grid size in wave modeling. Wave modeling with high-order finite differences was carried out to reduce the numerical error. This study focused on variations in the numerical order to suppress the dispersion due to numerical errors. The wave equation used in modeling was discretized to higher orders for the spatial term, while the time term was discretized up to the second order, with every layer unabsorbed. The results showed that high-order FD was effective in reducing numerical dispersion. Thus, subsurface layers could be distinguished and observed clearly. However, from the modeling results, the wave energy decreased with increasing distance, so the layer interfaces were unclear. To increase the wave energy, we propose a new source in modeling. Furthermore, to reduce the computational time we propose a proportional grid after numerical dispersion has disappeared. This method can effectively increase the energy of reflected and transmitted waves at a certain depth. The results also showed that the computational time of high-order FD is relatively low, so this method can be used in solving dispersion problems

Reducing Numerical Dispersion with High-Order Finite Difference to Increase Seismic Wave Energy: -

The numerical dispersion of 2D acoustic wave modeling has become an interesting subject in wave modeling in producing better subsurface images. Numerical dispersion is often caused by error accumulation with increased grid size in wave modeling. Wave modeling with high-order finite differences was carried out to reduce the numerical error. This study focused on variations in the numerical order to suppress the dispersion due to numerical errors. The wave equation used in modeling was discretized to higher orders for the spatial term, while the time term was discretized up to the second order, with every layer unabsorbed. The results showed that high-order FD was effective in reducing numerical dispersion. Thus, subsurface layers could be distinguished and observed clearly. However, from the modeling results, the wave energy decreased with increasing distance, so the layer interfaces were unclear. To increase the wave energy, we propose a new source in modeling. Furthermore, to reduce the computational time we propose a proportional grid after numerical dispersion has disappeared. This method can effectively increase the energy of reflected and transmitted waves at a certain depth. The results also showed that the computational time of high-order FD is relatively low, so this method can be used in solving dispersion problems

Efficient Estimation of the Robustness Region of Biological Models with Oscillatory Behavior

Robustness is an essential feature of biological systems, and any mathematical model that describes such a system should reflect this feature. Especially, persistence of oscillatory behavior is an important issue. A benchmark model for this phenomenon is the Laub-Loomis model, a nonlinear model for cAMP oscillations in Dictyostelium discoideum. This model captures the most important features of biomolecular networks oscillating at constant frequencies. Nevertheless, the robustness of its oscillatory behavior is not yet fully understood. Given a system that exhibits oscillating behavior for some set of parameters, the central question of robustness is how far the parameters may be changed, such that the qualitative behavior does not change. The determination of such a “robustness region” in parameter space is an intricate task. If the number of parameters is high, it may be also time consuming. In the literature, several methods are proposed that partially tackle this problem. For example, some methods only detect particular bifurcations, or only find a relatively small box-shaped estimate for an irregularly shaped robustness region. Here, we present an approach that is much more general, and is especially designed to be efficient for systems with a large number of parameters. As an illustration, we apply the method first to a well understood low-dimensional system, the Rosenzweig-MacArthur model. This is a predator-prey model featuring satiation of the predator. It has only two parameters and its bifurcation diagram is available in the literature. We find a good agreement with the existing knowledge about this model. When we apply the new method to the high dimensional Laub-Loomis model, we obtain a much larger robustness region than reported earlier in the literature. This clearly demonstrates the power of our method. From the results, we conclude that the biological system underlying is much more robust than was realized until now

Analisa Investasi Dan Studi Kelayakan Proyek Pembangunan Perumahan Griya Asri Di Karanganyar

Kebutuhan akan pangan, sandang dan papan mutlak pada saat ini sangat diperlukan bagi setiap masyarakat. Salah satunya kebutuhan primer yang di butuhkan masyarakat berupa papan atau rumah. mengikuti perkembangan zaman saat ini tingkat populasi penduduk di karanganyar yang semakin padat yang diimbangi infrastuktur yang baik dan akses jalan yang baik dan memadai, maka usaha properti di daerah ini sangat potensial. Metode Penelitian ini dengan menggunakan kuisioner yang bertujuan untuk mengetahui tingkat perekonomian dan karakteristik dalam kehidupan masyarakat mengenai perbedaan selera/ekspetasi masyarakat akan kebutuhan perumahan. kemudian hal ini dapat membuka peluang bagi pengembang (developer) untuk memberi solusi dalam memenuhi kebutuhan akan hunian bagi masyarakat dan mewujudkanya berupa sebuah perumahan bagi mereka. yang sebelumya memperhatikan kajian beberapa aspek diantaranya aspek lokasi, aspek sosial, aspek lingkungan, aspek pasar, aspek teknis aspek finansial, aspek ekonomi, dan aspek hukum, agar nantinya setelah diterima atau dilaksanakan dapat mencapai hasil yang sesuai dan berjalan lancar dengan apa yang telah direncanakan. Hasil dari perhitungan rencana anggaran biaya yaitu biaya total proyek sebesar Rp. 8.435.159.662 dan estimasi total pendapatan sebesar Rp. 10.865.046.140 dengan umur investasi selama 2 tahun dan bunga 10% pertahun. pada analisa keuangan diperoleh, Net Present Value Rp 1.045.855.711 Internal Rate of Return 14,40%, Benefit Cost Ratio 1,12, Indeks Profitabilitas 1,12 dan Break event point 77,63 % dari penjualan rumah. dari hasil di atas dapat diambil kesimpulan bahwa investasi proyek tersebut dapat diterima

Identifying optimal models to represent biochemical systems.

Biochemical systems involving a high number of components with intricate interactions often lead to complex models containing a large number of parameters. Although a large model could describe in detail the mechanisms that underlie the system, its very large size may hinder us in understanding the key elements of the system. Also in terms of parameter identification, large models are often problematic. Therefore, a reduced model may be preferred to represent the system. Yet, in order to efficaciously replace the large model, the reduced model should have the same ability as the large model to produce reliable predictions for a broad set of testable experimental conditions. We present a novel method to extract an "optimal" reduced model from a large model to represent biochemical systems by combining a reduction method and a model discrimination method. The former assures that the reduced model contains only those components that are important to produce the dynamics observed in given experiments, whereas the latter ensures that the reduced model gives a good prediction for any feasible experimental conditions that are relevant to answer questions at hand. These two techniques are applied iteratively. The method reveals the biological core of a model mathematically, indicating the processes that are likely to be responsible for certain behavior. We demonstrate the algorithm on two realistic model examples. We show that in both cases the core is substantially smaller than the full model

Flow chart of the method to approximate the robustness region around a nominal point .

<p>The approximated region is obtained by scanning the parameter space along orthogonal directions starting at .</p

The EGFR biochemical network.

<p>A solid arrow represents a reaction with two kinetic parameters and a dashed arrow represents a reaction with one kinetic parameter. (A) The full network from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083664#pone.0083664-Kholodenko1" target="_blank">[25]</a>, (B) The optimal network to produce the dynamics of the five target components for any experimental condition in (22).</p

Illustration of an admissible region for a system with two parameters.

<p>Initially, the admissible region of the system is . In this situation, a reduced model can be obtained either by setting or . When a new dataset from a new experiment is incorporated, the admissible region shrinks to . Thus, . Now, a reduced model can only be obtained when .</p