Grha Lansia Dan Anak Yatim Piatu Di Surabaya

— Grha Lansia dan Anak Yatim Piatu adalahsebuah fasilitas hunian dan berkumpul bagi lansia dananak yatim piatu di Surabaya. Dengan kapasitas 100lansia dan 60 anak-anak, tempat ini dilengkapi dengantempat bagi para lansia dan anak-anak untukmengembangkan hobi mereka. Khusus untuk lansia,dilengkapi dengan fasilitas olah raga dan treatmentberupa gym, salon, jogging track, spa, dan klinikdengan area fisioterapi bagi mereka yang mulaimengalami hambatan fisik. Bangunan dibentuk sepertiterpisah sehingga banyak menciptakan ruang luar diantara bangunan, sekaligus ditata sehinggamenimbulkan kesan mengundang pada bagiandepannya. Tampak bangunan dibuat seperti tampakperumahan pada umumnya, dengan banyak memainkanbidang geometri

Penentuan Konstanta A dan K dalam Persamaan Mark-Houwink- Sakurada (MHS) untuk Menentukan Massa Molekul Poli (Asam Laktat) Diol

Poli(asam laktat) diol (PLA-OH) telah disintesis melalui reaksi polimerisasi kondensasi asam laktat dan 1,4-butanadiol. Berat molekul rata-rata (Mn, Mw, dan Mz) PLA-OH ditentukan dengan analisis menggunakan Gel Permeation Chromatography (GPC). Viskositas intrinsiknya diukur pada konsentrasi 0,2 g/dL dan temperatur 298 K menggunakan pelarut kloroform. Melalui metode numerik berhasil ditentukan nilai a dan K dalam persamaan Mark- Houwink-Sakurada untuk PLA-OH, yaitu [η] = 3,532 x 10-4Mv 0,628 = 3,532 x 10-4qMHS 0,628 = 3,415 x 10-4Mw 0,62

Optimization of Alginate-Based Encapsulation Utilization For Viability and Stability of The Mesenchymal Stem Cell

In the past few decades, attention and research in the field of stem cell are progressing very rapidly. Hospitals in Indonesia have been using stem cells as an alternative to cure some illnesses like diabetes, heart disease, fractures and joints, dental implants, etc. Currently, adult stem cells can be obtained not only from the spinal cord and peripheral vessels, but also from fat tissues of the human body, where it can be isolated as adherent stem cells (mesenchymal stem cells). Consideration of fat tissue as the source of mesenchymal stem cells (MSCs) for autologous tissue engineering is because they are readily available in abundant quantities through minimal invasive procedures, as well as easily cultured and propagated. It is possible to proliferate and differentiate into the desired direction of the network. Stem cell growth requires conditions to grow such as requiring optimum growing conditions such as an environmental temperature of 37°C and a concentration of 5% CO2. Maintenance of MSCs also requires a subculture process, i.e. the process of moving MSCs from a full culture medium to new media; continuous subculture process can cause changes in MSCs. The viability of stem cells may be disrupted by micro-conditions in wounds such as hypoxia, oxidative stress, and inflammation. Therefore, the purpose of this research was to investigate whether alginate-based encapsulation can increase and maintenance stem cell growth at different temperature by using some concentration of alginate and CaCl2 as the formula. Results shown that alginat with low concentration and CaCl2 100mM is suitable for MSCs growth (as in MTT result shown) at 25°C temperature. This can be due to the MSCs encapsulated can adapt and grow within the alginate microcapsule with low concentration. In addition, the media may also easier to get into the microcapsule alginate

Data assimilation in slow-fast systems using homogenized climate models

A deterministic multiscale toy model is studied in which a chaotic fast subsystem triggers rare transitions between slow regimes, akin to weather or climate regimes. Using homogenization techniques, a reduced stochastic parametrization model is derived for the slow dynamics. The reliability of this reduced climate model in reproducing the statistics of the slow dynamics of the full deterministic model for finite values of the time scale separation is numerically established. The statistics however is sensitive to uncertainties in the parameters of the stochastic model. It is investigated whether the stochastic climate model can be beneficial as a forecast model in an ensemble data assimilation setting, in particular in the realistic setting when observations are only available for the slow variables. The main result is that reduced stochastic models can indeed improve the analysis skill, when used as forecast models instead of the perfect full deterministic model. The stochastic climate model is far superior at detecting transitions between regimes. The observation intervals for which skill improvement can be obtained are related to the characteristic time scales involved. The reason why stochastic climate models are capable of producing superior skill in an ensemble setting is due to the finite ensemble size; ensembles obtained from the perfect deterministic forecast model lacks sufficient spread even for moderate ensemble sizes. Stochastic climate models provide a natural way to provide sufficient ensemble spread to detect transitions between regimes. This is corroborated with numerical simulations. The conclusion is that stochastic parametrizations are attractive for data assimilation despite their sensitivity to uncertainties in the parameters.Comment: Accepted for publication in Journal of the Atmospheric Science

Estimating model evidence using data assimilation

We review the field of data assimilation (DA) from a Bayesian perspective and show that, in addition to its by now common application to state estimation, DA may be used for model selection. An important special case of the latter is the discrimination between a factual model–which corresponds, to the best of the modeller's knowledge, to the situation in the actual world in which a sequence of events has occurred–and a counterfactual model, in which a particular forcing or process might be absent or just quantitatively different from the actual world. Three different ensemble‐DA methods are reviewed for this purpose: the ensemble Kalman filter (EnKF), the ensemble four‐dimensional variational smoother (En‐4D‐Var), and the iterative ensemble Kalman smoother (IEnKS). An original contextual formulation of model evidence (CME) is introduced. It is shown how to apply these three methods to compute CME, using the approximated time‐dependent probability distribution functions (pdfs) each of them provide in the process of state estimation. The theoretical formulae so derived are applied to two simplified nonlinear and chaotic models: (i) the Lorenz three‐variable convection model (L63), and (ii) the Lorenz 40‐variable midlatitude atmospheric dynamics model (L95). The numerical results of these three DA‐based methods and those of an integration based on importance sampling are compared. It is found that better CME estimates are obtained by using DA, and the IEnKS method appears to be best among the DA methods. Differences among the performance of the three DA‐based methods are discussed as a function of model properties. Finally, the methodology is implemented for parameter estimation and for event attribution

SENYAWA KALKON BARU BERSIFAT ANTI-BAKTERI DARI TUMBUHAN CRYPTOCARYA COSTATA (LAURACEAE)

Suatu kalkon telah diisolasi dari kulit batang Cryptocarya costata. Isolat diperoleh dari fraksi kloroform, setelah fraksinasi dengan teknik kromatografi yang dilanjutkan dengan rekristalisasi dalam heksana dan etilasetat, dihasilkan kristal jarum berwarna kuning dengan titik leleh 167-169oC. Elusidasi struktur isolat berdasarkan spektrum UV, IR, 1 D dan 2D NMR, maka dapat ditetapkan bahwa senyawa isolat adalah 2’,4’-Dihidroksi-3’,6’-dimetoksicalkon. Uji sitotoksik terhadap E.coli, menunjukkan aktivitas positif dengan nilai hambat 35,4 %, dan pertama kali ditemukan dari tumbuhan Cryptocarya Kata kunci : Kalkon, Sitotoksik, Cryptocarya costat

Evaluating Data Assimilation Algorithms

Data assimilation leads naturally to a Bayesian formulation in which the posterior probability distribution of the system state, given the observations, plays a central conceptual role. The aim of this paper is to use this Bayesian posterior probability distribution as a gold standard against which to evaluate various commonly used data assimilation algorithms. A key aspect of geophysical data assimilation is the high dimensionality and low predictability of the computational model. With this in mind, yet with the goal of allowing an explicit and accurate computation of the posterior distribution, we study the 2D Navier-Stokes equations in a periodic geometry. We compute the posterior probability distribution by state-of-the-art statistical sampling techniques. The commonly used algorithms that we evaluate against this accurate gold standard, as quantified by comparing the relative error in reproducing its moments, are 4DVAR and a variety of sequential filtering approximations based on 3DVAR and on extended and ensemble Kalman filters. The primary conclusions are that: (i) with appropriate parameter choices, approximate filters can perform well in reproducing the mean of the desired probability distribution; (ii) however they typically perform poorly when attempting to reproduce the covariance; (iii) this poor performance is compounded by the need to modify the covariance, in order to induce stability. Thus, whilst filters can be a useful tool in predicting mean behavior, they should be viewed with caution as predictors of uncertainty. These conclusions are intrinsic to the algorithms and will not change if the model complexity is increased, for example by employing a smaller viscosity, or by using a detailed NWP model