Chemical characterization of rainwater at Akkalkuwa, India

The chemical composition of rainwater changes from place to place and region to region under the influence of several major factors, viz., topography, its distance from sea and overall rainfall pattern. The present study investigated the chemical composition of precipitation at Akkalkuwa, district Nandurbar, in the State Maharashtra during southwest monsoon. The rainwater samples were collected on event basis during June-September 2008 and were analyzed for pH, major anions (F, Cl, NO3, SO4) and cations (Ca, Mg, Na, K, NH4). The pH varied from 6.0 and 6.8 with an average of 6.29 ± 0.23 indicating alkaline nature and dominance of Ca in precipitation. The relative magnitude of major ions in precipitation follows the pattern as Ca>Cl>Na>SO4>NO3>HCO3>NH4>Mg>K>F>H. The Neutralization factor (NF) was found to be NFCa = 0.95, NFNH4 = 0.31, NFMg = 0.27 and NFK = 0.08 indicating below cloud process in which crustal components are responsible for neutralization of anions. Significant correlation of NH4 with SO4 and NO3 was observed with correlation coefficient of r = 0.79 and 0.75, respectively.

Design of Educational Learning Management Cloud Process with Blockchain 4.0 based E-Portfolio

The development of technology in education has produced many innovations to support the learning process. The large variety of learning media is the digital competence of education, which is very much needed in higher education . However, from several universities, there are still many that do not have adequate digital competence. This study uses a survey method that will be calculated quantitatively through a Likert Scale calculation. The design of this cooperative education management system uses E-Portfolio Integration stored in cloud computing based on Blockchain technology. The Blockchain process will be helpful as a literary platform for researchers who will start research on Blockchain technology for digital education so that the results of digital competence cannot be taken over by third parties and are transparent, and this is the solution to the problems in this research. Based on the study results, it can be seen that the Student-Centered iLearning Blockchain (SCi-B) with E-Portfolio is supported by Blockchain technology that can meet the needs of universities and improve cooperative education on digital competence.

Cavitation Bubble Cloud Break-Off Mechanisms at Micro-Channels

This paper provides new physical insight into the coupling between flow dynamics and cavitation bubble cloud behaviour at conditions relevant to both cavitation inception and the more complex phenomenon of flow “choking” using a multiphase compressible framework. Understanding the cavitation bubble cloud process and the parameters that determine its break-off frequency is important for control of phenomena such as structure vibration and erosion. Initially, the role of the pressure waves in the flow development is investigated. We highlight the differences between “physical” and “artificial” numerical waves by comparing cases with different boundary and differencing schemes. We analyse in detail the prediction of the coupling of flow and cavitation dynamics in a micro-channel 20 m high containing Diesel at pressure differences 7 MPa and 8.5 MPa, corresponding to cavitation inception and "choking" conditions respectively. The results have a very good agreement with experimental data and demonstrate that pressure wave dynamics, rather than the “re-entrant jet dynamics” suggested by previous studies, determine the characteristics of the bubble cloud dynamics under “choking” conditions.

Multiscale Receptive Fields Graph Attention Network for Point Cloud Classification

Understanding the implication of point cloud is still challenging in the aim of classification or segmentation for point cloud due to its irregular and sparse structure. As we have known, PointNet architecture as a ground-breaking work for point cloud process can learn shape features directly on unordered 3D point cloud and has achieved favorable performance, such as 86% mean accuracy and 89.2% overall accuracy for classification task, respectively. However, this model fails to consider the fine-grained semantic information of local structure for point cloud. Then, a multiscale receptive fields graph attention network (named after MRFGAT) by means of semantic features of local patch for point cloud is proposed in this paper, and the learned feature map for our network can well capture the abundant features information of point cloud. The proposed MRFGAT architecture is tested on ModelNet datasets, and results show it achieves state-of-the-art performance in shape classification tasks, such as it outperforms GAPNet (Chen et al.) model by 0.1% in terms of OA and compete with DGCNN (Wang et al.) model in terms of MA.

PEMANFAATAN FOTO UDARA FORMAT KECIL UNTUK PEMETAAN BIDANG TANAH DI SUB DAS BOMPON

Remote sensing technology such as small format aerial photography (SFAP) from unmanned aerial vehicle (UAV) or drone can be a solution in accelerating the land parcel mapping in Indonesia. This study aims to determine the level of geometric and planimetric accuracy of SFAP data output in land parcel mapping and describe the step of data processing. The results showed that in land parcel mapping, the main SFAP output data is orthophoto. Therefore, the processing should not need to do the dense cloud process, the orthophoto build process can use DEM from sparse cloud for time efficiency and reduce the relief displacement effect. The orthophoto geometry accuracy is CE90 of 0.44, so it is very well used for mapping 1: 2500 scale, while DSM has a LE90 value of 2.80. Planimetry accuracy of the land parcel distance has met the tolerance standard, while planimetry accuracy of the land parcel area there is 1 parcel that does not meet the tolerance standard. However, in general, SFAP can be used as a basis for land parcel mapping in slightly hilly areas such as Bompon watershed.Keywords: Small format aerial photo, land parcel, geometric accuracy, planimetric accuracy Teknologi penginderaan jauh seperti foto udara format kecil (FUFK) dari pesawat nir awak bisa menjadi solusi dalam percepatan pemetaan bidang tanah di Indonesia. Penelitian ini bertujuan untuk mengetahui tingkat ketelitian geometrik dan planimetrik data keluaran FUFK dalam pemetaan bidang tanah serta mendeskripsikan tahapan-tahapan pengolahan datanya. Hasil penelitian menunujukkan bahwa dalam pemetaan bidang tanah, data keluaran FUFK yang utama adalah ortofoto. Oleh karena itu sebaiknya proses pengolahan tidak perlu melakukan proses dense cloud, proses build orthophoto bisa menggunakan DEM dari sparse cloud untuk efisiensi waktu serta mengurangi efek relief displacement. Ketelitian geometri ortofoto yaitu CE90 sebesar 0,44, sehingga sangat baik digunakan untuk pemetaan skala 1:2500, sedangkan DSM memiliki nilai LE90 sebesar 2,80. Ketelitian planimetrik terhadap jarak bidang tanah telah memenuhi standar toleransi, sedangkan ketelitian planimetrik terhadap luas bidang tanah terdapat 1 bidang yang tidak memenuhi standar toleransi. Walaupun begitu, secara umum FUFK bisa digunakan sebagai dasar pemetaan bidang tanah di wilayah yang sedikit berbukit seperti Sub DAS Bompon.Kata Kunci: Foto udara format kecil, bidang tanah, ketelitian geometrik, ketelitian planimetrik.

A Secure Communication using Smart Mobile and Cloud Process

Information storage and security is one of key areas where much research is been done in this digital world where we communicate the data over using third party devices such as cloud by using smart devices such as mobiles so the security is an quite challenging factor where we access our data across the globe and with the social media coming into factor for the storage and accessibility of the data so there are many risk factors coming into process so we need to implement a smart and secure system for the authentication threats so here in this paper we implement a smart system in which face recognition aunthication system is implement between the cloud and mobile activity which give more security in terms of data storage and communication and then we evaluate using different graphs and also analyses the attacks

Void Formation: Does the Void-in-Cloud Process Matter?

We investigate the basic properties of voids from high resolution, cosmological N-body simulations of Λ–dominated cold dark matter (ΛCDM) models, in order to compare with the analytical model of Sheth and van de Weygaert (SvdW) for void statistics. For the subsample of five dark matter simulations in the ΛCDM cosmology with box sizes ranging from 1000h−1Mpc to 8 h−1Mpc, we find that the standard void–in–cloud effect is too simplified to explain several properties of identified small voids in simulations. (i) The number density of voids is found to be larger than the prediction of the analytical model up to 2 orders of magnitude below 1h−1Mpc scales. The Press-Schechter model with the linear critical threshold of void δv = −2.71, or a naive power law, is found to provide an excellent agreement with the void size function, suggesting that the void-in-cloud effect does not suppress as much voids as predicted by the SvdW model. (ii) We then measured the density and velocity profiles of small voids, and find that they are mostly partially collapsing underdensities, instead of being completely crushed in the standard void–in–cloud scenario. (iii) Finally, we measure the void distributions in four different tidal environments, and find that the void–in-void effect alone can explain the correlation between distribution and environments, whereas the void–in–cloud effect is only weakly influencing the abundance of voids, even in filaments and clusters.