Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Brazilian Auctions for Photovoltaic Projects Using Traditional and Real Option Approaches

The Brazilian government provides incentive mechanisms for photovoltaic projects to improve the power capacity in the country, to enhance the diversification of energy sources and promote cleaner energy production. In energy auctions, the government uses feed-in tariff, offers low-cost sources of financing and offers tax breaks, among other initiatives to stimulate investment in photovoltaic projects. This paper uses traditional and real option approaches to evaluate the mechanism of feed-in tariff in the auctions and the access to sources of financing with lower costs. The evaluation focuses on a specific restrictive clause in the contract for the winners of the energy auctions. Based on the financial evaluation the paper shows that more flexibility in the contract for auction winners may result in reduced bid prices in the auction, i.e. a lower feed-in-tariff incentive or a reduction in the need for credit lines from the Brazilian financial agency.
Issues and Challenges in Big Graph Modelling for Smart City: An Extensive Survey

The recent evolution in highly connected data has developed new research paradigms in efficient storage and fast retrieval. Graph databases naturally model the relationships among data and hence perform efficient while representing exponentially growing complex network of relationships. Management of graph database has three prominent challenges with respect to data models, storage models and indexing models. In this paper, an extensive survey on widely used techniques to deal with these challenges along with their parametric comparisons based upon various identified parameters is presented. This paper intends to deliver a state-of-the-art of popular approaches to logically organize the big graphs along with physical storage designing and developing an optimized indexing mechanism. In addition, the strengths and weaknesses of each approach are discussed with precise emphasis on certain application domains.
Assessment of Internalization of Iron in Molday Ion Rhodamine B Labelled Ovine Adipose Derived Mesenchymal Stem Cells

Ovine adipose derived mesenchymal stem cells (oADMSCs) are isolated, expanded and passaged up to passage 6. In-vitro cultured cells at passage 4, 5 and 6 were used for Molday Ion Rhodamine-B (MIRB) (25μl/ml concentration) labelling and the internalization of iron was assessed 72 hrs after labelling. Quantification was done by colorimeteric iron assay using 10M HCl and 5 per cent Pottasium Ferrocyanide. In labelled oADMSCs, the quantity of iron is 17-18.2pg of Fe/oADMSCs. In this concentration viability and differentiation potential of labelled oADMSCs was not affected. The results of this study infer that MIRB can be used for labeling the oADMSCs for tracking Mesenchymal stem cells without affecting its viability.
Re-Ranking Feature Selection Algorithm for Detecting the Availability and Requirement of Resources Tweets during Disaster

Social media is an essential source of information during a disaster. The number of posts is increased on social media during a disaster. It includes infrastructure damage, availability, requirement of resources and also non-related to the target class. Detecting the target class such as availability and requirement of resources is a challenging task among the posts. Most of the prior works focused on detection of the earthquake events during a disaster. However, it includes more classes and they are not focused on the specifically availability and requirement of resources. To address this problem, re-ranking feature selection algorithm is proposed using χ2-statistic feature selection and maximum frequency for detecting the availability and requirement of resources. It is tested on the real-time Nepal earthquake tweet dataset. The proposed re-ranking feature selection algorithm with standard baselines such as BOW model, χ2-statistic feature selection and Maximum frequency is compared. The proposed re-ranking feature selection algorithm outperforms the standard baseline in terms of precision for detecting the availability and requirement of resources during a disaster.
Hydrological Modelling of Surface Runoff for Temengor Reservoir Using GR4H Model.

Hydrological models are mainly used for predicting system behavior and understanding various hydrological processes, whereby, various parameters that define the characteristics of the model were used. In this study, a conceptual rainfall runoff model Génie Rural à 4 paramétres Horaires (GR4H) was developed for Temengor catchment to provide an estimate of the inflow contribution and lake level of Temengor Reservoir. Inflow data was calculated from the differences in the lake storage at each time step and accounting for the discharge from the power station. Since the lake level data at sub-daily time scale was too noisy to result in reasonably smooth inflow series data, denoising technique using filtering methods in both time and frequency domains for inflow data were applied. The model was calibrated and validated using 3 years rainfall data from 5 stations. The model performance with the optimum parameters value was evaluated based on the regression coefficient and Nash Sutcliffe Efficiency. It can be seen that the GR4H model are able to perform well in simulating the amount of inflow into the Temengor reservoir.
International Conference on Sustainable Computing in Science, Technology & Management (SUSCOM-2019)

There is an emerging attention for the various automatic classification frameworks to distinguish characters in software structure when information is looked over piece of paper records as it is noticed that the amount of daily identifications and records which are in published manner recognized with various businesses. This is popularly known as the document image exploration. To utilize Optical Character Recognition efficiently for character categorization so as to achieve image analysis, the data is utilizing in Grid assemblies. For high processing of documents, the heavy industries require a product as the framework which is known as character recognition classification. Also, there is need to create character recognition programming agenda to achieve document analysis in the form of image processing which deals with the organization of the material to the electronic reading arrangement. So this paper deals with the efficient learning approach which deals with the automatic classification from using processing of the images and machine learning for the optical character recognition process. In this research we have worked on the feature extraction using Independent component analysis with the swarm intelligence approach with Firefly algorithm, because it helps us to reduce more error probabilities and reduces the false positive and negative rates and increase the high learning rate. The learning is achieved using neural network. It is noticed that the proposed approach is able to perform high in terms of high specificity, sensitivity and recognition rate through which it is noticed that the proposed approach is able to achieve high true positive and true negative rates. © 2019SUSCOM. Hosting by Elsevier SSRN. All rights reserved. Peer review under responsibility of International Conference on Sustainable Computing in Science, Technology and Management.
EBET Agency Requested to Review All Pervious Study Based on DOI Theory and Toe Framework

EBET Agency's [www.ebetagency. com and www.ebet. agency], first trusted casino software in world accepted the cloud computing in their sport betting software in past decade, recently their adopt cloud computer in all part of their casino products such as poker, black jack, crash, backgammon and all sport betting and other popular casino game. This study requested by the CEO of EBET Agency Company, which was supported by grants from EBET Agency Company (Approximately 2,500,000$) to improve all the infrastructure in their casino software by using DOI theory and TOE framework. In this regard, we use EBET Agency’ as case study, and on the other hand, this company the first one in world which try to adoption of cloud computing in the world, which integrated all products and services regarding the bet and casino industry. In the next section of this article, we explained the EBET Agency, in addition we evaluated all pervious study which related to innovation adoption and respectively found all the critical success factors, which helped to propose hypothesis of the research as well as created an adoption model to accept cloud computing in EBET Agency Company.
Analysing Demand and Supply Projects on a Mine Compressed Air System

The cost of electricity in South Africa has been increasing at rates that are significantly higher than inflation. Eskom most recently proposed increases of up to 15%. Various methods have already been introduced to reduce energy consumption at different types of mining operations. Efficient energy use has been and will continuously be a big focus going into the 4th industrial revolution (4IR). As found from literature, compressed air systems are one of the largest energy consumers at typical South African mines. This paper focuses on improvements on mine compressed air systems and limiting factors of reducing said energy consumption on an already optimized mine. Through literature, it was found that specific energy can be used to analyse compressors. This method was used to characterise compressor systems and isolate areas where improvement projects may have a significant effect on a given mine’s compressor energy consumption. The methods for proposing and implementing further projects are discussed with illustration thereof as well as the corresponding effects. Current initiatives, if sustained may result, in as much as R4.5 million per annum in energy savings. A properly analysed system combined with well tailored, investigated and implemented projects can lead to further energy savings of up to R20 million per annum.
A Rapid Fabrication Technique for Self-Assembled Collagen Based Multicellular, Heterogeneous, 3D Tissue Constructs

Although monolayer cell culture models are considered to be the gold standard for <i>in vitro</i> modeling of pathophysiological events, they cannot reconstruct <i>in vivo</i> like gradient of gases and nutrients and lack proper cell-cell and cell-matrix interactions. Spherical cellular aggregates, otherwise known as multicellular spheroids, are widely used as three-dimensional <i>in vitro</i> models to mimic natural <i>in vivo</i> cellular microenvironment for applications such as drug screening. Although very useful, the previously established techniques are limited to low cell numbers, usually slow, and sometimes have limitations in terms of the cell type that can be used. Here, a versatile technique based on rapid self-assembly of cells and extracellular matrix materials and shapes using microfabricated molds is introduced to form multicellular tissue constructs. The self-assembly process takes less than 6 hrs and produces a mechanically robust tissue construct that could be handled easily. We demonstrate that a variety of shapes including spherical, cuboidal, dumbbell- and cross-like could be fabricated using this approach. Interestingly, the structures formed with non-spherical shapes were able to retain that shape even after removal from the molds and during long term cell culture. This versatile approach is applicable to a variety of cell types (breast cancer cell lines MCF-7, MDA-MB-321, Hs-578T; osteosarcoma cell line SaOS-2; endothelial cell line HUVEC) as well as a range of cell numbers (10<sup>4</sup>-10<sup>6</sup>). Furthermore, we also show that the constructs could be spatially patterned to position various cell types in a precisely controlled way. Such heterogeneous constructs that are formed provide physiologically relevant cell densities, 3D structure as well as close positioning of multiple types of cells that are not possible using other fabrication approaches. This fabrication approach will find significant applications in developing 3D cell culture models for drug discovery as well as tissue grafts for implantation.
An Evaluation of Constituents in Paste for Silicon Solar Cells with Floating Contact Method: A Case Study of Tellurium Oxide Effects

Metallization contacts on emitter of silicon solar cells cause shunting of p-n junction and induce carrier recombination, resulting in a loss in open-circuit voltage Voc of the cells, which must severely limit cell efficiency. But, the mechanism of the shunting and the recombination has not been understood enough yet, especially on the contacts with conductive paste, because the shunting and the recombination caused by the paste come of multiple effects of each constituent in the paste such as silver metal, glass frit, and some additives. Thus, the effects of the each constituent in the paste should be clarified to elucidate the mechanism and to achieve an advanced paste for high-efficiency silicon solar cells. In our previous study, effects of aluminum in silver/aluminum paste and effects of glass frit itself on the shunting and the recombination in n-type solar cells were clearly shown by using “floating contact method”, and its effectiveness on the paste evaluation were demonstrated in the solar cells. In this study, the floating contact method was applied to conventional p-type solar cells with silver paste to clarify effects of tellurium oxide on the silver paste, because the detailed effects of the tellurium oxide on the shunting and the recombination have not been clear yet even though the oxide is widely used for commercial paste. Our study clearly shows that the tellurium oxide reduces the shunting and the recombination caused by the silver paste in the solar cells, and also demonstrates the effectiveness of the floating contact method on the paste evaluation.
Hydrogen Effect on the Electron Structure and Grain Boundary Mobility in the Alpha-Iron

The hydrogen-caused change in the electron structure of the alpha-iron along with hydrogen effect on the mobility of grain boundaries has been studied using the first-principles atomic calculations and molecular dynamics. It is shown that hydrogen entry into the alpha-iron crystal lattice increases the density of electron states at the Fermi level, which suggests the increase in the concentration of free electrons. The studied spatial distribution of electron density gives the evidence for corresponding enhancement of the metallic character of interatomic bonds. The obtained result is consistent with the electron approach to the hypothesis of hydrogen-enhanced localized plasticity (HELP) for a mechanism of hydrogen embrittlement of the iron-based alloys. Using the molecular dynamics simulation, it is obtained that hydrogen segregation at the grain boundaries increases their mobility. This effect is attributed to the hydrogen-increased concentration of free electrons, which weakens rigidity of interatomic bonds along with the decrease of the grain boundary surface tension.
Project Carbon

Security is an important and serious issue for every type of network. Many network environments, specially those where computers are used as nodes are prone to an increasing number of security threats in the form of Trojan worm attacks and viruses that can damage the computer systems, servers and communication channels. Though Firewalls are used as a necessary security measure in a network environment but still different types of security issues keep on arising. In order to further strengthen the network from intruders, the concept of intrusion detection system (IDS) and intrusion prevention system (IPS) is gaining popularity. IDS is a process of monitoring the events occurring in a computer system or network and analyzing them for signs of possible incidents, which are violations or imminent threats or violations of computer security policies or standard security policies. Intrusion prevention system (IPS) is a process of performing intrusion detection and attempting to stop detected possible incidents. The main purpose of Intrusion Detection Systems (IDS) and Intrusion protection Systems (IPS) is to discover patterns of program and user activity, and determine what set of events indicate an attack. In the last years, the networking revolution has finally come of age. More than ever before, we see that the Internet is changing computing as we know it. The possibilities and opportunities are limitless; unfortunately, so too are the risks and chances of malicious intrusions. In Network Security, intrusion detection and prevention system is the act of detecting activity or action that attempt to compromise the confidentiality, integrity or availability of a resource. This paper presents an overview of the technologies and methodologies used in Network Intrusion Detection and Prevention Systems (NIDPS). <br> <br>Intrusion Detection and Prevention System (IDPS) technologies are differentiated by the types of events that IDPSs can recognize, by types of devices that IDPSs monitor and by activity. NIDPSs monitor and analyze the streams of network packets in order to detect security incidents. The main methodology used by NIDPSs is protocol analysis. Protocol analysis requires good knowledge of the theory of the main protocols, their definition, how each protocol works. <br> <br>
Power Aware Scheduling in Cloud using Ant Colony Optimization

In data centres all the virtual machines (VM) are hosted on the physical server, so there is a need for a large amount of energy for maintaining the servers. As the number of physical servers and resources such as power and storage are increased, consumption of the massive amount of electrical power at cloud data centres occurs and due to this, the more economical cost is also required. In this paper, we focused on minimizing the total power consumption of a data centre taking consideration of different parameters like IT equipment power consumption and cooling power consumption in cloud computing. Furthermore, for the same purpose, ant colony optimization (ACO) is used to provide the best mapping of VM to the server by optimizing the power consumption of the data centre. Simulation Result shows that the proposed algorithm performed better than the min-min algorithm.
Overview of Carbon Capture and Storage (CCS) Facilities Globally

The Carbon Capture and Storage (CCS) Facilities Database maintained by the Global CCS Institute contains a comprehensive list of CCS facilities around the world, covering large-scale, pilot and demonstration scale facilities and test centres. This report presents a complete overview of the status of global CCS facilities. The review spans the full life cycle of the facilities, from early development to in completion, and covers a wide range of industries and sectors. Globally, as of September 2018, there are 23 large-scale CCS facilities in operation and under construction, capturing ~ 40 million tonnes per annum (Mtpa) of CO2. There are over 28 key demonstration-scale CCS facilities in operation and under construction. These demonstration-scale CCS facilities have a cumulative CO2 capture capacity of over 3 Mtpa. <br><br>Early CCS facilities in the 1970s and 1980s involved processes in which CO2 was already routinely separated in a high purity form, such as in natural gas processing and fertiliser production for CO2 for enhanced oil recovery (EOR). The technology and techniques derived from these early operations have significantly advanced the deployment of CCS. Nowadays, the portfolio of CCS facilities covers a diverse range of sectors, including power generation, iron and steel manufacturing, cement production, chemical and hydrogen production, and bioenergy. Consistent learning from the existing portfolio of large-scale and demonstration-scale facilities, when combined with rising global R&amp;D efforts and commitments to emission reductions, can lower costs further and drive global CCS deployment using next generation and transformational technologies. <br><br>Since 1972, more than 230 million tonnes of anthropogenic CO2 has been captured and injected into deep storage complexes. Over half of that CO2 is concentrated in the United States and mostly in CO2-EOR operations. It is critical to maintain the momentum by growing the pipeline of facilities in development. That pipeline currently includes six large-scale facilities in advanced development and 14 in early development. However, the current pipeline of large-scale CCS deployment does not go anywhere near the envisaged role of CCS to meet the Paris Agreement climate goals. To avoid dangerous climate change, CCS must be deployed across a broad range of industrial processes, particularly in regions that are heavily reliant on fossil fuels, where CCS can deliver on emission reductions and meanwhile meet economic development and energy security goals. <br>
Object Detection Proposals: Evaluation of Image Processing Algorithms for Detecting Airborne Fungal Spores in Microscopic Images

The present research project studies the application of software algorithms for detecting object proposals in digital images based on their graphical characteristics. The fast generation of accurate object proposals is widely seen as a promising solution to the efficiency problems of heavy classification and object recognition algorithms, which require long time and huge computational power when applied on entire images. Instead, object detection proposals (ODPs) with smaller sizes and focused content are an optimum alternative for such classification algorithms. This report provides a systematic review and bench-marking of the state-of-the-art ODP algorithms. It further introduces a novel algorithm “Smart-Superpixels”, specifically developed for the purpose of airborne fungal spores’ detection in microscopic images. The bench-marking considers several qualitative and quantitative criteria regarding the algorithms speed, spatial efficiency, recall accuracy, localization precision and redundancy. The bench-marking results show that the introduced algorithm: Smart-Superpixels has the highest overall performance with its fast operation, relatively high spatial efficiency, high recall accuracy and localization precision, as well as low redundancy.
Approaches of Earthquake Magnitude Prediction Using Machine Learning Techniques

The prophecy of an earthquake is a very complicated task which has been resolved in different ways. However, there is a need for an elementary definition along with adequate catalogues. These areas have been marked with widely used geological parameters. Total characteristics of study are based on not only the training and testing data sets but also qualitative data about earthquake prediction. Machine learning techniques are applied separately to model the relationship between calculated seismic data and future earthquake occurrences. This paper provides a survey report on the implementation of different techniques for Earthquake Magnitude Prediction along with their advantages and drawbacks which will help in the further development and improvement of the system.
Investigation of Puea in Cognitive Radio Networks Using Energy Detection in Different Channel Model

Primary User Emulation Attack (PUEA) is one of the major threats to the spectrum sensing in cognitive radio networks. This paper studies the PUEA using energy detection that is based on the energy of the received signal. It discusses the impact of increasing the number of attackers on the performance of secondary user. Moreover, studying how the malicious user can emulate the Primary User (PU) signal is made. This is the first analytical method to study PUEA under a different number of attackers. The detection of the PUEA increases with increasing the number of attackers and decreases when changing the channel from log-normal to Rayleigh fading. <br>