Search results for: data bank

Authors: Mohamed El-Khateeb

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Encompassing inventory, warehousing, and transportation management, logistics is a crucial predictor of firm performance. This has been extensively proven by extant literature in business and operations management. Logistics is also a fundamental determinant of a country's ability to access international markets. Available studies in international and transport economics have shown that limited transport infrastructure and underperforming transport services can severely affect international competitiveness. However, the evidence lacks the overall impact of logistics performance-encompassing all inventory, warehousing, and transport components- on global trade. In order to fill this knowledge gap, the paper uses a gravitational trade model with 155 countries from all geographical regions between 2007 and 2018. Data on logistics performance is obtained from the World Bank's Logistics Performance Index (LPI). First, the relationship between logistics performance and a country’s total trade is estimated, followed by a breakdown by the economic sector. Then, the analysis is disaggregated according to the level of technological intensity of traded goods. Finally, after evaluating the intensive margin of trade, the relevance of logistics infrastructure and services for the extensive trade margin is assessed. Results suggest that: (i) improvements in both logistics infrastructure and services are associated with export growth; (ii) manufactured goods can significantly benefit from these improvements, especially when both exporting and importing countries increase their logistics performance; (iii) the quality of logistics infrastructure and services becomes more important as traded goods are technology-intensive; and (iv) improving the exporting country's logistics performance is essential in the intensive margin of trade while enhancing the importing country's logistics performance is more relevant in the extensive margin.

Keywords: low-cost, recycling, reuse, solid waste, wastewater treatment

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Authors: Zeba Mahmood

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The modern business organizations are adopting technological advancement to achieve competitive edge and satisfy their consumer. The development in the field of Information technology systems has changed the way of conducting business today. Business operations today rely more on the data they obtained and this data is continuously increasing in volume. The data stored in different locations is difficult to find and use without the effective implementation of Data mining and Knowledge management techniques. Organizations who smartly identify, obtain and then convert data in useful formats for their decision making and operational improvements create additional value for their customers and enhance their operational capabilities. Marketers and Customer relationship departments of firm use Data mining techniques to make relevant decisions, this paper emphasizes on the identification of different data mining and Knowledge management techniques that are applied to different business industries. The challenges and issues of execution of these techniques are also discussed and critically analyzed in this paper.

Keywords: knowledge, knowledge management, knowledge discovery in databases, business, operational, information, data mining

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Authors: Adarsh Shroff

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Big data is a collection of dataset so large and complex that it becomes difficult to process using data base management tools. To perform operations like search, analysis, visualization on big data by using data mining; which is the process of extraction of patterns or knowledge from large data set. In recent years, the data mining applications become stale and obsolete over time. Incremental processing is a promising approach to refreshing mining results. It utilizes previously saved states to avoid the expense of re-computation from scratch. This project uses i2MapReduce, an incremental processing extension to Map Reduce, the most widely used framework for mining big data. I2MapReduce performs key-value pair level incremental processing rather than task level re-computation, supports not only one-step computation but also more sophisticated iterative computation, which is widely used in data mining applications, and incorporates a set of novel techniques to reduce I/O overhead for accessing preserved fine-grain computation states. To optimize the mining results, evaluate i2MapReduce using a one-step algorithm and three iterative algorithms with diverse computation characteristics for efficient mining.

Keywords: big data, map reduce, incremental processing, iterative computation

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Authors: Sthembile Mthethwa

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With the rise in the usage of internet in the past decades, this presented an opportunity for users to transact with each other over the use of internet. Cryptocurrencies have been introduced, which allows users to transact with each other without the involvement of a third party i.e. the bank. These systems are widely known as cryptocurrencies or digital currencies and the first system to be introduced was Bitcoin which has been receiving a lot of attention. Bitcoin introduced a new technology known as the blockchain technology. In the past years, blockchain has been getting attention; whereby new applications are introduced that utilize blockchain. Yet, most people are still hesitant about the adoption of blockchain and the adoption of cryptocurrencies at large. Some people still do not understand the technology. Thus, it leads to the slow adoption of this technology. In this paper, a review of the blockchain is provided, whereby the different types of blockchain are discussed in details. Details of the things that contribute to the hindrance of the process of adoption are discussed.

Keywords: bitcoin, blockchain, cryptocurrency, payment system

Procedia PDF Downloads 262

Authors: Jerry Q. Cheng

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Currently, in analyzing large-scale recurrent event data, there are many challenges such as memory limitations, unscalable computing time, etc. In this research, a divide-and-conquer method is proposed using parametric frailty models. Specifically, the data is randomly divided into many subsets, and the maximum likelihood estimator from each individual data set is obtained. Then a weighted method is proposed to combine these individual estimators as the final estimator. It is shown that this divide-and-conquer estimator is asymptotically equivalent to the estimator based on the full data. Simulation studies are conducted to demonstrate the performance of this proposed method. This approach is applied to a large real dataset of repeated heart failure hospitalizations.

Keywords: big data analytics, divide-and-conquer, recurrent event data, statistical computing

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Authors: Benhammadi Hocine, Djamel Boubaya, Chaffai Hicham

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Meskiana Area is characterized by a semi-arid climate where the water supply for irrigation and industry is not sufficient as the priority goes for domestic use. To meet the increasing population growth and development, the authorities have considered building a new water retaining structure on some major temporary water streams. For this purpose Chebabta site on Oued Meskiana was chosen as the future dam site. It is large enough to store the desired volume of water. This study comes to investigate the conditions of the site and the adequacy of the ground as a foundation for the projected dam. The conditions of the site include the geological structure and mainly the presence of discontinuities in the formation on which the dam will be built, the nature of the lithologies under the foundation and the future lake, and the presence of any hazard. This site characterization is usually carried out using different methods in order to highlight any underground buried problematic structure. In this context, the different geophysical technics remain the most used ones. Three geophysical methods were used in the case of the Chebabta dam site, namely, electric survey, seismic refraction, and tomography. The choice of the technics and the location of the scan line was made on the basis of the available geological data. In this sense, profiles have been established on both banks of Oued Meskiana. The obtained results have allowed a better characterization of the geological structure, defining the limit between the surface cover and the bedrock, which is, in other words, the limit between the weathered zone and the bedrock. Their respective thicknesses were also determined by seismic refraction and electrical resistivity sounding. However, the tomography imaging technic has succeeded in positioning a fault structure passing through the right bank of the wadi.

Keywords: dam site, fault, geophysic, investigation, Meskiana

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Authors: Ashiff Khan, A. Seetharaman, Abhijit Dasgupta

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The new era of big data (BD) is influencing chemical industries tremendously, providing several opportunities to reshape the way they operate and help them shift towards intelligent manufacturing. Given the availability of free software and the large amount of real-time data generated and stored in process plants, chemical industries are still in the early stages of big data adoption. The industry is just starting to realize the importance of the large amount of data it owns to make the right decisions and support its strategies. This article explores the importance of professional competencies and data science that influence BD in chemical industries to help it move towards intelligent manufacturing fast and reliable. This article utilizes a literature review and identifies potential applications in the chemical industry to move from conventional methods to a data-driven approach. The scope of this document is limited to the adoption of BD in chemical industries and the variables identified in this article. To achieve this objective, government, academia, and industry must work together to overcome all present and future challenges.

Keywords: chemical engineering, big data analytics, industrial revolution, professional competence, data science

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Authors: M. Sumana, K. S. Hareesha

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Secure computations are essential while performing privacy preserving data mining. Distributed privacy preserving data mining involve two to more sites that cannot pool in their data to a third party due to the violation of law regarding the individual. Hence in order to model the private data without compromising privacy and information loss, secure multiparty computations are used. Secure computations of product, mean, variance, dot product, sigmoid function using the additive and multiplicative homomorphic property is discussed. The computations are performed on vertically partitioned data with a single site holding the class value.

Keywords: homomorphic property, secure product, secure mean and variance, secure dot product, vertically partitioned data

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Authors: M. C. Arcuri, L. Gai, F. Ielasi

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Italian Central Guarantee Fund (CGF) has the purpose to facilitate Small and Medium-sized Enterprises (SMEs)’ access to credit. The aim of the paper is to study the evaluation method adopted by the CGF with regard to SMEs requiring its intervention. This is even more important in the light of the recent CGF reform. We analyse an initial sample of more than 500.000 guarantees from 2012 to 2018. We distinguish between a counter-guarantee delivered to a mutual guarantee institution and a guarantee directly delivered to a bank. We investigate the impact of variables related to the operations and the SMEs on Altman Z’’-score and the score consistent with CGF methodology. We verify that the type of intervention affects the scores and the initial condition changes with the new assessment criterions. 

Keywords: banks, default risk, Italian guarantee fund, mutual guarantee institutions

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Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

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Authors: Vesna Kirandziska, Nevena Ackovska, Ana Madevska Bogdanova

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The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued.

Keywords: emotion recognition, facial recognition, signal processing, machine learning

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Authors: Sartaj Singh, Amar Singh, Ashok Sharma, Sandeep Kaur

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With upcoming threats in a digital world, we need to work continuously in the area of security in all aspects, from hardware to software as well as data modelling. The rise in social media activities and hunger for data by various entities leads to cybercrime and more attack on the privacy and security of persons. Cryptography has always been employed to avoid access to important data by using many processes. Symmetric key and asymmetric key cryptography have been used for keeping data secrets at rest as well in transmission mode. Various cryptosystems have evolved from time to time to make the data more secure. In this research article, we are studying various cryptosystems in asymmetric cryptography and their application with usefulness, and much emphasis is given to Elliptic curve cryptography involving algebraic mathematics.

Keywords: cryptography, symmetric key cryptography, asymmetric key cryptography

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Authors: Teguh Sugiarto, Amir Mohamadian Amiri

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Background: This study aims to explore if there is fraud in the company's financial report distribution using the number first digit Lucas, Fibonacci and Benford. Research methods: In this study, the author uses a number model contained in the first digit of the model Lucas, Fibonacci and Benford, to make a distinction between implementation by using the scale above and below 5%, the rate of occurrence of a difference against the digit number contained on Lucas, Fibonacci and Benford. If there is a significant difference above and below 5%, then the process of follow-up and detection of occurrence of fraud against the financial statements can be made. Findings: From research that has been done can be concluded that the number of frequency levels contained in the financial statements of PT Bank BRI Tbk in a year in the same conscientious results for model Lucas, Fibonacci and Benford.

Keywords: Lucas, Fibonacci, Benford, first digit

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Authors: Shota Gulbani

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At the present stage, for the countries with developing economies, studying, and researching financial markets, gains special importance, because the situation of financial markets shapes an exact views about the carried out economic policy of the country. Besides, it’s unimaginable any country with developed economy, without healthy and functioning financial markets, whereas, for any kind of business it has got a great importance in terms of finding diversified and alternative capital. In this regard; it should be noted that the segments of Georgian financial markets are developed quite unequally, as evidenced by the fact that the Georgian financial sector is represented by 93% of commercial banks, what does not create an conformable environment for non-bank financial institutions development. In spite of the fact that Georgia has got one of the best banking system of region, it is important to properly analyze that this system should not hinder the development of other participants of Georgian financial sector.

Keywords: financial markets, macroeconomics, investments, stock exchange

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Authors: Rong-Terng Juang

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Autonomous vehicles offer the possibility of significant benefits to social welfare. However, fully automated cars might not be going to happen in the near further. To speed the adoption of the self-driving technologies, many governments worldwide are passing laws requiring data recorders for the testing of autonomous vehicles. Currently, the self-driving vehicle, (e.g., shuttle bus) has to be monitored from a remote control center. When an autonomous vehicle encounters an unexpected driving environment, such as road construction or an obstruction, it should request assistance from a remote operator. Nevertheless, large amounts of data, including images, radar and lidar data, etc., have to be transmitted from the vehicle to the remote center. Therefore, this paper proposes a data compression method of in-vehicle networks for remote monitoring of autonomous vehicles. Firstly, the time-series data are rearranged into a multi-dimensional signal space. Upon the arrival, for controller area networks (CAN), the new data are mapped onto a time-data two-dimensional space associated with the specific CAN identity. Secondly, the data are sampled based on differential sampling. Finally, the whole set of data are encoded using existing algorithms such as Huffman, arithmetic and codebook encoding methods. To evaluate system performance, the proposed method was deployed on an in-house built autonomous vehicle. The testing results show that the amount of data can be reduced as much as 1/7 compared to the raw data.

Keywords: autonomous vehicle, data compression, remote monitoring, controller area networks (CAN), Lidar

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Authors: Sonia Rebai, Mohamed Naceur Azaiez, Dhafer Saidane

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In this study, we develop a performance evaluation model based on a multi-attribute utility approach aiming at reaching the sustainable banking (SB) status. This model is built accounting for various banks’ stakeholders in a win-win paradigm. In addition, it offers the opportunity for adopting a global measure of performance as an indication of a bank’s sustainability degree. This measure is referred to as banking sustainability performance index (BSPI). This index may constitute a basis for ranking banks. Moreover, it may constitute a bridge between the assessment types of financial and extra-financial rating agencies. A real application is performed on three French banks.

Keywords: multi-attribute utility theory, performance, sustainable banking, financial rating

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Authors: George-Anokwuru Chioma Chidinma Bernadette

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This paper investigated the effect of trade liberalization on domestic private investment in Nigeria from 1981 to 2020. To achieve this objective, secondary data on domestic private investment, trade openness, exchange rate and interest rate were sourced from the statistical bulletin of Nigeria’s apex bank. The Autoregressive Distributed Lag (ARDL) technique was used as the main analytical tool. The ARDL Bounds test revealed the existence of long run association among the variables. The results revealed that trade openness and exchange rate have positive and insignificant relationship with domestic private investment both in the long and short runs. At the same time, interest rate has negative relationship with domestic private investment both in the long and short runs. Therefore, it was concluded that there is no significant relationship between trade openness, exchange rate, interest rate and domestic private investment in Nigeria during the period of study. Based on the findings, the study recommended that government should formulate trade policies that will encourage the growth of domestic private investment in Nigeria. To achieve this, government should ensure consistency in trade policies and at the same time strengthen the existing policies to build investors’ confidence. Also, government should make available an investment-friendly environment, as well as monitor real sector operators to ensure that foreign exchange allocations are not diverted. Government should increase capital investment in education, housing, transportation, agriculture, health, power, road construction, national defense, among others that will help the various sectors of the economy to function very well thereby making the business environment friendly thereby enhancing the growth and development of the country.

Keywords: trade openness, domestic private investment, ARDL, exchange rate

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Authors: Gajanan M. Sonwane

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The computational studies on 2-phenazinamines with their protein targets have been carried out to design compounds with potential anticancer activity. This strategy of designing compounds possessing selectivity over specific tyrosine kinase has been achieved through G-QSAR and molecular docking studies. The objective of this research has been to design newer 2-phenazinamine derivatives as Bcr-Abl tyrosine kinase inhibitors by G-QSAR, molecular docking studies followed by wet-lab studies along with evaluation of their anticancer potential. Computational chemistry was done by using VLife MDS 4.3 and Autodock 4.2 followed by wet-lab experiments for synthesizing 2-phenazinamine derivatives. The chemical structures of ligands in 2D were drawn by employing Chemdraw 2D Ultra 8.0 and were converted into 3D. These were optimized by using a semi-empirical method called MOPAC. The protein structure was retrieved from RCSC protein data bank as a PDB file. The binding interactions of protein and ligands were done by using PYMOL. The molecular properties of the designed compounds were predicted in silico by using Osiris property explorer. The parent compound 2-phenazinamine was synthesized by reduction of 2, 4-dinitro-N-phenyl-benzenamine in the presence of tin chloride followed by cyclization in the presence of nitrobenzene and magnesium sulfate. The derivatization at the amino function of 2-phenazinamine was performed by treating parent compound with various aldehydes in the presence of dicyclohexylcarbodiimide (DCC) and urea to afford 2-(2-chlorophenyl)-3-(phenazine-2-yl) thiazolidine-4-one. Synthesized 39 novel derivatives of 2-phenazinamine and performed antioxidant activity, anti antiproliferative on the bulb of onion and anticancer activity on cell line showing significant competition with marked blockbuster drug imatinib.

Keywords: computer-aided drug design, tyrosin kinases, anticancer, docking

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Authors: Farzane Sadatnia, Bahram Fathi

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Today, all public and private organizations have found that the management of the world for key information related to the activities of a staff and its main essence and philosophy, though they constitute the management information systems are very helpful in this respect the right to apply systems can save a lot in terms of economic organizations including reducing the time decision - making, improve the quality of decision making, and cost savings to bring information systems is a backup system that can never be instead of logic and human reasoning, which can be used in the series is spreading, providing resources, and provide the necessary facilities, provide better services for users, balanced budget allocation, determine strengths and weaknesses and previous plans to review the current decisions and especially the decision . Hence; in this study attempts to the effect of an information system on a review of the organization.

Keywords: information system, planning, organization, coordination, control

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Authors: Samar M. Alqhtani, Suhuai Luo, Brian Regan

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Data fusion technology can be the best way to extract useful information from multiple sources of data. It has been widely applied in various applications. This paper presents a data fusion approach in multimedia data for event detection in twitter by using Dempster-Shafer evidence theory. The methodology applies a mining algorithm to detect the event. There are two types of data in the fusion. The first is features extracted from text by using the bag-ofwords method which is calculated using the term frequency-inverse document frequency (TF-IDF). The second is the visual features extracted by applying scale-invariant feature transform (SIFT). The Dempster - Shafer theory of evidence is applied in order to fuse the information from these two sources. Our experiments have indicated that comparing to the approaches using individual data source, the proposed data fusion approach can increase the prediction accuracy for event detection. The experimental result showed that the proposed method achieved a high accuracy of 0.97, comparing with 0.93 with texts only, and 0.86 with images only.

Keywords: data fusion, Dempster-Shafer theory, data mining, event detection

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Authors: Ioannis Iglezakis, Theodoros D. Trokanas, Panagiota Kiortsi

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This paper aims to explore major legal issues arising from the collection and processing of Health Big Data in the light of the new European secondary legislation for the protection of personal data of natural persons, placing emphasis on the General Data Protection Regulation 679/2016. Whether Big Health Data can be characterised as ‘personal data’ or not is really the crux of the matter. The legal ambiguity is compounded by the fact that, even though the processing of Big Health Data is premised on the de-identification of the data subject, the possibility of a combination of Big Health Data with other data circulating freely on the web or from other data files cannot be excluded. Another key point is that the application of some provisions of GPDR to Big Health Data may both absolve the data controller of his legal obligations and deprive the data subject of his rights (e.g., the right to be informed), ultimately undermining the fundamental right to the protection of personal data of natural persons. Moreover, data subject’s rights (e.g., the right not to be subject to a decision based solely on automated processing) are heavily impacted by the use of AI, algorithms, and technologies that reclaim health data for further use, resulting in sometimes ambiguous results that have a substantial impact on individuals. On the other hand, as the COVID-19 pandemic has revealed, Big Data analytics can offer crucial sources of information. In this respect, this paper identifies and systematises the legal provisions concerned, offering interpretative solutions that tackle dangers concerning data subject’s rights while embracing the opportunities that Big Health Data has to offer. In addition, particular attention is attached to the scope of ‘consent’ as a legal basis in the collection and processing of Big Health Data, as the application of data analytics in Big Health Data signals the construction of new data and subject’s profiles. Finally, the paper addresses the knotty problem of role assignment (i.e., distinguishing between controller and processor/joint controllers and joint processors) in an era of extensive Big Health data sharing. The findings are the fruit of a current research project conducted by a three-member research team at the Faculty of Law of the Aristotle University of Thessaloniki and funded by the Greek Ministry of Education and Religious Affairs.

Keywords: big health data, data subject rights, GDPR, pandemic

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Authors: Yong-Kyu Jung

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The fast growth in information technology has led to de-mands to access/process data. CPSs heavily depend on the time of hardware/software operations and communication over the network (i.e., real-time/parallel operations in CPSs (e.g., autonomous vehicles). Since data processing is an im-portant means to overcome the issue confronting data management, reducing the gap between the technological-growth and the data-complexity and channel-bandwidth. An adaptive perpetual data approximation method is intro-duced to manage the actual entropy of the digital spectrum. An ADAC implemented as an accelerator and/or apps for servers/smart-connected devices adaptively rescales digital contents (avg.62.8%), data processing/access time/energy, encryption/decryption overheads in AI/ML applications (facial ID/recognition).

Keywords: adaptive codec, AI, ML, HPC, cyber-physical, cybersecurity

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Authors: Bharathadas Sandhya

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Janamaithri Suraksha Project is the community policing project of Kerala police, fully sponsored by the Government of Kerala and in vogue in Kerala for the last ten years. The socio-economically weaker areas in the hilly terrains consisting of tribal hamlets are given special importance under the project. These hamlets are visited by the beat police officers, and they intervene in various issues in the hamlets. This study is based on data collected from 350 respondents living in the tribal hamlets of the Nilambur area in the District of Malappuram. The respondents were personally interviewed by the research team using a questionnaire consisting of 183 questions, seeking the details regarding their interaction with beat police officers, their ability to prevent or detect crimes, the menace of Maoists (extremist) presence, their interventions in other socio-economic problems like alcoholism, school dropout issues, lack of facilities for preparation for competitive examinations for educated youth, etc. The perception of the tribal population regarding the effectiveness of police intervention in their criminal justice complaints, the attitude of the police officers towards the tribal population when they approach the police station with a criminal complaint, are also studied. The general socio-economic problems of the tribal population as perceived by them are also brought out. Being the visible agency of the government, the police person coming on beat duty to the hamlet is generally seen by the tribal population as a representative to whom they can communicate the issues, even if it’s solution rests with another department like the forest or agriculture. The analysis of the primary data is carried out using computer applications. The amount of social justice benefits the tribal hamlets received through various government schemes, and their deficiencies are brought out in the study. From the conclusions of the study, certain suggestions for positive criminal justice and social justice intervention strategies are made out. The need for various government departments to work in tandem with each other so as to bring out more effectiveness in the socio-economic projects is evident from the study. Whether it is the need to obtain a transport to go to school or problem of drinking water or even opening a bank account, at least occasionally, the visiting beat police officer is of help to the tribal population. Mostly the tribal population feels free to approach the police with a criminal complaint without any inhibitions.

Keywords: community policing, beat police officer, criminal justice, social justice

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Authors: Serges Mendomo Meye, Li Guowei, Shen Zhenzhong, Gan Lei, Xu Liqun

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Due to the characteristics of high-heap and large-volume, the complex layers of waste and the high-water level of leachate, environmental pollution, and slope instability are easily produced. It is therefore of great significance to research the heterogeneous seepage field and stability of landfills. This paper focuses on the heterogeneous characteristics of the landfill piles and analyzes the seepage field and slope stability of the landfill using statistical and numerical analysis methods. The calculated results are compared with the field measurement and literature research data to verify the reliability of the model, which may provide the basis for the design, safe, and eco-friendly operation of the landfill. The main innovations are as follows: (1) The saturated-unsaturated seepage equation of heterogeneous soil is derived theoretically. The heterogeneous landfill is regarded as composed of infinite layers of homogeneous waste, and a method for establishing the heterogeneous seepage model is proposed. Then the formation law of the stagnant water level of heterogeneous landfills is studied. It is found that the maximum stagnant water level of landfills is higher when considering the heterogeneous seepage characteristics, which harms the stability of landfills. (2) Considering the heterogeneity weight and strength characteristics of waste, a method of establishing a heterogeneous stability model is proposed, and it is extended to the three-dimensional stability study. It is found that the distribution of heterogeneous characteristics has a great influence on the stability of landfill slope. During the operation and management of the landfill, the reservoir bank should also be considered while considering the capacity of the landfill.

Keywords: heterogeneous characteristics, leachate levels, saturated-unsaturated seepage, seepage field, slope stability

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Authors: Sašo Pečnik, Borut Žalik

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This paper presents a real-time visualization technique and filtering of classified LiDAR point clouds. The visualization is capable of displaying filtered information organized in layers by the classification attribute saved within LiDAR data sets. We explain the used data structure and data management, which enables real-time presentation of layered LiDAR data. Real-time visualization is achieved with LOD optimization based on the distance from the observer without loss of quality. The filtering process is done in two steps and is entirely executed on the GPU and implemented using programmable shaders.

Keywords: filtering, graphics, level-of-details, LiDAR, real-time visualization

Procedia PDF Downloads 290

Authors: Alireza Jalalvand, Maryam Saleh, Somayeh Behjat Khatouni, Zahra Bahri Najafi, Foroozan Fatahinia, Narges Ismailzadeh, Behrokh Farahmand

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Object: In the last two decades, the recent outbreak of Coronavirus (SARS-CoV-2) imposed a global pandemic in the world. Despite the increasing prevalence of the disease, there are no effective drugs to treat it. A suitable and rapid way to afford an effective drug and treat the global pandemic is a computational drug study. This study used molecular docking methods to examine the potential inhibition of over 50 antiviral drugs against three fundamental proteins of SARS-CoV-2. METHODS: Through a literature review, three important proteins (a key protease, RNA-dependent RNA polymerase (RdRp), and spike) were selected as drug targets. Three-dimensional (3D) structures of protease, spike, and RdRP proteins were obtained from the Protein Data Bank. Protein had minimal energy. Over 50 antiviral drugs were considered candidates for protein inhibition and their 3D structures were obtained from drug banks. The Autodock 4.2 software was used to define the molecular docking settings and run the algorithm. RESULTS: Five drugs, including indinavir, lopinavir, saquinavir, nelfinavir, and remdesivir, exhibited the highest inhibitory potency against all three proteins based on the binding energies and drug binding positions deduced from docking and hydrogen-bonding analysis. Conclusions: According to the results, among the drugs mentioned, saquinavir and lopinavir showed the highest inhibitory potency against all three proteins compared to other drugs. It may enter laboratory phase studies as a dual-drug treatment to inhibit SARS-CoV-2.

Keywords: covid-19, drug repositioning, molecular docking, lopinavir, saquinavir

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Authors: Juhi Sah

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Habitat is characterized by the inter-dependency of environmental elements. Anthropocentric development approach is increasing our vulnerability towards natural hazards. Hence, manmade interventions should have a higher level of sensitivity towards the natural settings. Sensitivity towards the environment can be assessed by the behavior of the stakeholders involved. This led to the establishment of a hypothesis: there exists a legitimate relationship between the behavioral sciences, land use evolution and environment conservation, in the planning process. An attempt has been made to establish this relationship by reviewing the existing set of knowledge and case examples pertaining to the three disciplines under inquiry. Understanding the scarce & deteriorating nature of fresh-water reserves of earth and experimenting the above concept, a case study of a growing urban center's river flood plain is selected, in a developing economy, India. Cases of urban flooding in Chennai, Delhi and other mega cities of India, imposes a high risk on the unauthorized settlement, on the floodplains of the rivers. The issue addressed here is the encroachment of floodplains, through psychological enlightenment and modification through knowledge building. The reaction of an individual or society can be compared to a cognitive process. This study documents all the stakeholders' behavior and perception for their immediate natural environment (water body), and produce various land uses suitable along a river in an urban settlement as per different stakeholder's perceptions. To assess and induce morally responsible behavior in a community (small scale or large scale), tools of psychological inquiry is used for qualitative analysis. The analysis will deal with varied data sets from two sectors namely: River and its geology, Land use planning and regulation. Identification of a distinctive pattern in the built up growth, river ecology degradation, and human behavior, by handling large quantum of data from the diverse sector and comments on the availability of relevant data and its implications, has been done. Along the whole river stretch, condition and usage of its bank vary, hence stakeholder specific survey questionnaires have been prepared to accurately map the responses and habits of the rational inhabitants. A conceptual framework has been designed to move forward with the empirical analysis. The classical principle of virtues says "virtue of a human depends on its character" but another concept defines that the behavior or response is a derivative of situations and to bring about a behavioral change one needs to introduce a disruption in the situation/environment. Owing to the present trends, blindly following the results of data analytics and using it to construct policy, is not proving to be in favor of planned development and natural resource conservation. Thus behavioral assessment of the rational inhabitants of the planet is also required, as their activities and interests have a large impact on the earth's pre-set systems and its sustenance.

Keywords: behavioral assessment, flood plain encroachment, land use planning, river sustenance

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Authors: Hasik Lee, Seung-Young Kho

Abstract:

Nowadays, automatic fare collection (AFC) system is widely used in many countries. However, smart card data from many of cities does not contain alighting information which is necessary to build OD matrices. Therefore, in order to utilize smart card data, destinations of passengers should be estimated. In this paper, kernel density estimation was used to forecast probabilities of alighting stations of bus passengers and applied to smart card data in Seoul, Korea which contains boarding and alighting information. This method was also validated with actual data. In some cases, stochastic method was more accurate than deterministic method. Therefore, it is sufficiently accurate to be used to build OD matrices.

Keywords: destination estimation, Kernel density estimation, smart card data, validation

Procedia PDF Downloads 335

Authors: Abubakar Dikko

Abstract:

The research investigates the causes of unemployment in Namibia, Nigeria and South Africa, and the role of Capital Accumulation in reducing the unemployment profile of these economies as proposed by the post-Keynesian economics. This is conducted through extensive review of literature on the NAIRU models and focused on the post-Keynesian view of unemployment within the NAIRU framework. The NAIRU (non-accelerating inflation rate of unemployment) model has become a dominant framework used in macroeconomic analysis of unemployment. The study views the post-Keynesian economics arguments that capital accumulation is a major determinant of unemployment. Unemployment remains the fundamental socio-economic challenge facing African economies. It has been a burden to citizens of those economies. Namibia, Nigeria and South Africa are great African nations battling with high unemployment rates. In 2013, the countries recorded high unemployment rates of 16.9%, 23.9% and 24.9% respectively. Most of the unemployed in these economies comprises of youth. Roughly about 40% working age South Africans has jobs, whereas in Nigeria and Namibia is less than that. Unemployment in Africa has wide implications on households which has led to extensive poverty and inequality, and created a rampant criminality. Recently in South Africa there has been a case of xenophobic attacks which were caused by the citizens of the country as a result of unemployment. The high unemployment rate in the country led the citizens to chase away foreigners in the country claiming that they have taken away their jobs. The study proposes that there is a strong relationship between capital accumulation and unemployment in Namibia, Nigeria and South Africa, and capital accumulation is responsible for high unemployment rates in these countries. For the economies to achieve steady state level of employment and satisfactory level of economic growth and development there is need for capital accumulation to take place. The countries in the study have been selected after a critical research and investigations. They are selected based on the following criteria; African economies with high unemployment rates above 15% and have about 40% of their workforce unemployed. This level of unemployment is the critical level of unemployment in Africa as expressed by International Labour Organization (ILO). The African countries with low level of capital accumulation. Adequate statistical measures have been employed using a time-series analysis in the study and the results revealed that capital accumulation is the main driver of unemployment performance in the chosen African countries. An increase in the accumulation of capital causes unemployment to reduce significantly. The results of the research work will be useful and relevant to federal governments and ministries, departments and agencies (MDAs) of Namibia, Nigeria and South Africa to resolve the issue of high and persistent unemployment rates in their economies which are great burden that slows growth and development of developing economies. Also, the result can be useful to World Bank, African Development Bank and International Labour Organization (ILO) in their further research and studies on how to tackle unemployment in developing and emerging economies.

Keywords: capital accumulation, unemployment, NAIRU, Post-Keynesian economics

Procedia PDF Downloads 247

Authors: Jae Won Shin

Abstract:

We develop an evaluated nuclear data based photonuclear reaction model of GEANT4 for a more accurate simulation of photon-induced neutron production. The evaluated photonuclear data libraries from the ENDF/B-VII.1 are taken as input. Incident photon energies up to 140 MeV which is the threshold energy for the pion production are considered. For checking the validity of the use of the data-based model, we calculate the photoneutron production cross-sections and yields and compared them with experimental data. The results obtained from the developed model are found to be in good agreement with the experimental data for (γ,xn) reactions.

Keywords: ENDF/B-VII.1, GEANT4, photoneutron, photonuclear reaction

Procedia PDF Downloads 264