Search results for: automated supply chain

Authors: Ye Xue, Zhenhua Deng

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Objectives: Facial soft tissue thickness (FSTT) data could be obtained from CT scans by measuring the face-to-skull distances at sparsely distributed anatomical landmarks by manually located on face and skull. However, automated measurement using 3D facial and skull models by dense points using open-source software has become a viable option due to the development of computed assisted imaging technologies. By utilizing dense FSTT information, it becomes feasible to generate plausible automated facial approximations. Therefore, establishing a comprehensive and detailed, densely calculated FSTT database is crucial in enhancing the accuracy of facial approximation. Materials and methods: This study utilized head CT scans from 250 Chinese adults of Han ethnicity, with 170 participants originally born and residing in northern China and 80 participants in southern China. The age of the participants ranged from 14 to 82 years, and all samples were divided into five non-overlapping age groups. Additionally, samples were also divided into three categories based on BMI information. The 3D Slicer software was utilized to segment bone and soft tissue based on different Hounsfield Unit (HU) thresholds, and surface models of the face and skull were reconstructed for all samples from CT data. Following procedures were performed unsing MeshLab, including converting the face models into hollowed cropped surface models amd automatically measuring the Hausdorff Distance (referred to as FSTT) between the skull and face models. Hausdorff point clouds were colorized based on depth value and exported as PLY files. A histogram of the depth distributions could be view and subdivided into smaller increments. All PLY files were visualized of Hausdorff distance value of each vertex. Basic descriptive statistics (i.e., mean, maximum, minimum and standard deviation etc.) and distribution of FSTT were analysis considering the sex, age, BMI and birthplace. Statistical methods employed included Multiple Regression Analysis, ANOVA, principal component analysis (PCA). Results: The distribution of FSTT is mainly influenced by BMI and sex, as further supported by the results of the PCA analysis. Additionally, FSTT values exceeding 30mm were found to be more sensitive to sex. Birthplace-related differences were observed in regions such as the forehead, orbital, mandibular, and zygoma. Specifically, there are distribution variances in the depth range of 20-30mm, particularly in the mandibular region. Northern males exhibit thinner FSTT in the frontal region of the forehead compared to southern males, while females shows fewer distribution differences between the northern and southern, except for the zygoma region. The observed distribution variance in the orbital region could be attributed to differences in orbital size and shape. Discussion: This study provides a database of Chinese individuals distribution of FSTT and suggested opening source tool shows fine function for FSTT measurement. By incorporating birthplace as an influential factor in the distribution of FSTT, a greater level of detail can be achieved in facial approximation.

Keywords: forensic anthropology, forensic imaging, cranial facial reconstruction, facial soft tissue thickness, CT, open-source tool

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Authors: Henni Sid Ahmed, Belbachir Mohamed Faouzi, Jean Caelen

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Behavior recognition has been studied for realizing drivers assisting system and automated navigation and is an important studied field in the intelligent Building. In this paper, a recognition method of behavior recognition separated from a real image was studied. Images were divided into several categories according to the actual weather, distance and angle of view etc. SIFT was firstly used to detect key points and describe them because the SIFT (Scale Invariant Feature Transform) features were invariant to image scale and rotation and were robust to changes in the viewpoint and illumination. My goal is to develop a robust and reliable system which is composed of two fixed cameras in every room of intelligent building which are connected to a computer for acquisition of video sequences, with a program using these video sequences as inputs, we use SIFT represented different images of video sequences, and SVM (support vector machine) Lights as a programming tool for classification of images in order to classify people’s behaviors in the intelligent building in order to give maximum comfort with optimized energy consumption.

Keywords: video analysis, people behavior, intelligent building, classification

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Authors: Chandragiri Nagadeep, M. V. V. S. Durga, Sadu Mahikshith

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Future generation is depended on new ideas and innovations that develops the country economical growth and technology standards so, Startups plays an important role in satisfying above goals. Startups includes support which is given by investing into it by investors but, single digit investors can’t keep supporting one startup and lot of security problems occurs while transferring large funds to startup’s bank account. Targeting security and most supportive funding, TogEther solves these issues by providing a platform where “Crowd Funding” is available in a decentralized way such that funding is done with digital currency called cryptocurrency where transactions are done in a secured way using “Block Chain Technology”. Not only Funding but also Ideas along with their documents can be presented and hosted with help of IPFS (Inter Planetary File System).

Keywords: blockchain, ethereum, web3, reactjs, interplanetary file system, funding

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Authors: Inna Vinokurova, N. Savvina

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The goal of research: to study the organization of multi-field hospital’s work environment in the Republic of Sakha (Yakutia), Autonomous public health care institution of Republic of Sakha (Yakutia) - Republican Hospital No. 1 - National Center of Medicine. Results: Autonomous public health care institution of Republic of Sakha (Yakutia) - Republican Hospital No. 1 - National Center of Medicine is a multidisciplinary, specialized hospital complex that provides specialized and high-tech medical care to children and adults in the Republic of Sakha (Yakutia) of the Russian Federation. There are 5 diagnostic and treatment centers (advisory and diagnostic, clinical, pediatric, perinatal, Republican cardiologic dispensary) with 45 clinical specialized departments with 727 cots, 5 resuscitation departments, 20 operating rooms and out-patient department with 905 visits in alternation in the National Center of Medicine. Annually more than 20,000 patients receive treatment in the hospital of the Republican Hospital of the Republic of Sakha (Yakutia), more than 70,000 patients visit out-patient sections, more than 2 million researches are done, more than 12,000 surgeries are performed, more than 2 thousand babies are delivered. National Center of Medicine has a great influence with such population’s health indicators as total mortality, birth rate, maternal, infant and perinatal mortality, circulatory system incidence. The work environment of the Republican Hospital of the Republic of Sakha (Yakutia) is represented by the following structural departments: pharmacy, blood transfusion department, sterilization department, laundry, dietetic department, infant-feeding centre, material and technical supply. More than 200 employees work in this service. The main function of these services is to provide on-time and fail-safe supply with all necessary: wear parts, medical supplies, donated blood and its components, foodstuffs, hospital linen , sterile instruments, etc. Thus, the activity of medical organization depends on the work environment, including quality health care, so it is a main part of multi-field hospital activity.

Keywords: organization of multi-field hospital’s, work environment, quality health care, pharmacy, blood transfusion department, sterilization department

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Authors: Davod Ahmadigheidari, Isabel Alvarez, Kate Sinclair, Marnie Davidson, Patrick Cortbaoui, Hugo Melgar-Quiñonez

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The burden of collecting water tends to disproportionately fall on women and girls in low-income countries. Specifically, women spend between one to eight hours per day fetching water for domestic use in Sub-Saharan Africa. While there has been research done on the global time burden for collecting water, it has been mainly focused on water quality parameters; leaving the relationship between water fetching and health outcomes understudied. There is little available evidence regarding the relationship between water fetching and maternal child care practices. The main objective of this study was to help fill the aforementioned gap in the literature. Data from two surveys in Ethiopia and Malawi conducted by CARE Canada in 2016-2017 were used. Descriptive statistics indicate that women were predominantly responsible for collecting water in both Ethiopia (87%) and Malawi (99%) respectively, with the majority spending more than 30 minutes per day on water collection. With regards to child care practices, in both countries, breastfeeding was relatively high (77% and 82%, respectively); and treatment for malnutrition was low (15% and 8%, respectively). However, the same consistency was not found for weighing; in Ethiopia only 16% took their children for weighting in contrast to 94% in Malawi. These three practices were summed to create one variable for regressions analyses. Unadjusted logistic regression findings showed that only in Ethiopia was time fetching water significantly associated with child care practices. Once adjusted for covariates, this relationship was no longer found to be significant. Adjusted logistic regressions also showed that the factors that did influence child care practices differed slightly between the two countries. In Ethiopia, a lack of access to community water supply (OR= 0.668; P=0.010), poor attitudes towards gender equality (OR= 0.608; P=0.001), no access to land and (OR=0.603; P=0.000), significantly decreased a women’s odd of using positive childcare practices. Notably, being young women between 15-24 years (OR=2.308; P=0.017), and 25-29 (OR=2.065; P=0.028) increased probability of using positive childcare practices. Whereas in Malawi, higher maternal age, low decision-making power, significantly decreased a women’s odd of using positive childcare practices. In conclusion, this study found that even though amount of time spent by women fetching water makes a difference for childcare practices, it is not significantly related to women’s child care practices when controlling the covariates. Importantly, women’s age contributes to child care practices in Ethiopia and Malawi.

Keywords: time fetching water, community water supply, women’s child care practices, Ethiopia, Malawi

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Authors: Naomi Grigoryan, Alexandros Loutsioli Daskalakis, Anna Elisse Uy, Yihe Huang, Aude Laurent (Webanck)

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In response to the building and construction sectors accounting for a third of all energy demand and emissions, the European Union has placed new laws and regulations in the construction sector that emphasize material circularity, energy efficiency, biodiversity, and social impact. Existing design tools assess sustainability in early-stage design for products or buildings; however, there is no standardized methodology for measuring the circularity performance of building components. Existing assessment methods for building components focus primarily on carbon footprint but lack the comprehensive analysis required to design for circularity. The research conducted in this paper covers the parameters needed to assess sustainability in the design process of architectural products such as doors, windows, and facades. It maps a framework for a tool that assists designers with real-time sustainability metrics. Considering the life cycle of building components such as façades, windows, and doors involves the life cycle stages applied to product design and many of the methods used in the life cycle analysis of buildings. The current industry standards of sustainability assessment for metal building components follow cradle-to-grave life cycle assessment (LCA), track Global Warming Potential (GWP), and document the parameters used for an Environmental Product Declaration (EPD). Developed by the Ellen Macarthur Foundation, the Material Circularity Indicator (MCI) is a methodology utilizing the data from LCA and EPDs to rate circularity, with a "value between 0 and 1 where higher values indicate a higher circularity+". Expanding on the MCI with additional indicators such as the Water Circularity Index (WCI), the Energy Circularity Index (ECI), the Social Circularity Index (SCI), Life Cycle Economic Value (EV), and calculating biodiversity risk and uncertainty, the assessment methodology of an architectural product's impact can be targeted more specifically based on product requirements, performance, and lifespan. Broadening the scope of LCA calculation for products to incorporate aspects of building design allows product designers to account for the disassembly of architectural components. For example, the Material Circularity Indicator for architectural products such as windows and facades is typically low due to the impact of glass, as 70% of glass ends up in landfills due to damage in the disassembly process. The low MCI can be combatted by expanding beyond cradle-to-grave assessment and focusing the design process on disassembly, recycling, and repurposing with the help of real-time assessment tools. Design for Disassembly and Urban Mining has been integrated within the construction field on small scales as project-based exercises, not addressing the entire supply chain of architectural products. By adopting more comprehensive sustainability metrics and incorporating uncertainty calculations, the sustainability assessment of building components can be more accurately assessed with decarbonization and disassembly in mind, addressing the large-scale commercial markets within construction, some of the most significant contributors to climate change.

Keywords: architectural products, early-stage design, life cycle assessment, material circularity indicator

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Authors: Sue S. Feldman, Bradley Tipper, Benjamin Schooley

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Nearly every community in the US has been impacted by opioid related addictions/deaths; it is a national problem that is threatening our social and economic welfare. Most believe that tackling this problem from a prevention perspective advances can be made toward breaking the chain of addiction. One mechanism, community based participatory research, involves the community in the prevention approach. This project combines that approach with a design science approach to develop an integrated solution. Findings suggested accountable care communities, transpersonal psychology, and social exchange theory as product kernel theories. Evaluation was conducted on a prototype.

Keywords: substance use and abuse recovery, community resource centers, accountable care communities, community based participatory research

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.

Keywords: apartment complex, big data, life-cycle building value analysis, machine learning

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Authors: Seok Woo Chang, Kee Yeon Kum, Kwang Shik Bae, WooCheol Lee

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Aim: The objective of this study was to compare the biocompatibilities and mineralization potential of ProRoot MTA and newly developed calcium phosphate based cement, Capseal. Materials and Methods: The biocompatibilities and mineralization-related gene expressions (Bone sialoprotein (BSP) and osteocalcin (OCN)) of ProRoot MTA and Capseal were also compared by a methylthiazol tetrazolium (MTT) assay and reverse transcription-polymerization chain reaction (RT-PCR) analysis on 1, 3, and 7 days, respectively. Empty rings were used as control group. The results were statistically analyzed by Kruskal-Wallis test with a Bonferroni correction. P-value of < 0.05 was considered significant. Results: The biocompatibilities of ProRoot MTA and Capseal were equally favorable. ProRoot MTA and Capseal affected the messenger RNA expression of osteocalcin and osteonectin. Conclusions: Based on the results, both ProRoot MTA and Capseal could be a useful biomaterial in clinical endodontics.

Keywords: biocompatibility, calcium silicate cement, MTT, RT-PCR

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Authors: Amit Verma, Simranjeet Kaur, Sandeep Kaur

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Test Case Prioritization (TCP) has gained wide spread acceptance as it often results in good quality software free from defects. Due to the increase in rate of faults in software traditional techniques for prioritization results in increased cost and time. Main challenge in TCP is difficulty in manually validate the priorities of different test cases due to large size of test suites and no more emphasis are made to make the TCP process automate. The objective of this paper is to detect the priorities of different test cases using an artificial neural network which helps to predict the correct priorities with the help of back propagation algorithm. In our proposed work one such method is implemented in which priorities are assigned to different test cases based on their frequency. After assigning the priorities ANN predicts whether correct priority is assigned to every test case or not otherwise it generates the interrupt when wrong priority is assigned. In order to classify the different priority test cases classifiers are used. Proposed algorithm is very effective as it reduces the complexity with robust efficiency and makes the process automated to prioritize the test cases.

Keywords: test case prioritization, classification, artificial neural networks, TF-IDF

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Authors: Mehrdad Rezaei, Reza Haghmaram, Nima Amjadi

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This paper proposes an operating and cost optimization model for micro-grid (MG). This model takes into account emission costs of NOx, SO2, and CO2, together with the operation and maintenance costs. Wind turbines (WT), photovoltaic (PV) arrays, micro turbines (MT), fuel cells (FC), diesel engine generators (DEG) with different capacities are considered in this model. The aim of the optimization is minimizing operation cost according to constraints, supply demand and safety of the system. The proposed genetic algorithm (GA), with the ability to fine-tune its own settings, is used to optimize the micro-grid operation.

Keywords: micro-grid, optimization, genetic algorithm, MG

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Authors: Julio Vicente Cateia

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This paper aims to assess the role of informality and trade facilitation on the export probability of Guinea-Bissau. We include informality in the Féchet function, which gives the expression for the country's supply probability. We find that Guinea-Bissau is about 7.2% less likely to export due to the 1% increase in informality. The export's probability increases by about 1.7%, 4%, and 1.1% due to a 1% increase in trade facilitation, R&D stock, and year of education. These results are significant at the usual levels. We suggest a development agenda aimed at reducing the level of informality in this country.

Keywords: development, trade, informality, trade facilitation, economy of Guinea-Bissau

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Authors: G. C. Tikkiwal, Mukesh Upadhyay

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During the surveillance operations at war or peace time, the Radar operator gets a scatter of targets over the screen. This may be a tracked vehicle like tank vis-à-vis T72, BMP etc, or it may be a wheeled vehicle like ALS, TATRA, 2.5Tonne, Shaktiman or moving the army, moving convoys etc. The radar operator selects one of the promising targets into single target tracking (STT) mode. Once the target is locked, the operator gets a typical audible signal into his headphones. With reference to the gained experience and training over the time, the operator then identifies the random target. But this process is cumbersome and is solely dependent on the skills of the operator, thus may lead to misclassification of the object. In this paper, we present a technique using mathematical and statistical methods like fast fourier transformation (FFT) and principal component analysis (PCA) to identify the random objects. The process of classification is based on transforming the audible signature of target into music octave-notes. The whole methodology is then automated by developing suitable software. This automation increases the efficiency of identification of the random target by reducing the chances of misclassification. This whole study is based on live data.

Keywords: radar target, FFT, principal component analysis, eigenvector, octave-notes, DSP

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Authors: Khalid Mahmoud Gaafar

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In human diets , ω-6 and ω-3 are important essential fatty acids for immunity and health. However, considerable alteration in dietary patterns and contents has resulted in change of the consumption of such fatty acids ,with subsequent increase in the consumption of ω-6 fatty acids and a marked decrease in the consumption of ω-3 fatty acids. This dietary alteration has led to an imbalance in the ratio for ω-6/ω-3, which at 20:1 now differs considerably from the original ratio (1:1). Therefore, dietary supplements such as eggs and meat enriched with omega 3 are necessary to increase the consumption of ω-3 to meet the recommended need for ω-3. Foods that supply ω-6 fatty acids include soybean, palm , sunflower, and rapeseed oils, whereas foods that supply ω-3 fatty acids such as linseed and fish oils. Lin seed oils contain Alpha – linolenic acid (ALA), which can be converted to DHA and EPA in the birds body, with linseed oil containing more than 50% ALA. On the other hand, high doses of omega 6 sources in the diet may have deleterious effects on humans. Maintaining an optimum ratio of ω-3 and ω-6fatty acids not only improves performance but also prevents these health risks. The ratio of n-6:ω-3 fatty acids also plays an important role in the immune response, production performance of broilers and designing meat enriched with ω-3 polyunsaturated fatty acids (PUFAs). Birds of three experimental groups fed on basal starter (0-2nd weeks), grower (3rd -4th weeks) and finisher (5th week) rations. The first is control group fed during the grower-finisher periods on basic diet with two replicate (one fed on basic diet contain vegetable oil and the other don’t) without any additives. The three experimental groups (T1 – T2 –T3) fed during the grower- finisher periods on diets free from vegetable oils and contain of 5% of extruded mixture of soybean and linseed (60%:40%). The second (T2) and third (T3) experimental groups supplemented with vitamin B12 and enzyme mixture. The first experimental groups don’t receive vitamins or enzymes. The obtained results showed a significant increased growth performance, immune response, highest antioxidant activity and serum HDL with lowest serum LDL and triglycerides levels in all experimental groups compared with control group, which was highly significant in group fed on vitamin B6.

Keywords: omega fatty acids, broiler, feeding, human health, growth performance, immunity

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Authors: Mohamed R. Mhereeg

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The paper discusses the implementation of the MultiAgent classification System (MACS) and utilizing it to provide an automated and accurate classification of end users developing applications in the spreadsheet domain. However, different technologies have been brought together to build MACS. The strength of the system is the integration of the agent technology with the FIPA specifications together with other technologies, which are the .NET widows service based agents, the Windows Communication Foundation (WCF) services, the Service Oriented Architecture (SOA), and Oracle Data Mining (ODM). Microsoft's .NET windows service based agents were utilized to develop the monitoring agents of MACS, the .NET WCF services together with SOA approach allowed the distribution and communication between agents over the WWW. The Monitoring Agents (MAs) were configured to execute automatically to monitor excel spreadsheets development activities by content. Data gathered by the Monitoring Agents from various resources over a period of time was collected and filtered by a Database Updater Agent (DUA) residing in the .NET client application of the system. This agent then transfers and stores the data in Oracle server database via Oracle stored procedures for further processing that leads to the classification of the end user developers.

Keywords: MACS, implementation, multi-agent, SOA, autonomous, WCF

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Authors: Odd Jarl Borch, Marina Z. Solesvik

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The paper analyses partner selection approaches related to large scale R&D-based innovation projects at the different stages of development. We emphasize innovation projects in the maritime value chain and how partners are selected to improve quality according to high spec customer demands, and to reduce investment costs on new production technology such as advanced offshore service vessels. We elaborate on the differences in innovation approach and especially the role that purposive inflows and outflows of knowledge from external partners may be used to accelerate internal innovation. We present three cases related to different projects in terms of specificity and scope. We explore how the partner selection criteria change over time when the goals move from wide scope to a very specific R&D tasks.

Keywords: partner selection, innovation, offshore industry, concept design

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Authors: Sirapop Nuannimnoi, Hojjat Baghban, Ching-Yao Huang

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Over the past decade, with the fast development of blockchain technology since the birth of Bitcoin, there has been a massive increase in the usage of Cryptocurrencies. Cryptocurrencies are not seen as an investment opportunity due to the market’s erratic behavior and high price volatility. With the recent success of deep reinforcement learning (DRL), portfolio management can be modeled and automated. In this paper, we propose a novel DRL-based multi-agent system to automatically make proper trading decisions on multiple cryptocurrencies and gain profits in the highly volatile cryptocurrency market. We also extend this multi-agent system with horizontal federated transfer learning for better adapting to the inclusion of new cryptocurrencies in our portfolio; therefore, we can, through the concept of diversification, maximize our profits and minimize the trading risks. Experimental results through multiple simulation scenarios reveal that this proposed algorithmic trading system can offer three promising key advantages over other systems, including maximized profits, minimized risks, and adaptability.

Keywords: cryptocurrency portfolio management, algorithmic trading, federated learning, multi-agent reinforcement learning

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Authors: Aarti Suryawanshi

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Royal Tourism has always been a popular niche activity for many tourists around the world. The United Kingdom being at the heart of it, has been a pioneering nation for Royal tourists. Though many other countries with monarchies such as India, Thailand, Japan, Spain, Netherlands, and many more have attracted tourists with the motivation to see and experience the royalty to their nations, the Middle Eastern countries have never really been the attraction for Royal tourists. Royalty in the middle east is fast emerging as a tourist product and also paving way to marketing opportunity that may lead to the increased popularity of the Royal Houses of the region. Dubai has been garnering the centre stage for futuristic developments, economic growth initiatives, and continuous efforts towards urbanisation which has brought the lime light on the Royal house of the Al Maktoum globally, along with the younger royal members being extensively recognised and appreciated for their public and private adventures which are shared through various social media platforms. The objective of this paper is to analyse the popularity of His Highness Sheikh Hamdan Bin Mohammed Bin Rashid Al Maktoum through social media platforms and the possibility of inducing Royal Tourism in Dubai. An empirical study has been performed to describe the automated repositioning of the city of Dubai as a royal tourism hub.

Keywords: royalty, royal tourism, monarchy, marketing strategy, repositioning

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Authors: Fernanda Varella Borges, Silvio Burrattino Melhado

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Motivated by the importance of public infrastructure projects in the civil construction chain, this research shows the study of project management methods and the infrastructure projects’ characteristics. The research aims at the objective of improving management efficiency by proposing guidelines for the application of project management methods in infrastructure projects. Through literature review and case studies, the research analyses two major infrastructure projects underway in Brazil, identifying the critical points for achieving its success. As a result, the proposed guidelines indicate that special attention should be given to the management of stakeholders, focusing on their knowledge and experience, their different interests, the efficient management of their communication, and their behavior in the day-by-day project management process.

Keywords: construction, infrastructure, project management, public projects

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Authors: Endrick Barnacin, Jean-Luc Henry, Jack Molinié, Jimmy Nagau, Hélène Delatte, Gérard Lebreton

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Palynology is a field of interest for many disciplines. It has multiple applications such as chronological dating, climatology, allergy treatment, and even honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time-consuming task that requires the intervention of experts in the field, which is becoming increasingly rare due to economic and social conditions. So, the automation of this task is a necessity. Pollen slides analysis is mainly a visual process as it is carried out with the naked eye. That is the reason why a primary method to automate palynology is the use of digital image processing. This method presents the lowest cost and has relatively good accuracy in pollen retrieval. In this work, we propose a system combining recognition and grouping of pollen. It consists of using a Logistic Model Tree to classify pollen already known by the proposed system while detecting any unknown species. Then, the unknown pollen species are divided using a cluster-based approach. Success rates for the recognition of known species have been achieved, and automated clustering seems to be a promising approach.

Keywords: pollen recognition, logistic model tree, expectation-maximization, local binary pattern

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Authors: Jitendra Kumar Quamara, Sohan Lal, Pushkar Raj

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Electrical conduction behavior of liquid crystalline polyurethane (LCPU) has been investigated under transient conditions in the operating temperature range 50-220°C at various electric fields of 4.35-43.45 kV/cm. The transient currents show the hyperbolic decay character and the decay exponent ∆t (one tenth decay time) dependent on field as well as on temperature. The increase in I0/Is values (where I0 represents the current observed immediately after applying the voltage and Is represents the steady state current) and the variation of mobility at high operating temperatures shows the appearance of mesophase. The origin of transient currents has been attributed to the dipolar nature of carbonyl (C＝O) groups in the main chain of LCPU and the trapping charge carriers.

Keywords: electrical conduction, transient current, liquid crystalline polymers, mesophase

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Authors: Laid Degaa, Bachir Bendjedia, Nassim Rizoug, Abdelkader Saidane

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Energy storage system is a key aspect for the development of clean cars. The work proposed here deals with the modeling of hybrid storage sources composed of a combination of lithium-ion battery and supercapacitors. Simulation results show the performance of the active model for a hybrid source and confirm the feasibility of our approach. In this context, sizing of the electrical energy supply is carried out. The aim of this sizing is to propose an 'optimal' solution that improves the performance of electric vehicles in term of weight, cost and aging.

Keywords: battery, electric vehicles, energy, hybrid storage, supercapacitor

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Authors: Girma Yohannis Bade, Olga Kolesnikova, Grigori Sidorov

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Named entity recognition (NER) is a building block for information extraction. Though the information extraction process has been automated using a variety of techniques to find and extract a piece of relevant information from unstructured documents, the discovery of targeted knowledge still poses a number of research difficulties because of the variability and lack of structure in Web data. NER, a subtask of information extraction (IE), came to exist to smooth such difficulty. It deals with finding the proper names (named entities), such as the name of the person, country, location, organization, dates, and event in a document, and categorizing them as predetermined labels, which is an initial step in IE tasks. This survey paper presents the roles and importance of NER to IE from the perspective of different algorithms and application area domains. Thus, this paper well summarizes how researchers implemented NER in particular application areas like finance, medicine, defense, business, food science, archeology, and so on. It also outlines the three types of sequence labeling algorithms for NER such as feature-based, neural network-based, and rule-based. Finally, the state-of-the-art and evaluation metrics of NER were presented.

Keywords: the role of NER, named entity recognition, information extraction, sequence labeling algorithms, named entity application area

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Authors: Kelly S. Parkinson, Katherine Y. Teh-White

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Futureye has a bespoke social licence to operate methodology which has successfully secured community approval and commercial return for fisheries which have faced regulatory and financial risk. This unique approach to fisheries management focuses on delivering improved social and environmental outcomes to support the fishing industry make steps towards achieving the United Nations SDGs. An SLO is the community’s implicit consent for a business or project to exist. An SLO must be earned and maintained alongside regulatory licences. In current and new operations, it helps you to anticipate and measure community concerns around your operations – leading to more predictable and sensible policy outcomes that will not jeopardise your commercial returns. Rising societal expectations and increasing activist sophistication mean the international fishing industry needs to resolve community concerns at each stage their supply chain. Futureye applied our tested social licence to operate (SLO) methodology to help Austral Fisheries who was being attacked by activists concerned about the sustainability of Patagonian Toothfish. Austral was Marine Stewardship Council certified, but pirates were making the overall catch unsustainable. Austral wanted to be carbon neutral. SLO provides a lens on the risk that helps industries and companies act before regulatory and political risk escalates. To do this assessment, we have a methodology that assesses the risk that we can then translate into a process to create a strategy. 1) Audience: we understand the drivers of change and the transmission of those drivers across all audience segments. 2) Expectation: we understand the level of social norming of changing expectations. 3) Outrage: we understand the technical and perceptual aspects of risk and the opportunities to mitigate these. 4) Inter-relationships: we understand the political, regulatory, and reputation system so that we can understand the levers of change. 5) Strategy: we understand whether the strategy will achieve a social licence through bringing the internal and external stakeholders on the journey. Futureye’s SLO methodologies helped Austral to understand risks and opportunities to enhance its resilience. Futureye reviewed the issues, assessed outrage and materiality and mapped SLO threats to the company. Austral was introduced to a new way that it could manage activism, climate action, and responsible consumption. As a result of Futureye’s work, Austral worked closely with Sea Shepherd who was campaigning against pirates illegally fishing Patagonian Toothfish as well as international governments. In 2016 Austral launched the world’s first carbon neutral fish which won Austral a thirteen percent premium for tender on the open market. In 2017, Austral received the prestigious Banksia Foundation Sustainability Leadership Award for seafood that is sustainable, healthy and carbon neutral. Austral’s position as a leader in sustainable development has opened doors for retailers all over the world. Futureye’s SLO methodology can identify the societal, political and regulatory risks facing fisheries and position them to proactively address the issues and become an industry leader in sustainability.

Keywords: carbon neutral, fisheries management, risk communication, social licence to operate, sustainable development

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Authors: G. Anjan Babu, G. Sumana, M. Rajasekhar

Abstract:

Many inherited diseases and non-hereditary disorders are common in the development of renal cystic diseases. Polycystic kidney disease (PKD) is a disorder developed within the kidneys in which grouping of cysts filled with water like fluid. PKD is responsible for 5-10% of end-stage renal failure treated by dialysis or transplantation. New experimental models, application of molecular biology techniques have provided new insights into the pathogenesis of PKD. Researchers are showing keen interest for developing an automated system by applying computer aided techniques for the diagnosis of diseases. In this paper a multi-layered feed forward neural network with one hidden layer is constructed, trained and tested by applying back propagation learning rule for the diagnosis of PKD based on physical symptoms and test results of urinanalysis collected from the individual patients. The data collected from 50 patients are used to train and test the network. Among these samples, 75% of the data used for training and remaining 25% of the data are used for testing purpose. Furthermore, this trained network is used to implement for new samples. The output results in normality and abnormality of the patient.

Keywords: dialysis, hereditary, transplantation, polycystic, pathogenesis

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Authors: S. S. Kharintsev, E. A. Chernykh, S. K. Saikin, A. I. Fishman, S. G. Kazarian

Abstract:

Recent advances in improving information storage performance are inseparably linked with circumvention of fundamental constraints such as the supermagnetic limit in heat assisted magnetic recording, charge loss tolerance in solid-state memory and the Abbe’s diffraction limit in optical storage. A substantial breakthrough in the development of nonvolatile storage devices with dimensional scaling has been achieved due to phase-change chalcogenide memory, which nowadays, meets the market needs to the greatest advantage. A further progress is aimed at the development of versatile nonvolatile high-speed memory combining potentials of random access memory and archive storage. The well-established properties of light at the nanoscale empower us to use them for recording optical information with ultrahigh density scaled down to a single molecule, which is the size of a pit. Indeed, diffraction-limited optics is able to record as much information as ~1 Gb/in2. Nonlinear optical effects, for example, two-photon fluorescence recording, allows one to decrease the extent of the pit even more, which results in the recording density up to ~100 Gb/in2. Going beyond the diffraction limit, due to the sub-wavelength confinement of light, pushes the pit size down to a single chromophore, which is, on average, of ~1 nm in length. Thus, the memory capacity can be increased up to the theoretical limit of 1 Pb/in2. Moreover, the field confinement provides faster recording and readout operations due to the enhanced light-matter interaction. This, in turn, leads to the miniaturization of optical devices and the decrease of energy supply down to ~1 μW/cm². Intrinsic features of light such as multimode, mixed polarization and angular momentum in addition to the underlying optical and holographic tools for writing/reading, enriches the storage and encryption of optical information. In particular, the finite extent of the near-field penetration, falling into a range of 50-100 nm, gives the possibility to perform 3D volume (layer-to-layer) recording/readout of optical information. In this study, we demonstrate a comprehensive evidence of isotropic-to-homeotropic phase transition of the azobenzene-functionalized polymer thin film exposed to light and dc electric field using near-field optical microscopy and scanning capacitance microscopy. We unravel a near-field Raman dichroism of a sub-10 nm thick epoxy-based side-chain azo-polymer films with polarization-controlled tip-enhanced Raman scattering. In our study, orientation of azo-chromophores is controlled with a bias voltage gold tip rather than light polarization. Isotropic in-plane and homeotropic out-of-plane arrangement of azo-chromophores in glassy environment can be distinguished with transverse and longitudinal optical near-fields. We demonstrate that both phases are unambiguously visualized by 2D mapping their local dielectric properties with scanning capacity microscopy. The stability of the polar homeotropic phase is strongly sensitive to the thickness of the thin film. We make an analysis of α-transition of the azo-polymer by detecting a temperature-dependent phase jump of an AFM cantilever when passing through the glass temperature. Overall, we anticipate further improvements in optical storage performance, which approaches to a single molecule level.

Keywords: optical memory, azo-dye, near-field, tip-enhanced Raman scattering

Procedia PDF Downloads 165

Authors: Hateem Zafar Kayani, Nageen Hussain

Abstract:

The main aim is to analyze the mutations of DNASE I gene in diabetic patients. A total of 120 diabetes patients and 120 controls were sampled. The total number of male diabetic patients included in the study was 79 (66%) while female patients were 41 (34%) in number. Exon 8 of the DNASE I gene was amplified by using thermo cycler. The possible band of interest was located at 165 base pairs. Two samples showed similar missense mutations at 127th position of exon 8 which replaced amino acid Arginine (Arg) to Glutamine (Gln). All controls showed no mutations. The association of diabetes with different levels of blood pressure and body mass index (BMI) were found to be significant.

Keywords: deoxyribonuclease I, polymerase chain reaction, insulin-dependent diabetes mellitus, non-insulin dependent diabetes mellitus

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Authors: Piyush Sharma, Devi Prasad Sharma, Sulabh Bansal

Abstract:

Diseases are fairly common in fruits and vegetables because of the changing climatic and environmental circumstances. Crop diseases, which are frequently difficult to control, interfere with the growth and output of the crops. Accurate disease detection and timely disease control measures are required to guarantee high production standards and good quality. In India, apples are a common crop that may be afflicted by a variety of diseases on the fruit, stem, and leaves. It is fungi, bacteria, and viruses that trigger the early symptoms of leaf diseases. In order to assist farmers and take the appropriate action, it is important to develop an automated system that can be used to detect the type of illnesses. Machine learning-based image processing can be used to: this research suggested a system that can automatically identify diseases in apple fruit and apple plants. Hence, this research utilizes the hybrid SVM-RBN model. As a consequence, the model may produce results that are more effective in terms of accuracy, precision, recall, and F1 Score, with respective values of 96%, 99%, 94%, and 93%.

Keywords: fruit plant disease, crop disease, machine learning, image processing, SVM-RBN

Procedia PDF Downloads 39

Authors: J. Andrade-Garda, A. Anguera, J. Ares-Casal, M. Hidalgo-Lorenzo, J.-A. Lara, D. Lizcano, S. Suárez-Garaboa

Abstract:

In the current constantly changing economic context, collaborative networks allow partners to undertake projects that would not be possible if attempted by them individually. These projects usually involve the performance of a group of tasks (named roles) that have to be distributed among the partners. Thus, an allocation/matching problem arises that will be referred to as Role-Partner Allocation problem. In real life this situation is addressed by negotiation between partners in order to reach ad hoc agreements. Besides taking a long time and being hard work, both historical evidence and economic analysis show that such approach is not recommended. Instead, the allocation process should be automated by means of a centralized matching scheme. However, as a preliminary step to start the search for such a matching mechanism (or even the development of a new one), the problem and its core components must be specified. To this end, this paper establishes (i) the definition of the problem and its constraints, (ii) the key features of the involved elements (i.e., roles and partners); and (iii) how to create preference lists both for roles and partners. Only this way it will be possible to conduct subsequent methodological research on the solution method.     

Keywords: collaborative network, matching, partner, preference list, role

Procedia PDF Downloads 209

Authors: Thulya Premathilake, Tharushi Perera, Hansi Thathsarani, Tharushi Nethmini, Dilshan De Silva, Piyumika Samarasekara

Abstract:

One of the most efficient ways to assist developers in grasping the source code is to make use of comments, which can be found throughout the code. When working in fields such as software development, having comments in your code that are of good quality is a fundamental requirement. Tackling software problems while making use of programs that have already been built. It is essential for the intention of the source code to be made crystal apparent in the comments that are added to the code. This assists programmers in better comprehending the programs they are working on and enables them to complete software maintenance jobs in a more timely manner. In spite of the fact that comments and documentation are meant to improve readability and maintainability, the vast majority of programmers place the majority of their focus on the actual code that is being written. This study provides a complete and comprehensive overview of the previous research that has been conducted on the topic of code comments. The study focuses on four main topics, including automated comment production, comment consistency, comment classification, and comment quality rating. One is able to get the knowledge that is more complete for use in following inquiries if they conduct an analysis of the proper approaches that were used in this study issue.

Keywords: code commenting, source code, software quality, quality assurance

Procedia PDF Downloads 65