Search results for: survival data

Authors: Satyanarayana Engu, Ahmed Mohd, V. Murugan

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We study the large time asymptotics of solutions to the Cauchy problem for a forced Burgers equation (FBE) with the initial data, which is continuous and summable on R. For which, we first derive explicit solutions of FBE assuming a different class of initial data in terms of Hermite polynomials. Later, by violating this assumption we prove the existence of a solution to the considered Cauchy problem. Finally, we give an asymptotic approximate solution and establish that the error will be of order O(t^(-1/2)) with respect to L^p -norm, where 1≤p≤∞, for large time.

Keywords: Burgers equation, Cole-Hopf transformation, Hermite polynomials, large time asymptotics

Procedia PDF Downloads 327

Authors: Alfredo Fortunato, Carmelofrancesco Origlia, Sara Laurita, Rossella Nicoletti

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In the post-pandemic recovery phase of tourism, the role of a Destination Management Organization (DMO) as a coordinated management system of all the elements that make up a destination (attractions, access, marketing, human resources, brand, pricing, etc.) is also becoming relevant for local territories. The objective of a DMO is to maximize the visitor's perception of value and quality while ensuring the competitiveness and sustainability of the destination, as well as the long-term preservation of its natural and cultural assets, and to catalyze benefits for the local economy and residents. In carrying out the multiple functions to which it is called, the DMO can leverage a collective intelligence that comes from the ability to pool information, explicit and tacit knowledge, and relationships of the various stakeholders: policymakers, public managers and officials, entrepreneurs in the tourism supply chain, researchers, data journalists, schools, associations and committees, citizens, etc. The DMO potentially has at its disposal large volumes of data and many of them at low cost, that need to be properly processed to produce value. Based on these assumptions, the paper presents a conceptual framework for building an information system to support the DMO in the intelligent management of a tourist destination tested in an area of southern Italy. The approach adopted is data-informed and consists of four phases: (1) formulation of the knowledge problem (analysis of policy documents and industry reports; focus groups and co-design with stakeholders; definition of information needs and key questions); (2) research and metadatation of relevant sources (reconnaissance of official sources, administrative archives and internal DMO sources); (3) gap analysis and identification of unconventional information sources (evaluation of traditional sources with respect to the level of consistency with information needs, the freshness of information and granularity of data; enrichment of the information base by identifying and studying web sources such as Wikipedia, Google Trends, Booking.com, Tripadvisor, websites of accommodation facilities and online newspapers); (4) definition of the set of indicators and construction of the information base (specific definition of indicators and procedures for data acquisition, transformation, and analysis). The framework derived consists of 6 thematic areas (accommodation supply, cultural heritage, flows, value, sustainability, and enabling factors), each of which is divided into three domains that gather a specific information need to be represented by a scheme of questions to be answered through the analysis of available indicators. The framework is characterized by a high degree of flexibility in the European context, given that it can be customized for each destination by adapting the part related to internal sources. Application to the case study led to the creation of a decision support system that allows: •integration of data from heterogeneous sources, including through the execution of automated web crawling procedures for data ingestion of social and web information; •reading and interpretation of data and metadata through guided navigation paths in the key of digital story-telling; •implementation of complex analysis capabilities through the use of data mining algorithms such as for the prediction of tourist flows.

Keywords: collective intelligence, data framework, destination management, smart tourism

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Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo Ferraioli

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In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring. SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, polarimetric SAR image, convolutional neural network, deep learnig, deep neural network

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Authors: Mary Jane B. Suarez

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Public speaking anxiety (PSA), also known as speech anxiety, is innumerably persistent in any traditional communication classes, especially for students who learn English as a second language. The speech anxiety intensifies when communication skills assessments have taken their toll in an online or a remote mode of learning due to the perils of the COVID-19 virus. Both teachers and students have experienced vast ambiguity on how to realize a still effective way to teach and learn speaking skills amidst the pandemic. Communication skills assessments like public speaking, oral presentations, and student reporting have defined their new meaning using Google Meet, Zoom, and other online platforms. Though using such technologies has paved for more creative ways for students to acquire and develop communication skills, the effectiveness of using such assessment tools stands in question. This mixed method study aimed to determine the factors that affected the public speaking skills of students in a communication class, to probe on the assessment gaps in assessing speaking skills of students attending online classes vis-à-vis the implementation of remote and blended modalities of learning, and to recommend ways on how to address the public speaking anxieties of students in performing a speaking task online and to bridge the assessment gaps based on the outcome of the study in order to achieve a smooth segue from online to on-ground instructions maneuvering towards a much better post-pandemic academic milieu. Using a convergent parallel design, both quantitative and qualitative data were reconciled by probing on the public speaking anxiety of students and the potential assessment gaps encountered in an online English communication class under remote and blended learning. There were four phases in applying the convergent parallel design. The first phase was the data collection, where both quantitative and qualitative data were collected using document reviews and focus group discussions. The second phase was data analysis, where quantitative data was treated using statistical testing, particularly frequency, percentage, and mean by using Microsoft Excel application and IBM Statistical Package for Social Sciences (SPSS) version 19, and qualitative data was examined using thematic analysis. The third phase was the merging of data analysis results to amalgamate varying comparisons between desired learning competencies versus the actual learning competencies of students. Finally, the fourth phase was the interpretation of merged data that led to the findings that there was a significantly high percentage of students' public speaking anxiety whenever students would deliver speaking tasks online. There were also assessment gaps identified by comparing the desired learning competencies of the formative and alternative assessments implemented and the actual speaking performances of students that showed evidence that public speaking anxiety of students was not properly identified and processed.

Keywords: blended learning, communication skills assessment, public speaking anxiety, speech anxiety

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Authors: Al Omari Moahmmed Ahmed

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In the Bayesian, we developed an approach by using non-informative prior with covariate and obtained by using Gauss quadrature method to estimate the parameters of the covariate and reliability function of the Weibull regression distribution with Type-I censored data. The maximum likelihood seen that the estimators obtained are not available in closed forms, although they can be solved it by using Newton-Raphson methods. The comparison criteria are the MSE and the performance of these estimates are assessed using simulation considering various sample size, several specific values of shape parameter. The results show that Bayesian with non-informative prior is better than Maximum Likelihood Estimator.

Keywords: non-informative prior, Bayesian method, type-I censoring, Gauss quardature

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Authors: Andres F. Ramirez, Carlos F. Valencia

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The increasing interest in renewable energies strategies application and the path for diminishing the use of carbon related energy sources have encouraged the development of novel strategies for integration of solar energy into the electricity network. A correct inclusion of the fluctuating energy output of a photovoltaic (PV) energy system into an electric grid requires improvements in the forecasting and simulation methodologies for solar energy potential, and the understanding not only of the mean value of the series but the associated underlying stochastic process. We present a methodology for synthetic generation of solar irradiance (shortwave flux) and air temperature bivariate time series based on copula functions to represent the cross-dependence and temporal structure of the data. We explore the advantages of using this nonlinear time series method over traditional approaches that use a transformation of the data to normal distributions as an intermediate step. The use of copulas gives flexibility to represent the serial variability of the real data on the simulation and allows having more control on the desired properties of the data. We use discrete zero mass density distributions to assess the nature of solar irradiance, alongside vector generalized linear models for the bivariate time series time dependent distributions. We found that the copula autoregressive methodology used, including the zero mass characteristics of the solar irradiance time series, generates a significant improvement over state of the art strategies. These results will help to better understand the fluctuating nature of solar energy forecasting, the underlying stochastic process, and quantify the potential of a photovoltaic (PV) energy generating system integration into a country electricity network. Experimental analysis and real data application substantiate the usage and convenience of the proposed methodology to forecast solar irradiance time series and solar energy across northern hemisphere, southern hemisphere, and equatorial zones.

Keywords: copula autoregressive, solar irradiance forecasting, solar energy forecasting, time series generation

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Authors: Dangut Maren David, Skaf Zakwan

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Adequate monitoring of vehicle component in other to obtain high uptime is the goal of predictive maintenance, the major challenge faced by businesses in industries is the significant cost associated with a delay in service delivery due to system downtime. Most of those businesses are interested in predicting those problems and proactively prevent them in advance before it occurs, which is the core advantage of Prognostic Health Management (PHM) application. The recent emergence of industry 4.0 or industrial internet of things (IIoT) has led to the need for monitoring systems activities and enhancing system-to-system or component-to- component interactions, this has resulted to a large generation of data known as big data. Analysis of big data represents an increasingly important, however, due to complexity inherently in the dataset such as imbalance classification problems, it becomes extremely difficult to build a model with accurate high precision. Data-driven predictive modeling for condition-based maintenance (CBM) has recently drowned research interest with growing attention to both academics and industries. The large data generated from industrial process inherently comes with a different degree of complexity which posed a challenge for analytics. Thus, imbalance classification problem exists perversely in industrial datasets which can affect the performance of learning algorithms yielding to poor classifier accuracy in model development. Misclassification of faults can result in unplanned breakdown leading economic loss. In this paper, an advanced approach for handling imbalance classification problem is proposed and then a prognostic model for predicting aircraft component replacement is developed to predict component replacement in advanced by exploring aircraft historical data, the approached is based on hybrid ensemble-based method which improves the prediction of the minority class during learning, we also investigate the impact of our approach on multiclass imbalance problem. We validate the feasibility and effectiveness in terms of the performance of our approach using real-world aircraft operation and maintenance datasets, which spans over 7 years. Our approach shows better performance compared to other similar approaches. We also validate our approach strength for handling multiclass imbalanced dataset, our results also show good performance compared to other based classifiers.

Keywords: prognostics, data-driven, imbalance classification, deep learning

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Authors: Ana Paula Lisboa Sohn, Filipa Dionísio Vieria, Nelson Casarotto, Idaulo José Cunha

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This paper aims at identifying and analyzing the knowledge transmission channels in textile and clothing clusters located in Brazil and in Europe. Primary data was obtained through interviews with key individuals. The collection of primary data was carried out based on a questionnaire with ten categories of indicators of knowledge transmission. Secondary data was also collected through a literature review and through international organizations sites. Similarities related to the use of the main transmission channels of knowledge are observed in all cases. The main similarities are: influence of suppliers of machinery, equipment and raw materials; imitation of products and best practices; training promoted by technical institutions and businesses; and cluster companies being open to acquire new knowledge. The main differences lie in the relationship between companies, where in Europe the intensity of this relationship is bigger when compared to Brazil. The differences also occur in importance and frequency of the relationship with the government, with the cultural environment, and with the activities of research and development. It is also found factors that reduce the importance of geographical proximity in transmission of knowledge, and in generating trust and the establishment of collaborative behavior.

Keywords: industrial clusters, interorganizational learning, knowledge transmission channels, textile and clothing industry

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Authors: Rommila Chandra, Harshika Choudhary

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The present-day tourism sector is globally developing at a fast pace, searching for new ideas and new venues. In the Indian Himalayan Region (IHR), tourism has experienced a vast growth and continuous diversification over the last few years, thus becoming one of the fastest-growing economic sectors in India. With its majestic landscape, high peaks, rich floral and faunal diversity, and cultural history, the IHR has continuously attracted tourists and pilgrims from across the globe. The IHR has attracted a vast range of visitors who seek adventure sports, natural and spiritual solace, peace, cultural assets, food, and festivals, etc. Thus, the multi-functionality of the region has turned tourism into a key component of economic growth for the rural communities in the hills. For the local mountain people, it means valuable economic opportunity for income generation, and for the government and entrepreneurs, it brings profits. As the urban cities gain attention and investment in India, efforts have to be made to protect, safeguard, and strengthen the cultural, spiritual, and natural heritage of IHR for sustainable livelihood development. Furthermore, the socio-economic and environmental insecurities, along with geographical isolation, adds to the challenging survival in the tough terrains of IHR, creating a major threat of outmigration, land abandonment, and degradation. The question the paper intends to answer is: whether the rural community of IHR is aware of the new global trends in rural tourism and the extent of their willingness to adapt to the evolving tourism industry, which impacts the rural economy, including sustainable livelihood opportunity. The objective of the paper is to discuss the integrated nature of rural tourism, which widely depends upon natural resources, cultural heritage, agriculture/horticulture, infrastructural development, education, social awareness, and willingness of the locals. The sustainable management of all these different rural activities can lead to long-term livelihood development and social upliftment. It highlights some gap areas and recommends fewcommunity-based coping measures which the local people can adopt amidst the disorganized sector of rural tourism. Lastly, the main contribution is the exploratory research of the rural tourism vulnerability in the IHR, which would further help in studying the resilience of the tourism sector in the rural parts of a developing nation.

Keywords: community-based approach, sustainable livelihood development, Indian Himalayan region, rural tourism

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Authors: Claude Takenga, Rolf-Dietrich Berndt, Erling Si, Markus Diem, Guohui Qiao, Melanie Gau, Michael Brandstoetter, Martin Kampel, Michael Dworzak

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In patients with acute lymphoblastic leukaemia (ALL), treatment response is increasingly evaluated with minimal residual disease (MRD) analyses. Flow Cytometry (FCM) is a fast and sensitive method to detect MRD. However, the interpretation of these multi-parametric data requires intensive operator training and experience. This paper presents a pipeline-software, as a ready-to-use FCM-based MRD-assessment tool for the daily clinical practice for patients with ALL. The new tool increases accuracy in assessment of FCM-MRD in samples which are difficult to analyse by conventional operator-based gating since computer-aided analysis potentially has a superior resolution due to utilization of the whole multi-parametric FCM-data space at once instead of step-wise, two-dimensional plot-based visualization. The system developed as a telemedical network reduces the work-load and lab-costs, staff-time needed for training, continuous quality control, operator-based data interpretation. It allows dissemination of automated FCM-MRD analysis to medical centres which have no established expertise for the benefit of an even larger community of diseased children worldwide. We established a telemedical network system for analysis and clinical follow-up and treatment monitoring of Leukaemia. The system is scalable and adapted to link several centres and laboratories worldwide.

Keywords: data security, flow cytometry, leukaemia, telematics platform, telemedicine

Procedia PDF Downloads 977

Authors: Abebe Mulugojam Negash, Yongbin Yu, Ekong Favour, Bekalu Nigus Dawit, Molla Woretaw Teshome, Aynalem Birtukan Yirga

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Breast cancer remains a significant health concern, necessitating advancements in diagnostic methodologies. Addressing this, our paper confronts the notable challenges in breast cancer classification, particularly the imbalance in datasets and the constraints in the accuracy and interpretability of prevailing deep learning approaches. We proposed an attention-based residual neural network (ResNet), which effectively combines the robust features of ResNet with an advanced attention mechanism. Enhanced through strategic data augmentation and positive weight adjustments, this approach specifically targets the issue of data imbalance. The proposed model is tested on the BreakHis dataset and achieved accuracies of 99.00%, 99.04%, 98.67%, and 98.08% in different magnifications (40X, 100X, 200X, and 400X), respectively. We evaluated the performance by using different evaluation metrics such as precision, recall, and F1-Score and made comparisons with other state-of-the-art methods. Our experiments demonstrate that the proposed model outperforms existing approaches, achieving higher accuracy in breast cancer classification.

Keywords: residual neural network, attention mechanism, positive weight, data augmentation

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Authors: Francesco Pinci

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This research investigates the significance of product packaging as an effective marketing tool. By using commercially available pasta as an example, the study specifically examines the visual components of packaging, including color, shape, packaging material, and logo. The insights gained from studies like this are particularly valuable to food and beverage companies as they provide marketers with a deeper understanding of the factors influencing consumer purchasing decisions. The research analyzes data collected through surveys conducted via Google Forms and visual data obtained using iMotions eye-tracker software. The results affirm the importance of packaging design elements, such as color and product information, in shaping consumer buying behavior.

Keywords: consumer behaviour, eyetracker, food marketing, neuromarketing

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Authors: Ahmed M. Hjazi, Bader M. Hjazi

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Protein 4.1 is a crucial component of complex interactions between the cytoskeleton and other junctional complex proteins. When the gene encoding this protein is altered, resulting in reduced expression, or when the protein is absent, the red cell undergoes a significant structural change. This research aims to achieve a deeper comprehension of the biochemical effects of red cell protein deficiency. A Tandem Mass Spectrometry Analysis (TMT-MS/MS) of patient cells lacking protein 4.1 compared to three healthy controls was achieved by the Proteomics Institute of the University of Bristol. The SDS-PAGE and Western blotting were utilized on the original patient sample and controls to partially confirm TMT MS/MS data analysis of the protein-4.1-deficient cells. Compared to healthy controls, protein levels in samples lacking protein 4.1 had a significantly higher concentration of proteins that probably originated from reticulocytes. This could occur if the patient has an elevated reticulocyte count. The increase in chaperone and reticulocyte-associated proteins was most notable in this study. This may result from elevated quantities of reticulocytes in patients with hereditary elliptocytosis.

Keywords: hereditary elliptocytosis, protein 4.1, red cells, tandem mass spectrometry data.

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Authors: Manvar Sagar, Nikul Virpariya

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The advancement in data mining techniques plays an important role in many applications. In context of privacy and security issues, the problems caused by association rule mining technique are investigated by many research scholars. It is proved that the misuse of this technique may reveal the database owner’s sensitive and private information to others. Many researchers have put their effort to preserve privacy in Association Rule Mining. Amongst the two basic approaches for privacy preserving data mining, viz. Randomization based and Cryptography based, the later provides high level of privacy but incurs higher computational as well as communication overhead. Hence, it is necessary to explore alternative techniques that improve the over-heads. In this work, we propose an efficient, collusion-resistant cryptography based approach for distributed Association Rule mining using Shamir’s secret sharing scheme. As we show from theoretical and practical analysis, our approach is provably secure and require only one time a trusted third party. We use secret sharing for privately sharing the information and code based identification scheme to add support against malicious adversaries.

Keywords: Privacy, Privacy Preservation in Data Mining (PPDM), horizontally partitioned database, EMHS, MFI, shamir secret sharing

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Authors: Seyed Abbas Tabatabaei, Fereydoon Moghadas Nejad, Mohammad Saed

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The bleeding prediction of the asphalt is one of the most complex subjects in the pavement engineering. In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) is used for modeling the effect of important parameters on bleeding is trained and tested with the experimental results. bleeding index based on the asphalt film thickness differential as target parameter,asphalt content, temperature depth of two centemeter, heavy traffic, dust to effective binder, Marshall strength, passing 3/4 sieves, passing 3/8 sieves,passing 3/16 sieves, passing NO8, passing NO50, passing NO100, passing NO200 as input parameters. Then, we randomly divided empirical data into train and test sections in order to accomplish modeling. We instructed ANFIS network by 72 percent of empirical data. 28 percent of primary data which had been considered for testing the approprativity of the modeling were entered into ANFIS model. Results were compared by two statistical criterions (R2, RMSE) with empirical ones. Considering the results, it is obvious that our proposed modeling by ANFIS is efficient and valid and it can also be promoted to more general states.

Keywords: bleeding, asphalt film thickness differential, Anfis Modeling

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Authors: Chanchai Supaartagorn

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Structured Query Language (SQL) is the standard de facto language to access and manipulate data in a relational database. Although SQL is a language that is simple and powerful, most novice users will have trouble with SQL syntax. Thus, we are presenting SQL generator tool which is capable of translating actions and displaying SQL commands and data sets simultaneously. The tool was developed based on Model-View-Controller (MVC) pattern. The MVC pattern is a widely used software design pattern that enforces the separation between the input, processing, and output of an application. Developers take full advantage of it to reduce the complexity in architectural design and to increase flexibility and reuse of code. In addition, we use White-Box testing for the code verification in the Model module.

Keywords: MVC, relational database, SQL, White-Box testing

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Authors: Lauren B. Birney

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Summer STEM Institute for Middle and High School Students at Pace University The STEM Collaboratory NYC® Summer Fellows Institute takes place on Pace University’s New York City campus during July and provides the following key features for all participants: (i) individual meetings with Pace faculty to discuss and refine future educational goals; (ii) mentorship, guidance, and new friendships with program leaders; and (iii) guest lectures from professionals in STEM disciplines and businesses. The Summer STEM Institute allows middle school and high school students to work in teams to conceptualize, develop, and build native mobile applications that teach and reinforce skills in the sciences and mathematics. These workshops enhance students’STEM problem solving techniques and teach advanced methods of computer science and engineering. Topics include: big data and analytics at the Big Data lab at Seidenberg, Data Science focused on social and environmental advancement and betterment; Natural Disasters and their Societal Influences; Algal Blooms and Environmental Impacts; Green CitiesNYC; STEM jobs and growth opportunities for the future; renew able energy and sustainable infrastructure; and climate and the economy. In order to better align the existing Summer STEM, Institute with the CCERS model and expand the overall network, Pace is actively recruiting new content area specialists from STEM industries and private sector enterprises to participate in an enhanced summer institute in order to1) nurture student progress and connect summer learning to school year curriculum, 2) increase peer-to-peer collaboration amongst STEM professionals and private sector technologists, and 3) develop long term funding and sponsorship opportunities for corporate sector partners to support CCERS schools and programs directly.

Keywords: environmental restoration science, citizen science, data science, STEM

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Authors: Dagnew Dessie Mengie

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The purpose of this study was to investigate German tourists' demand for Ethiopian tourism destinations. The author has made every effort to identify the differences in the preferences of German visitors’ demand in Ethiopia comparing with Egypt, Kenya, Tanzania, and South African tourism sectors if they are invited to visit at the same time. However, the demand of international tourism for Ethiopia currently lags behind these African countries. Therefore, to offer demand-driven tourism products, the Ethiopian government, Tour & Travel operators need to understand the important factors that affect international tourists’ decision to visit Ethiopian tourist destinations. The aim of this study was intended to analyze German Tourists’ Demand towards Ethiopian destination. The researcher aimed to identify the demand for German tourists’ preference to Ethiopian tourist destinations comparing to the above-mentioned African countries. For collecting and analysing data for this study, both quantitative and qualitative methods of research are being used in this study. The most significant data are collected by using the primary data collection method i.e. survey and interviews which are the most and large number of potential responses and feedback from nine German active tourists,12 Ethiopian tourism officials, four African embassies, and four well functioning private tour companies and secondary data collected from books, journals, previous research and electronic websites. based on the data analysis of the information gathered from interviews and questionnaires, the study disclosed that majority of German tourists have not that much high demand on Ethiopian Tourist destinations due to the following reasons; Many Germans are fascinated by adventures, safari and simply want to lie on the beach and relax. These interests have leaded them to look for other African countries which have these accesses. Uncomfortable infrastructure and transport problems attributed for the decreasing the number of German tourists in the country. Inadequate marketing operation of Ethiopian Tourism Authority and its delegates in advertising and clarifying the above irregularities which are raised by the tourists.

Keywords: environmental benefits of tourism, social benefits of tourism, economical benefits of tourism, political factors in tourism

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Authors: Alexandra P. Alexandropoulou, Andreas Fousteris, Eleni Didaskalou, Dimitrios A. Georgakellos

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As urbanization is becoming a major stress factor not only for the urban environment but also for the wellbeing of city dwellers, incorporating the issues of urban health in strategic city planning and policy-making has never been more relevant. The impact of urbanization can vary from low to severe and relates to all non-communicable diseases caused by the different functions of cities. Air pollution, noise pollution, water and soil pollution, availability of open green spaces, and urban heat island are the major factors that can compromise citizens' health. Urban health describes the effects of the social environment, the physical environment, and the availability and accessibility to health and social services. To assess the quality of urban wellbeing, all urban characteristics that might have an effect on citizens' health must be considered, evaluated, and introduced in integrated local planning. A series of indices and indicators can be used to better describe these effects and set the target values in policy making. Local strategic planning is one of the most valuable development tools a local city administration can possess; thus, it has become mandatory under Greek law for all municipalities. It involves a two-stage procedure; the first aims to collect, analyse and evaluate data on the current situation of the city (administrative data, population data, environmental data, social data, swot analysis), while the second aims to introduce a policy vision described and supported by distinct (nevertheless integrated) actions, plans and measures to be implemented with the aim of city development and citizen wellbeing. In this procedure, the element of health is often neglected or under-evaluated. A relative survey was conducted among all Greek local authorities in order to shed light on the current situation. Evidence shows that the rate of incorporation of health in strategic planning is lacking behind. The survey also highlights key hindrances and concerns raised by local officials and suggests a path for the way forward.

Keywords: urban health, strategic planning, local authorities, integrated development

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Authors: Mohamed Nasraoui

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Spatial ore distribution, ore heterogeneity and their links with geological processes involved in Niobium concentration are all factors for consideration when bridging field observations to extraction scheme. Indeed, mineralogy changes of Nb-hosting phases, their textural relationships with hydrothermal or secondary minerals, play a key control over mineral processing. This study based both on filed work and ore characterization presents data from several Nb-deposits related to carbonatite complexes. The results obtained by a wide range of analytical techniques, including, XRD, XRF, ICP-MS, SEM, Microprobe, Spectro-CL, FTIR-DTA and Mössbauer spectroscopy, demonstrate how geometallurgical assessment, at all stage of mine development, can greatly assist in the design of a suitable extraction flowsheet and data reconciliation.

Keywords: carbonatites, Nb-geometallurgy, Nb-mineralogy, mineral processing.

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Authors: Kevin Wang, Jian Ge, Yinan Zhao, Kevin Willis

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Kepler has discovered over 4000 exoplanets and candidates. However, current transit planet detection techniques based on the wavelet analysis and the Box Least Squares (BLS) algorithm have limited sensitivity in detecting minor planets with a low signal-to-noise ratio (SNR) and long periods with only 3-4 repeated signals over the mission lifetime of 4 years. This paper presents a novel precise-period transit signal detection methodology based on a new Graphics Processing Unit (GPU) Fast Folding algorithm in conjunction with a Convolutional Neural Network (CNN) to detect low SNR and/or long-period transit planet signals. A comparison with BLS is conducted on both simulated light curves and real data, demonstrating that the new method has higher speed, sensitivity, and reliability. For instance, the new system can detect transits with SNR as low as three while the performance of BLS drops off quickly around SNR of 7. Meanwhile, the GPU Fast Folding method folds light curves 25 times faster than BLS, a significant gain that allows exoplanet detection to occur at unprecedented period precision. This new method has been tested with all known transit signals with 100% confirmation. In addition, this new method has been successfully applied to the Kepler of Interest (KOI) data and identified a few new Earth-sized Ultra-short period (USP) exoplanet candidates and habitable planet candidates. The results highlight the promise for GPU Fast Folding as a replacement to the traditional BLS algorithm for finding small and/or long-period habitable and Earth-sized planet candidates in-transit data taken with Kepler and other space transit missions such as TESS(Transiting Exoplanet Survey Satellite) and PLATO(PLAnetary Transits and Oscillations of stars).

Keywords: algorithms, astronomy data analysis, deep learning, exoplanet detection methods, small planets, habitable planets, transit photometry

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Authors: Ye. Shapovalov, V. Shapovalov, O. Stryzhak, A. Salyuk

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Over the past decades, amount of scientific information has been growing exponentially. It became more complicated to process and systemize this amount of data. The approach to systematization of scientific information on the production of biogas based on the ontological IT platform “T.O.D.O.S.” has been developed. It has been proposed to select semantic characteristics of each work for their further introduction into the IT platform “T.O.D.O.S.”. An ontological graph with a ranking function for previous scientific research and for a system of selection of microorganisms has been worked out. These systems provide high performance of information management of scientific information.

Keywords: ontology-based analysis, analysis of scientific data, methanogenesis, microorganism hierarchy, 'T.O.D.O.S.'

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Authors: Vera Varazashvili, Murman Tsarakhov, Tamar Mirianashvili, Teimuraz Pavlenishvili, Tengiz Machaladze, Mzia Khundadze

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Standard Gibbs energy of formation ΔGfor(298.15) of lanthanide-iron double oxides of garnet-type crystal structure R3Fe5O12 - RIG (R – are rare earth ions) from initial oxides are evaluated. The calculation is based on the data of standard entropies S298.15 and standard enthalpies ΔH298.15 of formation of compounds which are involved in the process of garnets synthesis. Gibbs energy of formation is presented as temperature function ΔGfor(T) for the range 300-1600K. The necessary starting thermodynamic data were obtained from calorimetric study of heat capacity – temperature functions and by using the semi-empirical method for calculation of ΔH298.15 of formation. Thermodynamic functions for standard temperature – enthalpy, entropy and Gibbs energy - are recommended as reference data for technological evaluations. Through the isostructural series of rare earth-iron garnets the correlation between thermodynamic properties and characteristics of lanthanide ions are elucidated.

Keywords: calorimetry, entropy, enthalpy, heat capacity, gibbs energy of formation, rare earth iron garnets

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Authors: Manouchehr Amiri

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This article introduces a general notion of physical bit information that is compatible with the basics of quantum mechanics and incorporates the Shannon entropy as a special case. This notion of physical information leads to the Binary data matrix model (BDM), which predicts the basic results of quantum mechanics, general relativity, and black hole thermodynamics. The compatibility of the model with holographic, information conservation, and Landauer’s principles are investigated. After deriving the “Bit Information principle” as a consequence of BDM, the fundamental equations of Planck, De Broglie, Beckenstein, and mass-energy equivalence are derived.

Keywords: physical theory of information, binary data matrix model, Shannon information theory, bit information principle

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Authors: Felix Y. Mahenge, Abiud L. Kaswamila, Davis G. Mwamfupe

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Grapevine is a drought resistant crop, although in recent years it has been observed to be affect by climate change. This compelled investigation of grapevine farmers’ adaptation strategies to climate change in Dodoma, Tanzania. A mixed research approach was adopted. Likewise, purposive and random sampling techniques were used to select individuals for the study. About 248 grapevine farmers and 64 key informants and members of focus group discussions were involved. Primary data were collected through surveys, discussions, interviews, and observations, while secondary data were collected through documentary reviews. Quantitative data were analysed through descriptive statistics by means of IBM (SPSS) software while the qualitative data were analysed through content analysis. The findings indicate that climate change has adversely affected grapevine production leading to the occurrence of grapevine pests and diseases, drought which increases costs for irrigation and uncertainties which affect grapevine markets. For the purpose of lessening grapevine production constraints due to climate change, farmers have been using several adaptation strategies. Some of the strategies include application of pesticides, use of scarers to threaten birds, irrigation, timed pruning, manure fertilisers and diversification to other farm or non-farm activities. The use of pesticides and industrial fertilizers were regarded as increasing human health risks in the study area. The researchers recommend that the Tanzania government should strengthen the agricultural extension services in the study area so that the farmers undertake adaptation strategies with the consideration of human health safety.

Keywords: grapevine farmers, adaptation, climate change, human health

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Authors: Mahesh Kothari, K. D. Gharde

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The ANN and fuzzy logic (FL) models were developed to predict the runoff and sediment yield for catchment of Kal river, India using 21 years (1991 to 2011) rainfall and other hydrological data (evaporation, temperature and streamflow lag by one and two day) and 7 years data for sediment yield modelling. The ANN model performance improved with increasing the input vectors. The fuzzy logic model was performing with R value more than 0.95 during developmental stage and validation stage. The comparatively FL model found to be performing well to ANN in prediction of runoff and sediment yield for Kal river.

Keywords: transferred function, sigmoid, backpropagation, membership function, defuzzification

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Authors: Krismonika Khoirunnisa

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This research article aims to describe the variation of the Javanese lexicon "Sleep " and its distribution in the Jombang area, East Java. The objectives of this study were (1) to classify the variation of the "Sleep" lexicon in the Jombang area and (2) to design the fish rips for the variation of the "Sleep" lexicon according to their distribution. This type of research is a qualitative descriptive study using the method of leading proficiency, namely conducting interviews with speakers without directly meeting the speakers (interviews via WhatsApp and email as the media). This research article uses techniques record as support and tools for mapping and classifying data, collecting data in this study conducted at four points, namely the Kaliwungu village (Jombang City), Banjardowo village (District of Jombang), Mayangan Village (Subdistrict Jogoroto), and Karobelah village (Subdistrict Mojoagung) as a target investigators to conduct the interview. This study uses the dialectology theory as a basis for analyzing the data obtained. The results of this study found that the Javanese language variation "Sleep" has many different linguals, meanings, and forms even though they are in the same area (Jombang).

Keywords: geographical dialectology, lexicon variations, jombangan dialect, sssavanese language

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Authors: Abdullah H. Özcan, Dilara Hisar, Yetkin Sayar, Cem Ünsalan

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In this study, an automated building and tree detection method is proposed using DSM data and true orthophoto image. A multiscale matched filtering is used on DSM data. Therefore, first watershed transform is applied. Then, Otsu’s thresholding method is used as an adaptive threshold to segment each watershed region. Detected objects are masked with NDVI to separate buildings and trees. The proposed method is able to detect buildings and trees without entering any elevation threshold. We tested our method on ISPRS semantic labeling dataset and obtained promising results.

Keywords: building detection, local maximum filtering, matched filtering, multiscale

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Authors: Michio Kurosu

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Alterations in glycosylation not only directly impact cell growth and survival but also facilitate tumor-induced immunomodulation and eventual metastasis. Identification of cell type-specific glycoconjugates (tumor markers) has led to the discovery of new assay systems for certain cancers via immunodetection reagents. N- and O-linked glycans are the most abundant forms of glycoproteins. Recent studies of cancer immunotherapy are based on the immunogenicity of truncated O-glycan chains (e.g., Tn, sTn, T, and sLea/x). The prevalence of N-linked glycan changes in the development of tumor cells is known; however, therapeutic antibodies against N-glycans have not yet been developed. This is due to the lack of specificity of N-linked glycans between normal/healthy and cancer cells. Abnormal branching of N-linked glycans has been observed, particularly in solid cancer cells. While the discovery of drug-like glycosyltransferase inhibitors that block the biosynthesis of specific branching has a very low likelihood of success, altered glycosylation levels can be exploited by suppressing N-glycan biosynthesis through the inhibition of dolichyl-phosphate N-acetylglucosaminephosphotransferase1 (DPAGT1) activity. Inhibition of DPAGT1 function leads to changes of O-glycosylation on proteins associated with mitochondria and zinc finger binding proteins (indirect effects). On the basis of dynamic crosstalk between DPAGT1 and Snail/Slung/ZEB1 (a family of transcription factors that promote the repression of the adhesion molecules), we have developed pharmacologically acceptable selective DPAGT1 inhibitors. Tunicamycin kills a wide range of cancer and healthy cells in a non-selective manner. In sharp contrast, our DPAGT1 inhibitors display strong cytostatic effects against 16 solid cancers, which require the overexpression of DPAGT1 in their progression but do not affect the cell viability of healthy cells. The identified DPAGT1 inhibitors possess impressive anti-metastatic ability in various solid cancer cell lines and induce their mitochondrial structural changes, resulting in apoptosis. A prototype DPAGT1 inhibitor, APPB has already been proven to shrink solid tumors (e.g., pancreatic cancers, triple-negative breast cancers) in vivo while suppressing metastases and has strong synergistic effects when combined with current cytotoxic drugs (e.g., paclitaxel). At this conference, our discovery of selective DPAGT1 inhibitors with drug-like properties and proof-of-pharmaceutical concept studies of a novel DPAGT1 inhibitor are presented.

Keywords: DPAGT1 inhibitors, anti-metastatic drugs, natural product based drug designs, cytostatic effects

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Authors: Vinda B. Manurung, Laila Warkhaida, David Hutabarat, Sentanu Wisnuwardhana, Christovik Simatupang, Dhani Sanjaya, Ashadi, Redha B. Putra, Kiki Yustendi

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The geothermal drilling environment presents many obstacles that have limited the use of directional drilling and logging-while-drilling (LWD) technologies, such as borehole washout, mud losses, severe vibration, and high temperature. The case study presented in this paper demonstrates a practice to enhance data logging in geothermal drilling by deploying advanced telemetry and LWD technologies. This operation is aiming continuous improvement in geothermal drilling operations. The case study covers a 12.25-in. hole section of well XX-05 in Pad XX of the Sorik Marapi Geothermal Field. LWD string consists of electromagnetic (EM) telemetry, pressure while drilling (PWD), vibration (DDSr), and acoustic calliper (ACAL). Through this tool configuration, the operator acquired drilling mechanics and caliper logs in real-time and recorded mode, enabling effective monitoring of wellbore stability. Throughout the real-time acquisition, EM-PPM telemetry had provided a three times faster data rate to the surface unit. With the integration of Caliper data and Drilling mechanics data (vibration and ECD -equivalent circulating density), the borehole conditions were more visible to the directional driller, allowing for better control of drilling parameters to minimize vibration and achieve optimum hole cleaning in washed-out or tight formation sequences. After reaching well TD, the recorded data from the caliper sensor indicated an average of 8.6% washout for the entire 12.25-in. interval. Washout intervals were compared with loss occurrence, showing potential for the caliper to be used as an indirect indicator of fractured intervals and validating fault trend prognosis. This LWD case study has given added value in geothermal borehole characterization for both drilling operation and subsurface. Identified challenges while running LWD in this geothermal environment need to be addressed for future improvements, such as the effect of tool eccentricity and the impact of vibration. A perusal of both real-time and recorded drilling mechanics and caliper data has opened various possibilities for maximizing sensor usage in future wells.

Keywords: geothermal drilling, geothermal formation, geothermal technologies, logging-while-drilling, vibration, caliper, case study

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