Search results for: raw complex data

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

Procedia PDF Downloads 66

Authors: Muhammad Arifur Rahman, Talukdar M. Waliullah, Takashi Ushimaru

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Nucleophagy selectively degrades nuclear materials, especially nucleolus after nutrient starvation or inactivation of TORC1 kinase in budding yeast. Budding yeast phosphatidate (PA) phosphatase Pah1 that converts PA to diacylglycerol is essential for partitioning of lipid precursors between membrane and storage that is crucial for many aspects of cell growth and development. Pah1 is required for nuclear/ER membrane biogenesis and vacuole function, but whether Pah1 and its activator Nem1/Spo7 protein phosphatase complex are involved in autophagy is largely unknown. Loss of Pah1 causes expansion of the nucleus and fragmentation of the vacuole. Here we show that Pah1 is required for bulk autophagy and nucleophagy after TORC1 inactivation. Loss of Pah1 impaired nucleophagy severely and bulk autophagy to a lesser extent. Loss of the Pah1 activator Nem1-Spo7 protein phosphatase exhibited similar features.

Keywords: autophagy, Nem1/Spo7 phosphatase, Pah1, nucleophagy, TORC1

Procedia PDF Downloads 214

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

Procedia PDF Downloads 98

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

Procedia PDF Downloads 168

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: Martina Perinkova, Lenka Kolarcikova, Marketa Twrda

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Brownfields are one of the most important problems that must be solved by today's cities. The topic of this article is description of developing a comprehensive transformation of post-industrial area of the former iron factory national cultural heritage Lower Vítkovice. City of Ostrava used to be industrial superpower of the Czechoslovak Republic, especially in the area of coal mining and iron production, after declining industrial production and mining in the 80s left many unused areas of former factories generally brownfields and backfields. Since the late 90s we are observing how the city officials or private entities seeking to remedy this situation. Regeneration of brownfields is a very expensive and long-term process. The area is now rebuilt for tourists and residents of the city in the entertainment, cultural, and social center. It was necessary do the reconstruction of the industrial monuments. Equally important was the construction of new buildings, which helped reusing of the entire complex. This is a unique example of transformation of technical monuments and completion of necessary new objects, so that the area could start working again and reintegrate back into the urban system.

Keywords: brown fields, conversion, historical and industrial buildings, reconstruction

Procedia PDF Downloads 326

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: Getaneh Berie Tarekegn

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Wireless communication via unmanned aerial vehicles (UAVs) has drawn a great deal of attention due to its flexibility in establishing line-of-sight (LoS) communications. However, in complex urban and dynamic environments, the movement of UAVs can be blocked by trees and high-rise buildings that obstruct directional paths. With reconfigurable intelligent surfaces (RIS), this problem can be effectively addressed. To achieve this goal, accurate channel estimation in RIS-assisted UAV-enabled wireless communications is crucial. This paper proposes an accurate channel estimation model using long short-term memory (LSTM) for a multi-user RIS-assisted UAV-enabled wireless communication system. According to simulation results, LSTM can improve the channel estimation performance of RIS-assisted UAV-enabled wireless communication.

Keywords: channel estimation, reconfigurable intelligent surfaces, long short-term memory, unmanned aerial vehicles

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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: Eva Queheille, Franck Taillandier, Nadia Saiyouri

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Waste management has become a major issue in demolition works, because of its environmental impact (energy consumption, resource consumption, pollution…). However, improving waste management requires to take also into account the overall demolition process and to consider demolition main objectives (e.g. cost, delay). Establishing a strategy with these conflicting objectives (economic and environment) remains complex. In order to provide a decision-support for demolition companies, a multi-objective optimization model was developed. In this model, a demolition strategy is computed from a set of 80 decision variables (worker team composition, machines, treatment for each type of waste, choice of treatment platform…), which impacts the demolition objectives. The model has experimented on a real-case study (demolition of several buildings in France). To process the optimization, different optimization algorithms (NSGA2, MOPSO, DBEA…) were tested. Results allow the engineer in charge of this case, to build a sustainable demolition strategy without affecting cost or delay.

Keywords: deconstruction, life cycle assessment, multi-objective optimization, waste management

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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

Procedia PDF Downloads 403

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

Procedia PDF Downloads 414

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

Procedia PDF Downloads 83

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: S. Ramteke, K. S. Patel

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Aerosols are complex mixture of particulate matters (PM) inclusive of carbons, silica, elements, various salts, etc. Aerosols get deep into the human lungs and cause a broad range of health effects, in particular, respiratory and cardiovascular illnesses. They are one of the major culprits for the climate change. They are emitted by the high thermal processes i.e. vehicles, steel, sponge, cement, thermal power plants, etc. Raipur (22˚33'N to 21˚14'N and 82˚6'E) to 81˚38'E) is a growing industrial city in central India with population of two million. In this work, the distribution of inorganics (i.e. Cl⁻, NO³⁻, SO₄²⁻, NH₄⁺, Na⁺, K⁺, Mg²⁺, Ca²⁺, Al, Cr, Mn, Fe, Ni, Cu, Zn, and Pb) associated to the PM in the ambient air is described. The PM₁₀ in ambient air of Raipur city was collected for duration of one year (December 2014 - December 2015). The PM₁₀ was segregated into nine modes i.e. PM₁₀.₀₋₉.₀, PM₉.₀₋₅.₈, PM₅.₈₋₄.₇, PM₄.₇₋₃.₃, PM₃.₃₋₂.₁, PM₂.₁₋₁.₁, PM₁.₁₋₀.₇, PM₀.₇₋₀.₄ and PM₀.₄ to know their emission sources and health hazards. The analysis of ions and metals was carried out by techniques i.e. ion chromatography and TXRF. The PM₁₀ concentration (n=48) was ranged from 100-450 µg/m³ with mean value of 73.57±20.82 µg/m³. The highest concentration of PM₄.₇₋₃.₃, PM₂.₁₋₁.₁, PM₁.₁₋₀.₇ was observed in the commercial, residential and industrial area, respectively. The effect of meteorology i.e. temperature, humidity, wind speed and wind direction in the PM₁₀ and associated elemental concentration in the air is discussed.

Keywords: ambient aerosol, ions, metals, segregation

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Authors: Habtamu Abera Goshu, Ping Yan

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Copy number variation (CNV) is a significant marker of the genetic and phenotypic diversity among individuals that accounts for complex quantitative traits of phenotype and diseases via modulating gene dosage, position effects, alteration of downstream pathways, modification of chromosome structure, and position within the nucleus and disrupting coding regions in the genome. Associating copy number variations (CNVs) with growth and gene expression are a powerful approach for identifying genomic characteristics that contribute to phenotypic and genotypic variation. A previous study using next-generation sequencing illustrated that the choline kinase beta (CHKB), Krüpple-like factor 6 (KLF6), glypican 1(GPC1), and cholinergic receptor muscarinic 3 (CHRM3) genes reside within copy number variable regions (CNVRs) of yak populations that overlap with quantitative trait loci (QTLs) of meat quality and growth. As a result, this research aimed to determine the association of CNVs of the KLF6, CHKB, GPC1, and CHRM3 genes with growth traits in the Datong yak breed. The association between the CNV types of the KLF6, CHKB, GPC1, and CHRM3 genes and the growth traits in the Datong yak breed was determined by one-way analysis of variance (ANOVA) using SPSS software. The CNV types were classified as a loss (a copy number of 0 or 1), gain (a copy number >2), and normal (a copy number of 2) relative to the reference gene, BTF3 in the 387 individuals of Datong yak. These results indicated that the normal CNV types of the CHKB and GPC1 genes were significantly (P<0.05) associated with high body length, height and weight, and chest girth in six-month-old and five-year-old Datong yaks. On the other hand, the loss CNV types of the KLF6 gene is significantly (P<0.05) associated with body weight and length and chest girth at six-month-old and five-year-old Datong yaks. In the contrary, the gain CNV type of the CHRM3 gene is highly (P<0.05) associated with body weight, length, height, and chest girth in six-month-old and five-year-old. This work provides the first observation of the biological role of CNVs of the CHKB, KLF6, GPC1, and CHRM3 genes in the Datong yak breed and might, therefore, provide a novel opportunity to utilize data on CNVs in designing molecular markers for the selection of animal breeding programs for larger populations of various yak breeds. Therefore, we hypothesized that this study provided inclusive information on the application of CNVs of the CHKB, KLF6, GPC1, and CHRM3 genes in growth traits in Datong yaks and its possible function in bovine species.

Keywords: Copy number variation, growth traits, yak, genes

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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

Abstract:

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

Abstract:

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: Hilal Turkoglu Sasmazel, Ozan Ozkan, Seda Surucu

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Tissue engineering is the field of treating defects caused by injuries, trauma or acute/chronic diseases by using artificial scaffolds that mimic the extracellular matrix (ECM), the natural biological support for the tissues and cells within the body. The main aspects of a successful artificial scaffold are (i) large surface area in order to provide multiple anchorage points for cells to attach, (ii) suitable porosity in order to achieve 3 dimensional growth of the cells within the scaffold as well as proper transport of nutrition, biosignals and waste and (iii) physical, chemical and biological compatibility of the material in order to obtain viability throughout the healing process. By hybrid scaffolds where two or more different materials were combined with advanced fabrication techniques into complex structures, it is possible to combine the advantages of individual materials into one single structure while eliminating the disadvantages of each. Adding this to the complex structure provided by advanced fabrication techniques enables obtaining the desired aspects of a successful artificial tissue scaffold. In this study, fibroblast compatibility of poly(ε-caprolactone) (PCL)/chitosan core-shell electrospun hybrid scaffolds with proper mechanical, chemical and physical properties successfully developed in our previous study was investigated. Standard 7-day cell culture was carried out with L929 fibroblast cell line. The viability of the cells cultured with the scaffolds was monitored with 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) viability assay for every 48 h starting with 24 h after the initial seeding. In this assay, blank commercial tissue culture polystyrene (TCPS) Petri dishes, single electrospun PCL and single electrospun chitosan mats were used as control in order to compare and contrast the performance of the hybrid scaffolds. The adhesion, proliferation, spread and growth of the cells on/within the scaffolds were observed visually on the 3rd and the 7th days of the culture period with confocal laser scanning microscopy (CSLM) and scanning electron microscopy (SEM). The viability assay showed that the hybrid scaffolds caused no toxicity for fibroblast cells and provided a steady increase in cell viability, effectively doubling the cell density for every 48 h for the course of 7 days, as compared to TCPS, single electrospun PCL or chitosan mats. The cell viability on the hybrid scaffold was ~2 fold better compared to TCPS because of its 3D ECM-like structure compared to 2D flat surface of commercially cell compatible TCPS, and the performance was ~2 fold and ~10 fold better compared to single PCL and single chitosan mats, respectively, even though both fabricated similarly with electrospinning as non-woven fibrous structures, because single PCL and chitosan mats were either too hydrophobic or too hydrophilic to maintain cell attachment points. The viability results were verified with visual images obtained with CSLM and SEM, in which cells found to achieve characteristic spindle-like fibroblast shape and spread on the surface as well within the pores successfully at high densities.

Keywords: chitosan, core-shell, fibroblast, electrospinning, PCL

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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

Procedia PDF Downloads 88

Authors: Sham Kidane, Fitsum Gaim, Ibrahim Abdella, Sirak Asmerom, Yoel Ghebrihiwot, Simon Mulugeta, Natnael Ambassager

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The lack of annotated datasets is a bottleneck to the progress of NLP in low-resourced languages. The work presented here consists of large-scale annotated datasets and models for the named entity recognition (NER) system for the Tigrinya language. Our manually constructed corpus comprises over 340K words tagged for NER, with over 118K of the tokens also having parts-of-speech (POS) tags, annotated with 12 distinct classes of entities, represented using several types of tagging schemes. We conducted extensive experiments covering convolutional neural networks and transformer models; the highest performance achieved is 88.8% weighted F1-score. These results are especially noteworthy given the unique challenges posed by Tigrinya’s distinct grammatical structure and complex word morphologies. The system can be an essential building block for the advancement of NLP systems in Tigrinya and other related low-resourced languages and serve as a bridge for cross-referencing against higher-resourced languages.

Keywords: Tigrinya NER corpus, TiBERT, TiRoBERTa, BiLSTM-CRF

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Authors: Benalia Nadia, Zerzouri Nora, Ben Si Ali Nadia

Abstract:

Abstract: Among the many different modern heuristic optimization methods, genetic algorithms (GA) and the particle swarm optimization (PSO) technique have been attracting a lot of interest. The GA has gained popularity in academia and business mostly because to its simplicity, ability to solve highly nonlinear mixed integer optimization problems that are typical of complex engineering systems, and intuitiveness. The mechanics of the PSO methodology, a relatively recent heuristic search tool, are modeled after the swarming or cooperative behavior of biological groups. It is suitable to compare the performance of the two techniques since they both aim to solve a particular objective function but make use of distinct computing methods. In this article, PSO and GA optimization approaches are used for the parameter tuning of the power system stabilizer and Proportional integral derivative regulator. Load angle and rotor speed variations in the single machine infinite bus bar system is used to measure the performance of the suggested solution.

Keywords: SMIB, genetic algorithm, PSO, transient stability, power system stabilizer, PID

Procedia PDF Downloads 74

Authors: Muwaffaq Usman Adam, Isaac Quaye, Jim Anbazu, Yetimoni Kpeebi, Michael Osei-Assibey

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Urban informality, characterized by spontaneous and self-organized practices, plays a significant but often overlooked role in shaping the development of cities, particularly in the context of mixed-use and compact urban environments. This paper aims to explore the invisible planning processes inherent in informal practices and their influence on the urban form of Ghanaian cities. By examining the dynamic interplay between informality and formal planning, the study will discuss the ways in which informal actors shape and plan for mixed-use and compact development. Drawing on the synthesis of relevant secondary data, the research will begin by defining urban informality and identifying the factors that contribute to its prevalence in Ghanaian cities. It will delve into the concept of mixed-use and compact development, highlighting its benefits and importance in urban areas. Drawing on case studies, the paper will uncover the hidden planning processes that occur within informal settlements, showcasing their impact on the physical layout, land use, and spatial arrangements of Ghanaian cities. The study will also uncover the challenges and opportunities associated with informal planning. It examines the constraints faced by informal planners (actors) while also exploring the potential benefits and opportunities that emerge when informality is integrated into formal planning frameworks. By understanding the invisible planner, the research will offer valuable insights into how informal practices can contribute to sustainable and inclusive urban development. Based on the findings, the paper will present policy implications and recommendations. It highlights the need to bridge the policy gaps and calls for the recognition of informal planning practices within formal systems. Strategies are proposed to integrate informality into planning frameworks, fostering collaboration between formal and informal actors to achieve compact and mixed-use development in Ghanaian cities. This research underscores the importance of recognizing and leveraging the invisible planner in Ghanaian cities. By embracing informal planning practices, cities can achieve more sustainable, inclusive, and vibrant urban environments that meet the diverse needs of their residents. This research will also contribute to a deeper understanding of the complex dynamics between informality and planning, advocating for inclusive and collaborative approaches that harness the strengths of both formal and informal actors. The findings will likewise contribute to advancing our understanding of informality's role as an invisible yet influential planner, shedding light on its spatial planning implications on Ghanaian cities.

Keywords: informality, mixed-uses, compact development, land use, ghana

Procedia PDF Downloads 115