Search results for: earthquake disaster data collection

Authors: Xingyu Pan, Laure Tougne

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For recognizing coins, the graved release date is important information to identify precisely its monetary type. However, reading characters in coins meets much more obstacles than traditional character recognition tasks in the other fields, such as reading scanned documents or license plates. To address this challenging issue in a numismatic context, we propose a training-free approach dedicated to detection and recognition of the release date of the coin. In the first step, the date zone is detected by comparing histogram features; in the second step, a topology-based algorithm is introduced to recognize coin numbers with various font types represented by binary gradient map. Our method obtained a recognition rate of 92% on synthetic data and of 44% on real noised data.

Keywords: coin, detection, character recognition, topology

Procedia PDF Downloads 246

Authors: Noriyuki Suyama

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This paper discusses purchasing behavior in retail stores, with a particular focus on the impact of weather changes on customers' purchasing behavior. Weather conditions are one of the factors that greatly affect the management and operation of retail stores. However, there is very little research on the relationship between weather conditions and marketing from an academic perspective, although there is some importance from a practical standpoint and knowledge based on experience. For example, customers are more hesitant to go out when it rains than when it is sunny, and they may postpone purchases or buy only the minimum necessary items even if they do go out. It is not difficult to imagine that weather has a significant impact on consumer behavior. To the best of the authors' knowledge, there have been only a few studies that have delved into the purchasing behavior of individual customers. According to Hirata (2018), the economic impact of weather in the United States is estimated to be 3.4% of GDP, or "$485 billion ± $240 billion per year. However, weather data is not yet fully utilized. Representative industries include transportation-related industries (e.g., airlines, shipping, roads, railroads), leisure-related industries (e.g., leisure facilities, event organizers), energy and infrastructure-related industries (e.g., construction, factories, electricity and gas), agriculture-related industries (e.g., agricultural organizations, producers), and retail-related industries (e.g., retail, food service, convenience stores, etc.). This paper focuses on the retail industry and advances research on weather. The first reason is that, as far as the author has investigated the retail industry, only grocery retailers use temperature, rainfall, wind, weather, and humidity as parameters for their products, and there are very few examples of academic use in other retail industries. Second, according to NBL's "Toward Data Utilization Starting from Consumer Contact Points in the Retail Industry," labor productivity in the retail industry is very low compared to other industries. According to Hirata (2018) mentioned above, improving labor productivity in the retail industry is recognized as a major challenge. On the other hand, according to the "Survey and Research on Measurement Methods for Information Distribution and Accumulation (2013)" by the Ministry of Internal Affairs and Communications, the amount of data accumulated by each industry is extremely large in the retail industry, so new applications are expected by analyzing these data together with weather data. Third, there is currently a wealth of weather-related information available. There are, for example, companies such as WeatherNews, Inc. that make weather information their business and not only disseminate weather information but also disseminate information that supports businesses in various industries. Despite the wide range of influences that weather has on business, the impact of weather has not been a subject of research in the retail industry, where business models need to be imagined, especially from a micro perspective. In this paper, the author discuss the important aspects of the impact of weather on marketing strategies in the non-food retail industry.

Keywords: consumer behavior, weather marketing, marketing science, big data, retail marketing

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Authors: Anthony Proschka, Deepak Mishra, Merlyn Ramanan, Zurab Baratashvili

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Small and medium-sized businesses receive over 160 billion invoices every year. Since these documents exhibit many subtle differences in layout and text, extracting structured fields such as sender name, amount, and VAT rate from them automatically is an open research question. In this paper, existing work in template-based document extraction is extended, and a system is devised that is able to reliably extract all required fields for up to 70% of all documents in the data set, more than any other previously reported method. The approaches are described for 1) detecting through visual features which template a given document belongs to, 2) automatically generating extraction rules for a given new template by composing regular expressions from multiple components, and 3) computing confidence scores that indicate the accuracy of the automatic extractions. The system can generate templates with as little as one training sample and only requires the ground truth field values instead of detailed annotations such as bounding boxes that are hard to obtain. The system is deployed and used inside a commercial accounting software.

Keywords: data mining, information retrieval, business, feature extraction, layout, business data processing, document handling, end-user trained information extraction, document archiving, scanned business documents, automated document processing, F1-measure, commercial accounting software

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Authors: Mohammed Hmimou, Khalid Amediaz, Imane Sebari, Nabil Bounajma

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Annual crop production is one of the most important macroeconomic indicators for the majority of countries around the world. This information is valuable, especially for exporting countries which need a yield estimation before harvest in order to correctly plan the supply chain. When it comes to estimating agricultural yield, especially for arboriculture, conventional methods are mostly applied. In the case of the citrus industry, the sale before harvest is largely practiced, which requires an estimation of the production when the fruit is on the tree. However, conventional method based on the sampling surveys of some trees within the field is always used to perform yield estimation, and the success of this process mainly depends on the expertise of the ‘estimator agent’. The present study aims to propose a methodology based on the combination of unmanned aerial vehicle (UAV) images and terrestrial laser scanner (TLS) point cloud to estimate citrus production. During data acquisition, a fixed wing and rotatory drones, as well as a terrestrial laser scanner, were tested. After that, a pre-processing step was performed in order to generate point cloud and digital surface model. At the processing stage, a machine vision workflow was implemented to extract points corresponding to fruits from the whole tree point cloud, cluster them into fruits, and model them geometrically in a 3D space. By linking the resulting geometric properties to the fruit weight, the yield can be estimated, and the statistical distribution of fruits size can be generated. This later property, which is information required by importing countries of citrus, cannot be estimated before harvest using the conventional method. Since terrestrial laser scanner is static, data gathering using this technology can be performed over only some trees. So, integration of drone data was thought in order to estimate the yield over a whole orchard. To achieve that, features derived from drone digital surface model were linked to yield estimation by laser scanner of some trees to build a regression model that predicts the yield of a tree given its features. Several missions were carried out to collect drone and laser scanner data within citrus orchards of different varieties by testing several data acquisition parameters (fly height, images overlap, fly mission plan). The accuracy of the obtained results by the proposed methodology in comparison to the yield estimation results by the conventional method varies from 65% to 94% depending mainly on the phenological stage of the studied citrus variety during the data acquisition mission. The proposed approach demonstrates its strong potential for early estimation of citrus production and the possibility of its extension to other fruit trees.

Keywords: citrus, digital surface model, point cloud, terrestrial laser scanner, UAV, yield estimation, 3D modeling

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: M. M. Islam, S. Sharmin, M. Shah Newaz, N. S. Juyena, M. M. Rahman, P. K. Jha, F. Y. Bari

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The aim of the present study was to select the high quality ram by measuring the scrotal biometry which has an effect on semen parameters. Ten rams were selected in the present study. Eight ejaculates were collected from each ram using artificial vagina method. Scrotal circumference was measured before and after semen collection on weekly basis using the Scrotal tape. Bio-metries of scrotum (scrotal length and scrotal volume) were calculated. Semen was evaluated for macroscopic and microscopic characteristics. The average estimated scrotal circumference (cm) and scrotal volume (cm3) in 8 different age groups were 17.16±0.05 cm and 61.30±0.70 cm3, 17.17±0.62 cm and 63.67±4.49 cm3, 17.22±0.52 cm and 64.90±4.21 cm3, 17.72±0.37 cm and 67.10±4.20 cm3, 18.41±0.35cm and 69.52±4.12cm3, 18.45±0.36cm and 77.17±3.81 cm3, 18.55±0.41 cm and 78.72±4.90 cm3, 19.10±0.30 cm and 87.35±5.45 cm3 respectively. The body weight, scrotal circumference and scrotal volume increased with the progress of age (P < 0.05). Body weight of age group 381-410 days (13.62+1.48 kg) was significantly higher than group 169-200 days (10.17±0.05 kg) and 201-230 days (10.42±1.18 kg) (p < 0.05). Scrotal circumference (SC) of age group 381-410 days (19.10±0.30 cm) was significantly higher (p < 0.05) than other groups. In age group 381-410 days, scrotal volume (SCV) (87.35±5.45 cm3) was significantly higher than other first five groups (p < 0.05). Both scrotal circumference and scrotal volume development was positively correlated with the increasing of body weight (R2= 0.51). Semen volume increased accordingly with the increasing of ages, varied from 0.35±0.00 ml to 1.15+0.26 ml. Semen volume of age group 381-410 days (1.15±0.26 ml) was significantly higher than other age groups (p < 0.05) except age group 351-380 days (p > 0.05). Mass activity of different age groups varied from 2.75 (±0.35) to 4.25 (±0.29) ml in the scale of 1-5. Sperm concentration, progressive motility (%),progressively improved according to the increasing of ages, but significant changes in these parameters were seen when the animals reaches the age 291 days or more (p < 0.05). However, normal spermatozoa (%) improved significantly from the age of 261 days or more. Mass activity (mass) was positively correlated with sperm concentration (R2=0.568) and progressive motility (%) (R2=0.616). The relationships of semen volume with body weight and scrotal measurements and sperm concentration indicate that they are useful in evaluating rams for breeding soundness and genetic improvement for fertility in indigenous ram.

Keywords: breeding soundness, ram, semen quality, scrotal biometry

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Authors: Hassina Gheribi, Salim Boukebbab

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The measurement of the parts manufactured on CMM (coordinate measuring machine) is based on the association of a surface of perfect geometry to the group of dots palpated via a mathematical calculation of the distances between the palpated points and itself surfaces. Surfaces not being never perfect, they are measured by a number of points higher than the minimal number necessary to define them mathematically. However, the central problems of three-dimensional metrology are the estimate of, the orientation parameters, location and intrinsic of this surface. Including the numerical uncertainties attached to these parameters help the metrologist to make decisions to be able to declare the conformity of the part to specifications fixed on the design drawing. During this paper, we will present a data-processing model in Visual Basic-6 which makes it possible automatically to determine the whole of these parameters, and their uncertainties.

Keywords: coordinate measuring machines (CMM), associated surface, uncertainties of measurement, acquisition and modeling

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Authors: Shahin A. Abdel-Naby, Asad T. Hassan, Stuart Loch, Michael Fogle, Negil R. Badnell, Michael S. Pindzola

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Accurate and reliable laboratory astrophysical data for electron-ion recombination are needed for plasma modeling. Dielectronic recombination (DR) rate coefficients are calculated for boron-like nitrogen and oxygen ions using state-of-the-art multi-configuration Breit-Pauli atomic structure AUTOSTRUCTURE collisional package within the generalized collisional-radiative framework. The calculations are performed in intermediate coupling scheme associated with n = 0 (2  2) and n = 1 (2  3) core-excitations. Good agreements are found between the theoretically convoluted rate coefficients and the experimental measurements performed at CRYRING heavy-ion storage ring for both ions. Fitting coefficients for the rate coefficients are produced for these ions in the temperature range q2(102-107) K, where q is the ion charge before recombination.

Keywords: Atomic data, atomic processes, electron-ion collision, plasma

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Authors: Matthias Leitner, Gernot Pottlacher

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Experimental determination of critical point data like critical temperature, critical pressure, critical volume and critical compressibility of high-melting metals such as niobium is very rare due to the outstanding experimental difficulties in reaching the necessary extreme temperature and pressure regimes. Experimental techniques to achieve such extreme conditions could be diamond anvil devices, two stage gas guns or metal samples hit by explosively accelerated flyers. Electrical pulse-heating under increased pressures would be another choice. This technique heats thin wire samples of 0.5 mm diameter and 40 mm length from room temperature to melting and then further to the end of the stable phase, the spinodal line, within several microseconds. When crossing the spinodal line, the sample explodes and reaches the gaseous phase. In our laboratory, pulse-heating experiments can be performed under variation of the ambient pressure from 1 to 5000 bar and allow a direct determination of critical point data for low-melting, but not for high-melting metals. However, the critical point also can be estimated by extrapolating the liquid phase density according to theoretical models. A reasonable prerequisite for the extrapolation is the existence of data that cover as much as possible of the liquid phase and at the same time exhibit small uncertainties. Ohmic pulse-heating was therefore applied to determine thermal volume expansion, and from that density of niobium over the entire liquid phase. As a first step, experiments under ambient pressure were performed. The second step will be to perform experiments under high-pressure conditions. During the heating process, shadow images of the expanding sample wire were captured at a frame rate of 4 × 105 fps to monitor the radial expansion as a function of time. Simultaneously, the sample radiance was measured with a pyrometer operating at a mean effective wavelength of 652 nm. To increase the accuracy of temperature deduction, spectral emittance in the liquid phase is also taken into account. Due to the high heating rates of about 2 × 108 K/s, longitudinal expansion of the wire is inhibited which implies an increased radial expansion. As a consequence, measuring the temperature dependent radial expansion is sufficient to deduce density as a function of temperature. This is accomplished by evaluating the full widths at half maximum of the cup-shaped intensity profiles that are calculated from each shadow image of the expanding wire. Relating these diameters to the diameter obtained before the pulse-heating start, the temperature dependent volume expansion is calculated. With the help of the known room-temperature density, volume expansion is then converted into density data. The so-obtained liquid density behavior is compared to existing literature data and provides another independent source of experimental data. In this work, the newly determined off-critical liquid phase density was in a second step utilized as input data for the estimation of niobium’s critical point. The approach used, heuristically takes into account the crossover from mean field to Ising behavior, as well as the non-linearity of the phase diagram’s diameter.

Keywords: critical point data, density, liquid metals, niobium, ohmic pulse-heating, volume expansion

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Authors: Yayun Hsu, Henry Horng-Shing Lu

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In this paper, a new method is proposed to extending the method of connected component labeling from processing binary images to multi-scale modeling of images. By using the adaptive threshold of multi-scale attributes, this approach minimizes the possibility of missing those important components with weak intensities. In addition, the computational cost of this approach remains similar to that of the typical approach of component labeling. Then, this methodology is applied to grain boundary detection and Drosophila Brain-bow neuron segmentation. These demonstrate the feasibility of the proposed approach in the analysis of challenging microscopy images for scientific discovery.

Keywords: microscopic image processing, scientific data mining, multi-scale modeling, data mining

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Authors: Lincoln Panjaitan, Antonius Sumarlin

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The development of property businesses in the metropolitan area is growing rapidly forcing big real estate developers to come up with various strategies in winning the heart of consumers. This empirical research is focusing on how the two schools of marketing thoughts; namely, Modern and postmodern marketing employed by the preceding developers to retain consumers’ commitment toward their prospective brands. The data was collected from three different properties of PT. Intiland Tbk using accidental sampling technique. The data of 600 respondents was then put into Structural Equation Model (SEM). The result of the study suggests that both schools of thought can equally produce commitment and loyalty of consumers; however, the difference lays where the loyalty belongs to. The first is more toward developer’s brand and the latter is more toward the co-creation value of the housing community.

Keywords: consumer loyalty, consumer commitment, knowledge sharing platform, marketing mix

Procedia PDF Downloads 323

Authors: Peifang Hsieh

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The prevention of child abuse has aroused serious concerns in Taiwan because of the disparity between the increasing amount of reported child abuse cases that doubled over the past decade and the scarcity of social workers. New Taipei city, with the most population in Taiwan and over 70% of its 4 million citizens are migrant families in which the needs of children can be easily neglected due to insufficient support from relatives and communities, sees urgency for a social support system, by preemptively identifying and outreaching high-risk families of child abuse, so as to offer timely assistance and preventive measure to safeguard the welfare of the children. Big data analysis is the inspiration. As it was clear that high-risk families of child abuse have certain characteristics in common, New Taipei city decides to consolidate detailed background information data from departments of social affairs, education, labor, and health (for example considering status of parents’ employment, health, and if they are imprisoned, fugitives or under substance abuse), to cross-reference for accurate and prompt identification of the high-risk families in need. 'The Service Center for High-Risk Families' (SCHF) was established to integrate data cross-departmentally. By utilizing the machine learning 'random forest method' to build a risk prediction model which can early detect families that may very likely to have child abuse occurrence, the SCHF marks high-risk families red, yellow, or green to indicate the urgency for intervention, so as to those families concerned can be provided timely services. The accuracy and recall rates of the above model were 80% and 65%. This prediction model can not only improve the child abuse prevention process by helping social workers differentiate the risk level of newly reported cases, which may further reduce their major workload significantly but also can be referenced for future policy-making.

Keywords: child abuse, high-risk families, big data analysis, risk prediction model

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Authors: Colleen McMillan, Alice Schmidt-Hanbidge, Beth Archer-Kuhn, Heather Boynton, Judith Hughes

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It is well known that social work students enter the profession with higher scores of adverse childhood experiences (ACE). Add to that the fact that COVID-19 has forced higher education institutions to shift to online teaching and learning, where students, faculty and field educators in social work education have reported increased stressors as well as posing challenges in developing relationships with students and being able to identify mental health challenges including those related to trauma. This multi-institutional project included three Canadian post-secondary institutions at five sites (the University of Waterloo, the University of Calgary and the University of Manitoba) and partners; Desire To Learn (D2L), The Centre for Teaching Excellence at the University of Waterloo and the Taylor Institute for Teaching and Learning. A sequential mixed method research design was used. Survey data was collected from students, faculty and field education staff from the 3 universities using the Qualtrics Insight Platform, followed by virtual focus group data with students to provide greater clarity to the quantitative data. Survey data was analyzed using SPSS software, while focus group data was transcribed verbatim and organized with N-Vivo 12. Thematic analysis used line-by-line coding and constant comparative methods within and across focus groups. The following three objectives of the study were achieved: 1) Establish a Canadian baseline on trauma informed pedagogy and student experiences of trauma informed teaching in the online higher education environment during a pandemic; 2) Identify and document educator and student experiences of online learning regarding the ability to process trauma experiences; and, 3) Transfer the findings into a trauma informed pedagogical model for Social Work as a first step toward developing a universal trauma informed teaching model. The trauma informed pedagogy model would be presented in relation to the study findings.

Keywords: trauma informed pedagogy, higher education, social work, mental health

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Authors: Shahin A. Abdel-Naby, Asad T. Hassan

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Electron-ion recombination data are needed for plasma modeling. The recombination processes include radiative recombination (RR), dielectronic recombination (DR), and trielectronic recombination (TR). When a free electron is captured by an ion with simultaneous excitation of its core, a doubly-exited intermediate state may be formed. The doubly excited state relaxes either by electron emission (autoionization) or by radiative decay (photon emission). DR process takes place when the relaxation occurs to a bound state by photon emission. Reliable laboratory astrophysics data (theory and experiment) for DR rate coefficients are needed to determine the charge state distribution in photoionized sources such as X-ray binaries and active galactic nuclei. DR rate coefficients for NIII and OIV ions are calculated using state-of-the-art multi-configuration Breit-Pauli atomic structure AUTOSTRUCTURE collisional package within the generalized collisional-radiative framework. Level-resolved calculations for RR and DR rate coefficients from the ground and metastable initial states are produced in an intermediate coupling scheme associated with Δn = 0 (2→2) and Δn = 1 (2 →3) core-excitations. DR cross sections for these ions are convoluted with the experimental electron-cooler temperatures to produce DR rate coefficients. Good agreements are found between these rate coefficients and the experimental measurements performed at the CRYRING heavy-ion storage ring for both ions.

Keywords: atomic data, atomic process, electron-ion collision, plasmas

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Authors: Syahrir Andi Mangga, Ipranta

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The geological, paleomagnetic, geochemical and geophysical Investigation in The Sumatra Region has yielded some new data, has stimulated a reassessment of stratigraphy, structure, tectonic evolution and which can show a Sumatra geodynamic model. Sumatra island has in the margin of southwest part of the Eurasia plate in the Sundaland cratonic block and occurred as the amalgamation of allochtonous microplates, continental fragments, Island arc and accrctionary by foreland complex which assembled prior to Tertiary. The allochtonous rocks (terranes), can be divided into 4 (four) Terranes with Paleozoic to Mesosoic in age, had different origin, lithology and are separated by a Suture as main fault with trending NW-SE. The terranes are: the Tigapuluh-Bohorok (East Sumatra block / Sibumasu block), Permo-Carboniferous in age and is characterized by the rock types formed in glacio-marine and was intruded by Late Triassic to Early Jurrasic granitics, occupied in the Eastern part of Sumatra, the paleomagnetic data shown 41° South. Tanjung Karang - Gunung Kasih Terrane, is composed of higher metamorphic rocks and supposed to be pre-Carboniferous in age, covered by Mesozoic sedimentary rocks and were intruded by granitic-dioritic rocks, occupied in the Southern part of Sumatra, the paleomagnetic data shown 19° North. The Kuantan-Duabelas Mountain (West Sumatra block) is occupied by metamorphic, sedimentary and volcanic rocks of Paleozoic - Mesozoic (Carboniferous - Triassic) in age, contains a Cathaysion fauna and flora and are intruded by the Mesozoic granitoid rocks. The terrane occurred in the western part of Sumatra. Meanwhile, the Gumai-Garba (Waloya Terrane) which is occupied by the tectonite/melange, metasediment, carbonate and volcanic rocks of Mesozoic (Jurassic - Cretaceous) in age, are intruted by the Late Cretaceous granitoid rocks, the paleomagnetic data shown 30° - 31° South.

Keywords: tectonostratigraphy, amalgamation, allochtonous, terranes, sumatra

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Authors: Birendra Mahato, Shiva Narayan Chaudhary

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Tharus are indigenous inhabitants of the Tarai, the narrow strip of flat and fertile land that lies at the foothills of the Himalayas. They have unique rituals, festivals, and music, while their clothes and ornaments are similar to some ethnic groups of India. After the control of malaria in the Terai between 1955 to 1965, (with the help of the U.S. Government, the WHO and the government of Nepal) the rate of in-migration skyrocketed, particularly of Nepal’s hill people, whose culture, along with the use of the Western education system, was gradually adopted by the Tharus. Among the many challenges, the Tharus face is their assimilation into the dominant Nepali culture. .The Tharus are thus under threat of slowly losing their ethnic identity and cultural values. In order to preserve the indigenous Tharu Culture, Tharu Cultural Museum, and Research Center. It is a community based non-governmental and non-profitable museum established in 2005 by Tharu intellectuals, traditional leaders and youths. The main goal of the museum is to preserve Tharu culture through different activities with direct and indirect involvement of community people. It aims to preserve arts, culture, handicrafts, and artifacts related to Indigenous Tharu People. Similarly, the museum displays an exhibition about the Tharu histories, lives, culture, and their indigenous knowledge and skills. The paper aims to demonstrate that community museums can be one of the key hubs for the preservation of culture with the collection and promotion of cultural objects, artifacts, and intangible cultural heritages. The preservation of culture not only has contributed to establishing their ethnic identity but also has contributed to establishing their recognition of their political space in the mainstream politics - the constitution of Nepal has recognized Tharus to provide reservation for the political space.

Keywords: community museum, tharu, , identity, culture

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Authors: Teera Intararuang

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The purpose of this research is to study on the challenge and opportunity for waterway route management for promoting cultural tourism in Angsila District, Chonburi Province. To accomplish the goals and objectives, qualitative research will be applied. The research instruments used are observation, basic interviews, in-depth interviews, and interview key local performance. The study also uses both primary data and secondary data. From research result, it is revealed that all respondents had appreciated and strongly agree to promote their waterway route tourism as an intend for further increase for their income. However, it has some challenges to success this project due to natural obstacles such as water level, seasons and high temperature. Moreover, they lack financial support from government sectors also.

Keywords: Angsila community, waterway tourism route, cultural tourism, way of life

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Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

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Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: general appliance model, non intrusive load monitoring, events detection, unsupervised techniques;

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Authors: Lamia Bouzgarrou, Irtyah Merchaoui, Amira Omrane, Salma Kammoun, Amine Daafa, Neila Chaari

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Backgrounds: Currently, low back pain (LBP) is one of the most prevalent public health problems, which caused severe morbidity among a large portion of the adult population. It is also associated with heavy direct and indirect costs, in particular, related to absenteeism and early retirement. Health care workers are one of most occupational groups concerned by LBP, especially because of biomechanical and psycho-organizational risk factors. Our current study aims to investigate risk factors associated with chronic low back pain among Tunisian caregivers in university-hospitals. Methods: Cross-sectional study conducted over a period of 14 months, with a representative sample of caregivers, matched according to age, sex and work department, in two university-hospitals in Tunisia. Data collection included items related to socio-professional characteristics, the evaluation of the working capacity index (WAI), the occupational stress (Karazek job strain questionnaire); the quality of life (SF12), the musculoskeletal disorders Nordic questionnaire, and the examination of the spine flexibility (distance finger-ground, sit-stand maneuver and equilibrium test). Results: Totally, 293 caregivers were included with a mean age equal to 42.64 ± 11.65 years. A body mass index (BMI) exceeding 30, was noted in 20.82% of cases. Moreover, no regular physical activity was practiced in 51.9% of cases. In contrast, domestic activity equal or exceeding 20 hours per week, was reported by 38.22%. Job strain was noted in 19.79 % of cases and the work capacity was 'low' to 'average' among 27.64% of subjects. During the 12 months previous to the investigation, 65% of caregivers complained of LBP, with pain rated as 'severe' or 'extremely severe' in 54.4% of cases and with a frequency of discomfort exceeding one episode per week in 58.52% of cases. During physical examination, the mean distance finger-ground was 7.10 ± 7.5cm. Caregivers assigned to 'high workload' services had the highest prevalence of LBP (77.4%) compared to other categories of hospital services, with no statistically significant relationship (P = 0.125). LBP prevalence was statistically correlated with female gender (p = 0.01) and impaired work capacity (p < 10⁻³). Moreover, the increase of the distance finger-ground was statistically associated with LBP (p = 0.05), advanced age (p < 10⁻³), professional seniority (p < 10⁻³) and the BMI ≥ 25 (p = 0.001). Furthermore, others physical tests of spine flexibility were underperformed among LBP suffering workers with a statistically significant difference (sit-stand maneuver (p = 0.03); equilibrium test (p = 0.01)). According to the multivariate analysis, only the domestic activity exceeding 20H/week, the degraded quality of physical life, and the presence of neck pain were significantly corelated to LBP. The final model explains 36.7% of the variability of this complaint. Conclusion: Our results highlighted the elevate prevalence of LBP among caregivers in Tunisian public hospital and identified both professional and individual predisposing factors. The preliminary analysis supports the necessity of a multidimensional approach to prevent this critical occupational and public health problem. The preventive strategy should be based both on the improvement of working conditions, and also on lifestyle modifications, and reinforcement of healthy behaviors in these active populations.

Keywords: health care workers, low back pain, prevention, risk factor

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Authors: Yingkun Huang, Li Guo, Zekang Lan, Kai Tian

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Hard disk drives (HDD) failure of the exascale supercomputing system may lead to service interruption and invalidate previous calculations, and it will cause permanent data loss. Therefore, initiating corrective actions before hard drive failures materialize is critical to the continued operation of jobs. In this paper, a highly accurate analysis model based on CNN-LSTM and oversampling technique was proposed, which can correctly predict the necessity of a disk replacement even ten days in advance. Generally, the learning-based method performs poorly on a training dataset with long-tail distribution, especially fault prediction is a very classic situation as the scarcity of failure data. To overcome the puzzle, a new oversampling was employed to augment the data, and then, an improved CNN-LSTM with the shortcut was built to learn more effective features. The shortcut transmits the results of the previous layer of CNN and is used as the input of the LSTM model after weighted fusion with the output of the next layer. Finally, a detailed, empirical comparison of 6 prediction methods is presented and discussed on a public dataset for evaluation. The experiments indicate that the proposed method predicts disk failure with 0.91 Precision, 0.91 Recall, 0.91 F-measure, and 0.90 MCC for 10 days prediction horizon. Thus, the proposed algorithm is an efficient algorithm for predicting HDD failure in supercomputing.

Keywords: HDD replacement, failure, CNN-LSTM, oversampling, prediction

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Authors: Huawei Ma, Wencai Du, Shengbin Liang

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Smart Home Healthcare technologies for the elderly represent a transformative paradigm that leverages emerging technologies to provide the elderly’ health indicators and daily life monitoring, emergency calls, environmental monitoring, behavior perception, and other services to ensure the health and safety of the elderly who are aging in their own home. However, the excessive complexity in the main adopted framework has affected the acceptance and adoption of the elderly. Therefore, this paper proposes a lightweight synergy architecture of IoT data and service for elderly home smart health environment. It includes the modeling of IoT applications and their workflows, data interoperability, interaction, and storage paradigms to meet the growing needs of older people so that they can lead an active, fulfilling, and quality life.

Keywords: smart home healthcare, IoT, independent living, lightweight framework

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Authors: Ilham Benali, Nasser Hajji, Nawfel Acha

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The pharmaceutical sector is ongoing different constraints related to the Research and Development (R&D) costs, the patents extinction, the demand pressing, the regulatory requirement and the generics development, which drive leading firms in the sector to undergo technological change and to shift to biotechnological paradigm. Based on a large literature review, we present a background of innovation trajectory in pharmaceutical industry and reasons behind this technological transformation. Then we investigate the role that Information and Communication Technology (ICT) is playing in this revolution. In order to situate pharmaceutical firms in developing countries in this trajectory, and to examine the degree of their involvement in the innovation process, we did not find any previous empirical work or sources generating gathered data that allow us to analyze this phenomenon. Therefore, and for the case of Morocco, we tried to do it from scratch by gathering relevant data of the last five years from different sources. As a result, only about 4% of all innovative drugs that have access to the local market in the mentioned period are made locally which substantiates that the industrial model in pharmaceutical sector in developing countries is based on the 'license model'. Finally, we present another alternative, based on ICT use and big data tools that can allow developing countries to shift from status of simple consumers to active actors in the innovation process.

Keywords: biotechnologies, developing countries, innovation, information and communication technology, pharmaceutical firms

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Authors: Xu Runzhang, Yang Yanbing, Niu Yi, Zhang Mingyou, Liu Yu

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For a class of semilinear parabolic equations with linear dynamical boundary conditions in a bounded domain, we obtain both global solutions and finite time blow-up solutions when the initial data varies in the phase space H1(Ω). Our main tools are the comparison principle, the potential well method and the concavity method. In particular, we discuss the behavior of the solutions with the initial data at critical and high energy level.

Keywords: high energy level, critical energy level, linear dynamical boundary condition, semilinear parabolic equation

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Authors: Saowanit Sukparungsee

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Generally, the traditional Shewhart p chart has been developed by for charting the binomial data. This chart has been developed using the normal approximation with condition as low defect level and the small to moderate sample size. In real applications, however, are away from these assumptions due to skewness in the exact distribution. In this paper, a modified Exponentially Weighted Moving Average (EWMA) control chat for detecting a change in binomial data by improving square root transformations, namely ISRT p EWMA control chart. The numerical results show that ISRT p EWMA chart is superior to ISRT p chart for small to moderate shifts, otherwise, the latter is better for large shifts.

Keywords: number of defects, exponentially weighted moving average, average run length, square root transformations

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Authors: Fahad Y. Al-dawish

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The current trend by a large number of organizations and individuals to use cloud computing. Many consider it a significant shift in the field of computing. Cloud computing are distributed and parallel systems consisting of a collection of interconnected physical and virtual machines. With increasing request and profit of cloud computing infrastructure, diverse computing processes can be executed on cloud environment. Many organizations and individuals around the world depend on the cloud computing environments infrastructure to carry their applications, platform, and infrastructure. One of the major and essential issues in this environment related to allocating incoming tasks to suitable virtual machine (cloud task scheduling). Cloud task scheduling is classified as optimization problem, and there are several meta-heuristic algorithms have been anticipated to solve and optimize this problem. Good task scheduler should execute its scheduling technique on altering environment and the types of incoming task set. In this research project a cloud task scheduling methodology based on ant colony optimization ACO algorithm, we call it ACO-TS Ant Colony Optimization for Task Scheduling has been proposed and compared with different scheduling algorithms (Random, First Come First Serve FCFS, and Fastest Processor to the Largest Task First FPLTF). Ant Colony Optimization (ACO) is random optimization search method that will be used for assigning incoming tasks to available virtual machines VMs. The main role of proposed algorithm is to minimizing the makespan of certain tasks set and maximizing resource utilization by balance the load among virtual machines. The proposed scheduling algorithm was evaluated by using Cloudsim toolkit framework. Finally after analyzing and evaluating the performance of experimental results we find that the proposed algorithm ACO-TS perform better than Random, FCFS, and FPLTF algorithms in each of the makespaan and resource utilization.

Keywords: cloud Task scheduling, ant colony optimization (ACO), cloudsim, cloud computing

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Authors: Dilshad Ali Mohammed, Ziyad Nayef Shamsulddin Aldoski, Millet Salim Mohammed

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The increase in levels of traffic congestion along urban signalized arterials needs efficient traffic management. The application of traffic signal coordination can improve the traffic operation and safety for a series of signalized intersection along the arterials. The objective of this study is to evaluate the benefits achievable through actuated traffic signal coordination and make a comparison in control delay against the same signalized intersection in case of being isolated. To accomplish this purpose, a series of eight signalized intersections located on two major arterials in Duhok City was chosen for conducting the study. Traffic data (traffic volumes, link and approach speeds, and passenger car equivalent) were collected at peak hours. Various methods had been used for collecting data such as video recording technique, moving vehicle method and manual methods. Geometric and signalization data were also collected for the purpose of the study. The coupling index had been calculated to check the coordination attainability, and then time space diagrams were constructed representing one-way coordination for the intersections on Barzani and Zakho Streets, and others represented two-way coordination for the intersections on Zakho Street with accepted progression bandwidth efficiency. The results of this study show great progression bandwidth of 54 seconds for east direction coordination and 17 seconds for west direction coordination on Barzani Street under suggested controlled speed of 60 kph agreeable with the present data. For Zakho Street, the progression bandwidth is 19 seconds for east direction coordination and 18 seconds for west direction coordination under suggested controlled speed of 40 kph. The results show that traffic signal coordination had led to high reduction in intersection control delays on both arterials.

Keywords: bandwidth, congestion, coordination, traffic, signals, streets

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Authors: Xhaklina Cani, Ilirjan Malollari, Ismet Beqiraj, Lorina Lici

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Oil characterization is an essential step in the design, simulation, and optimization of refining facilities. To achieve optimal crude selection and processing decisions, a refiner must have exact information refer to crude oil quality. This includes crude oil TBP-curve as the main data for correct operation of refinery crude oil atmospheric distillation plants. Crude oil is typically characterized based on a distillation assay. This procedure is reasonably well-defined and is based on the representation of the mixture of actual components that boil within a boiling point interval by hypothetical components that boil at the average boiling temperature of the interval. The crude oil assay typically includes TBP distillation according to ASTM D-2892, which can characterize this part of oil that boils up to 400 C atmospheric equivalent boiling point. To model the yield curves obtained by physical distillation is necessary to compare the differences between the modelling and the experimental data. Most commercial use a different number of components and pseudo-components to represent crude oil. Laboratory tests include distillations, vapor pressures, flash points, pour points, cetane numbers, octane numbers, densities, and viscosities. The aim of the study is the drawing of true boiling curves for different crude oil resources in Albania and to compare the differences between the modeling and the experimental data for optimal characterization of crude oil.

Keywords: TBP distillation curves, crude oil, optimization, simulation

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Authors: Wei Hu, Wenguang Chen, Chong Dong

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In order to improve the efficiency and safety of photovoltaic power generation devices, this photovoltaic power generation system combines Artificial Intelligence (AI) and the Internet of Things (IoT) to control the chasing photovoltaic power generation device to track the sun to improve power generation efficiency and then convert energy management. The system uses artificial intelligence as the control terminal, the power generation device executive end uses the Linux system, and Exynos4412 is the CPU. The power generating device collects the sun image information through Sony CCD. After several power generating devices feedback the data to the CPU for processing, several CPUs send the data to the artificial intelligence control terminal through the Internet. The control terminal integrates the executive terminal information, time information, and environmental information to decide whether to generate electricity normally and then whether to convert the converted electrical energy into the grid or store it in the battery pack. When the power generation environment is abnormal, the control terminal authorizes the protection strategy, the power generation device executive terminal stops power generation and enters a self-protection posture, and at the same time, the control terminal synchronizes the data with the cloud. At the same time, the system is more intelligent, more adaptive, and longer life.

Keywords: photo-voltaic power generation, the pursuit of light, artificial intelligence, internet of things, photovoltaic array, power management

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Authors: Benniche Omar

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We are concerned with abstract fully nonlinear differential equations having the form y’(t)=Ay(t)+f(t,y(t)) where A is an m—dissipative operator (possibly multi—valued) defined on a subset D(A) of a Banach space X with values in X and f is a given function defined on I×X with values in X. We consider a graph K in I×X. We recall that K is said to be viable with respect to the above abstract differential equation if for each initial data in K there exists at least one trajectory starting from that initial data and remaining in K at least for a short time. The viability problem has been studied by many authors by using various techniques and frames. If K is closed, it is shown that a tangency condition, which is mainly linked to the dynamic, is crucial for viability. In the case when X is infinite dimensional, compactness and convexity assumptions are needed. In this paper, we are concerned with the notion of near viability for a given graph K with respect to y’(t)=Ay(t)+f(t,y(t)). Roughly speaking, the graph K is said to be near viable with respect to y’(t)=Ay(t)+f(t,y(t)), if for each initial data in K there exists at least one trajectory remaining arbitrary close to K at least for short time. It is interesting to note that the near viability is equivalent to an appropriate tangency condition under mild assumptions on the dynamic. Adding natural convexity and compactness assumptions on the dynamic, we may recover the (exact) viability. Here we investigate near viability for a graph K in I×X with respect to y’(t)=Ay(t)+f(t,y(t)) where A and f are as above. We emphasis that the t—dependence on the perturbation f leads us to introduce a new tangency concept. In the base of a tangency conditions expressed in terms of that tangency concept, we formulate criteria for K to be near viable with respect to y’(t)=Ay(t)+f(t,y(t)). As application, an abstract null—controllability theorem is given.

Keywords: abstract differential equation, graph, tangency condition, viability

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Authors: Victor Vasques Ribeiro

Abstract:

The Santa Cruz dos Navegantes beach is located in the city of Guarujá, in the central portion of the coast of the state of São Paulo. Next to this beach is located the Channel of the Port of Santos, configured as a source of plastic pellets for marine environments. On sandy beaches near the sources, especially during the winter and after cold front entrance events, the amounts of pellets can be very high. This study aimed to determine the influence of a cold front entry event of the winter of 2019 on the amount of pellets found on Santa Cruz dos Navegantes beach, besides assuming the proximity of the sources. During six consecutive collection campaigns, three of which were previous and three after the cold front entry peak, 30.0 square meters of surface sediments were sampled in each campaign. The color and shape of the pellets were determined to assume the length of the permanence of these granules in the marine environment and, consequently, the proximity of the sources. This beach was considered ideal for this type of research. The pellet pollution index (PPI) was from moderate to very high right after the peak of the cold front entry. The cold front peak event significantly influenced the amount of pellets found on the beach of Santa Cruz dos Navegantes. The factors that can bury the pellets in the sediments were classified as low when compared to other beaches in the region. Most of the pellets found were recently produced and lost to aquatic environments. Like the other beaches near Santos Bay, Santa Cruz dos Navegantes beach receives significant amounts of pellets that have nearby origins. Therefore, it was supposed that the activities of the Santos port complex are sources of pellets for the marine environment. This pollution can be further worsened in certain meteoceanographic events. The beaches of this region need to be constantly monitored and evaluated for pollution by pellets.

Keywords: beach, cold front, pellets, sources

Procedia PDF Downloads 180