Search results for: 3D plant data

Authors: Meenal Badki

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We address the issue of active feature determination, where the objective is to determine the set of examples on which additional data (such as lab tests) needs to be gathered, given a large number of examples with some features (such as demographics) and some examples with all the features (such as the complete Electronic Health Record). We note that certain features may be more costly, unique, or laborious to gather. Our proposal is a general active learning approach that is independent of classifiers and similarity metrics. It allows us to identify examples that differ from the full data set and obtain all the features for the examples that match. Our comprehensive evaluation shows the efficacy of this approach, which is driven by four authentic clinical tasks.

Keywords: feature determination, classification, active learning, sample-efficiency

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Authors: Mehran Bakhshi, Mohammad Raouf Hosseini, Mohammadhosein Rahimi

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The cadmium sulfide nanoparticles were synthesized from the nickel-cadmium cake of a hydrometallurgical zinc producing plant and sodium sulfide as Cd²⁺ and S^-2 sources, respectively. Also, the synthesis process was performed by using the secretions of Bacillus licheniformis as bio-surfactant. Initially, in order to obtain a cadmium rich solution, two following steps were carried out: 1) Alkaline leaching for the removal of zinc oxide from the cake, and 2) acidic leaching to dissolve cadmium from the remained solid residue. Afterward, the obtained CdSO₄ solution was used for the nanoparticle biosynthesis. Nanoparticles were characterized by the energy dispersive spectroscopy (EDS) and X-ray diffraction (XRD) to confirm the formation of CdS crystals with cubic structure. Also, transmission electron microscopy (TEM) was applied to determine the particle sizes which were in 2-10 nm range. Moreover, the presence of the protein containing bio-surfactants was approved by using infrared analysis (FTIR). In addition, the absorbance below 400 nm confirms quantum particles’ size. Finally, it was shown that valuable CdS quantum dots could be obtained from the industrial waste products via environment-friendly biological approaches.

Keywords: biosynthesis, cadmium cake, cadmium sulfide, nanoparticle, zinc smelter

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Authors: Kara Rosenblatt, Kevin Badgett, James Eldridge

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There is a preponderance of information regarding the overall effectiveness of charter schools and their ability to increase academic achievement compared to traditional district schools. Most research on the topic is focused on comparing long and short-term outcomes, academic achievement in mathematics and reading, and locale (i.e., urban, v. Rural). While the lingering unanswered questions regarding effectiveness continue to loom for school leaders, data on charter schools suggests that enrollment increases by 10% annually and that charter schools educate more than 2 million U.S. students across 40 states each year. Given the increasing share of U.S. students educated in charter schools, it is important to better understand possible differences in student achievement defined in multiple ways for students in charter schools and for those in Independent School District (ISD) settings in the state of Texas. Data were retrieved from the Texas Education Agency’s (TEA) repository that includes data organized annually and available on the TEA website. Specific data points and definitions of achievement were based on characterizations of achievement found in the relevant literature. Specific data points include but were not limited to graduation rate, student performance on standardized testing, and teacher-related factors such as experience and longevity in the district. Initial findings indicate some similarities with the current literature on long-term student achievement in English/Language Arts; however, the findings differ substantially from other recent research related to long-term student achievement in social studies. There are a number of interesting findings also related to differences between achievement for students in charters and ISDs and within different types of charter schools in Texas. In addition to findings, implications for leadership in different settings will be explored.

Keywords: charter schools, ISDs, student achievement, implications for PK-12 school leadership

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Authors: Md. Alamgir Hossain, Md. Zakir Hossain, Md. Atiqur Rahman

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Tidal power can play a vital role in integrating as new source of renewable energy to the off-grid power connection in isolated areas, namely Sandwip, in Bangladesh. It can reduce the present energy crisis and improve the social, environmental and economic perspective of Bangladesh. Tidal energy is becoming popular around the world due to its own facilities. The development of any country largely depends on energy sector improvement. Lack of energy sector is because of hampering progress of any country development, and the energy sector will be stable by only depend on sustainable energy sources. Renewable energy having environmental friendly is the only sustainable solution of secure energy system. Bangladesh has a huge potential of tidal power at different locations, but effective measures on this issue have not been considered sincerely. This paper summarizes the current energy scenario, and Bangladesh can produce power approximately 53.19 MW across the country to reduce the growing energy demand utilizing tidal energy as well as it is shown that Sandwip is highly potential place to produce tidal power, which is estimated approximately 16.49 MW by investing only US $10.37 million. Besides this, cost management for tidal power plant has been also discussed.

Keywords: sustainable energy, tidal power, cost analysis, power demand, gas crisis

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Authors: Baltazar Ndakidemi

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The resource-constrained farmers, especially those in sub-Saharan Africa, encounter significant challenges related to agriculture, notably diseases and pests. The sustainable means of pest management are not well known to farmers. As a result, some farmers use synthetic pesticides whose environmental impacts, ill health, and other negative impacts of synthetic pesticides on natural enemies have posed a great need for more sustainable means of pest management. Pesticidal plant resources can replace synthetic pesticides because their secondary metabolites can exhibit insecticidal activities such as deterrence, repellence, and pests' mortality. Additionally, the volatiles from these plants can have positive effects of attracting populations of natural enemies. Pesticidal plants can be grown as field margin plants or in strips for supporting natural enemies' populations. However, this is practically undetermined. Hence, there is a need to investigate the roles played by pesticidal plants in supporting natural enemies of pests and their applications in different cropping systems such as legumes. This study investigates different pesticidal plants with a high potential for pest control in agricultural fields. The information sheds light on potential plants that can be used for different crop pests.

Keywords: natural enemies, biological control, synthetic pesticides, pesticidal plants, predators, parasitoids

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Authors: Pimchanok Puksisuwan, Pitak Laorattanakul, Benya Cherdhirunkorn

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The use of aluminium dross as a raw material for geopolymer synthesis via mechanochemistry method was studied. The geopolymers were prepared using aluminium dross from secondary aluminium industry, fly ash from a biomass power plant and liquid alkaline activators, which is a mixture of sodium silicate solution (Na2SiO3) and sodium hydroxide solution (NaOH) (Na2SiO3/NaOH ratio 4:1, 3:1 and 2:1). Aluminium dross consists mostly of alumina (Al2O3), silicon oxide (SiO2) and aluminium nitride (AlN). The raw materials were mixed and milled using the high energy ball milling method for 5, 10 and 15 minutes in order to reduce the particle size. The milled powders were uniaxially pressed into a cylinder die with the pressure of 2200 psi. The cylinder samples were cured in the sealed plastic bags for 3, 7 and 14 days at the room temperature and 60°C for 24 hour. The mechanical property of geopolymers was investigated. In addition, scanning electron microscopy (SEM) and X-ray diffraction (XRD) analysis were carried out in order to study the microstructure and phase structures of the geopolymers, respectively. The results showed that aluminium dross could enhance the mechanical property of geopolymers product by mechanochemistry method and meet the TISI requirements.

Keywords: aluminium dross, fly ash, geopolymer, mechanochemistry

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Authors: Nadir Atayev, Mehman Hasanov

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Future spacecraft will require a structural change in the way data is transmitted due to the increase in the volume of data required for space communication. Current radio frequency communication systems are already facing a bottleneck in the volume of data sent to the ground segment due to their technological and regulatory characteristics. To overcome these issues, free space optics communication plays an important role in the integrated terrestrial space network due to its advantages such as significantly improved data rate compared to traditional RF technology, low cost, improved security, and inter-satellite free space communication, as well as uses a laser beam, which is an optical signal carrier to establish satellite-ground & ground-to-satellite links. In this approach, there is a need for high-speed and energy-efficient systems as a base platform for sending high-volume video & audio data. Nano Satellite and its branch CubeSat platforms have more technical functionality than large satellites, wheres cover an important part of the space sector, with their Low-Earth-Orbit application area with low-cost design and technical functionality for building networks using different communication topologies. Along the research theme developed in this regard, the output parameter indicators for the FSO of the optical communication transceiver subsystem on the existing CubeSat platforms, and in the direction of improving the mentioned parameters of this communication methodology, 3D optical switch and laser beam controlled optical transponder with 2U CubeSat structural subsystems and application in the Low Earth Orbit satellite network topology, as well as its functional performance and structural parameters, has been studied accordingly.

Keywords: cubesat, free space optics, nano satellite, optical laser communication.

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Authors: Lassi Lehto, Jaakko Kahkonen, Juha Oksanen, Tapani Sarjakoski

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The use of raster-formatted data sets in geospatial analysis is increasing rapidly. At the same time, geographic data are being introduced into disciplines outside the traditional domain of geoinformatics, like climate change, intelligent transport, and immigration studies. These developments call for better methods to deliver raster geodata in an efficient and easy-to-use manner. Data cube technologies have traditionally been used in the geospatial domain for managing Earth Observation data sets that have strict requirements for effective handling of time series. The same approach and methodologies can also be applied in managing other types of geospatial data sets. A cloud service-based geodata cube, called GeoCubes Finland, has been developed to support online delivery and analysis of most important geospatial data sets with national coverage. The main target group of the service is the academic research institutes in the country. The most significant aspects of the GeoCubes data repository include the use of multiple resolution levels, cloud-optimized file structure, and a customized, flexible content access API. Input data sets are pre-processed while being ingested into the repository to bring them into a harmonized form in aspects like georeferencing, sampling resolutions, spatial subdivision, and value encoding. All the resolution levels are created using an appropriate generalization method, selected depending on the nature of the source data set. Multiple pre-processed resolutions enable new kinds of online analysis approaches to be introduced. Analysis processes based on interactive visual exploration can be effectively carried out, as the level of resolution most close to the visual scale can always be used. In the same way, statistical analysis can be carried out on resolution levels that best reflect the scale of the phenomenon being studied. Access times remain close to constant, independent of the scale applied in the application. The cloud service-based approach, applied in the GeoCubes Finland repository, enables analysis operations to be performed on the server platform, thus making high-performance computing facilities easily accessible. The developed GeoCubes API supports this kind of approach for online analysis. The use of cloud-optimized file structures in data storage enables the fast extraction of subareas. The access API allows for the use of vector-formatted administrative areas and user-defined polygons as definitions of subareas for data retrieval. Administrative areas of the country in four levels are available readily from the GeoCubes platform. In addition to direct delivery of raster data, the service also supports the so-called virtual file format, in which only a small text file is first downloaded. The text file contains links to the raster content on the service platform. The actual raster data is downloaded on demand, from the spatial area and resolution level required in each stage of the application. By the geodata cube approach, pre-harmonized geospatial data sets are made accessible to new categories of inexperienced users in an easy-to-use manner. At the same time, the multiresolution nature of the GeoCubes repository facilitates expert users to introduce new kinds of interactive online analysis operations.

Keywords: cloud service, geodata cube, multiresolution, raster geodata

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Authors: Shirin Aghanajafizadeh

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Nest site selection of Crested Lark was investigated in Boroyeh wildlife sanctuary of Harat during spring 2014. Habitat variables such as number of plant species, soil texture, distance to the nearest water resources, farms and roads were compared in the species presence plots with absence ones. Our analysis showed that the average number of Zygophyllum atriplicoidesand, Artemisia sieberi were higher while fine-textured soil percent cover (with very little and gravel) was lower in species presence plots than control plots. We resulted that the most affecting factor in the species nest site selection is the number of Z .atriplicoides and soil texture. Z. atriplicoides and A. sieberi can provide cover for nests and chickens against predators and environmental harsh events such as sunshine and wind. The stability of built nest forces the birds to select sites with not fine-textured soil. Some of the nests were detected in Alfalfa farms that can be related to its cover producing capability.

Keywords: habitat selection, Yazd Province, presence and absence plots, habitat variables

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Authors: Sonia Machraoui, Salam Labidi, Karunakara Naregundi

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Environmental radiation measurements are useful to identify areas of potential natural radiation hazard particularly in areas of phosphate industries where enhanced radiation levels are expected to be present. Measurements of primordial radionuclides concentrations have been carried out in samples collected from Sfax City around the SIAPE phosphate industry of Tunis. The samples analysed include fish, beef meat, egg, and vegetables as well as in soil and grass. Measurements were performed by gamma spectrometry method using a 42% relative efficiency N-type HPGe detector. The activity concentrations of radionuclides were measured by gamma ray spectrometry. As expected, the concentrations of radionuclides belonging to uranium and thorium series were low in food materials. In all the samples analysed, the 137Cs concentration was below detection level, except meat samples which showed the activity concentration of 2.4 Bq kg-1 (dry wt.) The soil to grass transfer factor was found to be similar to those reported in literature. The effective dose to the population due to intake of food products were also estimated and are presented in this paper.

Keywords: effective doses, phosphate industry, transfer coefficients, Tunisia

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Authors: I Dewa Gede Arya Putra

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Principal Component Analysis (PCA) is a mathematical procedure that uses orthogonal transformation techniques to change a set of data with components that may be related become components that are not related to each other. This can have an impact on clustering wind speed characteristics in Indonesia. This study uses data daily wind speed observations of the Site Meteorological Station network for 30 years. Multicollinearity tests were also performed on all of these data before doing clustering with PCA. The results show that the four main components have a total diversity of above 80% which will be used for clusters. Division of clusters using Ward's method obtained 3 types of clusters. Cluster 1 covers the central part of Sumatra Island, northern Kalimantan, northern Sulawesi, and northern Maluku with the climatological pattern of wind speed that does not have an annual cycle and a weak speed throughout the year with a low-speed ranging from 0 to 1,5 m/s². Cluster 2 covers the northern part of Sumatra Island, South Sulawesi, Bali, northern Papua with the climatological pattern conditions of wind speed that have annual cycle variations with low speeds ranging from 1 to 3 m/s². Cluster 3 covers the eastern part of Java Island, the Southeast Nusa Islands, and the southern Maluku Islands with the climatological pattern of wind speed conditions that have annual cycle variations with high speeds ranging from 1 to 4.5 m/s².

Keywords: PCA, cluster, Ward's method, wind speed

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Authors: Mahda Foroughi, Bruno de Anderade, Ana Pereira Roders

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Social media platforms have been increasingly used by locals and tourists to express their opinions about buildings, cities, and built heritage in particular. Most recently, scholars have been using social media to conduct innovative research on built heritage and heritage management. Still, the application of artificial intelligence (AI) methods to analyze social media data for heritage management is seldom explored. This paper investigates the potential of short texts (sentences and hashtags) shared through social media as a data source and artificial intelligence methods for data analysis for revealing the cultural significance (values and attributes) of built heritage. The city of Yazd, Iran, was taken as a case study, with a particular focus on windcatchers, key attributes conveying outstanding universal values, as inscribed on the UNESCO World Heritage List. This paper has three subsequent phases: 1) state of the art on the intersection of public participation in heritage management and social media research; 2) methodology of data collection and data analysis related to coding people's voices from Instagram and Twitter into values of windcatchers over the last ten-years; 3) preliminary findings on the comparison between opinions of locals and tourists, sentiment analysis, and its association with the values and attributes of windcatchers. Results indicate that the age value is recognized as the most important value by all interest groups, while the political value is the least acknowledged. Besides, the negative sentiments are scarcely reflected (e.g., critiques) in social media. Results confirm the potential of social media for heritage management in terms of (de)coding and measuring the cultural significance of built heritage for windcatchers in Yazd. The methodology developed in this paper can be applied to other attributes in Yazd and also to other case studies.

Keywords: social media, artificial intelligence, public participation, cultural significance, heritage, sentiment analysis

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Authors: Erin Lynne Plettenberg, Jeremy Vickery

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In an access-control situation, the role of a user determines whether a data request is appropriate. This paper combines vetted web mining and logic modeling to build a lightweight system for determining the role of a health care provider based only on their prior authorized requests. The model identifies provider roles with 100% recall from very little data. This shows the value of vetted web mining in AI systems, and suggests the impact of the ICD classification on medical practice.

Keywords: electronic medical records, information extraction, logic modeling, ontology, vetted web mining

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Authors: Hiba Naccache

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The present research investigated whether gender differences affect performance in a simple math quiz in statistics course. Participants of this study comprised a sample of 567 statistics students in two different universities in Lebanon. Data were collected through a simple math quiz. Analysis of quantitative data indicated that there wasn’t a significant difference in math performance between males and females. The results suggest that improvements in student performance may depend on improved mastery of basic algebra especially for females. The implications of these findings and further recommendations were discussed.

Keywords: gender, education, math, statistics

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Authors: David Nogueiras Blanco, Amir Alwash, Arnaud Gaudinat, René Schneider

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At a time when the access to and the sharing of information are crucial in the world of research, the use of technologies such as persistent identifiers (PIDs), Current Research Information Systems (CRIS), and ontologies may create platforms for information sharing if they respond to the need of disambiguation of their data by assuring interoperability inside and between other systems. INCIPIT-CRIS is a continuation of the former INCIPIT project, whose goal was to set up an infrastructure for a low-cost attribution of PIDs with high granularity based on Archival Resource Keys (ARKs). INCIPIT-CRIS can be interpreted as a logical consequence and propose a research information management system developed from scratch. The system has been created on and around the Schema.org ontology with a further articulation of the use of ARKs. It is thus built upon the infrastructure previously implemented (i.e., INCIPIT) in order to enhance the persistence of URIs. As a consequence, INCIPIT-CRIS aims to be the hinge between previously separated aspects such as CRIS, ontologies and PIDs in order to produce a powerful system allowing the resolution of disambiguation problems using a combination of an ontology such as Schema.org and unique persistent identifiers such as ARK, allowing the sharing of information through a dedicated platform, but also the interoperability of the system by representing the entirety of the data as RDF triplets. This paper aims to present the implemented solution as well as its simulation in real life. We will describe the underlying ideas and inspirations while going through the logic and the different functionalities implemented and their links with ARKs and Schema.org. Finally, we will discuss the tests performed with our project partner, the Swiss Institute of Bioinformatics (SIB), by the use of large and real-world data sets.

Keywords: current research information systems, linked data, ontologies, persistent identifier, schema.org, semantic web

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Authors: Simone Leonardi, Luca Ardito

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Micro-influencers are effective elements in the marketing strategies of companies and institutions because of their capability to create an hyper-engaged audience around a specific topic of interest. In recent years, many scientific approaches and commercial tools have handled the task of detecting this type of social media users. These strategies adopt solutions ranging from rule based machine learning models to deep neural networks and graph analysis on text, images, and account information. This work compares the existing solutions and proposes an ensemble method to generalize them with different input data and social media platforms. The deployed solution combines deep learning models on unstructured data with statistical machine learning models on structured data. We retrieve both social media accounts information and multimedia posts on Twitter and Instagram. These data are mapped into feature vectors for an eXtreme Gradient Boosting (XGBoost) classifier. Sixty different topics have been analyzed to build a rule based gold standard dataset and to compare the performances of our approach against baseline classifiers. We prove the effectiveness of our work by comparing the accuracy, precision, recall, and f1 score of our model with different configurations and architectures. We obtained an accuracy of 0.91 with our best performing model.

Keywords: deep learning, gradient boosting, image processing, micro-influencers, NLP, social media

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Authors: Suriya

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Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

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Authors: Majno Ajbani

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Difficulties in teaching and learning English pronunciation in Sindh province, Pakistan Abstract Sindhi language is widely spoken in Sindh province, and it is one of the difficult languages of the world. Sindhi language has fifty-two alphabets which have caused a serious issue in learning and teaching of English pronunciation for teachers and students of Colleges and Universities. This study focuses on teachers’ and students’ need for extensive training in the pronunciation that articulates the real pronunciation of actual words. The study is set to contribute in the sociolinguistic studies of English learning communities in this region. Data from 200 English teachers and students was collected by already tested structured questionnaire. The data was analysed using SPSS 20 software. The data analysis clearly demonstrates the higher range of inappropriate pronunciations towards English learning and teaching. The anthropogenic responses indicate 87 percentages teachers and students had an improper pronunciation. This indicates the substantial negative effects on academic and sociolinguistic aspects. It is suggested an improper speaking of English, based on rapid changes in geopolitical and sociocultural surroundings.

Keywords: alphabets, pronunciation, sociolinguistic, anthropogenic, imprudent, malapropos

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Authors: Jazim Sohail, Filipe Teixeira-Dias

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Mild traumatic brain injury (mTBI) within non-helmeted contact sports is a growing concern due to the serious risk of potential injury. Extensive research is being conducted looking into head kinematics in non-helmeted contact sports utilizing instrumented mouthguards that allow researchers to record accelerations and velocities of the head during and after an impact. This does not, however, allow the location of the impact on the head, and its magnitude and orientation, to be determined. This research proposes and validates two methods to quantify impact locations from instrumented mouthguard kinematic data, one using rigid body dynamics, the other utilizing machine learning. The rigid body dynamics technique focuses on establishing and matching moments from Euler’s and torque equations in order to find the impact location on the head. The methodology is validated with impact data collected from a lab test with the dummy head fitted with an instrumented mouthguard. Additionally, a Hybrid III Dummy head finite element model was utilized to create synthetic kinematic data sets for impacts from varying locations to validate the impact location algorithm. The algorithm calculates accurate impact locations; however, it will require preprocessing of live data, which is currently being done by cross-referencing data timestamps to video footage. The machine learning technique focuses on eliminating the preprocessing aspect by establishing trends within time-series signals from instrumented mouthguards to determine the impact location on the head. An unsupervised learning technique is used to cluster together impacts within similar regions from an entire time-series signal. The kinematic signals established from mouthguards are converted to the frequency domain before using a clustering algorithm to cluster together similar signals within a time series that may span the length of a game. Impacts are clustered within predetermined location bins. The same Hybrid III Dummy finite element model is used to create impacts that closely replicate on-field impacts in order to create synthetic time-series datasets consisting of impacts in varying locations. These time-series data sets are used to validate the machine learning technique. The rigid body dynamics technique provides a good method to establish accurate impact location of impact signals that have already been labeled as true impacts and filtered out of the entire time series. However, the machine learning technique provides a method that can be implemented with long time series signal data but will provide impact location within predetermined regions on the head. Additionally, the machine learning technique can be used to eliminate false impacts captured by sensors saving additional time for data scientists using instrumented mouthguard kinematic data as validating true impacts with video footage would not be required.

Keywords: head impacts, impact location, instrumented mouthguard, machine learning, mTBI

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Authors: Nashrawan Naser Taha, Andrew M. Cox

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In a multi-cultural learning context, where ties are weak and dynamic, combining qualitative with quantitative research methods may be more effective. Such a combination may also allow us to answer different types of question, such as about people’s perception of the network. In this study the use of observation, interviews and photos were explored as ways of enhancing data from social network questionnaires. Integrating all of these methods was found to enhance the quality of data collected and its accuracy, also providing a richer story of the network dynamics and the factors that shaped these changes over time.

Keywords: mixed methods, social network analysis, multi-cultural learning, social network dynamics

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Authors: M. Mueller, M. Kuehn, M. Voelker

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In small and medium-sized enterprises (SMEs), the challenge is to create a well-grounded and reliable basis for process analysis, optimization and planning due to a lack of data. SMEs have limited access to methods with which they can effectively and efficiently analyse processes and identify cause-and-effect relationships in order to generate the necessary database and derive optimization potential from it. The implementation of digitalization within the framework of Industry 4.0 thus becomes a particular necessity for SMEs. For these reasons, the abstract presents an analysis methodology that is subject to the objective of developing an SME-appropriate methodology for efficient, temporarily feasible data collection and evaluation in flexible production and logistics systems as a basis for process analysis and optimization. The overall methodology focuses on retrospective, event-based tracing and analysis of material flow objects. The technological basis consists of Bluetooth low energy (BLE)-based transmitters, so-called beacons, and smart mobile devices (SMD), e.g. smartphones as receivers, between which distance data can be measured and derived motion profiles. The distance is determined using the Received Signal Strength Indicator (RSSI), which is a measure of signal field strength between transmitter and receiver. The focus is the development of a software-based methodology for interpretation of relative movements of transmitters and receivers based on distance data. The main research is on selection and implementation of pattern recognition methods for automatic process recognition as well as methods for the visualization of relative distance data. Due to an existing categorization of the database regarding process types, classification methods (e.g. Support Vector Machine) from the field of supervised learning are used. The necessary data quality requires selection of suitable methods as well as filters for smoothing occurring signal variations of the RSSI, the integration of methods for determination of correction factors depending on possible signal interference sources (columns, pallets) as well as the configuration of the used technology. The parameter settings on which respective algorithms are based have a further significant influence on result quality of the classification methods, correction models and methods for visualizing the position profiles used. The accuracy of classification algorithms can be improved up to 30% by selected parameter variation; this has already been proven in studies. Similar potentials can be observed with parameter variation of methods and filters for signal smoothing. Thus, there is increased interest in obtaining detailed results on the influence of parameter and factor combinations on data quality in this area. The overall methodology is realized with a modular software architecture consisting of independently modules for data acquisition, data preparation and data storage. The demonstrator for initialization and data acquisition is available as mobile Java-based application. The data preparation, including methods for signal smoothing, are Python-based with the possibility to vary parameter settings and to store them in the database (SQLite). The evaluation is divided into two separate software modules with database connection: the achievement of an automated assignment of defined process classes to distance data using selected classification algorithms and the visualization as well as reporting in terms of a graphical user interface (GUI).

Keywords: event-based tracing, machine learning, process classification, parameter settings, RSSI, signal smoothing

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Authors: Kazuki Kamada, Haruka Yamashita

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In Japan, taxi is one of the popular transportations and taxi industry is one of the big businesses. However, in recent years, there has been a difficult problem of reducing the number of taxi drivers. In the taxi business, mainly three passenger catching methods are applied. One style is "cruising" that drivers catches passengers while driving on a road. Second is "waiting" that waits passengers near by the places with many requirements for taxies such as entrances of hospitals, train stations. The third one is "dispatching" that is allocated based on the contact from the taxi company. Above all, the cruising taxi drivers need the experience and intuition for finding passengers, and it is difficult to decide "the destination for cruising". The strong recommendation system for the cruising taxies supports the new drivers to find passengers, and it can be the solution for the decreasing the number of drivers in the taxi industry. In this research, we propose a method of recommending a destination for cruising taxi drivers. On the other hand, as a machine learning technique, the embedding models that embed the high dimensional data to a low dimensional space is widely used for the data analysis, in order to represent the relationship of the meaning between the data clearly. Taxi drivers have their favorite courses based on their experiences, and the courses are different for each driver. We assume that the course of cruising taxies has meaning such as the course for finding business man passengers (go around the business area of the city of go to main stations) and course for finding traveler passengers (go around the sightseeing places or big hotels), and extract the meaning of their destinations. We analyze the cruising history data of taxis based on the embedding model and propose the recommendation system for passengers. Finally, we demonstrate the recommendation of destinations for cruising taxi drivers based on the real-world data analysis using proposing method.

Keywords: taxi industry, decision making, recommendation system, embedding model

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Authors: Nasim Nosoudi, Amir Zadeh, Hunter White, Joshua Conrad, Joon W. Shim

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De novo Malignancy has become one of the major causes of death after transplantation, so early cancer diagnosis and detection can drastically improve survival rates post-transplantation. Most previous work focuses on using artificial intelligence (AI) to predict transplant success or failure outcomes. In this work, we focused on predicting de novo malignancy after liver transplantation using AI. We chose the patients that had malignancy after liver transplantation with no history of malignancy pre-transplant. Their donors were cancer-free as well. We analyzed 254,200 patient profiles with post-transplant malignancy from the US Organ Procurement and Transplantation Network (OPTN). Several popular data mining methods were applied to the resultant dataset to build predictive models to characterize de novo malignancy after liver transplantation. Recipient's bilirubin, creatinine, weight, gender, number of days recipient was on the transplant waiting list, Epstein Barr Virus (EBV), International normalized ratio (INR), and ascites are among the most important factors affecting de novo malignancy after liver transplantation

Keywords: De novo malignancy, bilirubin, data mining, transplantation

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Authors: Ikele Chika Bright, Mgbenka Bernard Obialo, Ikele Chioma Faith

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Icthyophthiriasis is a prevalent protozoan (ectoparasite) mostly affecting cultured and aquarium fishes. The majority of the chemotherapeutants lack efficacy for completely eliminating Ich parasite without affecting the environment and they are not safe for human health. The present work is focused on the evaluating different immersion treatments of African catfish (Clarias gariepinus) infected with ichthyophthiriasis and treated with a non-chemical and environmental friendly parasiticides Moringa oleifera. A total number of 800 apparently healthy parasites free (examined) post juvenile catfish were obtained from a reputable farm, disinfected with potassium permanganate in a quarantine tank to remove any possible external parasites. The fish were further challenged with approximately 44,000 infective stages of theronts which were obtained through serial passages by cohabitation. Seven groups (A-G) of post Juvenile were used for the experiment which was carried out into three stages; Dips (60minutes), short term treatment (24-96h) and prolong bath treatment (0-15 days). The concentrations selected were dependent on the outcome of the LC50 of the plant material from which dose-dependent factors were used to select various concentrations of the treatment. In Dips treatment, group D-G were treated with 1,500mg/L, 2500mg/L., 3500mg/L and 4500mg/L, short-term treatment was treated with 150mg/L, 250mg/L, 350mg/L and 450mg/L and prolong bath was treated with 15mg/L, 25mg/L, 35mg/L and 45mg/L of the plant extract whereas group A, B and C were normal control, Ich- infested not treated and Ich- infested treated with standard drug (Acriflavin), respectively. The various types of treatment applied with corresponding concentrations showed almost complete elimination of the adult parasites (trophonts) both in the gills and the body smear, thereby making M. oleifera a potential parasiticides. There were serious pathological alterations in the skin and gills which are usually the main point for Ich parasites invasion but no significant morphological characteristics was noted among the treated groups subjected to different immersion treatment patterns. Epitheliocystis, aneurysm, oedema, hemorrhage, and localization of the adult parasite in the gills were the overall common observations made in the gills whereas degeneration of muscle fibre, dermatitis, hemorrhage, oedema, abscess formation and keratinisation were observed in the skin. However, there are no pathological changes in the control group. Moreover, biochemical parameters such as urea, creatinine, albumin., globulin, total protein, ALT, AST), blood chemistry (sodium, chloride, potassium, bicarbonate), antioxidants (CAT, SOD, GPx, LPO), enzymatic activities (myeloperoxidase, thioreadoxin reductase), Inflammatory response (C-reactive protein), Stress markers (lactate dehydrogenase), heamatological parameters (RBC, PCV, WBC, HB and differential count), lipid profile (total cholesterol, tryglycerides , high density lipoprotein and low density lipoprotein) all showed various significant (P<0.05) and no significant (P>0.05) responses among the Ich-infested fish treated under three immersion treatments. It is suggested that M. oleifera may serve as an alternatives to chemotherapeutants for control of Ichthyophthiriasis in African catfish Clarias gariepinus.

Keywords: Icthyophthirius multifilis, immersion treatment, pathophysiology, African catfish

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Authors: F. Z. Aliyat, B. Ben Messaoud, L. Nassiri, E. Bouiamrine, J. Ibijbijen

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The present study aims to assess the biological nitrogen fixation in the soybean tested in different Moroccan soils combined with the rhizobial inoculation. These effects were evaluated by the plant growth mainly by the aerial biomass production, total nitrogen content and the proportion of the nitrogen fixed. This assessment clearly shows that the inoculation with bacteria increases the growth of soybean. Five different soils and a control (peat) were used. The rhizobial inoculation was performed by applying the peat that contained a mixture of 2 strains Sinorhizobium fredii HH103 and Bradyrhizobium. The biomass, the total nitrogen content and the proportion of nitrogen fixed were evaluated under different treatments. The essay was realized at the greenhouse the Faculty of Sciences, Moulay Ismail University. The soybean has shown a great response for the parameters assessed. Moreover, the best response was reported by the inoculated plants compared to non- inoculated and to the absolute control. Finally, good production and the best biological nitrogen fixation present an important ecological technology to improve the sustainable production of soybean and to ensure the increase of the fertility of soils.

Keywords: biological nitrogen fixation, inoculation, rhizobium, soybean

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Authors: Malihe Shahidan, Azin Madhi, Meisam Shahbaz

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In recent decades, the role of supply chains in sustaining businesses and establishing their superiority in the market has been under focus. The realization of the goals and strategies of a business enterprise is largely dependent on the cooperation of the chain, including suppliers, distributors, retailers, etc. Supply chains can potentially be disrupted by both internal and external factors. In this paper, resilience strategies have been identified and analyzed in three levels: sourcing, producing, and distributing by considering economic depression as a current risk factor for the machine-made rugs industry. In this study, semi-structured interviews for data gathering and thematic analysis for data analysis are applied. Supply chain data has been gathered from seven rug factories before and after the economic depression through semi-structured interviews. The identified strategies were derived from literature review and validated by collecting data from a group of eighteen industry and university experts, and the results were analyzed using statistical tests. Finally, the outsourcing of new products and products in the new market, the development and completion of the product portfolio, the flexibility in the composition and volume of products, the expansion of the market to price-sensitive, direct sales, and disintermediation have been determined as strategies affecting supply chain resilience of machine-made rugs' industry during an economic depression.

Keywords: distribution, economic depression, machine-made rug, outsourcing, production, sourcing, supply chain, supply chain resilience

Procedia PDF Downloads 142

Authors: Chapman Eze Nnadozie

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Relational databases constitute a very vital tool for the effective management and administration of both personal and organizational data. Data access ranges from a single user database management software to a more complex distributed server system. This paper intends to appraise the use a programming language extension like structured query language (SQL) to establish links to a relational database (Microsoft Access 2013) using Visual C++ 9 programming language environment. The methodology used involves the creation of tables to form a database using Microsoft Access 2013, which is Object Linking and Embedding (OLE) database compliant. The SQL command is used to query the tables in the database for easy extraction of expected records inside the visual C++ environment. The findings of this paper reveal that records can easily be accessed and manipulated to filter exactly what the user wants, such as retrieval of records with specified criteria, updating of records, and deletion of part or the whole records in a table.

Keywords: data access, database, database management system, OLE, programming language, records, relational database, software, SQL, table

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Authors: Yunfeng Shan, Xiaoliang Wang, Youjun Zhou

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Whole-genome data from two lungfish species, along with other species, present a valuable opportunity to reassess the longstanding debate regarding the evolutionary relationships among tetrapods, lungfishes, and coelacanths. However, the use of bootstrap support has become outdated for large-scale phylogenomic data. Without robust phylogenetic support, the phylogenetic trees become meaningless. Therefore, it is necessary to re-evaluate the phylogenies of tetrapods, lungfishes, and coelacanths using novel measures of phylogenetic support specifically designed for phylogenomic data, as the previous phylogenies were based on 100% bootstrap support. Our findings consistently provide strong evidence favoring lungfish as the closest living relative of tetrapods. This conclusion is based on high gene support confidence with confidence intervals exceeding 95%, high internode certainty, and high gene concordance factor. The evidence stems from two datasets containing recently deciphered whole genomes of two lungfish species, as well as five previous datasets derived from lungfish transcriptomes. These results yield fresh insights into the three hypotheses regarding the phylogenies of tetrapods, lungfishes, and coelacanths. Importantly, these hypotheses are not mere conjectures but are substantiated by a significant number of genes. Analyzing real biological data further demonstrates that the inclusion of additional taxa diminishes the number of orthologues and leads to more diverse tree topologies. Consequently, gene trees and species trees may not be identical even when whole-genome sequencing data is utilized. However, it is worth noting that many gene trees can accurately reflect the species tree if an appropriate number of taxa, typically ranging from six to ten, are sampled. Therefore, it is crucial to carefully select the number of taxa and an appropriate outgroup while excluding fast-evolving taxa as outgroups to mitigate the adverse effects of long-branch attraction (LBA) and achieve an accurate reconstruction of the species tree. This is particularly important as more whole-genome sequencing data becomes available.

Keywords: gene support confidence (GSC), origin of land vertebrates, coelacanth, two whole genomes of lungfishes, confidence intervals

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Authors: Dana R. Thomson, Catherine Linard, Sabine Vanhuysse, Jessica E. Steele, Michal Shimoni, Jose Siri, Waleska Caiaffa, Megumi Rosenberg, Eleonore Wolff, Tais Grippa, Stefanos Georganos, Helen Elsey

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The Sustainable Development Goals (SDGs) and Urban Health Equity Assessment and Response Tool (Urban HEART) identify dozens of key indicators to help local decision-makers prioritize and track inequalities in health outcomes. However, presentations and discussions at the International Conference on Urban Health (ICUH) 2017 suggested that additional indicators are needed to make decisions and policies. A local decision-maker may realize that malaria or road accidents are a top priority. However, s/he needs additional health determinant indicators, for example about standing water or traffic, to address the priority and reduce inequalities. Health determinants reflect the physical and social environments that influence health outcomes often at community- and societal-levels and include such indicators as access to quality health facilities, access to safe parks, traffic density, location of slum areas, air pollution, social exclusion, and social networks. Indicator identification and disaggregation are necessarily constrained by available datasets – typically collected about households and individuals in surveys, censuses, and administrative records. Continued advancements in earth observation, data storage, computing and mobile technologies mean that new sources of health determinants indicators derived from 'big data' are becoming available at fine geographic scale. Big data includes high-resolution satellite imagery and aggregated, anonymized mobile phone data. While big data are themselves not representative of the population (e.g., satellite images depict the physical environment), they can provide information about population density, wealth, mobility, and social environments with tremendous detail and accuracy when combined with population-representative survey, census, administrative and health system data. The aim of this paper is to (1) flag to data scientists important indicators needed by health decision-makers at the city and sub-city scale - ideally free and publicly available, and (2) summarize for local decision-makers new datasets that can be generated from big data, with layperson descriptions of difficulties in generating them. We include SDGs and Urban HEART indicators, as well as indicators mentioned by decision-makers attending ICUH 2017.

Keywords: health determinant, health outcome, mobile phone, remote sensing, satellite imagery, SDG, urban HEART

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Authors: Sangita Pokhrel, Nalinda Somasiri, Rebecca Jeyavadhanam, Swathi Ganesan

Abstract:

Tourism is a booming industry with huge future potential for global wealth and employment. There are countless data generated over social media sites every day, creating numerous opportunities to bring more insights to decision-makers. The integration of Big Data Technology into the tourism industry will allow companies to conclude where their customers have been and what they like. This information can then be used by businesses, such as those in charge of managing visitor centers or hotels, etc., and the tourist can get a clear idea of places before visiting. The technical perspective of natural language is processed by analysing the sentiment features of online reviews from tourists, and we then supply an enhanced long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We have constructed a web review database using a crawler and web scraping technique for experimental validation to evaluate the effectiveness of our methodology. The text form of sentences was first classified through Vader and Roberta model to get the polarity of the reviews. In this paper, we have conducted study methods for feature extraction, such as Count Vectorization and TFIDF Vectorization, and implemented Convolutional Neural Network (CNN) classifier algorithm for the sentiment analysis to decide the tourist’s attitude towards the destinations is positive, negative, or simply neutral based on the review text that they posted online. The results demonstrated that from the CNN algorithm, after pre-processing and cleaning the dataset, we received an accuracy of 96.12% for the positive and negative sentiment analysis.

Keywords: counter vectorization, convolutional neural network, crawler, data technology, long short-term memory, web scraping, sentiment analysis

Procedia PDF Downloads 75