Search results for: flight test data

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: Luvuno N. Jele, Warwick W. Hastie, Andrew Green

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Calcarenite is a clastic sedimentary beach rock composed of more than 50% sand sized (0.0625 – 2 mm) carbonate grains. In South Africa, these rocks occur as a narrow belt along most of the coast of KwaZulu-Natal and sporadically along the coast of the Eastern Cape. Calcarenites contain a high percentage of calcium carbonate, and due to a number of its physical and structural features, like porosity, cementing material, sedimentary structures, grain shape, and grain size; they are more prone to chemical and mechanical weathering. The objective of the research is to study the strength and compressibility characteristics of the calcarenites along the coast of KwaZulu-Natal to be able to better understand the geotechnical behaviour of these rocks, which may help to predict areas along the coast which may be potentially susceptible to failure/differential settling resulting in damage to property. A total of 148 cores were prepared and analyzed. Cores were analyzed perpendicular and parallel to bedding. Tests were carried out in accordance with the relevant codes and recommendations of the International Society for Rock Mechanics, American Standard Testing Methods, and Committee of Land and Transport Standard Specifications for Road and Bridge Works for State Road Authorities. Test carried out included: x-ray diffraction, petrography, shape preferred orientation (SPO), 3-D Tomography, rock porosity, rock permeability, ethylene glycol, slake durability, rock water absorption, Duncan swelling index, triaxial compressive strength, Brazilian tensile strength and uniaxial compression test with elastic modulus. The beach-rocks have a uniaxial compressive strength (UCS) ranging from 17,84Mpa to 287,35Mpa and exhibit three types of failure; (1) single sliding shear failure, (2) complete cone development, and (3) splitting failure. Brazilian tensile strength of the rocks ranges from 2.56 Mpa to 12,40 Ma, with those tested perpendicular to bedding showing lower tensile strength. Triaxial compressive tests indicate calcarenites have strength ranging from 86,10 Mpa to 371,85 Mpa. Common failure mode in the triaxial test is a single sliding shear failure. Porosity of the rocks varies from 1.25 % to 26.52 %. Rock tests indicate that the direction of loading, whether it be parallel to bedding or perpendicular to bedding, plays no significantrole in the strength and durability of the calcarenites. Porosity, cement type, and grain texture play major roles.UCS results indicate that saturated cores are weaker in strength compared to dry samples. Thus, water or moisture content plays a significant role in the strength and durability of the beach-rock. Loosely packed, highly porous and low magnesian-calcite bearing calcarenites show a decrease in strength compared to the densely packed, low porosity and high magnesian-calcite bearing calcarenites.

Keywords: beach-rock, calcarenite, cement, compressive, failure, porosity, strength, tensile, grains

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Authors: Md, MCS, MPH Munyangi Wa Nkola Jerome

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The pandemic of COVID-19, a recently discovered contagious respiratory disease called SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus 2 Majority of people infected with SARS-CoV-2: Asymptomatic or mildly ill 14% of patients will develop severe illness requiring hospitalization and oxygen support, and 5% of these will be transferred to an intensive care unit, Urgent need for new treatments that can be used quickly to avoid transfer of patients to intensive care and death. Objective: To evaluate the clinical activity (efficacy) of ArtiCovid Hypothesis: Administration of 3 times a teaspoon per day by COVID patients (symptomatic, mild, or moderate forms) results in the disappearance of symptoms and improvement of biological parameters (including viral suppression). Clinical efficacy: the disappearance of clinical signs after seven days of treatment; reduction in the rate of patients transferred to intensive care units for mechanical ventilation and a decrease in mortality related to this infection Paraclinical efficacy: improvement of biological parameters (mainly d-dimer, CRP) Virological efficacy: suppression of the viral load after seven days of treatment (control test on the seventh day is negative) Pilot study using a standardized solution based on Artemisia annua (ARTICOVID) Obtaining authorization from the health authorities of the province of Central Kongo Recruitment of volunteer patients, mainly in the Kinkanda HospitalCarrying out tests before and after treatment as well as analyses before and after treatment. The protocol obtained the approval of the ethics committee 50 patients who completed the treatment were aged between 2 and 70 years, with an average age of 36 yearsMore half were male (56%). One in four patients was a health professional (25%) Of the 12 health professionals, 4 were physicians. For those who reported the date of onset of the disease, the average duration between the appearance of the first symptoms and the medical consultation was 5 days. The 50 patients put on ARTICOVID were discharged alive with CRP levels substantially normalizedAfter seven to eight days, the control test came back negative. This pilot study suggests that ARTICOVID may be effective against COVID-19 infection.

Keywords: artiCovid, DRC, Covid-19, SARS_COV_2

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Authors: Jacob T. Liberty, Wilfred I. Okonkwo

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The present study designed and constructed a post-harvest passive solar tomato dryer of dimension 176 x 152 x 54cm for drying tomato. Quality of the dried crop was evaluated and compared with the fresh ones. The solar dryer consist of solar collector (air heater), 110 x 61 x 10 x 10cm, the drying chamber, 102 x54cm, removal heat storage unit, 40 x 35 x 13cm and drying trays, 43 x 42cm. The physicochemical properties of this crop were evaluated before and after drying. Physicochemical properties evaluated includes moisture, protein, fat, fibre, ash, carbohydrate and vitamin C, contents. The fresh, open and solar dried samples were analysed for their proximate composition using the recommended method of AOAC. Also, statistical analysis of the data was conducted using analysis of variance (ANOVA) using completely Randomize Design (CRD) and means were separated by Duncan’s New Multiple Range test (DNMRT). Proximate analysis showed that solar dried tomato had significantly (P < 0.05) higher protein, fibre, ash, carbohydrate and vitamin C except for the fat content that was significantly (P < 0.05) higher for all the open sun dried samples than the solar dried and fresh product. The nutrient which is highly affected by sun drying is vitamin C. Result indicates that moisture loss in solar dried tomato was faster and lower than the open dried samples and as such makes the solar dried products of lesser tendency to mould and bacterial growth. Also, the open sun dried samples had to be carried into the sheltered place each time it rained. The solar dried produce is of high quality. Further processing of the dried crops will involve packaging for commercial purposes. This will also help in making these agricultural product available in a relatively cheap price in off season and also avert micronutrient deficiencies in diet especially among the low-income groups in Nigeria.

Keywords: tomato, passive solar dryer, physicochemical properties, removal heat storage

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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: Werner Grommes

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The institute of occupational safety and health works in a working group for building a normative standard for illuminated warning vests and did a lot of experiments and measurements as basic work (cooperation). Intelligent car headlamps are able to suppress conventional warning vests with retro-reflective stripes as a disturbing light. Illuminated warning vests are therefore required for occupational safety. However, they must not pose any danger to the wearer or other persons. Here, the risks of the batteries (lithium types), the maximum brightness (glare) and possible interference radiation from the electronics on the implant carrier must be taken into account. The all-around visibility, as well as the required range, play an important role here. For the study, many luminance measurements of already commercially available LEDs and electroluminescent warning vests, as well as their electromagnetic interference fields and aspects of electrical safety, were measured. The results of this study showed that LED lighting is all far too bright and causes strong glare. The integrated controls with pulse modulation and switching regulators cause electromagnetic interference fields. Rechargeable lithium batteries can explode depending on the temperature range. Electroluminescence brings even more hazards. A test method was developed for the evaluation of visibility at distances of 50, 100, and 150 m, including the interview of test persons. A measuring method was developed for the detection of glare effects at close range with the assignment of the maximum permissible luminance. The electromagnetic interference fields were tested in the time and frequency ranges. A risk and hazard analysis were prepared for the use of lithium batteries. The range of values for luminance and risk analysis for lithium batteries were discussed in the standards working group. These will be integrated into the standard. This paper gives a brief overview of the topics of illuminated warning vests, which takes into account the risks and hazards for the vest wearer or others

Keywords: illuminated warning vest, optical tests and measurements, risks, hazards, optical glare effects, LED, E-light, electric luminescent

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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

Procedia PDF Downloads 387

Authors: M. Azarbarmas, J. M. Cabrera, J. Calvo, M. Aghaie-Khafri

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The modeling of hot deformation behavior for unseen conditions is important in metal-forming. In this study, the hot deformation of IN718 has been characterized in the temperature range 950-1100 and strain rate range 0.001-0.1 s-1 using hot compression tests. All stress-strain curves showed the occurrence of dynamic recrystallization. These curves were implemented quantitatively in mathematics, and then constitutive equation indicating the relationship between the flow stress and hot deformation parameters was obtained successfully.

Keywords: compression test, constitutive equation, dynamic recrystallization, hot working

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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: Mae C. Pavilario

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This study employs a sequential explanatory mixed method. Quantitatively it investigated the profile of grade VII students. Qualitatively, the prevailing contextual variables that affect their frustration-level were sought based on their perspective and that of their parents and teachers. These students were categorized as frustration-level in reading based on the data on word list of the Philippine Informal Reading Inventory (Phil-IRI). The researcher-made reading factor instrument translated to local dialect (Hiligaynon) was subjected to cross-cultural translation to address content, semantic, technical, criterion, or conceptual equivalence, the open-ended questions, and one unstructured interview was utilized. In the profile of the 26 participants, the 12 males are categorized as grade II and grade III frustration-levels. The prevailing contextual variables are personal-“having no interest in reading”, “being ashamed and fear of having to read in front of others” for extremely high frustration level; social environmental-“having no regular reading schedule at home” for very high frustration level and personal- “having no interest in reading” for high frustration level. Kendall Tau inferential statistical tool was used to test the significant relationship in the prevailing contextual variables that affect frustration-level readers when grouped according to perspective. Result showed that significant relationship exists between students-parents perspectives; however, there is no significant relationship between students’ and teachers’, and parents’ and teachers’ perspectives. The themes in the narratives of the participants on frustration-level readers are existence of speech defects, undesirable attitude, insufficient amount of reading materials, lack of close supervision from parents, and losing time and focus on task. Intervention was designed.

Keywords: contextual variables, frustration-level readers, perspective, inquiry

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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: 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

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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: Ilze Dimanta, Zane Rutkovska, Vizma Nikolajeva, Janis Kleperis, Indrikis Muiznieks

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Majority of word’s steadily increasing energy consumption is provided by non-renewable fossil resources. Need to find an alternative energy resource is essential for further socio-economic development. Hydrogen is renewable, clean energy carrier with high energy density (142 MJ/kg, accordingly – oil has 42 MJ/kg). Biological hydrogen production is an alternative way to produce hydrogen from renewable resources, e.g. using organic waste material resource fermentation that facilitate recycling of sewage and are environmentally benign. Hydrogen gas is produced during the fermentation process of bacteria in anaerobic conditions. Bacteria are producing hydrogen in the liquid phase and when thermodynamic equilibrium is reached, hydrogen is diffusing from liquid to gaseous phase. Because of large quantities of available crude glycerol and the highly reduced nature of carbon in glycerol per se, microbial conversion of it seems to be economically and environmentally viable possibility. Such industrial organic waste product as crude glycerol is perspective for usage in feedstock for hydrogen producing bacteria. The process of biodiesel production results in 41% (w/w) of crude glycerol. The developed lab-scale test system (experimental bioreactor) with hydrogen micro-electrode (Unisense, Denmark) was used to determine hydrogen production yield and rate in the liquid phase. For hydrogen analysis in the gas phase the RGAPro-100 mass-spectrometer connected to the experimental test-system was used. Fermentative bacteria strains were tested for hydrogen gas production rates. The presence of hydrogen in gaseous phase was measured using mass spectrometer but registered concentrations were comparatively small. To decrease the hydrogen partial pressure in liquid phase reactor with a system for continuous bubbling with inert gas was developed. H2 production rate for the best producer in liquid phase reached 0,40 mmol H2/l, in gaseous phase - 1,32 mmol H2/l. Hydrogen production rate is time dependent – higher rate of hydrogen production is at the fermentation process beginning when concentration increases, but after three hours of fermentation, it decreases.

Keywords: bio-hydrogen, fermentation, experimental bioreactor, crude glycerol

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Authors: Sujo Meghwar, Zulfqar Ali laghari, Kanji Harijan, Muhib Ali Lagari, G. M. Mastoi, Ali Mohammad Rind

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Thar Desert is the most populous Desert of the world. Climate variation in Thar Desert has induced an increase in the magnitude of drought. The variation in climate variation has caused a decrease in natural vegetations. Some plant species are eliminated forever. We have applied the SPI (standardized precipitation index) climate model to investigate the drought induced by climate change. We have gathered the anthropogenic response through a developed questionnaire. The data was analyzed in SPSS version 18. The met-data of two meteorological station elaborated by the time series has suggested an increase in temperature from 1-2.5 centigrade, the decrease in rain fall rainfall from 5-25% and reduction in humidity from 5-12 mm in the 20th century. The anthropogenic responses indicate high impact of climate change on human life and vegetations. Triangle data, we have collected, gives a new insight into the understanding of an association between climate change, drought and human activities.

Keywords: Thar desert, human impact, vegetations, temperature, rainfall, humidity

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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: Roberta Pecoraro, Elena Maria Scalisi

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Engineered Nanoparticles (ENPs) and Nanomaterials (ENMs) concern an active research area and a sector in full expansion. They have physical-chemical characteristics and small size that improve their performance compared to common materials. Due to the increase in their production and their subsequent release into the environment, new strategies are emerging to assess risk of nanomaterials. NPs can be released into the environment through aquatic systems by human activities and exert toxicity on living organisms. We evaluated the potential toxic effect of cerium oxide (CeO2) nanoparticles because it’s used in different fields due to its peculiar properties. In order to assess nanoparticles toxicity, Fish Embryo Toxicity (FET) test was performed. Powders of CeO2 NPs supplied by the CNR-IMM of Catania are indicated as CeO2 type 1 (as-prepared) and CeO2 type 2 (modified), while CeO2 type 3 (commercial) is supplied by Sigma-Aldrich. Starting from a stock solution (0.001g/10 ml dilution water) of each type of CeO2 NPs, the other concentration solutions were obtained adding 1 ml of the stock solution to 9 ml of dilution water, leading to three different solutions of concentration (10-4, 10-5, 10-6 g/ml). All the solutions have been sonicated to avoid natural tendency of NPs to aggregate and sediment. FET test was performed according to the OECD guidelines for testing chemicals using our internal protocol procedure. A number of eight selected fertilized eggs were placed in each becher filled with 5 ml of each concentration of the three types of CeO2 NPs; control samples were incubated only with dilution water. Replication was performed for each concentration. During the exposure period, we observed four endpoints (embryo coagulation, lack of formation of somites, failure to lift the yolk bag, no heartbeat) by a stereomicroscope every 24 hours. Immunohistochemical analysis on treated larvae was performed to evaluate the expression of metallothioneins (MTs), Heat Shock Proteins 70 (HSP70) and 7-ethoxyresorufin-O-diethylase (EROD). Our results have not shown evident alterations on embryonic development because all embryos completed the development and the hatching of the eggs, started around the 48th hour after exposure, took place within the last observation at 72 hours. A good reactivity, both in the embryos and in the newly hatched larvae, was found. The presence of heartbeat has also been observed in embryos with reduced mobility confirming their viability. A higher expression of EROD biomarker was observed in the larvae exposed to the three types of CeO2, showing a clear difference with the control. A weak positivity was found for MTs biomarker in treated larvae as well as in the control. HSP70 are expressed homogeneously in all the type of nanoparticles tested but not too much greater than control. Our results are in agreement with other studies in the literature, in which the exposure of Danio rerio larvae to other metal oxide nanoparticles does not show adverse effects on survival and hatching time. Further studies are necessary to clarify the role of these NPs and also to solve conflicting opinions.

Keywords: Danio rerio, endpoints, fish embryo toxicity test, metallic nanoparticles

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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: Amanda Julia Binay, Patricia Elise Suarez

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Female representation in media is an important subject in the discussion of gender equality, especially in impactful and influential media like film. As the Filipino film industry continues to grow and evolve, the need for analysis on Filipino female representation on screen is imperative. Additionally, there has been limited research made on female representation in the Philippine film scene. Thus, the paper aims to analyze the presence and evolution of female representation in Philippine cinema and compare the findings with that of American films to see how Filipino filmmakers hold their own against the standards of international movements that call for more and better female representation, especially in Hollywood. The participants selected were Filipino and American films released within the years 1975 to 2020 in five (5) year intervals. Twenty (20) critically acclaimed and highest-grossing Filipino films and twenty (20) critically acclaimed and highest-grossing Hollywood films were then subject to the Bechdel and Peirce tests to obtain statistical measures of their female representation. The findings of the study reveal that the presence of female representation in Philippine film history has been consistent and has continued to grow and evolve throughout the years, with strong female leads with vibrant characteristics and diverse stories. However, analysis of female representation regarding American films has shown an extreme lack thereof with more misogynistic, sexist, and limiting ideals. Thus, the study concludes that the state of female representation in Philippine cinema and film industry holds its own when compared to American cinema and film industry and even outperforms it in many aspects of female representation, such as consistent inclusion and depiction of multi-dimensional female leads and female relationships. Hence, the study implies that women’s consistent presence in Philippine cinema mirrors Filipino women’s prominent role in Philippine society and that American cinema must continue to make efforts to change their portrayals of female characters, leads, and relationships to make them more grounded in reality.

Keywords: female representation, gender studies, feminism, philippine cinema, American cinema, bechdel test, peirce test, comparative analysis

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

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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

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Authors: Lindy Van Vliet, Saloni Phadke, Anthea Nelson, Ann Gallant

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Holistic and patient-centred palliative care and support require an integrated system of care that includes health and social service providers working together to ensure that patients and families have access to the care they need. The objective of this study is to further explore and understand the benefits and challenges of mobilizing multidisciplinary care teams for health care professionals and social service providers. Drawing on an interpretivist, exploratory, qualitative design, our multidisciplinary research team (medicine, nursing and social work) conducted interviews with 15 health care and social service providers in the Ottawa region. Interview data was audio-recorded, transcribed, and analyzed using a reflexive thematic analysis approach. The data deepens our understandings of the facilitators and barriers posed by multidisciplinary care teams. Three main findings emerged: First, the data highlighted the benefits of multidisciplinary care teams for both patient outcomes and quality of life and provider mental health; second, the data showed that the lack of a system-wide integrated communication system reduces the quality of patient care and increases provider stress while working in multidisciplinary care teams; finally, the data demonstrated the existence of implicit hierarchies between disciplines, this coupled with different disciplinary perspectives of palliative care provision can lead to friction and challenges within care teams. These findings will have important implications for the future of palliative care as they will help to facilitate and build stronger person-centred/relationship-centred palliative care practices by naming the challenges faced by multidisciplinary palliative care teams and providing examples of best practices.

Keywords: public health palliative care, palliative care nursing, care networks, integrated health care, palliative care approach, public health, multidisciplinary work, care teams

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Authors: Ren Moses, Jaqueline Masaki

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This paper addresses the need for accurate and updated vehicle classification system through a thorough evaluation of vehicle class categories to identify errors arising from the existing system and proposing modifications. The data collected from two permanent traffic monitoring sites in Florida were used to evaluate the performance of the existing vehicle classification table. The vehicle data were collected and classified by the automatic vehicle classifier (AVC), and a video camera was used to obtain ground truth data. The Federal Highway Administration (FHWA) vehicle classification definitions were used to define vehicle classes from the video and compare them to the data generated by AVC in order to identify the sources of misclassification. Six types of errors were identified. Modifications were made in the classification table to improve the classification accuracy. The results of this study include the development of updated vehicle classification table with a reduction in total error by 5.1%, a step by step procedure to use for evaluation of vehicle classification studies and recommendations to improve FHWA 13-category rule set. The recommendations for the FHWA 13-category rule set indicate the need for the vehicle classification definitions in this scheme to be updated to reflect the distribution of current traffic. The presented results will be of interest to States’ transportation departments and consultants, researchers, engineers, designers, and planners who require accurate vehicle classification information for planning, designing and maintenance of transportation infrastructures.

Keywords: vehicle classification, traffic monitoring, pavement design, highway traffic

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Authors: Chittana Phompila

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The current increased population pushes increasing demands for natural resources and living space. In Laos, urban areas have been expanding rapidly in recent years. The rapid urbanization can have negative impacts on landscapes, including forest and agriculture lands. The primary objective of this research were to map current urban areas in a large city in Savannakhet province, in Laos, 2) to compare changes in urbanization between 1990 and 2018, and 3) to estimate forest and agriculture areas lost due to expansions of urban areas during the last over twenty years within study area. Landsat 8 data was used and existing GIS data was collected including spatial data on rivers, lakes, roads, vegetated areas and other land use/land covers). GIS data was obtained from the government sectors. Object based classification (OBC) approach was applied in ECognition for image processing and analysis of urban area using. Historical data from other Landsat instruments (Landsat 5 and 7) were used to allow us comparing changes in urbanization in 1990, 2000, 2010 and 2018 in this study area. Only three main land cover classes were focused and classified, namely forest, agriculture and urban areas. Change detection approach was applied to illustrate changes in built-up areas in these periods. Our study shows that the overall accuracy of map was 95% assessed, kappa~ 0.8. It is found that that there is an ineffective control over forest and land-use conversions from forests and agriculture to urban areas in many main cities across the province. A large area of agriculture and forest has been decreased due to this conversion. Uncontrolled urban expansion and inappropriate land use planning can lead to creating a pressure in our resource utilisation. As consequence, it can lead to food insecurity and national economic downturn in a long term.

Keywords: urbanisation, forest cover, agriculture areas, Landsat 8 imagery

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