Search results for: earthquake disaster data collection

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: Aarnav Singh, Jatin Moolchandani

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The proposed research work aims to build a query-based AI chatbot that can answer any question related to any topic. A chatbot is software that converses with users via text messages. In the proposed system, we aim to build a chatbot that generates a response based on the user’s query. For this, we use natural language processing to analyze the query and some set of texts to form a concise answer. The texts are obtained through web-scrapping and filtering all the credible sources from a web search. The objective of this project is to provide a chatbot that is able to provide simple and accurate answers without the user having to read through a large number of articles and websites. Creating an AI chatbot that can answer a variety of user questions on a variety of topics is the goal of the proposed research project. This chatbot uses natural language processing to comprehend user inquiries and provides succinct responses by examining a collection of writings that were scraped from the internet. The texts are carefully selected from reliable websites that are found via internet searches. This project aims to provide users with a chatbot that provides clear and precise responses, removing the need to go through several articles and web pages in great detail. In addition to exploring the reasons for their broad acceptance and their usefulness across many industries, this article offers an overview of the interest in chatbots throughout the world.

Keywords: Chatbot, Artificial Intelligence, natural language processing, web scrapping

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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: Paola Millare, Nelinda Catherine Pangilinan

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Background and Aim: Cervical cancer is the most commonly diagnosed gynecological malignancy during pregnancy. Owing to the rarity of the disease, and the complexity of all factors that have to be taken into consideration, standardization of treatment is very difficult. Cervical cancer is the second most common malignancy among women. The treatment of cancer during pregnancy is most challenging in the case of cervical cancer, since the pregnant uterus itself is affected. This report aims to present a case of cervical cancer in a pregnant woman and how to manage this case and several issues accompanied with it. Methods: This is a case of a 28 year-old, Gravida 4 Para 2 (1111), who presented with watery to mucoid, whitish, non-foul smelling and increasing in amount. Internal examination revealed normal external genitalia, parous outlet, cervix was transformed into a fungating mass measuring 5x4 cm, with left parametrial involvement, body of uterus was enlarged to 24 weeks size, no adnexal mass or tenderness. She had cervical punch biopsy, which revealed, adenocarcinoma, well-differentiated cervical tissue. Standard management for cases with stage 2B cervical carcinoma was to start radiation or radical hysterectomy. In the case of patients diagnosed with cervical cancer and currently pregnant, these kind of management will result to fetal loss. The patient still declined the said management and opted to delay the treatment and wait for her baby to reach at least term and proceed to cesarean section as route of delivery. Results: The patient underwent an elective cesarean section at 37th weeks age of gestation, with an outcome of a term, live baby boy APGAR score 7,9 birthweight 2600 grams. One month postpartum, the patient followed up and completed radiotherapy, chemotherapy and brachytherapy. She was advised to go back after 6 months for monitoring. On her last check up, an internal examination was done which revealed normal external genitalia, vagina admits 2 fingers with ease, there is a palpable fungating mass at the cervix measuring 2x2 cm. A repeat gynecologic oncologic ultrasound was done revealing cervical mass, endophytic, grade 1 color score with stromal invasion 35% post radiation reactive lymph nodes with intact paracolpium, pericervical, and parametrial involvement. The patient was then advised to undergo pelvic boost and for close monitoring of the cervical mass. Conclusion: Cervical cancer in pregnancy is rare but is a dilemma for women and their physicians. Treatment should be multidisciplinary and individualized following careful counseling. In this case, the treatment was clearly on the side of preventing the progression of cervical cancer while she is pregnant, however due to ethical reasons, the management deviates on the right of the patient to decide for her own health and her unborn child. The collaborative collection of data relating to treatment and outcome is strongly encouraged.

Keywords: cancer, cervical, ethical, pregnancy

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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: Liping Wu, Durga Bhakta Pokharel, Junhua Dong, Changgang Wang, Lin Zhao, Wei Ke, Nan Chen

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Hazenite conversion coating was deposited on AZ31 Mg alloy in a deaerated phosphate solution containing 0.1 M K₂HPO₄ and 0.1 M Na₂HPO₄ (Na₀.₁K0₀.₁) with pH 9 at −0.8 V. The coating mechanism of hazenite was elucidated by in situ potentiostatic current decay, scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FT-IR), electron probe micro-analyzer (EPMA) and differential scanning calorimetry (DSC). The volume of H₂ evolved during potentiostatic polarization was measured by a gas collection apparatus. The degradation resistance of the hazenite coating was evaluated in simulated body fluid (SBF) at 37℃ by using potentiodynamic polarization (PDP). The results showed that amorphous Mg(OH)₂ was deposited first, followed by the transformation of Mg(OH)₂ to amorphous MgHPO₄, subsequently the conversion of MgHPO₄ to crystallized K-struvite (KMgPO₄·6H₂O), finally the crystallization of crystallized hazenite (NaKMg₂(PO₄)₂·14H₂O). The deposited coating was composed of four layers where the inner layer is comprised of Mg(OH)₂, the middle layer of Mg(OH)₂ and MgHPO₄, the top layer of Mg(OH)₂, MgHPO₄ and K-struvite, the topmost layer of Mg(OH)₂, MgHPO₄, K-struvite and hazenite (NaKMg₂(PO₄)₂·14H₂O). The PD results showed that the hazenite coating decreased the corrosion rate by two orders of magnitude.

Keywords: magnesium alloy, potentiostatic technique, hazenite, mineral conversion coating

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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: 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: 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: Bianca Koroschetz, Emma Mäenpää

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The purpose of this paper is to study consumer practices and meanings of different antifouling methods in order to identify the main barriers for sustainable consumption of antifouling products in the Baltic Sea. The Baltic Sea is considered to be an important tourism area. More than 3.5 million leisure boaters use the sea for recreational boating. Most leisure boat owners use toxic antifouling paint to keep barnacles from attaching to the hull. Attached barnacles limit maneuverability and add drag which in turn increases fuel costs. Antifouling paint used to combat barnacles causes particular problems, as the use of these products continuously adds to the distribution of biocides in the coastal ecosystem and leads to the death of marine organisms. To keep the Baltic Sea as an attractive tourism area measures need to be undertaken to stop the pollution coming from toxic antifouling paints. The antifouling market contains a wide range of environment-friendly alternative products such as a brush wash for boats, hand scrubbing devices, hull covers and boat lifts. Unfortunately, not a lot of boat owners use these environment-friendly alternatives and instead prefer the use of the traditional toxic copper paints. We ask “Why is the unsustainable consumption of toxic paints still predominant when there is a big range of environment-friendly alternatives available? What are the barriers for sustainable consumption?” Environmental psychology has concentrated on developing models of human behavior, including the main factors that influence pro-environmental behavior. The main focus of these models was directed to the individual’s attitudes, principals, and beliefs. However, social practice theory emphasizes the importance to study practices, as they have a stronger explanatory power than attitude-behavior to explain unsustainable consumer behavior. Thus, the study focuses on describing the material, meaning and competence of antifouling practice in order to understand the social and cultural embeddedness of the practice. Phenomenological interviews were conducted with boat owners using antifouling products such as paints and alternative methods. This data collection was supplemented with participant observations in marinas. Preliminary results indicate that different factors such as costs, traditions, advertising, frequency of use, marinas and application of method impact on the consumption of antifouling products. The findings have shown that marinas have a big influence on the consumption of antifouling goods. Some marinas are very active in supporting the sustainable consumption of antifouling products as for example in Stockholm area several marinas subsidize costs for using environmental friendly alternatives or even forbid toxic paints. Furthermore the study has revealed that environmental friendly methods are very effective and do not have to be more expensive than painting with toxic paints. This study contributes to a broader understanding why the unsustainable consumption of toxic paints is still predominant when a big range of environment-friendly alternatives exist. Answers to this phenomenon will be gained by studying practices instead of attitudes offering a new perspective on environmental issues.

Keywords: antifouling paint, Baltic Sea, boat tourism, sustainable consumption

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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: 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

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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: Pin-Hua Chou

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MACAN (Museum of Modern and Contemporary Art in Nusantara) is a new private art museum in Jakarta, Indonesia. Facing challenges of rapidly changing social, cultural environments, MACAN is responding by devoting themselves to not only presenting famous international artists but also constructing the context of artists from Indonesia by interdisciplinary education and cultural exchange. This paper discusses the exhibitions, collections and the activities of MACAN. The purpose of this museum is to make people aware of the dialogue between local and international artist, and also Indonesia’s own art history. Yet how they build up the cultural consciousness for their audience inside and outside Indonesia? What strategy or method do they adapt to enhance general understanding of their own history and the relation between Indonesia and the world through their exhibition? MACAN has tried to convey their mission by every action they took since its opening (2017). The discussion begins with the premise that the initiative of MACAN offers us a new vision to better understand how a modern and contemporary art museum can make an effort to connect art with cultural identity and stimulate the awareness of recognition in Indonesia. This paper will adopt a case study, curator interview, and document analysis. Last but not least, the paper seeks to contribute towards the narrative of its first exhibition Art Turns, World Turns, Exploring the collection of the MACAN, as well as the possibility of raising audience’s cultural consciousness by a variety of public programs.

Keywords: contemporary art museum, challenges for art museum curators today, culture heritage, museum collections and exhibitions

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Authors: A. S. Adegoke, O. N. Popoola

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The central purpose of management in any organization is that of coordinating the efforts of people towards the achievement of its goal. Effective and productive management is the function of leadership. Leadership plays a critical role in helping groups, organizations and societies to achieve their goals. Factors considered to make leadership to be effective are intelligence, social maturity, inner motivation and achievement drives and lastly, human relations attitudes. The factors affecting leadership style and effectiveness were examined. Also, the study examined which of the various leadership style best befits an organization and discussed the ways in which the style was determined. In order to meet the objectives of this study, different types of methods of data gathering were carried out. The methods include data from primary and secondary sources. The primary sources include personal interview, personal observation, and questionnaire while data from secondary sources were derived from various books, journal write up and other documentary records. Data were collected from respondents through questionnaire, and the field research carried out through oral interview to test each of the related hypotheses. From the data analysed it was determined that 45% strongly agreed that leadership traits are inborn not acquired and 28.3% agreed that leadership traits are inborn, while 11.7% and 10% strongly disagreed and disagreed respectively and 5% were undecided. 48.4% strongly agreed, and 43.3% agreed that environmental factors determined the appropriate style of leadership to be employed while 3.3% strongly disagreed, 1.7% disagreed and 3.3% were undecided. From the study, no single style of leadership is appropriate in any situation instead of concentrating on single leadership style; leader can vary approaches depending on forces in the leaders, characteristic of the subordinates, situational forces of the organization, lastly the expectations and behaviour of superior.

Keywords: hypothesis, leadership, management, organization

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Authors: Ghazal Faraj, András Micsik

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Shapes Constraint Language (SHACL), a World Wide Web Consortium (W3C) language, provides well-defined shapes and RDF graphs, named "shape graphs". These shape graphs validate other resource description framework (RDF) graphs which are called "data graphs". The structural features of SHACL permit generating a variety of conditions to evaluate string matching patterns, value type, and other constraints. Moreover, the framework of SHACL supports high-level validation by expressing more complex conditions in languages such as SPARQL protocol and RDF Query Language (SPARQL). SHACL includes two parts: SHACL Core and SHACL-SPARQL. SHACL Core includes all shapes that cover the most frequent constraint components. While SHACL-SPARQL is an extension that allows SHACL to express more complex customized constraints. Validating the efficacy of dataset mapping is an essential component of reconciled data mechanisms, as the enhancement of different datasets linking is a sustainable process. The conventional validation methods are the semantic reasoner and SPARQL queries. The former checks formalization errors and data type inconsistency, while the latter validates the data contradiction. After executing SPARQL queries, the retrieved information needs to be checked manually by an expert. However, this methodology is time-consuming and inaccurate as it does not test the mapping model comprehensively. Therefore, there is a serious need to expose a new methodology that covers the entire validation aspects for linking and mapping diverse datasets. Our goal is to conduct a new approach to achieve optimal validation outcomes. The first step towards this goal is implementing SHACL to validate the mapping between the International Committee for Documentation (CIDOC) conceptual reference model (CRM) and one of its ontologies. To initiate this project successfully, a thorough understanding of both source and target ontologies was required. Subsequently, the proper environment to run SHACL and its shape graphs were determined. As a case study, we performed SHACL over a CIDOC-CRM dataset after running a Pellet reasoner via the Protégé program. The applied validation falls under multiple categories: a) data type validation which constrains whether the source data is mapped to the correct data type. For instance, checking whether a birthdate is assigned to xsd:datetime and linked to Person entity via crm:P82a_begin_of_the_begin property. b) Data integrity validation which detects inconsistent data. For instance, inspecting whether a person's birthdate occurred before any of the linked event creation dates. The expected results of our work are: 1) highlighting validation techniques and categories, 2) selecting the most suitable techniques for those various categories of validation tasks. The next plan is to establish a comprehensive validation model and generate SHACL shapes automatically.

Keywords: SHACL, CIDOC-CRM, SPARQL, validation of ontology mapping

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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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Authors: Laura Micheli, Majd Hijazi, Mahmoud Faytarouni

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The damage reported by oil and gas industrial facilities revealed the utmost vulnerability of steel liquid storage tanks to seismic events. The failure of steel storage tanks may yield devastating and long-lasting consequences on built and natural environments, including the release of hazardous substances, uncontrolled fires, and soil contamination with hazardous materials. It is, therefore, fundamental to reliably predict the damage that steel liquid storage tanks will likely experience under future seismic hazard events. The seismic performance of steel liquid storage tanks is usually assessed using vulnerability curves obtained from the numerical simulation of a tank under different hazard scenarios. However, the computational demand of high-fidelity numerical simulation models, such as finite element models, makes the vulnerability assessment of liquid storage tanks time-consuming and often impractical. As a solution, this paper presents a surrogate model-based strategy for predicting seismic-induced damage in steel liquid storage tanks. In the proposed strategy, the surrogate model is leveraged to reduce the computational demand of time-consuming numerical simulations. To create the data set for training the surrogate model, field damage data from past earthquakes reconnaissance surveys and reports are collected. Features representative of steel liquid storage tank characteristics (e.g., diameter, height, liquid level, yielding stress) and seismic excitation parameters (e.g., peak ground acceleration, magnitude) are extracted from the field damage data. The collected data are then utilized to train a surrogate model that maps the relationship between tank characteristics, seismic hazard parameters, and seismic-induced damage via a data-driven surrogate model. Different types of surrogate algorithms, including naïve Bayes, k-nearest neighbors, decision tree, and random forest, are investigated, and results in terms of accuracy are reported. The model that yields the most accurate predictions is employed to predict future damage as a function of tank characteristics and seismic hazard intensity level. Results show that the proposed approach can be used to estimate the extent of damage in steel liquid storage tanks, where the use of data-driven surrogates represents a viable alternative to computationally expensive numerical simulation models.

Keywords: damage prediction , data-driven model, seismic performance, steel liquid storage tanks, surrogate model

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Authors: Adel Ali Elshaibani

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Risk management indicators augment the reporting infrastructure, particularly for the board and senior management, to identify, monitor, and manage risks. This enhancement facilitates improved decision-making throughout the banking organization. Decision tree analysis is a tool that visually outlines potential outcomes, costs, and consequences of complex decisions. It is particularly beneficial for analyzing quantitative data and making decisions based on numerical values. By calculating the expected value of each outcome, decision tree analysis can help assess the best course of action. In the context of banking, decision tree analysis can assist lenders in evaluating a customer’s creditworthiness, thereby preventing losses. However, applying these tools in developing countries may face several limitations, such as data availability, lack of technological infrastructure and resources, lack of skilled professionals, cultural factors, and cost. Moreover, decision trees can create overly complex models that do not generalize well to new data, known as overfitting. They can also be sensitive to small changes in the data, which can result in different tree structures and can become computationally expensive when dealing with large datasets. In conclusion, while risk management indicators and decision tree analysis are beneficial for decision-making in banks, their effectiveness is contingent upon how they are implemented and utilized by the board of directors, especially in the context of developing countries. It’s important to consider these limitations when planning to implement these tools in developing countries.

Keywords: risk management indicators, decision tree analysis, developing countries, board of directors, bank performance, risk management strategy, banking institutions

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Authors: Miguel José Oliveira, Fátima Farinha, Elisa M. J. da Silva, Rui Lança, Manuel Duarte Pinheiro, Cátia Miguel

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The Observatory of Sustainability of the Algarve Region for Tourism (OBSERVE) will be a valuable tool to assess the sustainability of this region. The OBSERVE tool is designed to provide data and maintain an up-to-date, consistent set of indicators defined to describe the region on the environmental, sociocultural, economic and institutional domains. This ongoing two-year project has the active participation of the Algarve’s stakeholders, since they were consulted and asked to participate in the discussion for the indicators proposal. The environmental and sociocultural indicators chosen must indicate the characteristics of the region and should be in alignment with other global systems used to monitor the sustainability. This paper presents a review of sustainability indicators systems that support the first proposal for the environmental and sociocultural indicators. Others constraints are discussed, namely the existing data and the data available in digital platforms in a format suitable for automatic importation to the platform of OBSERVE. It is intended that OBSERVE will be a valuable tool to assess the sustainability of the region of Algarve.

Keywords: Algarve, development, environmental indicators, observatory, sociocultural indicators, sustainability, tourism

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Authors: Oludare Isaac Abiodun, Esther Omolara Abiodun

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The purpose of this paper is to demonstrate that cyberterrorism is existing and poses a threat to computer security and national security. Nowadays, people have become excitedly dependent upon computers, phones, the Internet, and the Internet of things systems to share information, communicate, conduct a search, etc. However, these network systems are at risk from a different source that is known and unknown. These network systems risk being caused by some malicious individuals, groups, organizations, or governments, they take advantage of vulnerabilities in the computer system to hawk sensitive information from people, organizations, or governments. In doing so, they are engaging themselves in computer threats, crime, and terrorism, thereby making the use of computers insecure for others. The threat of cyberterrorism is of various forms and ranges from one country to another country. These threats include disrupting communications and information, stealing data, destroying data, leaking, and breaching data, interfering with messages and networks, and in some cases, demanding financial rewards for stolen data. Hence, this study identifies many ways that cyberterrorists utilize the Internet as a tool to advance their malicious mission, which negatively affects computer security and safety. One could identify causes for disparate anomaly behaviors and the theoretical, ideological, and current forms of the likelihood of cyberterrorism. Therefore, for a countermeasure, this paper proposes the use of previous and current computer security models as found in the literature to help in countering cyberterrorism

Keywords: cyberterrorism, computer security, information, internet, terrorism, threat, digital forensic solution

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Authors: Sagwata A. Manyike, Hulisani Magada

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The South African National Biodiversity Institute is in the process of developing a guideline which sets out how biodiversity can be incorporated into land use (zoning) schemes. South Africa promulgated its Spatial Planning and Land Use Management Act in 2015 and the act seeks, amongst other things, to bridge the gap between spatial planning and land use management within the country. In addition, the act requires local governments to develop wall-to-wall land use schemes for their entire jurisdictions as they had previously only developed them for their urban areas. At the same time, South Africa has a rich history of systematic conservation planning whereby Critical Biodiversity Areas and Ecological Support Areas have been spatially delineated at a scale appropriate for spatial planning and land use management at the scale of local government. South Africa is also in the process of spatially delineating ecological infrastructure which is defined as naturally occurring ecosystems which provide valuable services to people such as water and climate regulation, soil formation, disaster risk reduction, etc. The Biodiversity and Land Use Project, which is funded by the Global Environmental Facility through the United Nations Development Programme is seeking to explore ways in which biodiversity information and ecological infrastructure can be incorporated into the spatial planning and land use management systems of local governments. Towards this end, the Biodiversity and Land Use Project have developed a guideline which sets out how local governments can integrate biodiversity into their land-use schemes as a way of not only ensuring sustainable development but also as a way helping them prepare for climate change. In addition, by incorporating biodiversity into land-use schemes, the project is exploring new ways of protecting biodiversity through land use schemes. The Guideline for Incorporating Biodiversity into Land Use Schemes was developed as a response to the fact that the National Land Use Scheme Guidelines only indicates that local governments needed to incorporate biodiversity without explaining how this could be achieved. The Natioanl Guideline also failed to specify which biodiversity-related layers are compatible with which land uses or what the benefits of incorporating biodiversity into the schemes will be for that local government. The guideline, therefore, sets out an argument for why biodiversity is important in land management processes and proceeds to provide a step by step guideline for how schemes can integrate priority biodiversity layers. This guideline will further be added as an addendum to the National Land Use Guidelines. Although the planning act calls for local government to have wall to wall schemes within 5 years of its enactment, many municipalities will not meet this deadline and so this guideline will support them in the development of their new schemes.

Keywords: biodiversity, climate change, land use schemes, local government

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Authors: Myung Hwan Na, Ho- Chun Song, EunHee Hong

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We widely use normal linear mixed-effects model to analysis data in repeated measurement. In case of detecting heteroscedasticity and the non-normality of the population distribution at the same time, normal linear mixed-effects model can give improper result of analysis. To achieve more robust estimation, we use heavy tailed linear mixed-effects model which gives more exact and reliable analysis conclusion than standard normal linear mixed-effects model.

Keywords: reliability, tires, field data, linear mixed-effects model

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