Search results for: geographic data streams

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: 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: 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: 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: Valliyil Govindankutty

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Human civilizations across the globe stand testimony to water being one of the major interaction points with nature. The interactions with nature especially in drier areas revolve around water, be it harnessing, transporting, usage and management. Many ingenious ideas were born, nurtured and developed for harnessing, transporting, storing and distributing water through the areas in the drier parts of the world. Many methods of water extraction, collection and management could be found throughout the world, some of which are associated with efficient, sustained use of surface water, ground water and rain water. Karez is one such ingenious method of collection, transportation, storage and distribution of ground water. Most of the Karez systems in India were developed during reign of Muslim dynasties with ruling class descending from Persia or having influential connections and inviting expert engineers from there. Karez have strongly influenced the village socio-economic organisations due to multitude of uses they were brought into. These are masterpiece engineering structures to collect groundwater and direct it, through a subsurface gallery with a gradual slope, to surface canals that provide water to settlements and agricultural fields. This ingenious technology, karez was result of need for harnessing groundwater in arid areas like that of Bidar. The study views this traditional technology in historical perspective linked to sustainable utilization and management of groundwater and above all the immediate environment. The karez system is one of the best available demonstration of human ingenuity and adaptability to situations and locations of water scarcity. Bidar, capital of erstwhile Bahmani sultanate with a history of more than 700 years or more is one of the heritage cities of present Karnataka State. The unique water systems of Bidar along with other historic entities have been listed under World Heritage Watch List by World Monument Fund. The Historical or cultural landscape in Bidar is very closely associated to the natural resources of the region, Karez systems being one of the best examples. The Karez systems were the lifeline of Bidar’s historical period providing potable water, fulfilling domestic and irrigation needs, both within and outside the fort enclosures. These systems are still functional, but under great pressure and threat of rapid and unplanned urbanisation. The change in land use and fragmentation of land are already paving way for irreversible modification of the karez cultural and geographic landscape. The Paper discusses the significance of character defining elements of Naubad Karez Landscape, highlights the importance of conserving cultural heritage and presents a geographical approach to its revival.

Keywords: Karez, groundwater, traditional water harvesting, cultural heritage landscape, urban planning

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Authors: Benjamin D Reese Jr

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Implicit bias is understood as unconscious attitudes, stereotypes, or associations that can influence the cognitions, actions, decisions, and interactions of an individual without intentional control. These unconscious attitudes or stereotypes are often targeted toward specific groups of people based on their gender, race, age, perceived sexual orientation or other social categories. Since the late 1980s, there has been a proliferation of research that hypothesizes that the operation of implicit bias is the result of the brain needing to process millions of bits of information every second. Hence, one’s prior individual learning history provides ‘shortcuts’. As soon as one see someone of a certain race, one have immediate associations based on their past learning, and one might make assumptions about their competence, skill, or danger. These assumptions are outside of conscious awareness. In recent years, an alternative conceptualization has been proposed. The ‘bias of crowds’ theory hypothesizes that a given context or situation influences the degree of accessibility of particular biases. For example, in certain geographic communities in the United States, there is a long-standing and deeply ingrained history of structures, policies, and practices that contribute to racial inequities and bias toward African Americans. Hence, negative biases among groups of people towards African Americans are more accessible in such contexts or communities. This theory does not focus on individual brain functioning or cognitive ‘shortcuts.’ Therefore, attempts to modify individual perceptions or learning might have negligible impact on those embedded environmental systems or policies that are within certain contexts or communities. From the ‘bias of crowds’ perspective, high levels of racial bias in a community can be reduced by making fundamental changes in structures, policies, and practices to create a more equitable context or community rather than focusing on training or education aimed at reducing an individual’s biases. The current paper acknowledges and supports the foundational role of long-standing structures, policies, and practices that maintain racial inequities, as well as inequities related to other social categories, and highlights the critical need to continue organizational, community, and national efforts to eliminate those inequities. It also makes a case for providing individual leaders with a deep understanding of the dynamics of how implicit biases impact cognitions, actions, decisions, and interactions so that those leaders might more effectively develop structural changes in the processes and systems under their purview. This approach incorporates both the importance of an individual’s learning history as well as the important variables within the ‘bias of crowds’ theory. The paper also offers a model for leadership education, as well as examples of structural changes leaders might consider.

Keywords: implicit bias, unconscious bias, bias, inequities

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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: 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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Authors: Eyob Seife, Molla Alemayaehu, Tesfalem Teshome, Bereket Seyoum, Behailu Getachew

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Globally, immunization averts between 2 and 3 million deaths yearly, but Vaccine-Preventable Diseases still account for more in Sub-Saharan African countries and takes the majority of under-five deaths yearly, which indicates the need for consistent and on-time information to have evidence-based decision so as to save lives of these vulnerable groups. However, ensuring data of sufficient quality and promoting an information-use culture at the point of collection remains critical and challenging, especially in remote areas where the Ararso district is selected based on a hypothesis of there is a difference in reported and recounted immunization data consistency. Data quality is dependent on different factors where organizational, behavioral, technical and contextual factors are the mentioned ones. A cross-sectional quantitative study was conducted on September 2022 in the Ararso district. The study used the world health organization (WHO) recommended data quality self-assessment (DQS) tools. Immunization tally sheets, registers and reporting documents were reviewed at 4 health facilities (1 health center and 3 health posts) of primary health care units for one fiscal year (12 months) to determine the accuracy ratio, availability and timeliness of reports. The data was collected by trained DQS assessors to explore the quality of monitoring systems at health posts, health centers, and at the district health office. A quality index (QI), availability and timeliness of reports were assessed. Accuracy ratios formulated were: the first and third doses of pentavalent vaccines, fully immunized (FI), TT2+ and the first dose of measles-containing vaccines (MCV). In this study, facility-level results showed poor timeliness at all levels and both over-reporting and under-reporting were observed at all levels when computing the accuracy ratio of registration to health post reports found at health centers for almost all antigens verified. A quality index (QI) of all facilities also showed poor results. Most of the verified immunization data accuracy ratios were found to be relatively better than that of quality index and timeliness of reports. So attention should be given to improving the capacity of staff, timeliness of reports and quality of monitoring system components, namely recording, reporting, archiving, data analysis and using information for decisions at all levels, especially in remote and areas.

Keywords: accuracy ratio, ararso district, quality of monitoring system, regular immunization program, timeliness of reports, Somali region-Ethiopia

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Authors: Gunalan Vasudevan

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The Malaysia construction industry generates a large quantity of construction and demolition waste nowadays. In the handbook for demolition work only comprised small portion of demolition waste management. It is important to study and determine the ways to provide a practical guide for the professional in the building industry about handling the demolition waste. In general, demolition defined as tearing down or wrecking of structural work or architectural work of the building and other infrastructures work such as road, bridge and etc. It’s a common misconception that demolition is nothing more than taking down a structure and carrying the debris to a landfill. On many projects, 80-90% of the structure is kept for reuse or recycling which help the owner to save cost. Demolition contractors required a lot of knowledge and experience to minimize the impact of demolition work to the existing surrounding area. For data collecting method, postal questionnaires and interviews have been selected to collect data. Questionnaires have distributed to 80 respondents from the construction industry in Klang Valley. 67 of 80 respondents have replied the questionnaire while 4 people have interviewed. Microsoft Excel and Statistical Package for Social Science version 17.0 were used to analyze the data collected.

Keywords: demolition, waste management, construction material, Malaysia

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Authors: Yizhen Zhao, Adam S. Z. Belloum, Goncalo Maia Da Costa, Zhiming Zhao

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Machine learning has evolved from an area of academic research to a real-word applied field. This change comes with challenges, gaps and differences exist between common practices in academic environments and the ones in production environments. Following continuous integration, development and delivery practices in software engineering, similar trends have happened in machine learning (ML) systems, called MLOps. In this paper we propose a framework that helps to streamline and introduce best practices that facilitate the ML lifecycle in an industrial setting. This framework can be used as a template that can be customized to implement various machine learning experiment. The proposed framework is modular and can be recomposed to be adapted to various use cases (e.g. data versioning, remote training on cloud). The framework inherits practices from DevOps and introduces other practices that are unique to the machine learning system (e.g.data versioning). Our MLOps practices automate the entire machine learning lifecycle, bridge the gap between development and operation.

Keywords: cloud computing, continuous development, data versioning, DevOps, industrial setting, MLOps

Procedia PDF Downloads 271

Authors: Víctor D. Rodríguez, Edith P. Estupiñán, Juan C. Martínez

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The evolution in mobile broadband technologies has allowed to increase the download rates in users considering the current services. The evaluation of technical parameters at the link level is of vital importance to validate the quality and veracity of the connection, thus avoiding large losses of data, time and productivity. Some of these failures may occur between the eNodeB (Evolved Node B) and the user equipment (UE), so the link between the end device and the base station can be observed. LTE (Long Term Evolution) is considered one of the IP-oriented mobile broadband technologies that work stably for data and VoIP (Voice Over IP) for those devices that have that feature. This research presents a technical analysis of the connection and channeling processes between UE and eNodeB with the TAC (Tracking Area Code) variables, and analysis of performance variables (Throughput, Signal to Interference and Noise Ratio (SINR)). Three measurement scenarios were proposed in the city of Bogotá using QualiPoc, where two operators were evaluated (Operator 1 and Operator 2). Once the data were obtained, an analysis of the variables was performed determining that the data obtained in transmission modes vary depending on the parameters BLER (Block Error Rate), performance and SNR (Signal-to-Noise Ratio). In the case of both operators, differences in transmission modes are detected and this is reflected in the quality of the signal. In addition, due to the fact that both operators work in different frequencies, it can be seen that Operator 1, despite having spectrum in Band 7 (2600 MHz), together with Operator 2, is reassigning to another frequency, a lower band, which is AWS (1700 MHz), but the difference in signal quality with respect to the establishment with data by the provider Operator 2 and the difference found in the transmission modes determined by the eNodeB in Operator 1 is remarkable.

Keywords: BLER, LTE, network, qualipoc, SNR.

Procedia PDF Downloads 119

Authors: Rama Umesh Pandey, Jyoti Yadav

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Flooding is one of the waterborne disasters that causes extensive destruction in urban areas. Developing countries are at a higher risk of such damage and more than half of the global flooding events take place in Asian countries including India. Urban flooding is more of a human-induced disaster rather than natural. This is highly influenced by the anthropogenic factors, besides metrological and hydrological causes. Unplanned urbanization and poor management of cities enhance the impact manifold and cause huge loss of life and property in urban areas. It is an irony that urban areas have been facing water scarcity in summers and flooding during monsoon. This paper is an attempt to highlight the factors responsible for flooding in a city especially from an urban planning perspective and to suggest mitigating measures through spatial planning interventions. Analysis has been done in two stages; first is to assess the impacts of previous flooding events and second to analyze the factors responsible for flooding at macro and micro level in cities. Bhopal, a city in Central India having nearly two million population, has been selected for the study. The city has been experiencing flooding during heavy rains in monsoon. The factors responsible for urban flooding were identified through literature review as well as various case studies from different cities across the world and India. The factors thus identified were analyzed for both macro and micro level influences. For macro level, the previous flooding events that have caused huge destructions were analyzed and the most affected areas in Bhopal city were identified. Since the identified area was falling within the catchment of a drain so the catchment area was delineated for the study. The factors analyzed were: rainfall pattern to calculate the return period using Weibull’s formula; imperviousness through mapping in ArcGIS; runoff discharge by using Rational method. The catchment was divided into micro watersheds and the micro watershed having maximum impervious surfaces was selected to analyze the coverage and effect of physical infrastructure such as: storm water management; sewerage system; solid waste management practices. The area was further analyzed to assess the extent of violation of ‘building byelaws’ and ‘development control regulations’ and encroachment over the natural water streams. Through analysis, the study has revealed that the main issues have been: lack of sewerage system; inadequate storm water drains; inefficient solid waste management in the study area; violation of building byelaws through extending building structures ether on to the drain or on the road; encroachments by slum dwellers along or on to the drain reducing the width and capacity of the drain. Other factors include faulty culvert’s design resulting in back water effect. Roads are at higher level than the plinth of houses which creates submersion of their ground floors. The study recommends spatial planning interventions for mitigating urban flooding and strategies for management of excess rain water during monsoon season. Recommendations have also been made for efficient land use management to mitigate water logging in areas vulnerable to flooding.

Keywords: mitigating strategies, spatial planning interventions, urban flooding, violation of development control regulations

Procedia PDF Downloads 331

Authors: Clive L. Morley

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Gravity models and panel data modelling of tourism flows are receiving renewed attention, after decades of general neglect. Such models have quite different underpinnings from conventional demand models derived from micro-economic theory. They operate at a different level of data and with different theoretical bases. These differences have important consequences for the interpretation of the results and their policy and managerial implications. This review compares and contrasts the two model forms, clarifying the distinguishing features and the estimation requirements of each. In general, gravity models are not recommended for use to address specific management and marketing purposes.

Keywords: gravity models, micro-economics, demand models, marketing

Procedia PDF Downloads 445

Authors: Pantaleon Lutta, Mohamed Sedky, Mohamed Hassan

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The inability of organizations to put in place management control measures for Internet of Things (IoT) complexities persists to be a risk concern. Policy makers have been left to scamper in finding measures to combat these security and privacy concerns. IoT forensics is a cumbersome process as there is no standardization of the IoT products, no or limited historical data are stored on the devices. This paper highlights why IoT forensics is a unique adventure and brought out the legal challenges encountered in the investigation process. A quadrant model is presented to study the conflicting aspects in IoT forensics. The model analyses the effectiveness of forensic investigation process versus the admissibility of the evidence integrity; taking into account the user privacy and the providers’ compliance with the laws and regulations. Our analysis concludes that a semi-automated forensic process using machine learning, could eliminate the human factor from the profiling and surveillance processes, and hence resolves the issues of data protection (privacy and confidentiality).

Keywords: cloud forensics, data protection Laws, GDPR, IoT forensics, machine Learning

Procedia PDF Downloads 154

Authors: Jingde Li, Hong Hao

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Recent oil and gas accidents have reminded us the severe consequences of gas explosion on structure damage and financial loss. In order to protect the structures and personnel, engineers and researchers have been working on numerous different explosion mitigation methods. Amongst, venting is the most economical approach to mitigate gas explosion overpressure. In this paper, venting is used as the overpressure alleviation method. A theoretical method and a numerical technique are presented to predict the internal and external pressure from vented gas explosion in a large enclosure. Under idealized conditions, a number of experiments are used to calibrate the accuracy of the theoretically calculated data. A good agreement between the theoretical results and experimental data is seen. However, for realistic scenarios, the theoretical method over-estimates internal pressures and is incapable of predicting external pressures. Therefore, a CFD simulation procedure is proposed in this study to estimate both the internal and external overpressure from a large-scale vented explosion. Satisfactory agreement between CFD simulation results and experimental data is achieved.

Keywords: vented gas explosion, internal pressure, external pressure, CFD simulation, FLACS, ANSYS Fluent

Procedia PDF Downloads 164

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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The increasing use of deep learning applications in production is becoming a competitive advantage. Predictive quality enables the assurance of product quality by using data-driven forecasts via machine learning models as a basis for decisions on test results. The use of real Bosch production data along the value chain of hydraulic valves is a promising approach to classifying the leakage of directional valves.

Keywords: artificial neural networks, classification, hydraulics, predictive quality, deep learning

Procedia PDF Downloads 251

Authors: Fatima Al-Aswadi, Chan Yong

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With the large volume of unstructured data that increases day by day on the web, the motivation of representing the knowledge in this data in the machine processable form is increased. Ontology is one of the major cornerstones of representing the information in a more meaningful way on the semantic Web. The goal of Ontology learning from text is to elicit and represent domain knowledge in the machine readable form. This paper aims to give a follow-up review on the ontology learning systems from text and some of their defects. Furthermore, it discusses how far the ontology learning process will enhance in the future.

Keywords: concept discovery, deep learning, ontology learning, semantic relation, semantic web

Procedia PDF Downloads 529

Authors: Susan C. Graham

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Islands have a long established allure for travellers the world over. From earliest accounts of human history, travellers were drawn by the sense of islandness embodied by these destinations. The concept of islandness describes the essence of what makes islands unique relative to non-islands and extends beyond geographic interpretations by attempting to capture the specific sense of self-exhibited by islanders in relation to their connection to place. The themes most strongly associated with islandness include a) a strong connection to water as both the life blood and a physical barrier, b) a unique culture and robust arts community that is deeply linked to both the island and islanders, c) an appreciation of and for nature, d) a rich sense of history and tradition connected to the place, e) a sense of community and belonging that arose through shared triumphs and struggles, and f) a profound awareness of independence, separateness, and uniqueness derived from both physical and social experience. The island brand, like all brands, is a marketing tactic designed to succinctly express a specific value proposition in simplistic ways which might include a brand symbol, logo, slogan, or representation meant to distinguish one brand from another. If a value proposition is the identification of attributes that separate one brand from another by highlighting the brand’s uniqueness, then presumably island brands may, at least in part, emphasize islandness as part of the destination brand. Yet it may in naïve to expect all islands to brand themselves using similar themes when islands can differ so substantially in terms of population, geography, political climate, economy, culture, and history. Of particular interest is the increased focus on tourism among 'cold-water' islands. This paper will examine the incorporation of themes related to islandness in tourism branding among cold-water, warm-water, and temperate-water islands. The tourism logos of 83 islands were collected and assessed for the use of themes related to islandness, namely water, arts and culture, nature, history and tradition, community and belongingness, and independence, separateness, and uniqueness. The ratings for each theme related to islandness for each of the 83 island destinations were then analyzed to identify if differences exist between cold-water, warm-water, and temperate-water islands. A general consensus of what constitutes 'cold-water' destinations is lacking, therefore a water temperature of 15C was adopted using the guidelines from the National Center for Cold Water Safety. Among these 83 islands, the average high and average low water temperatures of 196 specific locations, including the capital, northern, and southern most points of each island, was recorded to determine if the location was a cold-water (average high and low below 15C), warm-water (average high and low above 15C), or temperate-water (average high above 15C and low below 15C) location.

Keywords: branding, cold-water, islands, tourism

Procedia PDF Downloads 226