Search results for: data mining analytics

Authors: Michael Picard

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This proposal offers to shed light on the changing legal geography of the global waste economy. Global waste recycling has become a multi-billion-dollar industry. NASDAQ predicts the emergence of a worldwide 1,296G$ waste management market between 2017 and 2022. Underlining this evolution, a new generation of preferential waste-trade agreements has emerged in the Pacific. In the last decade, Japan has concluded a series of bilateral treaties with Asian countries, and most recently with China. An agreement between Tokyo and Beijing was formalized on 7 May 2008, which forged an economic partnership on waste transfer and mining. The agreement set up International Recycling Zones, where certified recycling plants in China process industrial waste imported from Japan. Under the joint venture, Chinese companies salvage the embedded value from Japanese industrial discards, reprocess them and send them back to Japanese manufacturers, such as Mitsubishi and Panasonic. This circular economy is designed to convert surplus garbage into surplus value. Ever since the opening of Sino-Japanese eco-parks, millions of tons of plastic and e-waste have been exported from Japan to China every year. Yet, quite unexpectedly, China has recently closed its waste market to imports, jeopardizing Japan’s billion-dollar exports to China. China notified the WTO that, by the end of 2017, it would no longer accept imports of plastics and certain metals. Given China’s share of Japanese waste exports, a complete closure of China’s market would require Japan to find new uses for its recyclable industrial trash generated domestically every year. It remains to be seen how China will effectively implement its ban on waste imports, considering the economic interests at stake. At this stage, what remains to be clarified is whether China's ban on waste imports will negatively affect the recycling trade between Japan and China. What is clear, though, is the rapid transformation in the legal geography of waste mining in East-Asia. For decades, East-Asian waste trade had been tied up in an ‘ecologically unequal exchange’ between the Japanese core and the Chinese periphery. This global unequal waste distribution could be measured by the Environmental Stringency Index, which revealed that waste regulation was 39% weaker in the Global South than in Japan. This explains why Japan could legally export its hazardous plastic and electronic discards to China. The asymmetric flow of hazardous waste between Japan and China carried the colonial heritage of international law. The legal geography of waste distribution was closely associated to the imperial construction of an ecological trade imbalance between the Japanese source and the Chinese sink. Thus, China’s recent decision to ban hazardous waste imports is a sign of a broader ecological shift. As a global economic superpower, China announced to the world it would no longer be the planet’s junkyard. The policy change will have profound consequences on the global circulation of waste, re-routing global waste towards countries south of China, such as Vietnam and Malaysia. By the time the Berlin Conference takes place in May 2018, the presentation will be able to assess more accurately the effect of the Chinese ban on the transboundary movement of waste in Asia.

Keywords: Asia, ecological unequal exchange, global waste trade, legal geography

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Authors: Philipp Ruf, Massiwa Chabbi, Christoph Reich, Djaffar Ould-Abdeslam

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In recent years, convolutional neural networks (CNN) have demonstrated high performance in image analysis, but oftentimes, there is only structured data available regarding a specific problem. By interpreting structured data as images, CNNs can effectively learn and extract valuable insights from tabular data, leading to improved predictive accuracy and uncovering hidden patterns that may not be apparent in traditional structured data analysis. In applying a single neural network for analyzing multimodal data, e.g., both structured and unstructured information, significant advantages in terms of time complexity and energy efficiency can be achieved. Converting structured data into images and merging them with existing visual material offers a promising solution for applying CNN in multimodal datasets, as they often occur in a medical context. By employing suitable preprocessing techniques, structured data is transformed into image representations, where the respective features are expressed as different formations of colors and shapes. In an additional step, these representations are fused with existing images to incorporate both types of information. This final image is finally analyzed using a CNN.

Keywords: CNN, image processing, tabular data, mixed dataset, data transformation, multimodal fusion

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Authors: Cristiana Ortigão, Antonio Couto, Thiago Gabriel

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In the wake of Samarco Fundão’s failure in 2015 followed by Vale’s Brumadinho disaster in 2019, the Brazilian National Mining Agency started a comprehensive dam safety programmed to rank dam safety risks and establish monitoring and analysis procedures. This paper focuses on the use of accelerometers for static and dynamic applications. Static applications may employ tiltmeters, as an example shown later in this paper. Dynamic monitoring of a structure with accelerometers yields its dynamic signature and this technique has also been successfully used in Brazil and this paper gives an example of tailings dam.

Keywords: instrumentation, dynamic, monitoring, tailings, dams, tiltmeters, automation

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Authors: M. Belén Prendes-Gero, Celestino González-Nicieza, M. Inmaculada Alvarez-Fernández

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Roof collapse is one of the problems with a higher frequency in most of the mines of all countries, even now. There are many reasons that may cause the roof to collapse, namely the mine stress activities in the mining process, the lack of vigilance and carelessness or the complexity of the geological structure and irregular operations. This work is the result of the analysis of one accident produced in the “Mary” coal exploitation located in northern Spain. In this accident, the roof of a crossroad of excavated galleries to exploit the “Morena” Layer, 700 m deep, collapsed. In the paper, the work done by the forensic team to determine the causes of the incident, its conclusions and recommendations are collected. Initially, the available documentation (geology, geotechnics, mining, etc.) and accident area were reviewed. After that, laboratory and on-site tests were carried out to characterize the behaviour of the rock materials and the support used (metal frames and shotcrete). With this information, different hypotheses of failure were simulated to find the one that best fits reality. For this work, the software of finite differences in three dimensions, FLAC 3D, was employed. The results of the study confirmed that the detachment was originated as a consequence of one sliding in the layer wall, due to the large roof span present in the place of the accident, and probably triggered as a consequence of the existence of a protection pillar insufficient. The results allowed to establish some corrective measures avoiding future risks. For example, the dimensions of the protection zones that must be remained unexploited and their interaction with the crossing areas between galleries, or the use of more adequate supports for these conditions, in which the significant deformations may discourage the use of rigid supports such as shotcrete. At last, a grid of seismic control was proposed as a predictive system. Its efficiency was tested along the investigation period employing three control equipment that detected new incidents (although smaller) in other similar areas of the mine. These new incidents show that the use of explosives produces vibrations which are a new risk factor to analyse in a next future.

Keywords: forensic analysis, hypothesis modelling, roof failure, seismic monitoring

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: student performance, supervised machine learning, classification, cross-validation, prediction

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Authors: Ashima Anurag Sharma

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Optical fiber based networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This research paper is targeted to show the simultaneous delivery of triple play service (data, voice, and video). The comparison between various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be decreases due to increase in bit error rate.

Keywords: BER, PON, TDMPON, GPON, CWDM, OLT, ONT

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Authors: Fuad M. Alkoot

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Different experimental technologies such as microarray sequencing have been proposed to generate high-resolution genetic data, in order to understand the complex dynamic interactions between complex diseases and the biological system components of genes and gene products. However, the generated samples have a very large dimension reaching thousands. Therefore, hindering all attempts to design a classifier system that can identify diseases based on such data. Additionally, the high overlap in the class distributions makes the task more difficult. The data we experiment with is generated for the identification of autism. It includes 142 samples, which is small compared to the large dimension of the data. The classifier systems trained on this data yield very low classification rates that are almost equivalent to a guess. We aim at reducing the data dimension and improve it for classification. Here, we experiment with applying a multistage PCA on the genetic data to reduce its dimensionality. Results show a significant improvement in the classification rates which increases the possibility of building an automated system for autism detection.

Keywords: PCA, gene expression, dimensionality reduction, classification, autism

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Authors: Evelyn Castle, Adam Thompson

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Background: In 2013, the Kano State Primary Health Care Management Board redesigned its Routine immunization supply chain from diffused pull to direct delivery push. It addressed issues around stockouts and reduced time spent by health facility staff collecting, and reporting on vaccine usage. The health care board sought the help of a 3PL for twice-monthly deliveries from its cold store to 484 facilities across 44 local governments. eHA’s Health Delivery Systems group formed a 3PL to serve 326 of these new facilities in partnership with the State. We focused on designing and implementing a technology system throughout. Basic methodologies: GIS Mapping: - Planning the delivery of vaccines to hundreds of health facilities requires detailed route planning for delivery vehicles. Mapping the road networks across Kano and Bauchi with a custom routing tool provided information for the optimization of deliveries. Reducing the number of kilometers driven each round by 20%, - reducing cost and delivery time. Direct Delivery Information System: - Vaccine Direct Deliveries are facilitated through pre-round planning (driven by health facility database, extensive GIS, and inventory workflow rules), manager and driver control panel customizing delivery routines and reporting, progress dashboard, schedules/routes, packing lists, delivery reports, and driver data collection applications. Move: Last Mile Logistics Management System: - MOVE has improved vaccine supply information management to be timely, accurate and actionable. Provides stock management workflow support, alerts management for cold chain exceptions/stock outs, and on-device analytics for health and supply chain staff. Software was built to be offline-first with user-validated interface and experience. Deployed to hundreds of vaccine storage site the improved information tools helps facilitate the process of system redesign and change management. Findings: - Stock-outs reduced from 90% to 33% - Redesigned current health systems and managing vaccine supply for 68% of Kano’s wards. - Near real time reporting and data availability to track stock. - Paperwork burdens of health staff have been dramatically reduced. - Medicine available when the community needs it. - Consistent vaccination dates for children under one to prevent polio, yellow fever, tetanus. - Higher immunization rates = Lower infection rates. - Hundreds of millions of Naira worth of vaccines successfully transported. - Fortnightly service to 326 facilities in 326 wards across 30 Local Government areas. - 6,031 cumulative deliveries. - Over 3.44 million doses transported. - Minimum travel distance covered in a round of delivery is 2000 kms & maximum of 6297 kms. - 153,409 kms travelled by 6 drivers. - 500 facilities in 326 wards. - Data captured and synchronized for the first time. - Data driven decision making now possible. Conclusion: eHA’s Vaccine Direct delivery has met challenges in Kano and Bauchi State and provided a reliable delivery service of vaccinations that ensure t health facilities can run vaccination clinics for children under one. eHA uses innovative technology that delivers vaccines from Northern Nigerian zonal stores straight to healthcare facilities. Helped healthcare workers spend less time managing supplies and more time delivering care, and will be rolled out nationally across Nigeria.

Keywords: direct delivery information system, health delivery system, GIS mapping, Northern Nigeria, vaccines

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Authors: V. Ibujes, A. Guevara, P. Barreto

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Concentration of arsenic represents a serious threat to human health. It is a bioaccumulable toxic element and is transferred through the food chain. In Ecuador, values of 0.0423 mg/kg As are registered in potatoes of the skirts of the Tungurahua volcano. The increase of arsenic contamination in Ecuador is mainly due to mining activity, since the process of gold extraction generates toxic tailings with mercury. In the Province of Azuay, due to the mining activity, the soil reaches concentrations of 2,500 to 6,420 mg/kg As whereas in the province of Tungurahua it can be found arsenic concentrations of 6.9 to 198.7 mg/kg due to volcanic eruptions. Since the contamination by arsenic, the present investigation is directed to the remediation of the soils in the provinces of Azuay and Tungurahua by phytoremediation technique and the definition of a methodology of extraction by means of analysis of arsenic in the system soil-plant. The methodology consists in selection of two types of plants that have the best arsenic removal capacity in synthetic solutions 60 μM As, a lower percentage of mortality and hydroponics resistance. The arsenic concentrations in each plant were obtained from taking 10 ml aliquots and the subsequent analysis of the ICP-OES (inductively coupled plasma-optical emission spectrometry) equipment. Soils were contaminated with synthetic solutions of arsenic with the capillarity method to achieve arsenic concentration of 13 and 15 mg/kg. Subsequently, two types of plants were evaluated to reduce the concentration of arsenic in soils for 7 weeks. The global variance for soil types was obtained with the InfoStat program. To measure the changes in arsenic concentration in the soil-plant system, the Rhizo and Wenzel arsenic extraction methodology was used and subsequently analyzed with the ICP-OES (optima 8000 Pekin Elmer). As a result, the selected plants were bluegrass and llanten, due to the high percentages of arsenic removal of 55% and 67% and low mortality rates of 9% and 8% respectively. In conclusion, Azuay soil with an initial concentration of 13 mg/kg As reached the concentrations of 11.49 and 11.04 mg/kg As for bluegrass and llanten respectively, and for the initial concentration of 15 mg/kg As reached 11.79 and 11.10 mg/kg As for blue grass and llanten after 7 weeks. For the Tungurahua soil with an initial concentration of 13 mg/kg As it reached the concentrations of 11.56 and 12.16 mg/kg As for the bluegrass and llanten respectively, and for the initial concentration of 15 mg/kg As reached 11.97 and 12.27 mg/kg Ace for bluegrass and llanten after 7 weeks. The best arsenic extraction methodology of soil-plant system is Wenzel.

Keywords: blue grass, llanten, phytoremediation, soil of Azuay, soil of Tungurahua, synthetic arsenic solution

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Authors: Fei Gao, Rodolfo C. Raga Jr.

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This research proposal will ascertain the major risk factors for diabetes and to design a predictive model for risk assessment. The project aims to improve diabetes early detection and management by utilizing data science techniques, which may improve patient outcomes and healthcare efficiency. The phase relation values of each attribute were used to analyze and choose the attributes that might influence the examiner's survival probability using Diabetes Health Indicators Dataset from Kaggle’s data as the research data. We compare and evaluate eight machine learning algorithms. Our investigation begins with comprehensive data preprocessing, including feature engineering and dimensionality reduction, aimed at enhancing data quality. The dataset, comprising health indicators and medical data, serves as a foundation for training and testing these algorithms. A rigorous cross-validation process is applied, and we assess their performance using five key metrics like accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). After analyzing the data characteristics, investigate their impact on the likelihood of diabetes and develop corresponding risk indicators.

Keywords: diabetes, risk factors, predictive model, risk assessment, data science techniques, early detection, data analysis, Kaggle

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Authors: Chang Qin, Daham Mustafa, Abderrahmane Khiat, Pierre Bienert, Paulo Zanini

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Nowadays, companies are facing lots of challenges in the process of digital transformation, which can be a complex and costly undertaking. Digital transformation involves the collection and analysis of large amounts of data, which can create challenges around data management and governance. Furthermore, it is also challenged to integrate data from multiple systems and technologies. Although with these pains, companies are still pursuing digitalization because by embracing advanced technologies, companies can improve efficiency, quality, decision-making, and customer experience while also creating different business models and revenue streams. In this paper, the issue that data is stored in data silos with different schema and structures is focused. The conventional approaches to addressing this issue involve utilizing data warehousing, data integration tools, data standardization, and business intelligence tools. However, these approaches primarily focus on the grammar and structure of the data and neglect the importance of semantic modeling and semantic standardization, which are essential for achieving data interoperability. In this session, the challenge of data silos in Industry 4.0 is addressed by developing a semantic modeling approach compliant with Asset Administration Shell (AAS) models as an efficient standard for communication in Industry 4.0. The paper highlights how our approach can facilitate the data mapping process and semantic lifting according to existing industry standards such as ECLASS and other industrial dictionaries. It also incorporates the Asset Administration Shell technology to model and map the company’s data and utilize a knowledge graph for data storage and exploration.

Keywords: data interoperability in industry 4.0, digital integration, industrial dictionary, semantic modeling

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Authors: Elkington Sibusiso Mnguni

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In South Africa, mining has been a key economic sector since the discovery of gold in 1886 in the Witwatersrand region, where the city of Johannesburg is located. However, some mines have since been decommissioned, and the continuous pumping of acid mine drainage (AMD) also stopped causing the AMD to rise towards the ground surface. This posed a serious environmental risk to the groundwater resources and river systems in the region. This paper documents the development and extent of the environmental damage as well as the measures implemented by the government to alleviate such damage. The study will add to the body of knowledge on the subject of AMD treatment to prevent environmental degradation. The method used to gather and collate relevant data and information was the desktop study. The key findings include the social and environmental impact of the AMD, which include the pollution of water sources for domestic use leading to skin and other health problems and the loss of biodiversity in some areas. It was also found that the technical intervention of constructing a plant to pump and treat the AMD using the high-density sludge technology was the most effective short-term solution available while a long-term solution was being explored. Some successes and challenges experienced during the implementation of the project are also highlighted. The study will be a useful record of the current status of the AMD treatment interventions in the region.

Keywords: acid mine drainage, groundwater resources, pollution, river systems, technical intervention, high density sludge

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Authors: Mohammad Talei, Neda Ranjbar Nosheri, Reza Kazemi Gorzadini

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Today municipalities are searching for the new tools for increasing the public participation in different levels of urban planning. This approach of urban planning involves the community in planning process using participatory approaches instead of the long traditional top-down planning methods. These tools can be used to obtain the particular problems of urban furniture form the residents’ point of view. One of the tools that is designed with this goal is public participation GIS (PPGIS) that enables citizen to record and following up their feeling and spatial knowledge regarding main problems of the city, specifically urban furniture, in the form of maps. However, despite the good intentions of PPGIS, its practical implementation in developing countries faces many problems including the lack of basic supporting infrastructure and services and unavailability of sophisticated public participatory models. In this research we develop a PPGIS using of Web 2 to collect voluntary geodataand to perform spatial analysis based on Spatial OnLine Analytical Processing (SOLAP) and Spatial Data Mining (SDM). These tools provide urban planners with proper informationregarding the type, spatial distribution and the clusters of reported problems. This system is implemented in a case study area in Tehran, Iran and the challenges to make it applicable and its potential for real urban planning have been evaluated. It helps decision makers to better understand, plan and allocate scarce resources for providing most requested urban furniture.

Keywords: PPGIS, spatial information, urbanizing futures, urban planning

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Authors: Bilquis Ferdousi

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With the exponential growth of the online business, the threat to consumers’ privacy and data security has become a serious challenge. This literature review-based study focuses on a better understanding of those threats and what legislative measures have been taken to address those challenges. Research shows that people are increasingly involved in online business using different digital devices and platforms, although this practice varies based on age groups. The threat to consumers’ privacy and data security is a serious hindrance in developing trust among consumers in online businesses. There are some legislative measures taken at the federal and state level to protect consumers’ privacy and data security. The study was based on an extensive review of current literature on protecting consumers’ privacy and data security and legislative measures that have been taken.

Keywords: privacy, data security, legislation, online business

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Authors: Vijayan Narayananayar

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Identifying learning gaps is crucial in ensuring learners have the necessary knowledge and skills to succeed. The Learning and Teaching (L&T) approach requires tutors to identify gaps in knowledge and improvise learning activities to close them. One approach to identifying learning gaps is through diagnostic assessment, which uses well-structured questions and answer options. The paper focuses on the use of diagnostic assessment as a learning and teaching approach in a foundational module at a polytechnic. The study used diagnostic assessment over two semesters, including the COVID and post-COVID semesters, to identify gaps in learning. The design of the diagnostic activity, pedagogical intervention, and survey responses completed by learners were analyzed. Results showed that diagnostic assessment can be an effective tool for identifying learning gaps and designing interventions to address them. Additionally, the use of diagnostic assessment provides an opportunity for tutors to engage with learners on a one-to-one basis, tailoring teaching to individual needs. The paper also discusses the design of diagnostic questions and answer options, including characteristics that need to be considered in achieving the target of identifying learning gaps. The implications of using diagnostic assessment as a learning and teaching approach include bridging the gap between theory and practice, and ensuring learners are equipped with skills necessary for their future careers. This paper can be useful in helping educators and practitioners to incorporate diagnostic assessment into their L&T approach.

Keywords: assessment, learning & teaching, diagnostic assessment, analytics

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Authors: Orhun Vural, Oguz Bayat, Rustu Akay, Osman N. Ucan

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This study presents a new parallel approach clustering of GPS data. Evaluation has been made by comparing execution time of various clustering algorithms on GPS data. This paper aims to propose a parallel based on neighborhood K-means algorithm to make it faster. The proposed parallelization approach assumes that each GPS data represents a vehicle and to communicate between vehicles close to each other after vehicles are clustered. This parallelization approach has been examined on different sized continuously changing GPS data and compared with serial K-means algorithm and other serial clustering algorithms. The results demonstrated that proposed parallel K-means algorithm has been shown to work much faster than other clustering algorithms.

Keywords: parallel k-means algorithm, parallel clustering, clustering algorithms, clustering on flowing data

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Authors: Yamma Rose, Kone Martine, Yonli Arsène, Wanko Ngnien Adrien

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Artisanal gold mining has seen a resurgence in recent years in Burkina Faso with its corollary of soil and water pollution. Indeed, in addition to visible impacts, it generates discharges rich in trace metal elements and acids. This pollution has significant environmental consequences, making these lands unusable while the population depends on the natural environment for its survival. The goal of this study is to assess the decontamination potential of Chrysopogon zizanioides on two artisanal gold processing sites in Burkina Faso. The cyanidation sites of Nebia (1Ha) and Nimbrogo (2Ha) located respectively in the Central West and Central South regions were selected. The soils were characterized to determine the initial pollution levels before the implementation of phytoremediation. After development of the site, parallel trenches equidistant 6 m apart, 30 cm deep, 40 cm wide and opposite to the water flow direction were dug and filled with earth amended with manure. The Chrysopogon zizanioides plants were transplanted 5 cm equidistant into the trenches. The mere fact that Chrysopogon zizanioides grew in the polluted soil is an indication that this plant tolerates and resists the toxicity of trace elements present on the site. The characterization shows sites very polluted with free cyanide 900 times higher than the national standard, the level of Hg in the soil is 5 times more than the limit value, iron and Zn are respectively 1000 times and 200 more than the tolerated environmental value. At time T1 (6 months) and T2 (12 months) of culture, Chrysopogon zizanioides showed less development on the Nimbrogo site than that of the Nebia site. Plant shoots and associated soil samples were collected and analyzed for total As, Hg, Fe and Zn concentration. The trace element content of the soil, the bioaccumulation factor and the hyper accumulation thresholds were also determined to assess the remediation potential. The concentration of As and Hg in the soil was below international risk thresholds, while that of Fe and Zn was well above these thresholds. The CN removal efficiency at the Nebia site is respectively 29.90% and 68.62% compared to 6.6% and 60.8% at Nimbrogo at time T1 and T2.

Keywords: chrysopogon zizanioides, in-situ phytoremediation, polluted soils, micropollutants

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Authors: Paulo Sergio Altman Ferreira

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Relationship development plays an essential role in every logistics company. Logistics companies are service-based businesses essentially performing the flow of materials, housing, and inventory management for a wide range of customers. The service encounter between the logistics provider’s personnel and the customers may form a connection that will demonstrate a strong impact, not only to the customers' overall satisfaction but may also provide the perception of individualized services. Logistics services must drive value. It also shows a close influence on the quality and costs of client-centered services. If we describe logistics value creation as the function of quality perception of the client divided by service costs, there is a requirement to better outline and explain the measures and analytics for logistics costs and relationship performance. This critical shift to understand logistics services is a relevant contribution to capture how relationship value can be quantified. This might involve changing our current perspective on logistics providers beyond uniquely measuring the services in terms of activities, personnel levels, and financial/costs ratios. This paper argues that measuring value creation accomplishments of logistics services needs to consider the relational improvements for the wider range of logistics companies. Accurate logistics value requires a description of the financial impact of the relational perspective of the service.

Keywords: logistics services providers, financial metrics, relationship management, value creation

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Authors: N. D. Iswarya, M. A. Maluk Mohamed, N. Vijaya

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Grid is infrastructure that allows the deployment of distributed data in large size from multiple locations to reach a common goal. Scheduling data intensive applications becomes challenging as the size of data sets are very huge in size. Only two solutions exist in order to tackle this challenging issue. First, computation which requires huge data sets to be processed can be transferred to the data site. Second, the required data sets can be transferred to the computation site. In the former scenario, the computation cannot be transferred since the servers are storage/data servers with little or no computational capability. Hence, the second scenario can be considered for further exploration. During scheduling, transferring huge data sets from one site to another site requires more network bandwidth. In order to mitigate this issue, this work focuses on incorporating cognitive science in scheduling. Cognitive Science is the study of human brain and its related activities. Current researches are mainly focused on to incorporate cognitive science in various computational modeling techniques. In this work, the problem solving approach of human brain is studied and incorporated during the data intensive scheduling in grid environments. Here, a cognitive engine is designed and deployed in various grid sites. The intelligent agents present in CE will help in analyzing the request and creating the knowledge base. Depending upon the link capacity, decision will be taken whether to transfer data sets or to partition the data sets. Prediction of next request is made by the agents to serve the requesting site with data sets in advance. This will reduce the data availability time and data transfer time. Replica catalog and Meta data catalog created by the agents assist in decision making process.

Keywords: data grid, grid workflow scheduling, cognitive artificial intelligence

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Authors: Xinge Liao, Francesc Xavier Roige Ventura, Dolores Sanchez Aguilera

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With the development of the Internet, the study of tourism behavior has rapidly expanded from the traditional physical market to the online market. Data on the Internet is characterized by dynamic changes, and new data appear all the time. In recent years the generation of a large volume of data was characterized, such as forums, blogs, and other sources, which have expanded over time and space, together they constitute large-scale Internet data, known as Big Data. This data of technological origin that derives from the use of devices and the activity of multiple users is becoming a source of great importance for the study of geography and the behavior of tourists. The study will focus on cultural heritage tourist practices in the context of Big Data. The research will focus on exploring the characteristics and behavior of Chinese tourists in relation to the cultural heritage of Catalonia. Geographical information, target image, perceptions in user-generated content will be studied through data analysis from Weibo -the largest social networks of blogs in China. Through the analysis of the behavior of heritage tourists in the Big Data environment, this study will understand the practices (activities, motivations, perceptions) of cultural tourists and then understand the needs and preferences of tourists in order to better guide the sustainable development of tourism in heritage sites.

Keywords: Barcelona, Big Data, Catalonia, cultural heritage, Chinese tourism market, tourists’ behavior

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Authors: Darusalam, Jorish Hulstijn, Marijn Janssen

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Media has revealed a variety of corruption cases in the regional and local governments all over the world. Many governments pursued many anti-corruption reforms and have created a system of checks and balances. Three types of corruption are faced by citizens; administrative corruption, collusion and extortion. Accountability is one of the benchmarks for building transparent government. The public sector is required to report the results of the programs that have been implemented so that the citizen can judge whether the institution has been working such as economical, efficient and effective. Open Data is offering solutions for the implementation of good governance in organizations who want to be more transparent. In addition, Open Data can create transparency and accountability to the community. The objective of this paper is to build a framework of open data for accountability to combating corruption. This paper will investigate the relationship between open data, and accountability as part of anti-corruption initiatives. This research will investigate the impact of open data implementation on public organization.

Keywords: open data, accountability, anti-corruption, framework

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Authors: Yuyun Wabula, B. J. Dewancker

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In the past decade, the social networking app has been growing very rapidly. Geolocation data is one of the important features of social media that can attach the user's location coordinate in the real world. This paper proposes the use of geolocation data from the Twitter social media application to gain knowledge about urban dynamics, especially on human mobility behavior. This paper aims to explore the relation between geolocation Twitter with the existence of people in the urban area. Firstly, the study will analyze the spread of people in the particular area, within the city using Twitter social media data. Secondly, we then match and categorize the existing place based on the same individuals visiting. Then, we combine the Twitter data from the tracking result and the questionnaire data to catch the Twitter user profile. To do that, we used the distribution frequency analysis to learn the visitors’ percentage. To validate the hypothesis, we compare it with the local population statistic data and land use mapping released by the city planning department of Makassar local government. The results show that there is the correlation between Twitter geolocation and questionnaire data. Thus, integration the Twitter data and survey data can reveal the profile of the social media users.

Keywords: geolocation, Twitter, distribution analysis, human mobility

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Authors: Sheldon Liu, Gordon Wang, Lei Liu, Xuefeng Liu

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Anomaly detection, also referred to as one-class classification, plays a crucial role in identifying product images that deviate from the expected distribution. This study introduces Data-centric Anomaly Detection with Diffusion Models (DCADDM), presenting a systematic strategy for data collection and further diversifying the data with image generation via diffusion models. The algorithm addresses data collection challenges in real-world scenarios and points toward data augmentation with the integration of generative AI capabilities. The paper explores the generation of normal images using diffusion models. The experiments demonstrate that with 30% of the original normal image size, modeling in an unsupervised setting with state-of-the-art approaches can achieve equivalent performances. With the addition of generated images via diffusion models (10% equivalence of the original dataset size), the proposed algorithm achieves better or equivalent anomaly localization performance.

Keywords: diffusion models, anomaly detection, data-centric, generative AI

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Authors: Devika Nair, Sean Bellairs, Ken Evans

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Flow and erosion modeling provides an avenue for simulating the fine suspended sediment in surface water systems like streams and creeks. Fine suspended sediment is highly mobile, and many contaminants that may have been released by any sort of catchment disturbance attach themselves to these sediments. Therefore, a knowledge of fine suspended sediment transport is important in assessing contaminant transport. The CAESAR-Lisflood Landform Evolution Model, which includes a hydrologic model (TOPMODEL) and a hydraulic model (Lisflood), is being used to assess the sediment movement in tropical streams on account of a disturbance in the catchment of the creek and to determine the dynamics of sediment quantity in the creek through the years by simulating the model for future years. The accuracy of future simulations depends on the calibration and validation of the model to the past and present events. Calibration and validation of the model involve finding a combination of parameters of the model, which, when applied and simulated, gives model outputs similar to those observed for the real site scenario for corresponding input data. Calibrating the sediment output of the CAESAR-Lisflood model at the catchment level and using it for studying the equilibrium conditions of the landform is an area yet to be explored. Therefore, the aim of the study was to calibrate the CAESAR-Lisflood model and then validate it so that it could be run for future simulations to study how the landform evolves over time. To achieve this, the model was run for a rainfall event with a set of parameters, plus discharge and sediment data for the input point of the catchment, to analyze how similar the model output would behave when compared with the discharge and sediment data for the output point of the catchment. The model parameters were then adjusted until the model closely approximated the real site values of the catchment. It was then validated by running the model for a different set of events and checking that the model gave similar results to the real site values. The outcomes demonstrated that while the model can be calibrated to a greater extent for hydrology (discharge output) throughout the year, the sediment output calibration may be slightly improved by having the ability to change parameters to take into account the seasonal vegetation growth during the start and end of the wet season. This study is important to assess hydrology and sediment movement in seasonal biomes. The understanding of sediment-associated metal dispersion processes in rivers can be used in a practical way to help river basin managers more effectively control and remediate catchments affected by present and historical metal mining.

Keywords: erosion modelling, fine suspended sediments, hydrology, surface water systems

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Authors: Elizabeta Krstić Vukelja

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Digitization of personal data is a consequence of the development of information and communication technologies that create a new work environment with many advantages and challenges, but also potential threats to privacy and personal data protection. Regulation (EU) 2016/679 of the European Parliament and of the Council is becoming a law and obligation that should address the issues of personal data protection and information security. The existence of the Regulation leads to the conclusion that national legislation in the field of virtual environment, protection of the rights of EU citizens and processing of their personal data is insufficiently effective. In the health system, special emphasis is placed on the processing of special categories of personal data, such as health data. The healthcare industry is recognized as a particularly sensitive area in which a large amount of medical data is processed, the digitization of which enables quick access and quick identification of the health insured. The protection of the individual requires quality IT solutions that guarantee the technical protection of personal categories. However, the real problems are the technical and human nature and the spatial limitations of the application of the Regulation. Some conclusions will be drawn by analyzing the implementation of the basic principles of the Regulation on the example of the Croatian health care system and comparing it with similar activities in other EU member states.

Keywords: regulation, healthcare system, personal dana protection, quality data assurance

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Authors: Dilsad Akal

Abstract:

Aim: Companies communicate with each other and with their costumers via call centers. Call centers are defined as stressful because of their uncertain working hours, inadequate relief time, performance based system and heavy workload. In literature, this sector is defined as risky as mining sector by means of health and safety. The aim of this research is to enlight the relatively dark area. Subject and Methods: The collection of data for this study completed during April-May 2015 for the two selected call centers in different parts of Turkey. The applied question mostly investigated the health conditions of call center workers. Electromagnetic field measurements were completed at the same time with applying the question poll. The ratio of employee accessibility noted as 73% for the first call center and 87% for the second. Results: The results of electromagnetic field measurements were as between 371 V/m-32 V/m for the first location and between 370 V/m-61 V/m for the second. The general complaints of the employees for both workplaces can be counted as; inadequate relief time, inadequate air conditioning, disturbance, poor thermal conditions, inadequate or extreme lighting. Furthermore, musculoskeletal discomfort, stress, ear and eye discomfort are main health problems of employees. Conclusion: The measured values and the responses to the question poll were found parallel with the other similar research results in literature. At the end of this survey, a risk map of workplace was prepared in terms of safety and health at work in general and some suggestions for resolution were provided.

Keywords: call center, health and safety, electromagnetic field, risk map

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Authors: Sandra Rizkallah, Amir F. Atiya

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This work presents a novel word to vector representation that is based on embedding the words into a sphere, whereby the dot product of the corresponding vectors represents the similarity between any two words. Embedding the vectors into a sphere enabled us to take into consideration the antonymity between words, not only the synonymity, because of the suitability to handle the polarity nature of words. For example, a word and its antonym can be represented as a vector and its negative. Moreover, we have managed to extract an adequate vocabulary. The obtained results show that the proposed approach can capture the essence of the language, and can be generalized to estimate a correct similarity of any new pair of words.

Keywords: natural language processing, word to vector, text similarity, text mining

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Authors: Nagi Mekhiel

Abstract:

Many applications use vector operations by applying single instruction to multiple data that map to different locations in conventional memory. Transferring data from memory is limited by access latency and bandwidth affecting the performance gain of vector processing. We present a memory system that makes all of its content available to processors in time so that processors need not to access the memory, we force each location to be available to all processors at a specific time. The data move in different orbits to become available to other processors in higher orbits at different time. We use this memory to apply parallel vector operations to data streams at first orbit level. Data processed in the first level move to upper orbit one data element at a time, allowing a processor in that orbit to apply another vector operation to deal with serial code limitations inherited in all parallel applications and interleaved it with lower level vector operations.

Keywords: Memory Organization, Parallel Processors, Serial Code, Vector Processing

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Authors: David Percy

Abstract:

Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

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Authors: Madiha Ijaz, Muhammad Akram, Sima Mir

Abstract:

Underground mining of coal is carried out manually in Pakistan. Exposure to ergonomic hazards (musculoskeletal disorders) are very common among the coal cutters of these mines. Cutting coal in narrow spaces poses a great threat to both upper and lower limbs of these workers. To observe the prevalence of such hazards, a thorough study was conducted on 600 workers from 30 mines (20 workers from 1 mine), located in two districts of province Punjab, Pakistan. Rapid Upper Limb Assessment sheet and Rapid Entire Body Assessment sheet were used for the study along with a standard Nordic Musculoskeleton disorder questionnaire. SPSS, 25, software was used for data analysis on upper and lower limb disorders, and regression analysis models were run for upper and lower back pain. According to the results obtained, it was found that work stages (drilling & blasting, coal cutting, timbering & supporting, etc.), wok experience and number of repetitions performed/minute were significant (with p-value 0.00,0.004 and 0.009, respectively) for discomfort in upper and lower limb. Age got p vale 0.00 for upper limb and 0.012 for lower limb disorder. The task of coal cutting was strongly associated with the pain in upper back (with odd ratios13.21, 95% confidence interval (CI)14.0-21.64)) and lower back pain (3.7, 95% confidence interval 1.3-4.2). scored on RULA and REBA sheets, every work-stage was ranked at 7-highest level of risk involved. Workers were young (mean value of age= 28.7 years) with mean BMI 28.1 kg/m2

Keywords: workplace hazards, ergonomic disorders, limb disorders, MSDs.

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