Search results for: data clustering

Authors: Alam Ali, Ashok Kumar Pathak

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Path analysis is a statistical technique used to evaluate the strength of the direct and indirect effects of variables. One or more structural regression equations are used to estimate a series of parameters in order to find the better fit of data. Sometimes, exogenous variables do not show a significant strength of their direct and indirect effect when the assumption of classical regression (ordinary least squares (OLS)) are violated by the nature of the data. The main motive of this article is to investigate the efficacy of the copula-based regression approach over the classical regression approach and calculate the direct and indirect effects of variables when data violates the OLS assumption and variables are linked through an elliptical copula. We perform this study using a well-organized numerical scheme. Finally, a real data application is also presented to demonstrate the performance of the superiority of the copula approach.

Keywords: path analysis, copula-based regression models, direct and indirect effects, k-fold cross validation technique

Procedia PDF Downloads 68

Authors: Kamil Jonski

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Last year vividly demonstrated, that populism and political instability can endanger democratic institutions in countries regarded as democratic transition champions (Poland) or cornerstones of liberal order (UK, US). So called ‘illiberal democracy’ is winning hearts and minds of voters, keen to believe that rule of strongman is a viable alternative to perceived decay of western values and institutions. These developments pose a serious threat to the democratic institutions (including rule of law), proven critical for both personal freedom and economic development. Although scholars proposed some structural explanations of the illiberal wave (notably focusing on inequality, stagnant incomes and drawbacks of globalization), they seem to have little predictive value. Indeed, events like Trump’s victory, Brexit or Polish shift towards populist nationalism always came as a surprise. Intriguingly, in the case of US election, simple rules like ‘Bread and Peace model’ gauged prospects of Trump’s victory better than pundits and pollsters. This paper attempts to compile set of indicators, in order to gauge various democracies’ vulnerability to populism, instability and pursuance of ‘illiberal’ projects. Among them, it identifies the gap between consensus assessment of institutional performance (as measured by WGI indicators) and citizens’ subjective assessment (survey based confidence in institutions). Plotting these variables against each other, reveals three clusters of countries – ‘predictable’ (good institutions and high confidence, poor institutions and low confidence), ‘blind’ (poor institutions, high confidence e.g. Uzbekistan or Azerbaijan) and ‘disillusioned’ (good institutions, low confidence e.g. Spain, Chile, Poland and US). It seems that this clustering – carried out separately for various institutions (like legislature, executive and courts) and blended with economic indicators like inequality and living standards (using PCA) – offers reasonably good watchlist of countries, that should ‘expect the unexpected’.

Keywords: illiberal democracy, populism, political instability, political risk measurement

Procedia PDF Downloads 199

Authors: Vaibhav Barve

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Reversible information hiding has drawn a lot of interest as of late. Being reversible, we can restore unique computerized data totally. It is a plan where mystery data is put away in digital media like image, video, audio to maintain a strategic distance from unapproved access and security reason. By and large JPEG bit stream is utilized to store this key data, first JPEG bit stream is encrypted into all around sorted out structure and then this secret information or key data is implanted into this encrypted region by marginally changing the JPEG bit stream. Valuable pixels suitable for information implanting are computed and as indicated by this key subtle elements are implanted. In our proposed framework we are utilizing RC4 algorithm for encrypting JPEG bit stream. Encryption key is acknowledged by framework user which, likewise, will be used at the time of decryption. We are executing enhanced least significant bit supplanting steganography by utilizing genetic algorithm. At first, the quantity of bits that must be installed in a guaranteed coefficient is versatile. By utilizing proper parameters, we can get high capacity while ensuring high security. We are utilizing logistic map for shuffling of bits and utilization GA (Genetic Algorithm) to find right parameters for the logistic map. Information embedding key is utilized at the time of information embedding. By utilizing precise picture encryption and information embedding key, the beneficiary can, without much of a stretch, concentrate the incorporated secure data and totally recoup the first picture and also the original secret information. At the point when the embedding key is truant, the first picture can be recouped pretty nearly with sufficient quality without getting the embedding key of interest.

Keywords: data embedding, decryption, encryption, reversible data hiding, steganography

Procedia PDF Downloads 285

Authors: Tyler T. Procko, Steve Collins

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New features in .NET (6 and above) permit streamlined access to information residing in JSON-capable relational databases, such as SQL Server (2016 and above). Traditional methods of data access now comparatively involve unnecessary steps which compromise system performance. This work posits that the established ORM (Object Relational Mapping) based methods of data access in applications and APIs result in common issues, e.g., object-relational impedance mismatch. Recent developments in C# and .NET Core combined with a framework of modern SQL Server coding conventions have allowed better technical solutions to the problem. As an amelioration, this work details the language features and coding conventions which enable this streamlined approach, resulting in an open-source .NET library implementation called Codeless Data Access (CODA). Canonical approaches rely on ad-hoc mapping code to perform type conversions between the client and back-end database; with CODA, no mapping code is needed, as JSON is freely mapped to SQL and vice versa. CODA streamlines API data access by improving on three aspects of immediate concern to web developers, database engineers and cybersecurity professionals: Simplicity, Speed and Security. Simplicity is engendered by cutting out the “middleman” steps, effectively making API data access a whitebox, whereas traditional methods are blackbox. Speed is improved because of the fewer translational steps taken, and security is improved as attack surfaces are minimized. An empirical evaluation of the speed of the CODA approach in comparison to ORM approaches ] is provided and demonstrates that the CODA approach is significantly faster. CODA presents substantial benefits for API developer workflows by simplifying data access, resulting in better speed and security and allowing developers to focus on productive development rather than being mired in data access code. Future considerations include a generalization of the CODA method and extension outside of the .NET ecosystem to other programming languages.

Keywords: API data access, database, JSON, .NET core, SQL server

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Authors: Umair Shafique, Hafiz Usman Zia, Fiaz Majeed, Samina Naz, Javeria Ahmed, Maleeha Zainab

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The Internet of Things (IoT) has become a hot topic for the last couple of years. This innovative technology has shown promising progress in various areas, and the world has witnessed exponential growth in multiple application domains. Researchers are working to investigate its aptitudes to get the best from it by harnessing its true potential. But at the same time, IoT networks open up a new aspect of vulnerability and physical threats to data integrity, privacy, and confidentiality. It's is due to centralized control, data silos approach for handling information, and a lack of standardization in the IoT networks. As we know, blockchain is a new technology that involves creating secure distributed ledgers to store and communicate data. Some of the benefits include resiliency, integrity, anonymity, decentralization, and autonomous control. The potential for blockchain technology to provide the key to managing and controlling IoT has created a new wave of excitement around the idea of putting that data back into the hands of the end-users. In this manuscript, we have proposed a model that combines blockchain and IoT networks to address potential security and privacy issues in the healthcare domain. Then we try to describe various application areas, challenges, and future directions in the healthcare sector where blockchain platforms merge with IoT networks.

Keywords: IoT, blockchain, cryptocurrency, healthcare, consensus, data

Procedia PDF Downloads 171

Authors: Bruce X. B. Yu, Yan Liu, Keith C. C. Chan

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We propose to record Activities of Daily Living (ADLs) of elderly people using a vision-based system so as to provide better assistive and personalization technologies. Current ADL-related research is based on data collected with help from non-elderly subjects in laboratory environments and the activities performed are predetermined for the sole purpose of data collection. To obtain more realistic datasets for the application, we recorded ADLs for the elderly with data collected from real-world environment involving real elderly subjects. Motivated by the need to collect data for more effective research related to elderly care, we chose to collect data in the room of an elderly person. Specifically, we installed Kinect, a vision-based sensor on the ceiling, to capture the activities that the elderly subject performs in the morning every day. Based on the data, we identified 12 morning activities that the elderly person performs daily. To recognize these activities, we created a HARELCARE framework to investigate into the effectiveness of existing Human Activity Recognition (HAR) algorithms and propose the use of a transfer learning algorithm for HAR. We compared the performance, in terms of accuracy, and training progress. Although the collected dataset is relatively small, the proposed algorithm has a good potential to be applied to all daily routine activities for healthcare purposes such as evidence-based diagnosis and treatment.

Keywords: daily activity recognition, healthcare, IoT sensors, transfer learning

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Authors: Mayada Al Meghari

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Recently, grid middlewares have provided large integrated use of network resources as the shared data and the CPU to become a virtual supercomputer. In this work, we present the design and implementation of the middleware for Data Warehouse Signature, DWS Framework. The aim of using the middleware in our DWS framework is to achieve the high performance by the parallel computing. This middleware is developed on Alchemi.Net framework to increase the security among the network nodes through the authentication and group-key distribution model. This model achieves the key security and prevents any intermediate attacks in the middleware. This paper presents the flow process structures of the middleware design. In addition, the paper ensures the implementation of security for DWS middleware enhancement with the authentication and group-key distribution model. Finally, from the analysis of other middleware approaches, the developed middleware of DWS framework is the optimal solution of a complete covering of security issues.

Keywords: middleware, parallel computing, data warehouse, security, group-key, high performance

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Authors: Ayodele Ajayi, John Ajayi

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This paper investigates the effect of corporate governance with a view to determining the relationship between board size and bank performance. Data for the study were obtained from the audited financial statements of five sampled banks listed on the Nigerian Stock Exchange. Panel data technique was adopted and analysis was carried out with the use of multiple regression and pooled ordinary least square. Results from the study show that the larger the board size, the greater the profit implying that corporate governance is positively correlated with bank performance.

Keywords: corporate governance, banks performance, board size, pooled data

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Authors: Sadia Nasrin Tisha, Mushfika Sharmin Rahman, Javier Orduz

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Machine learning is applied in a variety of fields throughout the world. The healthcare sector has benefited enormously from it. One of the most effective approaches for predicting human heart diseases is to use machine learning applications to classify data and predict the outcome as a classification. However, with the rapid advancement of quantum technology, quantum computing has emerged as a potential game-changer for many applications. Quantum algorithms have the potential to execute substantially faster than their classical equivalents, which can lead to significant improvements in computational performance and efficiency. In this study, we applied quantum machine learning concepts to predict coronary heart diseases from text data. We experimented thrice with three different features; and three feature sets. The data set consisted of 100 data points. We pursue to do a comparative analysis of the two approaches, highlighting the potential benefits of quantum machine learning for predicting heart diseases.

Keywords: quantum machine learning, SVM, QSVM, matrix product state

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Authors: Brenden M. Shutt, Lubjana Beshaj, Paul L. Goethals, Ambrose Kam

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Communication security is of particular interest to military data networks. A relatively novel approach to network security is blockchain, a cryptographically secured distribution ledger with a decentralized consensus mechanism for data transaction processing. Recent advances in blockchain technology have proposed new techniques for both data validation and trust management, as well as different frameworks for managing dataflow. The purpose of this work is to test the feasibility of different blockchain architectures as applied to military command and control networks. Various architectures are tested through discrete-event simulation and the feasibility is determined based upon a blockchain design’s ability to maintain long-term stable performance at industry standards of throughput, network latency, and security. This work proposes a consortium blockchain architecture with a computationally inexpensive consensus mechanism, one that leverages a Proof-of-Identity (PoI) concept and a reputation management mechanism.

Keywords: blockchain, consensus mechanism, discrete-event simulation, fog computing

Procedia PDF Downloads 136

Authors: Toleen Alaqqad, Kadi Alshabramiy, Suad Zaafarany, Basma Musallam

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CADmapper is a significant tool of software for transforming geographic data into realistic CAD drawings. It speeds up and simplifies the conversion process by automating it. This allows architects, urban planners, engineers, and geographic information system (GIS) experts to solely concentrate on the imaginative and scientific parts of their projects. While the future incorporation of AI has the potential for further improvements, CADmapper's current capabilities make it an indispensable asset in the business. It covers a combination of 2D and 3D city and urban area models. The user can select a specific square section of the map to view, and the fee is based on the dimensions of the area being viewed. The procedure is straightforward: you choose the area you want, then pick whether or not to include topography. 3D architectural data (if available), followed by selecting whatever design program or CAD style you want to publish the document which contains more than 200 free broad town plans in DXF format. If you desire to specify a bespoke area, it's free up to 1 km2.

Keywords: cadmaper, gdata, 2d and 3d data conversion, automated cad drawing, urban planning software

Procedia PDF Downloads 63

Authors: Prashansa Singh, Rohit Bajaj, Manjot Kaur

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In the modern agricultural landscape, precision farming has emerged as a pivotal strategy for enhancing crop yield and optimizing resource utilization. This paper introduces an innovative Crop Recommendation System (CRS) that leverages the Internet of Things (IoT) technology and the Message Queuing Telemetry Transport (MQTT) protocol to collect critical environmental and soil data via sensors deployed across agricultural fields. The system is designed to address the challenges of real-time data acquisition, efficient data transmission, and dynamic crop recommendation through the application of advanced Artificial Intelligence (AI) and Machine Learning (ML) models. The CRS architecture encompasses a network of sensors that continuously monitor environmental parameters such as temperature, humidity, soil moisture, and nutrient levels. This sensor data is then transmitted to a central MQTT server, ensuring reliable and low-latency communication even in bandwidth-constrained scenarios typical of rural agricultural settings. Upon reaching the server, the data is processed and analyzed by AI/ML models trained to correlate specific environmental conditions with optimal crop choices and cultivation practices. These models consider historical crop performance data, current agricultural research, and real-time field conditions to generate tailored crop recommendations. This implementation gets 99% accuracy.

Keywords: Iot, MQTT protocol, machine learning, sensor, publish, subscriber, agriculture, humidity

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Authors: Shivam Yadav, Neelam Atri

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Cyanobacteria are photoautotrophic prokaryotes able to grow in diverse ecological habitats, originated 2.5 - 3.5 billion years ago and brought oxygenic photosynthesis. Since then superoxide dismutases (SODs) acquired great significance due to their ability to catalyze detoxification of byproducts of oxygenic photosynthesis, i.e. superoxide radicals. Sequence information from several cyanobacterial genomes offers a unique opportunity to conduct a comprehensive comparative analysis of the superoxide dismutases family. In the present study, we extracted information regarding SODs from species of sequenced cyanobacteria and investigated their diversity, conservation, domain structure, and evolution. 144 putative SOD homologues were identified. SODs are present in all cyanobacterial species reflecting their significant role in survival. However, their distribution varies, fewer in unicellular marine strains whereas abundant in filamentous nitrogen-fixing cyanobacteria. Motifs and invariant amino acids typical in eukaryotic SODs were conserved well in these proteins. These SODs were classified into three major families according to their domain structures. Interestingly, they lack additional domains as found in proteins of other family. Phylogenetic relationships correspond well with phylogenies based on 16S rRNA and clustering occurs on the basis of structural characteristics such as domain organization. Similar conserved motifs and amino acids indicate that cyanobacterial SODs make use of a similar catalytic mechanism as eukaryotic SODs. Gene gain-and-loss is insignificant during SOD evolution as evidenced by absence of additional domain. This study has not only examined an overall background of sequence-structure-function interactions for the SOD gene family but also revealed variation among SOD distribution based on ecophysiological and morphological characters.

Keywords: comparative genomics, cyanobacteria, phylogeny, superoxide dismutases

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Authors: Mona Heydari, Ehsan Motamedian, Seyed Abbas Shojaosadati

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Prediction of perturbations after genetic manipulation (especially gene knockout) is one of the important challenges in systems biology. In this paper, a new algorithm is introduced that integrates microarray data into the metabolic model. The algorithm was used to study the change in the cell phenotype after knockout of Gss gene in Escherichia coli BW25113. Algorithm implementation indicated that gene deletion resulted in more activation of the metabolic network. Growth yield was more and less regulating gene were identified for mutant in comparison with the wild-type strain.

Keywords: metabolic network, gene knockout, flux balance analysis, microarray data, integration

Procedia PDF Downloads 576

Authors: Veronica S. Moertini, Vinsensius Kevin, Gede Karya

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Customer reviews have been collected by many kinds of e-commerce websites selling products, services, hotel rooms, tickets and so on. Each website collects its own customer reviews. The reviews can be crawled, collected from those websites and stored as big data. Text analysis techniques can be used to analyze that data to produce summarized information, such as customer opinions. Then, these opinions can be published by independent service provider websites and used to help customers in choosing the most suitable products or services. As the opinions are analyzed from big data of reviews originated from many websites, it is expected that the results are more trusted and accurate. Indonesian customers write reviews in Indonesian language, which comes with its own structures and uniqueness. We found that most of the reviews are expressed with “daily language”, which is informal, do not follow the correct grammar, have many abbreviations and slangs or non-formal words. Hadoop is an emerging platform aimed for storing and analyzing big data in distributed systems. A Hadoop cluster consists of master and slave nodes/computers operated in a network. Hadoop comes with distributed file system (HDFS) and MapReduce framework for supporting parallel computation. However, MapReduce has weakness (i.e. inefficient) for iterative computations, specifically, the cost of reading/writing data (I/O cost) is high. Given this fact, we conclude that MapReduce function is best adapted for “one-pass” computation. In this research, we develop an efficient technique for extracting or mining opinions from big data of Indonesian reviews, which is based on MapReduce with one-pass computation. In designing the algorithm, we avoid iterative computation and instead adopt a “look up table” technique. The stages of the proposed technique are: (1) Crawling the data reviews from websites; (2) cleaning and finding root words from the raw reviews; (3) computing the frequency of the meaningful opinion words; (4) analyzing customers sentiments towards defined objects. The experiments for evaluating the performance of the technique were conducted on a Hadoop cluster with 14 slave nodes. The results show that the proposed technique (stage 2 to 4) discovers useful opinions, is capable of processing big data efficiently and scalable.

Keywords: big data analysis, Hadoop MapReduce, analyzing text data, mining Indonesian reviews

Procedia PDF Downloads 200

Authors: Adji Achmad Rinaldo Fernandes

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SEM analysis is a complex multivariate analysis because it involves a number of exogenous and endogenous variables that are interconnected to form a model. The measurement model is divided into two, namely, the reflective model (reflecting) and the formative model (forming). Before carrying out further tests on SEM, there are assumptions that must be met, namely the linearity assumption, to determine the form of the relationship. There are three modeling approaches to path analysis, including parametric, nonparametric and semiparametric approaches. The aim of this research is to develop semiparametric SEM and obtain the best model. The data used in the research is secondary data as the basis for the process of obtaining simulation data. Simulation data was generated with various sample sizes of 100, 300, and 500. In the semiparametric SEM analysis, the form of the relationship studied was determined, namely linear and quadratic and determined one and two knot points with various levels of error variance (EV=0.5; 1; 5). There are three levels of closeness of relationship for the analysis process in the measurement model consisting of low (0.1-0.3), medium (0.4-0.6) and high (0.7-0.9) levels of closeness. The best model lies in the form of the relationship X1Y1 linear, and. In the measurement model, a characteristic of the reflective model is obtained, namely that the higher the closeness of the relationship, the better the model obtained. The originality of this research is the development of semiparametric SEM, which has not been widely studied by researchers.

Keywords: semiparametric SEM, measurement model, structural model, reflective model, formative model

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Authors: Judith Klostermann, Miguel Segura, Wilma Jans, Dragana Bojovic, Isadora Christel Jimenez, Francisco Doblas-Reyees, Judit Snethlage

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The Climate Data Store (CDS), developed by the Copernicus Climate Change Service (C3S) implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Union, is intended to become a key instrument for exploring climate data. The CDS contains both raw and processed data to provide information to the users about the past, present and future climate of the earth. It allows for easy and free access to climate data and indicators, presenting an important asset for scientists and stakeholders on the path for achieving a more sustainable future. The C3S Evaluation and Quality Control (EQC) is assessing the quality of the CDS by undertaking a comprehensive user requirement assessment to measure the users’ satisfaction. Recommendations will be developed for the improvement and expansion of the CDS datasets and products. User requirements will be identified on the fitness of the datasets, the toolbox, and the overall CDS service. The EQC function of the CDS will help C3S to make the service more robust: integrated by validated data that follows high-quality standards while being user-friendly. This function will be closely developed with the users of the service. Through their feedback, suggestions, and contributions, the CDS can become more accessible and meet the requirements for a diverse range of users. Stakeholders and their active engagement are thus an important aspect of CDS development. This will be achieved with direct interactions with users such as meetings, interviews or workshops as well as different feedback mechanisms like surveys or helpdesk services at the CDS. The results provided by the users will be categorized as a function of CDS products so that their specific interests will be monitored and linked to the right product. Through this procedure, we will identify the requirements and criteria for data and products in order to build the correspondent recommendations for the improvement and expansion of the CDS datasets and products.

Keywords: climate data store, Copernicus, quality, user engagement

Procedia PDF Downloads 143

Authors: Yamamah Abdulrazaq, Lahieb A. Abrahim, Samuel E. Davies, Iain Shewring

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An electric water heater (EWH) is a powerful appliance that uses electricity in residential, commercial, and industrial settings, and the ability to control them properly will result in cost savings and the prevention of blackouts on the national grid. This article discusses the usage of timers in EWH control strategies for demand-side management (DSM). Up to the authors' knowledge, there is no systematic mapping review focusing on the utilisation of EWH control strategies in DSM has yet been conducted. Consequently, the purpose of this research is to identify and examine main papers exploring EWH procedures in DSM by quantifying and categorising information with regard to publication year and source, kind of methods, and source of data for monitoring control techniques. In order to answer the research questions, a total of 31 publications published between 1999 and 2023 were selected depending on specific inclusion and exclusion criteria. The data indicate that direct load control (DLC) has been somewhat more prevalent than indirect load control (ILC). Additionally, the mixing method is much lower than the other techniques, and the proportion of Real-time data (RTD) to non-real-time data (NRTD) is about equal.

Keywords: demand side management, direct load control, electric water heater, indirect load control, non real-time data, real-time data

Procedia PDF Downloads 79

Authors: O. O. Sufyani, M. E. Oraiby, S. A. Qumaiy, A. I. Alaamri, Z. M. Eisa, A. M. Hakami, M. A. Attafi, O. M. Alhassan, W. M. Elsideeg, E. M. Noureldin, Y. A. Hobani, Y. Q. Majrabi, I. A. Khardali, A. B. Maashi, A. A. Al Mane, A. H. Hakami, I. M. Alkhyat, A. A. Sahly, I. M. Attafi

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According to the Food and Agriculture Organization (FAO) Pesticides Use Database, pesticide use in agriculture in Saudi Arabia has more than doubled from 4539 tons in 2009 to 10496 tons in 2019. Among pesticides, pyrethroids is commonly used in Saudi Arabia. Pesticides may increase susceptibility to a variety of diseases, particularly among pesticide workers, due to their extensive use, indiscriminate use, and long-term exposure. Therefore, analyzing blood chemo-profiles and evaluating the detected substances as biomarkers for pyrethroid pesticide exposure may assist to identify and predicting adverse effects of exposure, which may be used for both preventative and risk assessment purposes. The purpose of this study was to (a) analyze chemo-profiling by Gas Chromatography-Mass Spectrometry (GC-MS) analysis, (b) identify the most commonly detected chemicals in a time-exposure-dependent manner using a Venn diagram, and (c) identify their associated disease among pesticide workers using analyzer tools on the Comparative Toxicogenomics Database (CTD) website, (250 healthy male volunteers (20-60 years old) who deal with pesticides in the Jazan region of Saudi Arabia (exposure intervals: 1-2, 4-6, 6-8, more than 8 years) were included in the study. A questionnaire was used to collect demographic information, the duration of pesticide exposure, and the existence of chronic conditions. Blood samples were collected for biochemistry analysis and extracted by solid-phase extraction for gas chromatography-mass spectrometry (GC-MS) analysis. Biochemistry analysis reveals no significant changes in response to the exposure period; however, an inverse association between the albumin level and the exposure interval was observed. The blood chemo-profiling was differentially expressed in an exposure time-dependent manner. This analysis identified the common chemical set associated with each group and their associated significant occupational diseases. While some of these chemicals are associated with a variety of diseases, the distinguishing feature of these chemically associated disorders is their applicability for prevention measures. The most interesting finding was the identification of several chemicals; erucic acid, pelargonic acid, alpha-linolenic acid, dibutyl phthalate, diisobutyl phthalate, dodecanol, myristic Acid, pyrene, and 8,11,14-eicosatrienoic acid, associated with pneumoconiosis, asbestosis, asthma, silicosis and berylliosis. Chemical-disease association study also found that cancer, digestive system disease, nervous system disease, and metabolic disease were the most often recognized disease categories in the common chemical set. The hierarchical clustering approach was used to compare the expression patterns and exposure intervals of the chemicals found commonly. More study is needed to validate these chemicals as early markers of pyrethroid insecticide-related occupational disease, which might assist evaluate and reducing risk. The current study contributes valuable data and recommendations to public health.

Keywords: occupational, toxicology, chemo-profiling, pesticide, pyrethroid, GC-MS

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Authors: Mariia Khramova, Sergio Martinez, Duc Nguyen

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Modern electronic devices, particularly smartphones, are characterised by extremely high environmental footprint and short product lifecycle. Every year manufacturers release new models with even more superior performance, which pushes the customers towards new purchases. As a result, millions of devices are being accumulated in the urban mine. To tackle these challenges the concept of circular economy has been introduced to promote repair, reuse and recycle of electronics. In this case, electronic devices, that previously ended up in landfills or households, are getting the second life, therefore, reducing the demand for new raw materials. Smartphone reuse is gradually gaining wider adoption partly due to the price increase of flagship models, consequently, boosting circular economy implementation. However, along with reuse of communication device, circular economy approach needs to ensure the data of the previous user have not been 'reused' together with a device. This is especially important since modern smartphones are comparable with computers in terms of performance and amount of data stored. These data vary from pictures, videos, call logs to social security numbers, passport and credit card details, from personal information to corporate confidential data. To assess how well the data privacy requirements are followed on smartphones second-hand market, a sample of 100 Android smartphones has been purchased from IT Asset Disposition (ITAD) facilities responsible for data erasure and resell. Although devices should not have stored any user data by the time they leave ITAD, it has been possible to retrieve the data from 19% of the sample. Applied techniques varied from manual device inspection to sophisticated equipment and tools. These findings indicate significant barrier in implementation of circular economy and a limitation of smartphone reuse. Therefore, in order to motivate the users to donate or sell their old devices and make electronic use more sustainable, data privacy on second-hand smartphone market should be significantly improved. Presented research has been carried out in the framework of sustainablySMART project, which is part of Horizon 2020 EU Framework Programme for Research and Innovation.

Keywords: android, circular economy, data privacy, second-hand phones

Procedia PDF Downloads 126

Authors: Supasak Ngaoprasertwong

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The purpose of this research was to develop a model for Muay Thai competition management for promoting sport tourism in the next decade. Moreover, the model was appropriately initiated for practical use. This study also combined several methodologies, both quantitative research and qualitative research, to entirely cover all aspects of data, especially the tourists’ satisfaction toward Muay Thai competition. The data were collected from 400 tourists watching Muay Thai competition in 4 stadiums to create the model for Muay Thai competition to support the sport tourism in the next decade. Besides, Ethnographic Delphi Futures Research (EDFR) was applied to gather the data from certain experts in boxing industry or having significant role in Muay Thai competition in both public sector and private sector. The first step of data collection was an in-depth interview with 27 experts associated with Muay Thai competition, Muay Thai management, and tourism. The second step and the third step of data collection were conducted to confirm the experts’ opinions toward various elements. When the 3 steps of data collection were completely accomplished, all data were assembled to draft the model. Then the model was proposed to 8 experts to conduct a brainstorming to affirm it. According to the results of quantitative research, it found that the tourists were satisfied with personnel of competition at high level (x=3.87), followed by facilities, services, and safe high level (x=3.67). Furthermore, they were satisfied with operation in competition field at high level (x=3.62).Regarding the qualitative methodology including literature review, theories, concepts and analysis of qualitative research development of the model for Muay Thai competition to promote the sport tourism in the next decade, the findings indicated that there were 2 data sets as follows: The first one was related to Muay Thai competition to encourage the sport tourism and the second one was associated with Muay Thai stadium management to support the sport tourism. After the brain storming, “EE Muay Thai Model” was finally developed for promoting the sport tourism in the next decade (2015-2024).

Keywords: Muay Thai competition management, Muay Thai sport tourism, Muay Thai, Muay Thai for sport tourism management

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Authors: Nishimwe Clemence, Mchunu Gugu, Mukamusoni Dariya

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Background: Community health workers and extension workers can play an important role in supporting families to adopt health practices, encourage delivery in a health care facility, and ensure time referral of mothers and newborns if needed. Saving the lives of neonates should, therefore, be a significant health outcome in any maternal and newborn health program that is being implemented. Furthermore, about half of a million mothers die from pregnancy-related causes. Maternal and newborn deaths related to the period of postnatal care are neglected. Some authors emphasized that in developing countries, newborn mortality rates have been reduced much more slowly because of the lack of many necessary facility-based and outreach service. The aim of this review was to critically analyze the implementation and integration process of the maternal and newborn health care program by maternal community health workers, into the health care system, in Africa. Furthermore, it aims to reduce maternal and newborn mortality. We addressed the following review question: (1) what process is involved in the implementation and integration of the maternal and newborn health care program by maternal community health workers during antenatal, delivery and postnatal care into health system care in Africa? Methods: The database searched was from Health Source: Nursing/Academic Edition through academic search complete via EBSCO Host. An iterative approach was used to go through Google scholarly papers. The reviewers considered adapted Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidance, and the Mixed Methods Appraisal Tool (MMAT) was used. Synthesis method in integrative review following elements of noting patterns and themes, seeing plausibility, clustering, counting, making contrasts and comparisons, discerning commons and unusual patterns, subsuming particulars into general, noting relations between variability, finding intervening factors and building a logical chain of evidence, using data–based convergent synthesis design. Results: From the seventeen of studies included, results focused on three dimensions inspired by the literature on antenatal, delivery, and postnatal interventions. From this, further conceptual framework was elaborated. The conceptual framework process of implementation and integration of maternal and newborn health care program by maternal community health workers was elaborated in order to ensure the sustainability of community based intervention. Conclusions: the review revealed that the implementation and integration of maternal and newborn health care program require planning. We call upon governments, non-government organizations, the global health community, all stakeholders including policy makers, program managers, evaluators, educators, and providers to be involved in implementation and integration of maternal and newborn health program in updated policy and community-based intervention. Furthermore, emphasis should be placed on competence, responsibility, and accountability of maternal community health workers, their training and payment, collaboration with health professionals in health facilities, and reinforcement of outreach service. However, the review was limited in focus to the African context, where the process of maternal and newborn health care program has been poorly implemented.

Keywords: Africa, implementation of integration, maternal, newborn

Procedia PDF Downloads 157

Authors: T. Penkova, A. Korobko, V. Nicheporchuk, L. Nozhenkova, A. Metus

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This paper presents an approach of on-line control of the state of technosphere and environment objects based on the integration of Data Warehouse, OLAP and Expert systems technologies. It looks at the structure and content of data warehouse that provides consolidation and storage of monitoring data. There is a description of OLAP-models that provide a multidimensional analysis of monitoring data and dynamic analysis of principal parameters of controlled objects. The authors suggest some criteria of emergency risk assessment using expert knowledge about danger levels. It is demonstrated now some of the proposed solutions could be adopted in territorial decision making support systems. Operational control allows authorities to detect threat, prevent natural and anthropogenic emergencies and ensure a comprehensive safety of territory.

Keywords: decision making support systems, emergency risk assessment, natural and anthropogenic safety, on-line control, territory

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Authors: Sanele Lionel Khanyile, Emmanuel Nahayo

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Geomagnetic jerks are jumps observed in the second derivative of the main magnetic field that occurs on annual to decadal timescales. Understanding these jerks is crucial as they provide valuable insights into the complex dynamics of the Earth’s outer liquid core. In this study, we investigate the occurrence of geomagnetic jerks in geomagnetic observatory data collected at southern African magnetic observatories, Hermanus (HER), Tsumeb (TSU), Hartebeesthoek (HBK) and Keetmanshoop (KMH) between 2017 and 2023. The observatory data was processed and analyzed by retaining quiet night-time data recorded during quiet geomagnetic activities with the help of Kp, Dst, and ring current RC indices. Results confirm the occurrence of the 2019-2020 geomagnetic jerk in the region and identify the recent 2021 jerk detected with V-shaped secular variation changes in X and Z components at all four observatories. The highest estimated 2021 jerk secular acceleration amplitudes in X and Z components were found at HBK, 12.7 nT/year² and 19. 1 nT/year², respectively. Notably, the global CHAOS-7 model aptly identifies this 2021 jerk in the Z component at all magnetic observatories in the region.

Keywords: geomagnetic jerks, secular variation, magnetic observatory data, South Atlantic Anomaly

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Authors: Roberto Lorenzo

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Much has been said about the dynamics of the Latin American experience in the United States, however, there seems to be very little data regarding the perception of career identity. Although we do have some Latinos within the professional ranks, there is not nearly enough to claim that we have practiced enough cultural competence to create equity in the professional sphere in the United States. In this thesis, data will be provided regarding labor force statistics highlighting the industries that Latin Americans frequent. Also provided will be the citing of data that suggests further necessity of cultural competence within the professional realm regarding Latin Americans. In addition, methods that were spoken about over the course of our social work education will be discussed in order to connect to possible solutions to this issue.

Keywords: hiring, Latinos, professional equity, cultural competence

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Authors: Marek Dosbaba

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Within the mining sector, SEM-based Automated Mineralogy (AM) has been the standard application for quickly and efficiently handling mineral processing tasks. Over the last decade, the trend has been to analyze larger numbers of samples, often with a higher level of detail. This has necessitated a shift from interactive sample analysis performed by an operator using a SEM, to an increased reliance on offline processing to analyze and report the data. In response to this trend, TESCAN TIMA Mineral Analyzer is designed to quickly create a virtual copy of the studied samples, thereby preserving all the necessary information. Depending on the selected data acquisition mode, TESCAN TIMA can perform hyperspectral mapping and save an X-ray spectrum for each pixel or segment, respectively. This approach allows the user to browse through elemental distribution maps of all elements detectable by means of energy dispersive spectroscopy. Re-evaluation of the existing data for the presence of previously unconsidered elements is possible without the need to repeat the analysis. Additional tiers of data such as a secondary electron or cathodoluminescence images can also be recorded. To take full advantage of these information-rich datasets, TIMA utilizes a new archiving tool introduced by TESCAN. The dataset size can be reduced for long-term storage and all information can be recovered on-demand in case of renewed interest. TESCAN TIMA is optimized for network storage of its datasets because of the larger data storage capacity of servers compared to local drives, which also allows multiple users to access the data remotely. This goes hand in hand with the support of remote control for the entire data acquisition process. TESCAN also brings a newly extended open-source data format that allows other applications to extract, process and report AM data. This offers the ability to link TIMA data to large databases feeding plant performance dashboards or geometallurgical models. The traditional tabular particle-by-particle or grain-by-grain export process is preserved and can be customized with scripts to include user-defined particle/grain properties.

Keywords: Tescan, electron microscopy, mineralogy, SEM, automated mineralogy, database, TESCAN TIMA, open format, archiving, big data

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Authors: Leng Jian-Wei, Wang Lai-Jun, Li Ye

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In order to measure and analyze the transportation situation within the counties of Shaanxi province over a certain period of time and to promote the province's future transportation planning and development, this paper proposes a reasonable layout plan and compares model rationality. The study uses entropy weight method to measure the transportation advantages of 107 counties in Shaanxi province from three dimensions: road network density, trunk line influence and location advantage in 2013 and 2021, and applies spatial autocorrelation analysis method to analyze the spatial layout and development trend of county-level transportation, and conducts ordinary least square (OLS)regression on transportation impact factors and other influencing factors. The paper also compares the regression fitting degree of the Geographically weighted regression(GWR) model and the OLS model. The results show that spatially, the transportation advantages of Shaanxi province generally show a decreasing trend from the Weihe Plain to the surrounding areas and mainly exhibit high-high clustering phenomenon. Temporally, transportation advantages show an overall upward trend, and the phenomenon of spatial imbalance gradually decreases. People's travel demands have changed to some extent, and the demand for rapid transportation has increased overall. The GWR model regression fitting degree of transportation advantages is 0.74, which is higher than the OLS regression model's fitting degree of 0.64. Based on the evolution of transportation advantages, it is predicted that this trend will continue for a period of time in the future. To improve the transportation advantages of Shaanxi province increasing the layout of rapid transportation can effectively enhance the transportation advantages of Shaanxi province. When analyzing spatial heterogeneity, geographic factors should be considered to establish a more reliable model

Keywords: traffic dominance, GWR model, spatial autocorrelation analysis, temporal and spatial evolution

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Authors: Isaac Kayode Ogunlade

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Data acquisition (DAQ) is the process by which physical phenomena from the real world are transformed into an electrical signal(s) that are measured and converted into a digital format for processing, analysis, and storage by a computer. The DAQ is designed using PIC18F4550 microcontroller, communicating with Personal Computer (PC) through USB (Universal Serial Bus). The research deployed initial knowledge of data acquisition system and embedded system to develop a weather data acquisition device using LM35 sensor to measure weather parameters and the use of Artificial Intelligence(Artificial Neural Network - ANN)and statistical approach(Autoregressive Integrated Moving Average – ARIMA) to predict precipitation (rainfall). The device is placed by a standard device in the Department of Meteorology, Federal University of Technology, Akure (FUTA) to know the performance evaluation of the device. Both devices (standard and designed) were subjected to 180 days with the same atmospheric condition for data mining (temperature, relative humidity, and pressure). The acquired data is trained in MATLAB R2012b environment using ANN, and ARIMAto predict precipitation (rainfall). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Correction Square (R2), and Mean Percentage Error (MPE) was deplored as standardize evaluation to know the performance of the models in the prediction of precipitation. The results from the working of the developed device show that the device has an efficiency of 96% and is also compatible with Personal Computer (PC) and laptops. The simulation result for acquired data shows that ANN models precipitation (rainfall) prediction for two months (May and June 2017) revealed a disparity error of 1.59%; while ARIMA is 2.63%, respectively. The device will be useful in research, practical laboratories, and industrial environments.

Keywords: data acquisition system, design device, weather development, predict precipitation and (FUTA) standard device

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Authors: Iacopo Testi, Diego Pajarito, Nicoletta Roberto, Carmen Greco

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Today, a consistent segment of the world’s population lives in urban areas, and this proportion will vastly increase in the next decades. Therefore, understanding the key trends in urbanization, likely to unfold over the coming years, is crucial to the implementation of sustainable urban strategies. In parallel, the daily amount of digital data produced will be expanding at an exponential rate during the following years. The analysis of various types of data sets and its derived applications have incredible potential across different crucial sectors such as healthcare, housing, transportation, energy, and education. Nevertheless, in city development, architects and urban planners appear to rely mostly on traditional and analogical techniques of data collection. This paper investigates the prospective of the data science field, appearing to be a formidable resource to assist city managers in identifying strategies to enhance the social, economic, and environmental sustainability of our urban areas. The collection of different new layers of information would definitely enhance planners' capabilities to comprehend more in-depth urban phenomena such as gentrification, land use definition, mobility, or critical infrastructural issues. Specifically, the research results correlate economic, commercial, demographic, and housing data with the purpose of defining the youth economic discomfort index. The statistical composite index provides insights regarding the economic disadvantage of citizens aged between 18 years and 29 years, and results clearly display that central urban zones and more disadvantaged than peripheral ones. The experimental set up selected the city of Rome as the testing ground of the whole investigation. The methodology aims at applying statistical and spatial analysis to construct a composite index supporting informed data-driven decisions for urban planning.

Keywords: data science, spatial analysis, composite index, Rome, urban planning, youth economic discomfort index

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Authors: Annabelle Onyefulu-Kingston

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One of the major purposes of AI today is to evaluate and analyze millions of micro and macro data in order to determine what is relevant in a particular case and proffer it in an adequate manner. Microdata, as far as it relates to AI in international arbitration, is the millions of key issues specifically mentioned by either one or both parties or by their counsels, arbitrators, or arbitral tribunals in arbitral proceedings. This can be qualifications of expert witness and admissibility of evidence, amongst others. Macro data, on the other hand, refers to data derived from the resolution of the dispute and, consequently, the final and binding award. A notable example of this includes the rationale of the award and specific and general damages awarded, amongst others. This paper aims to critically evaluate and analyze the possibility of technological inclusion in international arbitration. This research will be imploring the qualitative method by evaluating existing literature on the consequence of applying AI to both micro and macro data in international arbitration, and how this can be of assistance to parties, counsels, and arbitrators.

Keywords: AI-based technologies, algorithms, arbitrators, international arbitration

Procedia PDF Downloads 85