Search results for: incomplete count data

Authors: Chaimaa Messaoudi, Rachida Fissoune, Hassan Badir

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Technological advances of computer science and data analysis are helping to provide continuously huge volumes of biological data, which are available on the web. Such advances involve and require powerful techniques for data integration to extract pertinent knowledge and information for a specific question. Biomedical exploration of these big data often requires the use of complex queries across multiple autonomous, heterogeneous and distributed data sources. Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontology. We provide a survey of some approaches and techniques for integrating biological data, we focus on those developed in the ontology community.

Keywords: biological ontology, linked data, semantic data integration, semantic web

Procedia PDF Downloads 428

Authors: Maximilian Elsen, Frank Tietze

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The development and diffusion of green technologies are crucial for achieving our ambitious climate targets. The Paris Agreement commits its members to develop strategies for achieving net zero greenhouse gas emissions by the second half of the century. Governments, executives, and academics are working on net-zero strategies and the business of rating organisations on their environmental, social and governance (ESG) performance has grown tremendously in its public interest. ESG data is now commonly integrated into traditional investment analysis and an important factor in investment decisions. Creating these metrics, however, is inherently challenging as environmental and social impacts are hard to measure and uniform requirements on ESG reporting are lacking. ESG metrics are often incomplete and inconsistent as they lack fully accepted reporting standards and are often of qualitative nature. This study explores the use of patent data for assessing the environmental performance of companies by focusing on their patented inventions in the space of climate change mitigation and adaptation technologies (CCMAT). The present study builds on the successful identification of CCMAT patents. In this context, the study adopts the Y02 patent classification, a fully cross-sectional tagging scheme that is fully incorporated in the Cooperative Patent Classification (CPC), to identify Climate Change Adaptation Technologies. The Y02 classification was jointly developed by the European Patent Office (EPO) and the United States Patent and Trademark Office (USPTO) and provides means to examine technologies in the field of mitigation and adaptation to climate change across relevant technologies. This paper develops sustainability-related metrics for firm-level patent portfolios. We do so by adopting a three-step approach. First, we identify relevant CCMAT patents based on their classification as Y02 CPC patents. Second, we examine the technological strength of the identified CCMAT patents by including more traditional metrics from the field of patent analytics while considering their relevance in the space of CCMAT. Such metrics include, among others, the number of forward citations a patent receives, as well as the backward citations and the size of the focal patent family. Third, we conduct our analysis on a firm level by sector for a sample of companies from different industries and compare the derived sustainability performance metrics with the firms’ environmental and financial performance based on carbon emissions and revenue data. The main outcome of this research is the development of sustainability-related metrics for firm-level environmental performance based on patent data. This research has the potential to complement existing ESG metrics from an innovation perspective by focusing on the environmental performance of companies and putting them into perspective to conventional financial performance metrics. We further provide insights into the environmental performance of companies on a sector level. This study has implications of both academic and practical nature. Academically, it contributes to the research on eco-innovation and the literature on innovation and intellectual property (IP). Practically, the study has implications for policymakers by deriving meaningful insights into the environmental performance from an innovation and IP perspective. Such metrics are further relevant for investors and potentially complement existing ESG data.

Keywords: climate change mitigation, innovation, patent portfolios, sustainability

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Authors: Mitakshara Sharma, S. K. Nema

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Diabetes mellitus (DM) is metabolic disorder prevalent in pandemic proportions, incurring significant morbidity and mortality due to associated vascular angiopathies. Platelet related thrombogenesis plays key role in pathogenesis of these complications. Most patients with type II DM suffer from preventable vascular complications and early diagnosis can help manage these successfully. These complications are attributed to platelet activation which can be recognised by the increase in Platelet Volume Indices(PVI) viz. Mean Platelet Volume(MPV) and Platelet Distribution Width(PDW). This study was undertaken with the aim of finding a relationship between PVI and vascular complications of Diabetes mellitus, their importance as a causal factor in these complications and use as markers for early detection of impending vascular complications in patients with poor glycaemic status. This is a cross-sectional study conducted for 2 years with total 930 subjects. The subjects were segregated in 03 groups on basis of glycosylated haemoglobin (HbA1C) as: - (a) Diabetic, (b) Non-Diabetic and (c) Subjects with Impaired fasting glucose(IFG) with 300 individuals in IFG and non-diabetic group & 330 individuals in diabetic group. The diabetic group was further divided into two groups: - (a) Diabetic subjects with diabetes related vascular complications (b) Diabetic subjects without diabetes related vascular complications. Samples for HbA1C and platelet indices were collected using Ethylene diamine tetracetic acid(EDTA) as anticoagulant and processed on SYSMEX-XS-800i autoanalyser. The study revealed stepwise increase in PVI from non-diabetics to IFG to diabetics. MPV and PDW of diabetics, IFG and non diabetics were 17.60 ± 2.04, 11.76 ± 0.73, 9.93 ± 0.64 and 19.17 ± 1.48, 15.49 ± 0.67, 10.59 ± 0.67 respectively with a significant p value 0.00 and a significant positive correlation (MPV-HbA1c r = 0.951; PDW-HbA1c r = 0.875). However, significant negative correlation was found between glycaemic levels and total platelet count (PC- HbA1c r =-0.164). MPV & PDW of subjects with and without diabetes related complications were (15.14 ± 1.04) fl & (17.51±0.39) fl and (18.96 ± 0.83) fl & (20.09 ± 0.98) fl respectively with a significant p value 0.00.The current study demonstrates raised platelet indices & reduced platelet counts in association with rising glycaemic levels and diabetes related vascular complications across various study groups & showed that platelet morphology is altered with increasing glycaemic levels. These changes can be known by measurements of PVI which are important, simple, cost effective, effortless tool & indicators of impending vascular complications in patients with deranged glycaemic control. PVI should be researched and explored further as surrogate markers to develop a clinical tool for early recognition of vascular changes related to diabetes and thereby help prevent them. They can prove to be more useful in developing countries with limited resources. This study is multi-parameter, comprehensive with adequately powered study design and represents pioneering effort in India on account of the fact that both Platelet indices (MPV & PDW) along with platelet count have been evaluated together for the first time in Diabetics, non diabetics, patients with IFG and also in the diabetic patients with and without diabetes related vascular complications.

Keywords: diabetes, HbA1C, IFG, MPV, PDW, PVI

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Authors: Claudia Horeanu

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Ever since the first horror films, Count Dracula, the image inspired mainly by the novel written by Bram Stoker, is an almost indispensable character in popular culture. In the shadow of his vampire image is a Romanian ruler, Vlad Țepeș, from Wallachia, a ruler who was also nicknamed Drăculea. The purpose of this research is to analyze the evolution of the image of Vlad Tepeș/Dracula in popular culture, identifying the reasons and themes associated with this character, and to explore how the figure of Vlad Tepeș/Dracula evolved according to social and political changes in different historical periods. It is also believed that there are elements that have remained constant in the depictions of Vlad the Impaler/Dracula.

Keywords: popular culture, dracula, vlad tepes, castlevania, vampire

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Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

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Authors: Lakshmi Prayaga, Chandra Prayaga. Aaron Wade, Gopi Shankar Mallu, Harsha Satya Pola

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Synthetic data generated by Generative Adversarial Networks and Autoencoders is becoming more common to combat the problem of insufficient data for research purposes. However, generating synthetic data is a tedious task requiring extensive mathematical and programming background. Open-source platforms such as the Synthetic Data Vault (SDV) and Mostly AI have offered a platform that is user-friendly and accessible to non-technical professionals to generate synthetic data to augment existing data for further analysis. The SDV also provides for additions to the generic GAN, such as the Gaussian copula. We present the results from two synthetic data sets (CTGAN data and CTGAN with Gaussian Copula) generated by the SDV and report the findings. The results indicate that the ROC and AUC curves for the data generated by adding the layer of Gaussian copula are much higher than the data generated by the CTGAN.

Keywords: synthetic data generation, generative adversarial networks, conditional tabular GAN, Gaussian copula

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Authors: Feng Tao, Ma Jing, Guo Xian, Wang Jing

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Named Data Networking (NDN) replaces IP address of traditional network with data name, and adopts dynamic cache mechanism. In the existing mechanism, however, only one-to-one search can be achieved because every data has a unique name corresponding to it. There is a certain mapping relationship between data content and data name, so if the data name is intercepted by an adversary, the privacy of the data content and user’s interest can hardly be guaranteed. In order to solve this problem, this paper proposes a one-to-many fuzzy search scheme based on order-preserving encryption to reduce the query overhead by optimizing the caching strategy. In this scheme, we use hash value to ensure the user’s query safe from each node in the process of search, so does the privacy of the requiring data content.

Keywords: NDN, order-preserving encryption, fuzzy search, privacy

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Authors: Chellammal Surianarayanan

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Healthcare industry is generating large amounts of data driven by various needs such as record keeping, physician’s prescription, medical imaging, sensor data, Electronic Patient Record(EPR), laboratory, pharmacy, etc. Healthcare data is so big and complex that they cannot be managed by conventional hardware and software. The complexity of healthcare big data arises from large volume of data, the velocity with which the data is accumulated and different varieties such as structured, semi-structured and unstructured nature of data. Despite the complexity of big data, if the trends and patterns that exist within the big data are uncovered and analyzed, higher quality healthcare at lower cost can be provided. Hadoop is an open source software framework for distributed processing of large data sets across clusters of commodity hardware using a simple programming model. The core components of Hadoop include Hadoop Distributed File System which offers way to store large amount of data across multiple machines and MapReduce which offers way to process large data sets with a parallel, distributed algorithm on a cluster. Hadoop ecosystem also includes various other tools such as Hive (a SQL-like query language), Pig (a higher level query language for MapReduce), Hbase(a columnar data store), etc. In this paper an analysis has been done as how healthcare big data can be processed and analyzed using Hadoop ecosystem.

Keywords: big data analytics, Hadoop, healthcare data, towards quality healthcare

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Authors: Byron Joseph A. Hallar, Annjeannette Alain D. Galang, Maria Visitacion N. Gumabay

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One of the unique challenges provided by the twenty-first century to Philippine higher education is the utilization of Big Data. The higher education system in the Philippines is generating burgeoning amounts of data that contains relevant data that can be used to generate the information and knowledge needed for accurate data-driven decision making. This study examines the status, relevance and future of Big Data and Analytics in Philippine higher education. The insights gained from the study may be relevant to other developing nations similarly situated as the Philippines.

Keywords: big data, data analytics, higher education, republic of the philippines, assessment

Procedia PDF Downloads 322

Authors: Janki Shankar, Shu-Ping Chen

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This Canadian study explored the perspectives of recent immigrant workers regarding occupational health and safety (OHS) and workplace conditions that increase workers’ vulnerability to sustaining injury or illness. Using an interpretive research approach and semi structured qualitative interviews, 42 recent immigrant workers from a range of industries operating in two cities in a province in Canada were interviewed. A constant comparative approach was used to identify key themes across the workers’ experiences. The findings revealed that these workers have an incomplete understanding of OHS. In many workplaces, poor job training, little worker support, lack of power in the workplace, and a poor workplace safety culture make it difficult for recent immigrant workers to acquire OHS information and implement safe work practices. This study proposes workplace policies and practices that will improve worker OHS awareness and make workplaces safer for immigrant workers.

Keywords: new immigrant workers, occupational health and safety, workplace challenges, policy, practice

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Authors: G. Julius P. Roy, Prateek Saxena, Sanjeev Singh

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Power distribution utilities two decades ago would collect data from its customers not later than a period of at least one month. The origin of SmartGrid and AMI has subsequently increased the sampling frequency leading to 1000 to 10000 fold increase in data quantity. This increase is notable and this steered to coin the tern Big Data in utilities. Power distribution industry is one of the largest to handle huge and complex data for keeping history and also to turn the data in to significance. Majority of the utilities around the globe are adopting SmartGrid technologies as a mass implementation and are primarily focusing on strategic interdependence and synergies of the big data coming from new information sources like AMI and intelligent SCADA, there is a rising need for new models of data management and resurrected focus on analytics to dissect data into descriptive, predictive and dictatorial subsets. The goal of this paper is to is to bring load disaggregation into smart energy toolkit for commercial usage.

Keywords: data management, analytics, energy data analytics, smart grid, smart utilities

Procedia PDF Downloads 763

Authors: P. Srinivasa Rao, K. Venkatesh Sharma, G. Sadhya Devi, V. Nagesh

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Privacy Preserving Data Publication is the main concern in present days because the data being published through the internet has been increasing day by day. This huge amount of data was named as Big Data by its size. This project deals the privacy preservation in the context of Big Data using a data warehousing solution called hive. We implemented Nearest Similarity Based Clustering (NSB) with Bottom-up generalization to achieve (v,l)-anonymity. (v,l)-Anonymity deals with the sensitivity vulnerabilities and ensures the individual privacy. We also calculate the sensitivity levels by simple comparison method using the index values, by classifying the different levels of sensitivity. The experiments were carried out on the hive environment to verify the efficiency of algorithms with Big Data. This framework also supports the execution of existing algorithms without any changes. The model in the paper outperforms than existing models.

Keywords: sensitivity, sensitive level, clustering, Privacy Preserving Data Publication (PPDP), bottom-up generalization, Big Data

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Authors: Behnam Tavakkol

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Uncertain data mining algorithms use different ways to consider uncertainty in data such as by representing a data object as a sample of points or a probability distribution. Fuzzy methods have long been used for clustering traditional (certain) data objects. They are used to produce non-crisp cluster labels. For uncertain data, however, besides some uncertain fuzzy k-medoids algorithms, not many other fuzzy clustering methods have been developed. In this work, we develop a fuzzy kernel k-medoids algorithm for clustering uncertain data objects. The developed fuzzy kernel k-medoids algorithm is superior to existing fuzzy k-medoids algorithms in clustering data sets with non-linearly separable clusters.

Keywords: clustering algorithm, fuzzy methods, kernel k-medoids, uncertain data

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Authors: Ferawati, H. Purwanto, Y. F. Kurnia, E. Purwati

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The aim of this study was to evaluate the microbiological quality and safety of meatball obtained from five different manufacturers around Payakumbuh City, West Sumatra, Indonesia. Microbiological analysis of meatball sample resulted in aerobic plate count range from 7 log CFU/gr to 8.623 log CFU/gr, respectively. Total coliform ranges from 1.041 log Most Probable Number (MPN)/gr to 3.380 log MPN/gr, respectively. Chemical analysis of meatball sample consisted of borax and formalin content. The result of qualitative detection of borax and formalin content on all meatball samples were not detected. Thus, it remains essential to include the significance of effective hygiene practices as an important safety measure in consumer education programmes.

Keywords: borax, formalin, meatball, microbiological quality

Procedia PDF Downloads 271

Authors: Abdoulie K. Ceesay

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Within the sequence of the whole genome, it is known that 99.9% of the human genome is similar, whilst our difference lies in just 0.1%. Among these minor dissimilarities, the most common type of genetic variations that occurs in a population is SNP, which arises due to nucleotide substitution in a protein sequence that leads to protein destabilization, alteration in dynamics, and other physio-chemical properties’ distortions. While causing variations, they are equally responsible for our difference in the way we respond to a treatment or a disease, including various cancer types. There are two types of SNPs; synonymous single nucleotide polymorphism (sSNP) and non-synonymous single nucleotide polymorphism (nsSNP). sSNP occur in the gene coding region without causing a change in the encoded amino acid, while nsSNP is deleterious due to its replacement of a nucleotide residue in the gene sequence that results in a change in the encoded amino acid. Predicting the effects of cancer related nsSNPs on protein stability, function, and dynamics is important due to the significance of phenotype-genotype association of cancer. In this thesis, Data of 5 oncogenes (ONGs) (AKT1, ALK, ERBB2, KRAS, BRAF) and 5 tumor suppressor genes (TSGs) (ESR1, CASP8, TET2, PALB2, PTEN) were retrieved from ClinVar. Five common in silico tools; Polyphen, Provean, Mutation Assessor, Suspect, and FATHMM, were used to predict and categorize nsSNPs as deleterious, benign, or neutral. To understand the impact of each variation on the phenotype, Maestro, PremPS, Cupsat, and mCSM-NA in silico structural prediction tools were used. This study comprises of in-depth analysis of 10 cancer gene variants downloaded from Clinvar. Various analysis of the genes was conducted to derive a meaningful conclusion from the data. Research done indicated that pathogenic variants are more common among ONGs. Our research also shows that pathogenic and destabilizing variants are more common among ONGs than TSGs. Moreover, our data indicated that ALK(409) and BRAF(86) has higher benign count among ONGs; whilst among TSGs, PALB2(1308) and PTEN(318) genes have higher benign counts. Looking at the individual cancer genes predisposition or frequencies of causing cancer according to our research data, KRAS(76%), BRAF(55%), and ERBB2(36%) among ONGs; and PTEN(29%) and ESR1(17%) among TSGs have higher tendencies of causing cancer. Obtained results can shed light to the future research in order to pave new frontiers in cancer therapies.

Keywords: tumor suppressor genes (TSGs), oncogenes (ONGs), non synonymous single nucleotide polymorphism (nsSNP), single nucleotide polymorphism (SNP)

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Authors: Nirmala Gopal, Sheetal Bhoola, Audecious Mugwagwa

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This paper discusses the continued infringement and exploitation of data by non-state actors for destructive purposes, emphasizing radical terrorist organizations. It will discuss how terrorist organizations access and use data to foster their nefarious agendas. It further examines how cybersecurity, designed as a tool to curb data exploitation, is ineffective in raising global citizens' concerns about how their data can be kept safe and used for its acquired purpose. The study interrogates several policies and data protection instruments, such as the Data Protection Act, Cyber Security Policies, Protection of Personal Information(PPI) and General Data Protection Regulations (GDPR), to understand data use and storage in democratic states. The study outcomes point to the fact that international cybersecurity and cybercrime legislation, policies, and conventions have not curbed violations of data access and use by radical terrorist groups. The study recommends ways to enhance cybersecurity and reduce cyber risks using democratic principles.

Keywords: cybersecurity, data exploitation, terrorist organizations, data democracy

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Authors: Eugenia Jilinguirian

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In the healthcare industry, data mining is essential since it transforms the field by collecting useful data from large datasets. Data mining is the process of applying advanced analytical methods to large patient records and medical histories in order to identify patterns, correlations, and trends. Healthcare professionals can improve diagnosis accuracy, uncover hidden linkages, and predict disease outcomes by carefully examining these statistics. Additionally, data mining supports personalized medicine by personalizing treatment according to the unique attributes of each patient. This proactive strategy helps allocate resources more efficiently, enhances patient care, and streamlines operations. However, to effectively apply data mining, however, and ensure the use of private healthcare information, issues like data privacy and security must be carefully considered. Data mining continues to be vital for searching for more effective, efficient, and individualized healthcare solutions as technology evolves.

Keywords: data mining, healthcare, big data, individualised healthcare, healthcare solutions, database

Procedia PDF Downloads 50

Authors: Patience Manjengwa-Hungwe, Carmen White

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Background: Odyssean malaria refers to malaria acquired by infected mosquito bites from malaria endemic to non-endemic regions by mechanical modes of transport, such as airplanes, water vessels, trains and vehicles. Odyssean Malaria is rare and is characterised by absence of travel history to malaria endemic areas. As not anticipated in non-endemic areas, late diagnosis and treatment lead to a high case fatality rate. On 26 September 2014, the Outbreak Response Unit at the National Institute of Communicable Diseases was notified of a suspected death from Odyssean Malaria in Johannesburg, Gauteng Province, a non-endemic area. The main objective of this investigation was to identify the etiological agent's mode and source of transmission. Methods: Epidemiological surveys were conducted with the deceased’s family and clinical details were obtained from doctors who treated the victim in Southrand, Johannesburg. Blood samples were collected prior to death and sent to the National Health Laboratory Services, Johannesburg laboratory for a full blood count, urea electrolytes, creatinine, and C-reactive protein. Environmental assessments and entomological investigations, including collection of mosquito and larvae, were conducted at the deceased’s home and surrounding areas and sent to the laboratory for analysis. Results: Epidemiological surveys revealed no travel history, no mechanical transmission through blood transfusion and no previous possible exposure of the victim to malaria mosquitoes. Laboratory findings indicated that the platelet count was low. A further smear revealed that the malaria parasite was present and malaria antigen for P. falciparum was positive. Entomological findings revealed that none of the six adult or larval mosquitoes collected on site were malaria vectors. Dumping sites found at the back of the house were identified as possible sites where mosquitoes from endemic places could possibly breed. Conclusion: Given that there was no travel history or the possibility of mechanical transmission (blood transfusion or needle), the research team concluded that it is highly probable that the infection was acquired through an infective Anopheles mosquito inadvertently translocated from a Malaria endemic area by mechanical modes of transport. We recommend that clinicians in non-endemic malaria areas be aware of this type of malaria and test for malaria in patients showing malaria-like symptoms.

Keywords: Odyssean Malaria, vector Bourne, malaria, epidemiological surveys

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Authors: Yunus Doğan, Ahmet Durap

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Coastal regions are the one of the most commonly used places by the natural balance and the growing population. In coastal engineering, the most valuable data is wave behaviors. The amount of this data becomes very big because of observations that take place for periods of hours, days and months. In this study, some statistical methods such as the wave spectrum analysis methods and the standard statistical methods have been used. The goal of this study is the discovery profiles of the different coast areas by using these statistical methods, and thus, obtaining an instance based data set from the big data to analysis by using data mining algorithms. In the experimental studies, the six sample data sets about the wave behaviors obtained by 20 minutes of observations from Mersin Bay in Turkey and converted to an instance based form, while different clustering techniques in data mining algorithms were used to discover similar coastal places. Moreover, this study discusses that this summarization approach can be used in other branches collecting big data such as medicine.

Keywords: clustering algorithms, coastal engineering, data mining, data summarization, statistical methods

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Authors: Anny Retnowati, Elisabeth Sundari

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This research aims to elaborate the meaning of personal data protection principles on patient access to health data in medical records in Indonesia and its implications. The method uses normative legal research by examining health law in Indonesia regarding the patient's right to access their health data in medical records. The data will be analysed qualitatively using the interpretation method to elaborate on the limitation of the meaning of personal data protection principles on patients' access to their data in medical records. The results show that patients only have the right to obtain copies of their health data in medical records. There is no right to inspect directly at any time. Indonesian health law limits the principle of patients' right to broad access to their health data in medical records. This restriction has implications for the reduction of personal data protection as part of human rights. This research contribute to show that a limitaion of personal data protection may abuse the human rights.

Keywords: access, health data, medical records, personal data, protection

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Authors: Martin Böhmer, Agatha Dabrowski, Boris Otto

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The trend of digitization significantly changes the role of data for enterprises. Data turn from an enabler to an intangible organizational asset that requires management and qualifies as a tradeable good. The idea of a networked economy has gained momentum in the data domain as collaborative approaches for data management emerge. Traditional organizational knowledge consequently needs to be extended by comprehensive knowledge about data. The knowledge about data is vital for organizations to ensure that data quality requirements are met and data can be effectively utilized and sovereignly governed. As this specific knowledge has been paid little attention to so far by academics, the aim of the research presented in this paper is to conceptualize it by proposing a “data knowledge model”. Relevant model entities have been identified based on a design science research (DSR) approach that iteratively integrates insights of various industry case studies and literature research.

Keywords: data management, digitization, industry 4.0, knowledge engineering, metamodel

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Authors: Wenqing Fan, Yixuan Cheng, Wei Huang

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The diversity and complexity of modern IT systems make it almost impossible for internal teams to find vulnerabilities in all software before the software is officially released. The emergence of threat intelligence and vulnerability reporting policy has greatly reduced the burden on software vendors and organizations to find vulnerabilities. However, to prove the existence of the reported vulnerability, it is necessary but difficult for security incident response team to build a deliberated vulnerable environment from the vulnerability report with limited and incomplete information. This paper presents a structured, standardized, machine-oriented vulnerability intelligence format, that can be used to automate the orchestration of Deliberated Vulnerable Environment (DVE). This paper highlights the important role of software configuration and proof of vulnerable specifications in vulnerability intelligence, and proposes a triad model, which is called DIR (Dependency Configuration, Installation Configuration, Runtime Configuration), to define software configuration. Finally, this paper has also implemented a prototype system to demonstrate that the orchestration of DVE can be automated with the intelligence.

Keywords: DIR triad model, DVE, vulnerability intelligence, vulnerability recurrence

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Authors: Raphael C. Zero, Marita V. Cardozo, Thiago A. S. S. Rocha, Mariana T. Kihara, Fernando A. Ávila, Fabrício S. Oliveira

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There are several fixative and preservative solutions for use on cadavers, many of them using formaldehyde as the fixative or anatomical part preservative. In some countries, such as Brazil, this toxic agent has been increasingly restricted. The objective of this study was to microbiologically identify and quantify the key agents in tanks containing 96GL ethanol or sodium chloride solutions, used respectively as fixatives and preservatives of cat cadavers. Eight adult cat corpses, three females and five males, with an average weight of 4.3 kg, were used. After injection via the external common carotid artery (120 ml/kg, 95% 96GL ethyl alcohol and 5% pure glycerin), the cadavers were fixed in a plastic tank with 96GL ethanol for 60 days. After fixing, they were stored in a 30% sodium chloride aqueous solution for 120 days in a similar tank. Samples were collected at the start of the experiment - before the animals were placed in the ethanol tanks, and monthly thereafter. The bacterial count was performed by Pour Plate Method in BHI agar (Brain Heart Infusion) and the plates were incubated aerobically and anaerobically for 24h at 37ºC. MacConkey agar, SPS agar (Sulfite Polymyxin Sulfadizine) and MYP Agar Base were used to isolate the microorganisms. There was no microbial growth in the samples prior to alcohol fixation. After 30 days of fixation in the alcohol solution, total aerobic and anaerobic (<1.0 x 10 CFU/ml) were found and Pseudomonas sp., Staphylococcus sp., Clostridium sp. were the identified agents. After 60 days in the alcohol fixation solution, total aerobes (<1.0 x 10 CFU/ml) and total anaerobes (<2.2 x 10 CFU/mL) were found, and the identified agents were the same. After 30 days of storage in the aqueous solution of 30% sodium chloride, total aerobic (<5.2 x 10 CFU/ml) and total anaerobes (<3.7 x 10 CFU/mL) were found and the agents identified were Staphylococcus sp., Clostridium sp., and fungi. After 60 days of sodium chloride storage, total aerobic (<3.0 x 10 CFU / ml) and total anaerobes (<7.0 x 10 CFU/mL) were found and the identified agents remained the same: Staphylococcus sp., Clostridium sp., and fungi. The microbiological count was low and visual inspection did not reveal signs of contamination in the tanks. There was no strong odor or purification, which proved the technique to be microbiologically effective in fixing and preserving the cat cadavers for the four-month period in which they are provided to undergraduate students of University of Veterinary Medicine for surgery practice. All experimental procedures were approved by the Municipal Legal Department (protocol 02.2014.000027-1). The project was funded by FAPESP (protocol 2015-08259-9).

Keywords: anatomy, fixation, microbiology, small animal, surgery

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Authors: Debjani Dasgupta, Aman Zalawadia, Anuj Thapa, Pranjali Sing, Ashish Dabade

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Organophosphate group pesticides are common pesticide group, which gain entry into food product due to incomplete removal of pesticide residues. The current food industry raw material handling process is not sufficient to eliminate pesticide residues. A neutralizer was used to neutralize the residues of pesticide on Vitis vinifera (Grapes). The water based dilution of neutralizer was demonstrated on fruits like grapes. Analysis for pesticides in water wash and neutralizer wash was carried out using GCMS. Fruits washed with neutralizer exhibited 72.95% removal of pesticides compared with normal water wash method. An economical chemical neutralizer can be used to remove such residues in raw material handling at industrial scale with minor modification in process to achieve minimum pesticide entry into final food products.

Keywords: GCMS, organophosphate, raw material handling, Vitis vinifera, pesticide neutralizer

Procedia PDF Downloads 256

Authors: J-C. Leneveu, A. Chereau, L. Mansart, T. Mesbah, M. Wyka

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Extracting and interpreting information from Big Data represent a stake for years to come in several sectors such as finance. Currently, numerous methods are used (such as Technical Analysis) to try to understand and to anticipate market behavior, with mixed results because it still seems impossible to exactly predict a financial trend. The increase of available data on Internet and their diversity represent a great opportunity for the financial world. Indeed, it is possible, along with these standard financial data, to focus on exogenous data to take into account more macroeconomic factors. Coupling the interpretation of these data with standard methods could allow obtaining more precise trend predictions. In this paper, in order to observe the influence of exogenous data price independent of other usual effects occurring in classical markets, behaviors of Bitcoin users are introduced in a model reconstituting Bitcoin value, which is elaborated and tested for prediction purposes.

Keywords: big data, bitcoin, data mining, social network, financial trends, exogenous data, global economy, behavioral finance

Procedia PDF Downloads 341

Authors: Arum Kingsley Chinedu, Ugwuowo Fidelis Ifeanyi, Oranye Henrietta Ebele

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The Poisson regression model (PRM) is a nonlinear model that belongs to the exponential family of distribution. PRM is suitable for studying count variables using appropriate covariates and sometimes experiences the problem of multicollinearity in the explanatory variables and outliers on the response variable. This study aims to address the problem of multicollinearity and outliers jointly in a Poisson regression model. We developed an estimator called the robust modified jackknife PCKL parameter estimator by combining the principal component estimator, modified jackknife KL and transformed M-estimator estimator to address both problems in a PRM. The superiority conditions for this estimator were established, and the properties of the estimator were also derived. The estimator inherits the characteristics of the combined estimators, thereby making it efficient in addressing both problems. And will also be of immediate interest to the research community and advance this study in terms of novelty compared to other studies undertaken in this area. The performance of the estimator (robust modified jackknife PCKL) with other existing estimators was compared using mean squared error (MSE) as a performance evaluation criterion through a Monte Carlo simulation study and the use of real-life data. The results of the analytical study show that the estimator outperformed other existing estimators compared with by having the smallest MSE across all sample sizes, different levels of correlation, percentages of outliers and different numbers of explanatory variables.

Keywords: jackknife modified KL, outliers, multicollinearity, principal component, transformed M-estimator.

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Authors: Jeroen S. de Bruin, Karin Schindler, Christian Schuh

Abstract:

With increasingly more mobile health applications appearing due to the popularity of smartphones, the possibility arises that these data can be used to improve the medical diagnostic process, as well as the overall quality of healthcare, while at the same time lowering costs. However, as of yet there have been no reports of a successful combination of patient-generated data from smartphones with data from clinical routine. In this paper, we describe how these two types of data can be combined in a secure way without modification to hospital information systems, and how they can together be used in a medical expert system for automatic nutritional classification and triage.

Keywords: mobile health, data integration, expert systems, disease-related malnutrition

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Authors: Shannon Steinmetz, Ellen Gethner

Abstract:

In this paper, a theoretical foundation is developed for patterned segmentation of audio using the geometry of music and statistical manifold. We demonstrate image content clustering using conic space sonification. The algorithm takes a geodesic curve as a model estimator of the three-parameter Gamma distribution. The random variable is parameterized by musical centricity and centric velocity. Model parameters predict audio segmentation in the form of duration and frame count based on the likelihood of musical geometry transition. We provide an example using a database of randomly selected images, resulting in statistically significant clusters of similar image content.

Keywords: sonification, musical information geometry, image, content extraction, automated quantification, audio segmentation, pattern recognition

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Authors: Sombol Mokhles

Abstract:

This paper tries to show the prospects of utilising (big)data for enabling just the transition of diverse cities. Our key purpose is to offer a framework of applications and implications of utlising (big) data in comparing sustainability transitions across different cities. Relying on the cosmopolitan comparison, this paper explains the potential application of (big) data but also its limitations. The paper calls for adopting a data-driven and just perspective in including different cities around the world. Having a just and inclusive approach at the front and centre ensures a just transition with synergistic effects that leave nobody behind.

Keywords: big data, just sustainable transition, cosmopolitan city comparison, cities

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Authors: Luke S. Carlos A. Thompson

Abstract:

The medical model of disability, though beneficial for the medical professional, is often exclusionary, restrictive and dehumanizing when applied to the lived experience of disability. As a result, a critique of this model was constructed called the social model of disability. Much of the language used to articulate the purpose behind the social model of disability can be summed up within the word inclusion. However, this essay asserts that inclusiveness is an incomplete aspiration. The social model, as it currently stands, does not aid in creating a society where those with impairments actually belong. Rather, the social model aids in lessening the visibility, or negative consequence of, difference. Therefore, the social model does not invite society to welcome those with physical and intellectual impairments. It simply aids society in ignoring the existence of impairment by removing explicit forms of exclusion. Rather than simple inclusion, then, this essay uses John Swinton’s concept of friendship and Jean Vanier’s understanding of belonging to better articulate the intended outcome of the social model—a society where everyone can belong.

Keywords: belong, community, differently-able, disability, exclusion, friendship, inclusion, normality

Procedia PDF Downloads 432