Search results for: negative data

Authors: Amara Rafik, Mostefa Belhadj Aissa

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Radar data is one of the many data sources used by ATM Air Traffic Management systems. These data come from air navigation radar antennas. These radars intercept signals emitted by the various aircraft crossing the controlled airspace and calculate the position of these aircraft and retransmit their positions to the Air Traffic Management System. For greater reliability, these radars are positioned in such a way as to allow their coverage areas to overlap. An aircraft will therefore be detected by at least one of these radars. However, the position coordinates of the same aircraft and sent by these different radars are not necessarily identical. Therefore, the ATM system must calculate a single position (radar track) which will ultimately be sent to the control position and displayed on the air traffic controller's monitor. There are several techniques for calculating the radar track. Furthermore, the geographical nature of the problem requires the use of a Geographic Information System (GIS), i.e. a geographical database on the one hand and geographical processing. The objective of this work is to propose a GIS for traffic simulation which reconstructs the evolution over time of aircraft positions from a multi-source radar data set and by applying these different techniques.

Keywords: ATM, GIS, radar data, simulation

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Authors: Itulelo Matiyabu Imaja, Manoj Maharaj, Patrick Ndayizigamiye

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Lack of transparency in public funds management is observed in many African countries. The DR Congo is among the most corrupted countries in Africa, and this is due mainly to lack of transparency and accountability in public funds management. Corruption has a negative effect on the welfare of the country’s citizens and the national economic growth. Public funds collection and allocation are the major areas whereby malpractices such as bribe, extortion, embezzlement, nepotism and other practices related to corruption are prevalent. Hence, there is a need to implement strong mechanisms to enforce transparency in public funds management. Many researchers have suggested some control mechanisms in curbing corruption in public funds management focusing mainly on law enforcement and administrative reforms with little or no insight on the role that ICT can play in preventing and curbing the corrupt behaviour. In the Democratic Republic of Congo (DRC), there are slight indications that the government of the DR Congo is integrating ICT to fight corruption in public funds collection and allocation. However, such government initiatives are at an infancy stage, with no tangible evidence on how ICT could be used effectively to address the issue of corruption in the context of the country. Hence, this research assesses the role that ICT can play for transparency in public funds management and suggest a framework for its adoption in the Democratic Republic of Congo. This research uses the revised Capability model (Capability, Empowerment, Sustainability model) as the guiding theoretical framework. The study uses the exploratory design methodology coupled with a qualitative approach to data collection and purposive sampling as sampling strategy.

Keywords: corruption, DR congo, ICT, management, public funds, transparency

Procedia PDF Downloads 344

Authors: J. K. D. M. P. Madara, R. B. L. Dharmawickreme, Linu John, Ivee Boiss

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Flemingia vestita, commonly known as ‘Sohphlang’ is an important medicinal plant found in the North-Eastern region of India, which is traditionally recognized for its anthelmintic properties. This study was aimed to evaluate the phytochemical constituents and antibacterial activity of the tuber skin extracts of the plant species. Methanol, acetone, and water were used to obtain the solvent extractions of the skin peel extracts. Concentrated extracts of skin peel were tested using previously established qualitative phytochemical assays. The antibacterial efficacy of methanol tuber skin extract was tested against Gram-negative and positive microorganisms, namely, Klebsiella pneumonia, Escherichia coli, Pseudomonas aeruginosa, Bacillus subtilis, and Mycobacterium tuberculosis strains. Agar well diffusion method was employed to determine the zone of inhibition of the plant extracts. Obtained data were statistically analyzed. Methanol extracts of Flemingia vestita were found to be effective against Bacillus subtilis and Mycobacterium tuberculosis at concentrations of 0.5 mg/ml. The reported zone of inhibition for the two strains was 13.3mm ± 0.57 and 16.3mm ± 4.9, respectively. However Klebsiella pneumoniae, Pseudomonas aeruginosa and Escherichia coli were resistant to the plant extracts with no zone of inhibition. Alkaloids, glycosides, and phenols were found to be present in aqueous, methanol, and acetone extracts of the plant in qualitative phytochemical analysis.

Keywords: flemingia vestita, antibacterial activity, phytochemical screening, well diffusion method

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Authors: Meng-Tzu Cheng, Louisa Rosenheck, Chen-Yen Lin, Eric Klopfer

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The purpose of the research is to explore some of the ways in which gameplay data can be analyzed to yield results that feedback into the learning ecosystem. Back-end data for all users as they played an MMOG, The Radix Endeavor, was collected, and this study reports the analyses on a specific genetics quest by using the data mining techniques, including the decision tree method. In the study, different reasons for quest failure between participants who eventually succeeded and who never succeeded were revealed. Regarding the in-game tools use, trait examiner was a key tool in the quest completion process. Subsequently, the results of decision tree showed that a lack of trait examiner usage can be made up with additional Punnett square uses, displaying multiple pathways to success in this quest. The methods of analysis used in this study and the resulting usage patterns indicate some useful ways that gameplay data can provide insights in two main areas. The first is for game designers to know how players are interacting with and learning from their game. The second is for players themselves as well as their teachers to get information on how they are progressing through the game, and to provide help they may need based on strategies and misconceptions identified in the data.

Keywords: MMOG, decision tree, genetics, gaming-learning interaction

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Authors: Marwa Hussien Mohamed, Mohamed Helmy Khafagy

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Map-Reduce is a programming model which is widely used to extract valuable information from enormous volumes of data. Map-reduce designed to support heterogeneous datasets. Apache Hadoop map-reduce used extensively to uncover hidden pattern like data mining, SQL, etc. The most important operation for data analysis is joining operation. But, map-reduce framework does not directly support join algorithm. This paper explains and compares two-way and multi-way map-reduce join algorithms for map reduce also we implement MR join Algorithms and show the performance of each phase in MR join algorithms. Our experimental results show that map side join and map merge join in two-way join algorithms has the longest time according to preprocessing step sorting data and reduce side cascade join has the longest time at Multi-Way join algorithms.

Keywords: Hadoop, MapReduce, multi-way join, two-way join, Ubuntu

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Authors: Aurelija Burinskienė, Dalė Dzemydienė, Arūnas Miliauskas

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This research aims at developing the approach for more effective freight delivery and transportation process management. The road congestions and the identification of causes are important, as well as the context information recognition and management. The measure of many parameters during the transportation period and proper control of driver work became the problem. The number of vehicles per time unit passing at a given time and point for drivers can be evaluated in some situations. The collection of data is mainly used to establish new trips. The flow of the data is more complex in urban areas. Herein, the movement of freight is reported in detail, including the information on street level. When traffic density is extremely high in congestion cases, and the traffic speed is incredibly low, data transmission reaches the peak. Different data sets are generated, which depend on the type of freight delivery network. There are three types of networks: long-distance delivery networks, last-mile delivery networks and mode-based delivery networks; the last one includes different modes, in particular, railways and other networks. When freight delivery is switched from one type of the above-stated network to another, more data could be included for reporting purposes and vice versa. In this case, a significant amount of these data is used for control operations, and the problem requires an integrated methodological approach. The paper presents an approach for providing e-services for drivers by including the assessment of the multi-component infrastructure needed for delivery of freights following the network type. The construction of such a methodology is required to evaluate data flow conditions and overloads, and to minimize the time gaps in data reporting. The results obtained show the possibilities of the proposing methodological approach to support the management and decision-making processes with functionality of incorporating networking specifics, by helping to minimize the overloads in data reporting.

Keywords: transportation networks, freight delivery, data flow, monitoring, e-services

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Authors: Jumana Majdi Qutub

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Rationale: Cloud computing has rapidly developed the past few years. Because of the importance of providing protection for personal data used in cloud computing, the role of data protection in promoting trust and confidence in users’ data has become an important policy priority. This research examines key regulatory challenges rose by the growing use and importance of cloud computing with focusing on protection of individuals personal data. Methodology: Describing and analyzing governance challenges facing policymakers and industry in Saudi Arabia, with an account of anticipated governance responses. The aim of the research is to describe and define the regulatory challenges on cloud computing for policy making in Saudi Arabia and comparing it with potential complied issues rose in respect of transported data to EU member state. In addition, it discusses information privacy issues. Finally, the research proposes policy recommendation that would resolve concerns surrounds the privacy and effectiveness of clouds computing frameworks for data protection. Results: There are still no clear regulation in Saudi Arabia specialized in legalizing cloud computing and specialty regulations in transferring data internationally and locally. Decision makers need to review the applicable law in Saudi Arabia that protect information in cloud computing. This should be from an international and a local view in order to identify all requirements surrounding this area. It is important to educate cloud computing users about their information value and rights before putting it in the cloud to avoid further legal complications, such as making an educational program to prevent giving personal information to a bank employee. Therefore, with many kinds of cloud computing services, it is important to have it covered by the law in all aspects.

Keywords: cloud computing, cyber crime, data protection, privacy

Procedia PDF Downloads 254

Authors: Mesbaholdin Salami, Farzad Movahedi Sobhani, Mohammad Sadegh Ghazizadeh

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Evaluation of organizational performance is among the most important measures that help organizations and entities continuously improve their efficiency. Organizations can use the existing data and results from the comparison of units under investigation to obtain an estimation of their performance. The Malmquist Productivity Index (MPI) is an important index in the evaluation of overall productivity, which considers technological developments and technical efficiency at the same time. This article proposed a model based on the multistage MPI, considering limited data (Grey’s theory). This model can evaluate the performance of units using limited and uncertain data in a multistage process. It was applied by the electricity market manager to Iran’s electric power supply chain (EPSC), which contains uncertain data, to evaluate the performance of its actors. Results from solving the model showed an improvement in the accuracy of future performance of the units under investigation, using the Grey’s system theory. This model can be used in all case studies, in which MPI is used and there are limited or uncertain data.

Keywords: Malmquist Index, Grey's Theory, CCR Model, network data envelopment analysis, Iran electricity power chain

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Authors: Rachna Jain, Sushila Madan, Bindu Garg

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Cloud computing is the key powerhouse in numerous organizations due to shifting of their data to the cloud environment. In recent years it has been observed that cloud-based-services are being used on large scale for content storage, distribution and processing. Various issues have been observed in cloud computing environment that need to be addressed. Security and privacy are found topmost concern area. In this paper, a novel security model is proposed to secure data by utilizing CDN services like image to icon conversion. CDN Service is a content delivery service which converts an image to icon, word to pdf & Latex to pdf etc. Presented model is used to convert an image into icon by keeping image secret. Here security of image is imparted so that image should be encrypted and decrypted by data owners only. It is also discussed in the paper that how server performs multiplication and selection on encrypted data without decryption. The data can be image file, word file, audio or video file. Moreover, the proposed model is capable enough to multiply images, encrypt them and send to a server application for conversion. Eventually, the prime objective is to encrypt an image and convert the encrypted image to image Icon by utilizing homomorphic encryption.

Keywords: cloud computing, user data security, homomorphic encryption, image multiplication, CDN service

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Authors: Mohammad Zainie Bin Hassan

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Background: Erectile dysfunction (ED) is a complication commonly seen among men with diabetes which can be assessed based upon International Index of Erectile Function (IIEF-5) questionnaire. Creatinine level is a blood test that indicates kidney functionality. Object: To evaluate the association between ED, determined by the IIEF-5scores and Creatinine level in diabetic men attending Temerloh Health Clinic, Pahang, Malaysia.Hence, to identify raising Creatinine level related with ED or not. Methods: All married diabetic patients will be investigated face to face after consented for answering the IIEF-5 questionnaire. Creatinine level will be taken by using standard method.Patients with no sexual partner, refuse to answer the questionnaire, cancer, stroke, heart disease and language barrier will be excluded.Data obtained from IIEF-5 score and Creatinine level will be analyzed by using Pearson correlation. All statistical value determined by p=0.05. ED will be categorized accordingly to IIEF-5 scores: no ED (22-25), mild (17-21), moderate (12-16), severe (8-11) and very severe (1-7). Results: A total of 450 patients were investigated with 385 patients were included (85.6% respondant rate) and 65 patients were excluded in this study with age range from 29 to 85 years old. 7% had no ED, 28% mild ED, 34% moderate ED, 16% severe ED and 15% had very severe ED. There was a significant negative correlation between Creatinine level and IIEF-5 scores (r=-0.218, p <0.001). This result implicated that poor kidney function which indicated by high Creatinine level associated significantly with erectile dysfunction. 93% had ED with a different range of severity which triggers for appropriate aggressive ED management among diabetics. Conclusion: The high level of Creatinine is associated with erectile dysfunction among diabetics in Temerloh Health Clinic.

Keywords: correlation, creatinine level, erectile dysfunction, ED, diabetes

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Authors: Peter B. M. Vranas

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An important question in moral philosophy is whether whatever is obligatory (i.e., morally required) is personally obligatory, namely obligatory for someone. A positive answer is uncontested in the literature: for example, if it is obligatory for you to keep your promises, it seems that it is obligatory for you you keep your promises. By using conceptual analysis, this paper defends a negative answer: some things are impersonally obligatory, namely obligatory, but not obligatory for anyone. For example, if each of us has promised to vote and thus has an obligation to vote, then it is obligatory that we all vote, but it is not obligatory for anyone that we all vote (because, for example, what is obligatory for you is that you vote, not that we all vote). The paper concludes that there is an important concept of impersonal obligatoriness irreducible to personal obligatoriness.

Keywords: impersonal obligatoriness, ought to be, ought to do, personal obligatoriness

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Authors: Lubabatu Sada Sodangi

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The rapid growth in exchange and accessibility of information via the internet makes many organisations acquire data on their own operation. The aim of data mining is to analyse the different behaviour of a dataset using observation. Although, the subset of the dataset being analysed may not display all the behaviours and relationships of the entire data and, therefore, may not represent other parts that exist in the dataset. There is a range of techniques used in data mining to determine the hidden or unknown information in datasets. In this paper, the performance of two algorithms Chi-Square Automatic Interaction Detection (CHAID) and multilayer perceptron (MLP) would be matched using an Adult dataset to find out the percentage of an/the adults that earn > 50k and those that earn <= 50k per year. The two algorithms were studied and compared using IBM SPSS statistics software. The result for CHAID shows that the most important predictors are relationship and education. The algorithm shows that those are married (husband) and have qualification: Bachelor, Masters, Doctorate or Prof-school whose their age is > 41<57 earn > 50k. Also, multilayer perceptron displays marital status and capital gain as the most important predictors of the income. It also shows that individuals that their capital gain is less than 6,849 and are single, separated or widow, earn <= 50K, whereas individuals with their capital gain is > 6,849, work > 35 hrs/wk, and > 27yrs their income will be > 50k. By comparing the two algorithms, it is observed that both algorithms are reliable but there is strong reliability in CHAID which clearly shows that relation and education contribute to the prediction as displayed in the data visualisation.

Keywords: data mining, CHAID, multi-layer perceptron, SPSS, Adult dataset

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Authors: Parul Suraia, Harshit Sosan Lakra

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Indigenous communities represent some of the most marginalized populations globally, with India labeled as tribals, experiencing particularly pronounced marginalization and a concerning decline in their numbers. These communities often inhabit geographically challenging regions characterized by low population densities, posing significant challenges to providing essential infrastructure services. Jharkhand, a Schedule 5 state, is infamous for its low-level health status due to disparities in access to health care. The primary objective of this study is to investigate the spatial inequalities in healthcare accessibility among tribal populations within the state and pinpoint critical areas requiring immediate attention. Health indicators were selected based on the tribal perspective and association of Sustainable Goal 3 (Good Health and Wellbeing) with other SDGs. Focused group discussions in which tribal people and tribal experts were done in order to finalize the indicators. Employing Principal Component Analysis, two essential indices were constructed: the Tribal Health Index (THI) and the Tribal Health Intervention Index (THII). Index values were calculated based on the district-wise secondary data for Jharkhand. The bivariate spatial association technique, Moran’s I was used to assess the spatial pattern of the variables to determine if there is any clustering (positive spatial autocorrelation) or dispersion (negative spatial autocorrelation) of values across Jharkhand. The results helped in facilitating targeting policy interventions in deprived areas of Jharkhand.

Keywords: tribal health, health spatial disparities, health status, Jharkhand

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Authors: Reinaldo Biscaro, Luciano R. Ferreira, Leonardo C. Biscaro, Raphael C. Biscaro, Isabela S. Vasconcelos, Laura C. R. Ferreira, Cristiano M. Galhardi, Erica P. Baciuk

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Introduction: The present study had the objective to present the profile of workers with shoulder disorders related to labor lawsuits in Brazil. The study analyzed the association between the worker’s health and the actions performed by the companies related to injured professional. The research method performed a retrospective, cross-sectional and quantitative database analysis. The documents of labor lawsuits with shoulder injury registered at the Regional Labor Court in the 15^th region (Campinas - São Paulo) were submitted to the medical examination and evaluated during the period from 2012 until 2015. The data collected were age, gender, onset of symptoms, length of service, current occupation, type of shoulder injury, referred complaints, type of acromion, associated or related diseases, company actions as CAT (workplace accident communication), compliance of NR7 by the organization (Environmental Risk Prevention Program - PPRA and Medical Coordination Program in Occupational Health - PCMSO). Results: From the 93 workers evaluated, there was a prevalence of men (58.1%), with a mean age of 42.6 y-o, and 54.8% were included in the age group 35-49 years. Regarding the length of work time in the company, 66.7% have worked for more than 5 years. There was an association between gender and current occupational status (p < 0.005), with predominance of women in household occupation (13 vs. 2) and predominance of unemployed men in job search situation (24 vs. 10) and reintegrated to work by judicial decision (8 vs. 2). There was also a correlation between pain and functional limitation (p < 0.01). There was a positive association of PPRA with the complaint of functional limitation and negative association with pain (p < 0.04). There was also a correlation between the sedentary lifestyle and the presence of PCMSO and PPRA (p < 0.04), and the absence of CAT in the companies (p < 0.001). It was concluded that the appearance or aggravation of osseous and articular shoulder pathologies in workers who have undertaken labor law suits seem to be associated with individual habits or inadequate labor practices. These data can help preventing the occurrence of these lesions by implementing local health promotion policies at work.

Keywords: work-related accidents, cross-sectional study, shoulder lesions, labor lawsuits

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Authors: Jae Hyun Lee

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Manufacturing companies use life-cycle inventory databases to analyze sustainability of their manufacturing processes. Life cycle inventory data provides reference data which may not be accurate for a specific company. Collecting accurate data of manufacturing processes for a specific company requires enormous time and efforts. An information model of typical manufacturing processes can reduce time and efforts to get appropriate reference data for a specific company. This paper shows an attempt to build an abstract information model which can be used to develop information models for specific manufacturing processes.

Keywords: process information model, sustainability, OWL, manufacturing

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Authors: D.Mendes, J. Henriques, P. Carvalho, T. Rocha, S. Paredes, R. Cabiddu, R. Trimer, R. Mendes, A. Borghi-Silva, L. Kaminsky, E. Ashley, R. Arena, J. Myers

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The continued exploration of clinically relevant predictive models continues to be an important pursuit. Cardiorespiratory fitness (CRF) portends clinical vital information and as such its accurate prediction is of high importance. Therefore, the aim of the current study was to develop a data-driven model, based on computational intelligence techniques and, in particular, clustering approaches, to predict CRF. Two prediction models were implemented and compared: 1) the traditional Wasserman/Hansen Equations; and 2) an interpretable clustering approach. Data used for this analysis were from the 'FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base'; in the present study a subset of 10690 apparently healthy individuals were utilized. The accuracy of the models was performed through the computation of sensitivity, specificity, and geometric mean values. The results show the superiority of the clustering approach in the accurate estimation of CRF (i.e., maximal oxygen consumption).

Keywords: cardiorespiratory fitness, data-driven models, knowledge extraction, machine learning

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Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

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Authors: R. McGrath, A. Trace, S. Curtin, C. McCreary

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Introduction: Although, in relation to Burning Mouth Syndrome (BMS), much energy has been expended on its definition and etiology, it still remains a contentious issue. There is agreement on the symptoms, but on little else; and approaches to treatment vary widely. However, it has been established that the condition has a detrimental effect on the sufferer’s quality of life. Much research focus has been put on the physical impact of the syndrome. Recently, some literature has turned the focus to social, functional, and psychological factors. However, there is very little qualitative research on how burning mouth syndrome affects the lives of sufferer’s and the present study seeks to remedy this. Method: The study recruited five male participants who took part in semi-structured interviews lasting between 30 and 50 minutes. Data was analysed using Interpretative Phenomenological Analysis. Results: The study identified four super-ordinate themes: Lack of Control due to Uncertainty about Condition; Disruption to Internal Sense of Self; Negative Future Expectation due to Chronic Symptoms; and Sense of BMS as an Intrusive Force. Aspects of these themes reflect areas of reduction in quality of life. Conclusion: BMS damages an individual’s quality of life in ways that have not been reflected in self-report surveys of health-related quality of life. The condition has serious implications for the individual's sense of self, identity, and future. The study recommends that further qualitative research be carried out in this area. Also, the use of therapeutic interventions with sufferers from BMS is recommended, which would help not only sufferers but best practice in relation to their treatment.

Keywords: burning mouth syndrome, interpretative phenomenological analysis, qualitative research, quality of life

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Authors: Rania Alshikhe, Vinita Jindal

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Digital innovation has played a crucial role in managing smart transportation. For this, big trajectory data collected from traveling vehicles, such as taxis through installed global positioning system (GPS)-enabled devices can be utilized. It offers an unprecedented opportunity to trace the movements of vehicles in fine spatiotemporal granularity. This paper aims to explore big trajectory data to measure the travel efficiency of road networks using the proposed statistical travel efficiency measure (STEM) across an entire city. Further, it identifies the cause of low travel efficiency by proposed least square approximation network-based causality exploration (LANCE). Finally, the resulting data analysis reveals the causes of low travel efficiency, along with the road segments that need to be optimized to improve the traffic conditions and thus minimize the average travel time from given point A to point B in the road network. Obtained results show that our proposed approach outperforms the baseline algorithms for measuring the travel efficiency of the road network.

Keywords: GPS trajectory, road network, taxi trips, digital map, big data, STEM, LANCE

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Authors: Lubica Hikkerova, Jean-Michel Sahut

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Environmental protection remains a global challenge but, since 2012, there has been a progressive decline in corporate engagement in environmental protection issues. This study seeks to investigate the role of financial capacity and governance in improving the level of environmental engagement of companies. The regression technique is applied to data on 351 large European companies from the ASSET4-ESG database for the 2007-2015 period. Firstly, the results show that the companies in the sample are fairly engaged in environmental protection, with a strong dispersion representing nearly four times the average. This means that the companies in the sample do not share the same level of engagement in matters of environmental protection, some being more committed than others. Secondly, the results reveal that the financial capacity of the company, as assessed through its indicators, has a significant effect on its level of environmental protection engagement in the present sample. This effect is more positive the higher the profits the company makes, and more negative the more heavily indebted or, the higher the rates of dividends it pays per share. Lastly, the results also show that a better quality of governance plays an important role in the decision to undertake actions leading to environmental protection. More specifically, the degree of management implication in the running of the business, the respect of the rights of the shareholders, the effectiveness of the control exerted by the board of directors, and, to a lesser extent, the independence of the audit committee, are variables which have a positive and significant influence on the level of environmental engagement of companies.

Keywords: financial capacity, corporate governance, environmental engagement, stakeholder theory, theory of organizational legitimacy, theory of resources and capabilities

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Authors: Mani Venkatesh, Catarina Delgado, Purvishkumar Patel

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The sustainable supply chain is gaining popularity among practitioners because of increased environmental degradation and stakeholder awareness. On the other hand supply chain, risk management is very crucial for the practitioners as it potentially disrupts supply chain operations. Prediction and addressing the risk caused by social issues in the supply chain is paramount importance to the sustainable enterprise. More recently, the usage of Big data analytics for forecasting business trends has been gaining momentum among professionals. The aim of the research is to explore the application of big data, predictive analytics in successfully mitigating supply chain social risk and demonstrate how such mitigation can help in achieving sustainability (environmental, economic & social). The method involves the identification and validation of social issues in the supply chain by an expert panel and survey. Later, we used a case study to illustrate the application of big data in the successful identification and mitigation of social issues in the supply chain. Our result shows that the company can predict various social issues through big data, predictive analytics and mitigate the social risk. We also discuss the implication of this research to the body of knowledge and practice.

Keywords: big data, sustainability, supply chain social sustainability, social risk, case study

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Authors: Elias K. Maragos, Petros E. Maravelakis

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In Dynamic Data Envelopment Analysis (DDEA), which is a subfield of Data Envelopment Analysis (DEA), the productivity of Decision Making Units (DMUs) is considered in relation to time. In this case, as it is accepted by the most of the researchers, there are outputs, which are produced by a DMU to be used as inputs in a future time. Those outputs are known as intermediates. The common models, in DDEA, do not take into account the shape of the distribution of those inputs, outputs or intermediates data, assuming that the distribution of the virtual value of them does not deviate from linearity. This weakness causes the limitation of the accuracy of the analytical power of the traditional DDEA models. In this paper, the authors, using the concept of piecewise linear inputs and outputs, propose an extended DDEA model. The proposed model increases the flexibility of the traditional DDEA models and improves the measurement of the dynamic performance of DMUs.

Keywords: Dynamic Data Envelopment Analysis, DDEA, piecewise linear inputs, piecewise linear outputs

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Authors: Mohamed El Horri, Amine Mouden, Reda Messaoudi, Mohamed Chekkal, Driss Benlaldj, Malika Baghdadi, Lahcene Benmahdi, Fatima Seghier

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Background/aim: Recently, the Von Willebrand factor antigen (vWF-Ag)has been identified as a new marker of portal hypertension (PH) and its complications. Few studies talked about its role in the prediction of esophageal varices. VWF-Ag is considered a non-invasive approach, In order to avoid the endoscopic burden, cost, drawbacks, unpleasant and repeated examinations to the patients. In our study, we aimed to evaluate the ability of this marker in the prediction of another complication of portal hypertension, which is portal hypertensive gastropathy (PHG), the one that is diagnosed also by endoscopic tools. Patients and methods: It is about a prospective study, which include 124 cirrhotic patients with no history of bleeding who underwent screening endoscopy for PH-related complications like esophageal varices (EVs) and PHG. Routine biological tests were performed as well as the VWF-Ag testing by both ELFA and Immunoturbidimetric techniques. The diagnostic performance of our marker was assessed using sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic curves. Results: 124 patients were enrolled in this study, with a mean age of 58 years [CI: 55 – 60 years] and a sex ratio of 1.17. Viral etiologies were found in 50% of patients. Screening endoscopy revealed the presence of PHG in 20.2% of cases, while for EVsthey were found in 83.1% of cases. VWF-Ag levels, were significantly increased in patients with PHG compared to those who have not: 441% [CI: 375 – 506], versus 279% [CI: 253 – 304], respectively (p <0.0001). Using the area under the receiver operating characteristic curve (AUC), vWF-Ag was a good predictor for the presence of PHG. With a value higher than 320% and an AUC of 0.824, VWF-Ag had an 84% sensitivity, 74% specificity, 44.7% positive predictive value, 94.8% negative predictive value, and 75.8% diagnostic accuracy. Conclusion: VWF-Ag is a good non-invasive low coast marker for excluding the presence of PHG in patients with liver cirrhosis. Using this marker as part of a selective screening strategy might reduce the need for endoscopic screening and the coast of the management of these kinds of patients.

Keywords: von willebrand factor, portal hypertensive gastropathy, prediction, liver cirrhosis

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Authors: Nidhi S. Chandra, Veena Agrawal, Debprasad Chattopadhyay

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Background: Hepatitis E virus (HEV) is a major cause of acute viral hepatitis in developing countries. It spreads feco orally mainly due to contamination of drinking water by sewage. There is limited data on the genotypic comparison of HEV isolates from sewage water and humans. The aim of this study was to identify genotype and conduct phylogenetic analysis of HEV isolates from sewage water and humans. Materials and Methods: 14 sewage water and 60 serum samples from acute sporadic hepatitis E cases (negative for hepatitis A, B, C) were tested for HEV-RNA by nested polymerase chain reaction (RTnPCR) using primers designed with in RdRp (RNA dependent RNA polymerase) region of open reading frame-1 (ORF-1). Sequencing was done by ABI prism 310. The sequences (343 nucleotides) were compared with each other and were aligned with previously reported HEV sequences obtained from GeneBank, using Clustal W software. A Phylogenetic tree was constructed by using PHYLIP version 3.67 software. Results: HEV-RNA was detected in 49/ 60 (81.67%) serum and 5/14 (35.71%) sewage samples. The sequences obtained from 17 serums and 2 sewage specimens belonged to genotype I with 85% similarity and clustering with previously reported human HEV sequences from India. HEV isolates from human and sewage in North West India are genetically closely related to each other. Conclusion: These finding suggest that sewage acts as reservoir of HEV. Therefore it is important that measures are taken for proper waste disposal and treatment of drinking water to prevent outbreaks and epidemics due to HEV.

Keywords: hepatitis E virus, nested polymerase chain reaction, open reading frame-1, nucleotidies

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Authors: Wi Sung Yoo, Seung Woo Lee, Youn Kyoung Hur, Sung Hwan Kim

Abstract:

Large-scale construction projects are continuously increasing, and the need for tools to monitor and evaluate the project success is emphasized. At the construction industry level, there are limitations in deriving performance evaluation factors that reflect the diversity of construction sites and systems that can objectively evaluate and manage performance. Additionally, there are difficulties in integrating structured and unstructured data generated at construction sites and deriving improvements. In this study, we propose the Key Performance Indicators (KPIs) to enable performance evaluation that reflects the increased diversity of construction sites and the unstructured data generated, and present a model for measuring performance by the derived indicators. The comprehensive performance of a unit construction site is assessed based on 6 areas (Time, Cost, Quality, Safety, Environment, Productivity) and 26 indicators. We collect performance indicator information from 30 construction sites that meet legal standards and have been successfully performed. And We apply data augmentation and optimization techniques into establishing measurement standards for each indicator. In other words, the KPI for construction site performance evaluation presented in this study provides standards for evaluating performance in six areas using institutional requirement data and document data. This can be expanded to establish a performance evaluation system considering the scale and type of construction project. Also, they are expected to be used as a comprehensive indicator of the construction industry and used as basic data for tracking competitiveness at the national level and establishing policies.

Keywords: key performance indicator, performance measurement, structured and unstructured data, data augmentation

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Authors: N. Borjalilu, P. Rabiei, A. Enjoo

Abstract:

Flight Data Monitoring (FDM) program assists an operator in aviation industries to identify, quantify, assess and address operational safety risks, in order to improve safety of flight operations. FDM is a powerful tool for an aircraft operator integrated into the operator’s Safety Management System (SMS), allowing to detect, confirm, and assess safety issues and to check the effectiveness of corrective actions, associated with human errors. This article proposes a model for safety risk assessment level of flight data in a different aspect of event focus based on fuzzy set values. It permits to evaluate the operational safety level from the point of view of flight activities. The main advantages of this method are proposed qualitative safety analysis of flight data. This research applies the opinions of the aviation experts through a number of questionnaires Related to flight data in four categories of occurrence that can take place during an accident or an incident such as: Runway Excursions (RE), Controlled Flight Into Terrain (CFIT), Mid-Air Collision (MAC), Loss of Control in Flight (LOC-I). By weighting each one (by F-TOPSIS) and applying it to the number of risks of the event, the safety risk of each related events can be obtained.

Keywords: F-topsis, fuzzy set, flight data monitoring (FDM), flight safety

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Authors: Valentin P. Velikov

Abstract:

Automatic program generation saves time, human resources, and allows receiving syntactically clear and logically correct modules. The 4-th generation programming languages are related to drawing the data and the processes of the subject area, as well as, to obtain a frame of the respective information system. The application can be separated in interface and business logic. That means, for an interactive generation of the needed system to be used an already existing toolkit or to be created a new one.

Keywords: computer science, graphical user interface, user dialog interface, dialog frames, data modeling, subject area modeling

Procedia PDF Downloads 298

Authors: Bo Wang

Abstract:

The geometric processing of multi-source remote sensing data using control data of different scale and different accuracy is an important research direction of multi-platform system for earth observation. In the existing block bundle adjustment methods, as the controlling information in the adjustment system, the approach using single observation scale and precision is unable to screen out the control information and to give reasonable and effective corresponding weights, which reduces the convergence and adjustment reliability of the results. Referring to the relevant theory and technology of quotient space, in this project, several subjects are researched. Multi-layer quotient space of multi-geometric features is constructed to describe and filter control data. Normalized granularity merging mechanism of multi-layer control information is studied and based on the normalized scale factor, the strategy to optimize the weight selection of control data which is less relevant to the adjustment system can be realized. At the same time, geometric positioning experiment is conducted using multi-source remote sensing data, aerial images, and multiclass control data to verify the theoretical research results. This research is expected to break through the cliché of the single scale and single accuracy control data in the adjustment process and expand the theory and technology of photogrammetry. Thus the problem to process multi-source remote sensing data will be solved both theoretically and practically.

Keywords: multi-source image geometric process, high precision geometric positioning, quotient space of multi-geometric features, optimized weight selection

Procedia PDF Downloads 280

Authors: Jose A. Aznar-Moreno, Xi Jiang, Stefan Burén, Luis M. Rubio

Abstract:

Integration of prokaryotic nitrogen fixation (nif) genes into the plastid genome for expression of functional nitrogenase components could render plants capable of assimilating atmospheric N2 making their crops less dependent of nitrogen fertilizers. The nitrogenase Fe protein component (NifH) has been used as proxy for expression and targeting of Nif proteins within plant and yeast cells. Here we use tobacco plants with the Azotobacter vinelandii nifH and nifM genes integrated into the plastid genome. NifH and its maturase NifM were constitutively produced in leaves, but not roots, during light and dark periods. Nif protein expression in transplastomic plants was stable throughout development. Chloroplast NifH was soluble, but it only showed in vitro activity when isolated from leaves collected at the end of the dark period. Exposing the plant extracts to elevated temperatures precipitated NifM and apo-NifH protein devoid of [Fe4S4] clusters, dramatically increasing the specific activity of remaining NifH protein. Our data indicate that the chloroplast endogenous [Fe-S] cluster biosynthesis was insufficient for complete NifH maturation, albeit a negative effect on NifH maturation due to excess NifM in the chloroplast cannot be excluded. NifH and NifM constitutive expression in transplastomic plants did not affect any of the following traits: seed size, germination time, germination ratio, seedling growth, emergence of the cotyledon and first leaves, chlorophyll content and plant height throughout development.

Keywords: NifH, chloroplast, nitrogen fixation, crop improvement, transplastomic plants, fertilizer, biotechnology

Procedia PDF Downloads 154

Authors: Teo Hao Jing, Shane Ho Ken Wae, Lee Jin Yu, Burra Venkata Durga Kumar

Abstract:

Current distributed healthcare systems face the challenge of interoperability of health data. Storing electronic health records (EHR) in local databases causes them to be fragmented. This problem is aggravated as patients visit multiple healthcare providers in their lifetime. Existing solutions are unable to solve this issue and have caused burdens to healthcare specialists and patients alike. Blockchain technology was found to be able to increase the interoperability of health data by implementing digital access rules, enabling uniformed patient identity, and providing data aggregation. Consortium blockchain was found to have high read throughputs, is more trustworthy, more secure against external disruptions and accommodates transactions without fees. Therefore, this paper proposes a blockchain-based model for data management applications. In this model, a consortium blockchain is implemented by using a delegated proof of stake (DPoS) as its consensus mechanism. This blockchain allows collaboration between users from different organizations such as hospitals and medical bureaus. Patients serve as the owner of their information, where users from other parties require authorization from the patient to view their information. Hospitals upload the hash value of patients’ generated data to the blockchain, whereas the encrypted information is stored in a distributed cloud storage.

Keywords: blockchain technology, data management applications, healthcare, interoperability, delegated proof of stake

Procedia PDF Downloads 132