Search results for: gold mining

Authors: K. Hema Shankari, R. Thirumalaiselvi, N. V. Balasubramanian

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The present paper addresses to the research in the area of regression testing with emphasis on automated tools as well as prioritization of test cases. The uniqueness of regression testing and its cyclic nature is pointed out. The difference in approach between industry, with business model as basis, and academia, with focus on data mining, is highlighted. Test Metrics are discussed as a prelude to our formula for prioritization; a case study is further discussed to illustrate this methodology. An industrial case study is also described in the paper, where the number of test cases is so large that they have to be grouped as Test Suites. In such situations, a genetic algorithm proposed by us can be used to reconfigure these Test Suites in each cycle of regression testing. The comparison is made between a proprietary tool and an open source tool using the above-mentioned metrics. Our approach is clarified through several tables.

Keywords: APFD metric, genetic algorithm, regression testing, RFT tool, test case prioritization, selenium tool

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Authors: A. Baloglu, A. Çınar

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Different tools and technologies were implemented for Crisis Response and Management (CRM) which is generally using available network infrastructure for information exchange. Depending on type of disaster or crisis, network infrastructure could be affected and it could not be able to provide reliable connectivity. Thus any tool or technology that depends on the connectivity could not be able to fulfill its functionalities. As a solution, a new message exchange framework has been developed. Framework provides offline/online information exchange platform for CRM Information Systems (CRMIS) and it uses XML compression and packet prioritization algorithms and is based on open source web technologies. By introducing offline capabilities to the web technologies, framework will be able to perform message exchange on unreliable networks. The experiments done on the simulation environment provide promising results on low bandwidth networks (56kbps and 28.8 kbps) with up to 50% packet loss and the solution is to successfully transfer all the information on these low quality networks where the traditional 2 and 3 tier applications failed.

Keywords: crisis response and management, XML messaging, web services, XML compression, mining

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Authors: C. Rana, S. K. Jain

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The enormous amount of Web data has challenged its usage in efficient manner in the past few years. As such, a range of techniques are applied to tackle this problem; prominent among them is personalization and recommender system. In fact, these are the tools that assist user in finding relevant information of web. Most of the e-commerce websites are applying such tools in one way or the other. In the past decade, a large number of recommendation algorithms have been proposed to tackle such problems. However, there have not been much research in the evaluation criteria for these algorithms. As such, the traditional accuracy and classification metrics are still used for the evaluation purpose that provides a static view. This paper studies how the evolution of user preference over a period of time can be mapped in a recommender system using a new evaluation methodology that explicitly using time dimension. We have also presented different types of experimental set up that are generally used for recommender system evaluation. Furthermore, an overview of major accuracy metrics and metrics that go beyond the scope of accuracy as researched in the past few years is also discussed in detail.

Keywords: collaborative filtering, data mining, evolutionary, clustering, algorithm, recommender systems

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Authors: Kuni Vergauwen, Ivan P. J. Huijnen, Astrid Depuydt, Jasmine Van Regenmortel, Mira Meeus

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A lower activity level and imbalanced activity pattern are frequently observed in persons with chronic fatigue syndrome (CFS) / myalgic encephalomyelitis (ME) due to debilitating fatigue and post-exertional malaise (PEM). Identification of measurement instruments to evaluate the activity level and pattern is therefore important. The objective is to identify measurement instruments suited to evaluate the activity level and/or pattern in patients with CFS/ME and review their psychometric properties. A systematic literature search was performed in the electronic databases PubMed and Web of Science until 12 October 2016. Articles including relevant measurement instruments were identified and included for further analysis. The psychometric properties of relevant measurement instruments were extracted from the included articles and rated based on the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist. The review was performed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A total of 49 articles and 15 unique measurement instruments were found, but only three instruments were evaluated in patients with CFS/ME: the Chronic Fatigue Syndrome-Activity Questionnaire (CFS-AQ), Activity Pattern Interview (API) and International Physical Activity Questionnaire-Short Form (IPAQ-SF), three self-report instruments measuring the physical activity level. The IPAQ-SF, CFS-AQ and API are all equally capable of evaluating the physical activity level, but none of the three measurement instruments are optimal to use. No studies about the psychometric properties of activity monitors in patients with CFS/ME were found, although they are often used as the gold standard to measure the physical activity pattern. More research is needed to evaluate the psychometric properties of existing instruments, including the use of activity monitors.

Keywords: chronic fatigue syndrome, data collection, physical activity, psychometrics

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Authors: Andrey V. Khromov, Antonida V. Makhotenko, Ekaterina A. Snigir, Svetlana S. Makarova, Natalia O. Kalinina, Valentin V. Makarov, Mikhail E. Taliansky

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Due to its simplicity, CRISPR/Cas9 has become widely used and capable of inducing mutations in the genes of organisms of various kingdoms. The aim of this work was to develop applications for the efficient modification of DNA coding sequences of phytoene desaturase (PDS), coilin and vacuolar invertase (Solanum tuberosum) genes, and to develop a new nanoparticles carrier efficient technology to deliver the CRISPR/Cas9 system for editing the plant genome. For each of the genes - coilin, PDS and vacuolar invertase, five single RNA guide (sgRNAs) were synthesized. To determine the most suitable nanoplatform, two types of NP platforms were used: magnetic NPs (MNPS) and gold NPs (AuNPs). To test the penetration efficiency, they were functionalized with fluorescent agents - BSA * FITS and GFP, as well as labeled Cy3 small-sized RNA. To measure the efficiency, a fluorescence and confocal microscopy were used. It was shown that the best of these options were AuNP - both in the case of proteins and in the case of RNA. The next step was to check the possibility of delivering components of the CRISPR/Cas9 system to plant cells for editing target genes. AuNPs were functionalized with a ribonucleoprotein complex consisting of Cas9 and corresponding to target genes sgRNAs, and they were biolistically bombarded to axillary buds and apical meristems of potato plants. After the treatment by the best NP carrier, potato meristems were grown to adult plants. DNA isolated from this plants was sent to a preliminary fragment of the analysis to screen out the non-transformed samples, and then to the NGS. The present work was carried out with the financial support from the Russian Science Foundation (grant No. 16-16-04019).

Keywords: biobombardment, coilin, CRISPR/Cas9, nanoparticles, NPs, PDS, sgRNA, vacuolar invertase

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Authors: Prince Dacosta Aboagye, Ayyoob Sharifi

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Cities respond to climate concerns mainly through their climate action plans (CAPs). A comprehensive content analysis of the dynamics in existing urban CAPs is not well represented in the literature. This literature void presents a difficulty in appreciating the strengths and weaknesses of urban CAPs. Here, we perform a qualitative content analysis (QCA) on CAPs from 278 cities worldwide and use text-mining tools to map and visualize the relevant data. Our analysis showed a decline in the number of CAPs developed and published following the global COVID-19 lockdown period. Evidently, megacities are leading the deep decarbonisation agenda. We also observed a transition from developing mainly mitigation-focused CAPs pre-COP21 to both mitigation and adaptation CAPs. A lack of inclusiveness in local climate planning was common among European and North American cities. The evidence is a catalyst for understanding the trends in existing urban CAPs to shape future urban climate planning.

Keywords: urban, climate action plans, strengths, weaknesses

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Authors: Surya R. Niraula, Devendra B Chhetry, Girish K. Singh, S. Nagesh, Frederick A. Connell

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Background: Although random sampling is generally considered to be the gold standard for population-based research, the majority of drug abuse research is based on non-random sampling despite the well-known limitations of this kind of sampling. Method: We compared the statistical properties of two surveys of drug abuse in the same community: one using snowball sampling of drug users who then identified “friend controls” and the other using a random sample of non-drug users (controls) who then identified “friend cases.” Models to predict drug abuse based on risk factors were developed for each data set using conditional logistic regression. We compared the precision of each model using bootstrapping method and the predictive properties of each model using receiver operating characteristics (ROC) curves. Results: Analysis of 100 random bootstrap samples drawn from the snowball-sample data set showed a wide variation in the standard errors of the beta coefficients of the predictive model, none of which achieved statistical significance. One the other hand, bootstrap analysis of the random-sample data set showed less variation, and did not change the significance of the predictors at the 5% level when compared to the non-bootstrap analysis. Comparison of the area under the ROC curves using the model derived from the random-sample data set was similar when fitted to either data set (0.93, for random-sample data vs. 0.91 for snowball-sample data, p=0.35); however, when the model derived from the snowball-sample data set was fitted to each of the data sets, the areas under the curve were significantly different (0.98 vs. 0.83, p < .001). Conclusion: The proposed method of random sampling of controls appears to be superior from a statistical perspective to snowball sampling and may represent a viable alternative to snowball sampling.

Keywords: drug abuse, matched case-control study, non-probability sampling, probability sampling

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Authors: Doyin Afolabi, Phillip Adewole, Oladipupo Sennaike

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Most well-known classifiers, including the decision tree algorithm, can make predictions on balanced datasets efficiently. However, the decision tree algorithm tends to be biased towards imbalanced datasets because of the skewness of the distribution of such datasets. To overcome this problem, this study proposes a weighted decision tree algorithm that aims to remove the bias toward the majority class and prevents the reduction of majority observations in imbalance datasets classification. The proposed weighted decision tree algorithm was tested on three imbalanced datasets- cancer dataset, german credit dataset, and banknote dataset. The specificity, sensitivity, and accuracy metrics were used to evaluate the performance of the proposed decision tree algorithm on the datasets. The evaluation results show that for some of the weights of our proposed decision tree, the specificity, sensitivity, and accuracy metrics gave better results compared to that of the ID3 decision tree and decision tree induced with minority entropy for all three datasets.

Keywords: data mining, decision tree, classification, imbalance dataset

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Authors: Sofia G. Meirinho, Luis G. Dias, António M. Peres, Lígia R. Rodrigues

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The emerging development of electrochemical aptasen sors has enabled the easy and fast detection of protein biomarkers in standard and real samples. Biomarkers are produced by body organs or tumours and provide a measure of antigens on cell surfaces. When detected in high amounts in blood, they can be suggestive of tumour activity. These biomarkers are more often used to evaluate treatment effects or to assess the potential for metastatic disease in patients with established disease. Osteopontin (OPN) is a protein found in all body fluids and constitutes a possible biomarker because its overexpression has been related with breast cancer evolution and metastasis. Currently, biomarkers are commonly used for the development of diagnostic methods, allowing the detection of the disease in its initial stages. A previously described RNA aptamer was used in the current work to develop a simple and sensitive electrochemical aptasensor with high affinity for human OPN. The RNA aptamer was biotinylated and immobilized on a gold electrode by avidin-biotin interaction. The electrochemical signal generated from the aptamer–target molecule interaction was monitored electrochemically using cyclic voltammetry in the presence of [Fe (CN) 6]−3/− as a redox probe. The signal observed showed a current decrease due to the binding of OPN. The preliminary results showed that this aptasensor enables the detection of OPN in standard solutions, showing good selectivity towards the target in the presence of others interfering proteins such as bovine OPN and bovine serum albumin. The results gathered in the current work suggest that the proposed electrochemical aptasensor is a simple and sensitive detection tool for human OPN and so, may have future applications in cancer disease monitoring.

Keywords: osteopontin, aptamer, aptasensor, screen-printed electrode, cyclic voltammetry

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Authors: Yangze Lan, Ruihua Xv, Feng Zhou, Yijia Shan, Longhao Zhang, Qinghui Xv

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Receiving and dispatching trains is an important part of railroad organization, and the risky evaluation of operating personnel is still reflected by scores, lacking further excavation of wrong answers and operating accidents. With natural language processing (NLP) technology, this study extracts the keywords and key phrases of 40 relevant risk events about receiving and dispatching trains and reclassifies the risk events into 8 categories, such as train approach and signal risks, dispatching command risks, and so on. Based on the historical risk data of personnel, the K-Means clustering method is used to classify the risk level of personnel. The result indicates that the high-risk operating personnel need to strengthen the training of train receiving and dispatching operations towards essential trains and abnormal situations.

Keywords: receiving and dispatching trains, natural language processing, risk evaluation, K-means clustering

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Authors: Tadayuki Kyoutani, Shigeyuki Haruyama, Ken Kaminishi, Zefry Darmawan

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Japan’s semiconductor industries have developed greatly in recent years. Many were started from a Small and Medium-sized Enterprises (SMEs) that found at a good circumstance and now become the prosperous industries in the world. Sustainable growth factors that support the creation of spirit value inside the Japanese company were strongly embedded through performance. Those factors were not clearly defined among each company. A series of literature research conducted to explore quantitative text mining about the definition of sustainable growth factors. Sustainable criteria were developed from previous research to verify the definition of the factors. A typical frame work was proposed as a systematical approach to develop sustainable growth factor in a specific company. Result of approach was review in certain period shows that factors influenced in sustainable growth was importance for the company to achieve the goal.

Keywords: SME, manufacture, sustainable, growth factor

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Authors: Adriano Araujo Carvalho, Arturo Alatrista Corrales

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This paper intends to show how electrical energy consumption and production data analysis were used to find opportunities to save energy at Taboada wastewater treatment plant in Callao, Peru. In order to access the data, it was used independent data networks for both electrical and process instruments, which were taken to analyze under an ISO 50001 energy audit, which considered, thus, Energy Performance Indexes for each process and a step-by-step guide presented in this text. Due to the use of aforementioned methodology and data mining techniques applied on information gathered through electronic multimeters (conveniently placed on substation switchboards connected to a cloud network), it was possible to identify thoroughly the performance of each process and thus, evidence saving opportunities which were previously hidden before. The data analysis brought both costs and energy reduction, allowing the plant to save significant resources and to be certified under ISO 50001.

Keywords: energy and production data analysis, energy management, ISO 50001, wastewater treatment plant energy analysis

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Authors: Salima Smiti, Ines Gasmi, Makram Soui

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Credit risk is the most important issue for financial institutions. Its assessment becomes an important task used to predict defaulter customers and classify customers as good or bad payers. To this objective, numerous techniques have been applied for credit risk assessment. However, to our knowledge, several evaluation techniques are black-box models such as neural networks, SVM, etc. They generate applicants’ classes without any explanation. In this paper, we propose to assess credit risk using rules classification method. Our output is a set of rules which describe and explain the decision. To this end, we will compare seven classification algorithms (JRip, Decision Table, OneR, ZeroR, Fuzzy Rule, PART and Genetic programming (GP)) where the goal is to find the best rules satisfying many criteria: accuracy, sensitivity, and specificity. The obtained results confirm the efficiency of the GP algorithm for German and Australian datasets compared to other rule-based techniques to predict the credit risk.

Keywords: credit risk assessment, classification algorithms, data mining, rule extraction

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Authors: Abhigyan Hedau, Priya Shelke, Riddhi Mirajkar, Shreyash Chaple, Mrunali Gadekar, Himanshu Akula

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The heart is the coordinating centre of the major endocrine glandular structure of the body, which produces hormones that profoundly affect the operations of the body, and diagnosing cardiovascular disease is a difficult but critical task. By extracting knowledge and information about the disease from patient data, data mining is a more practical technique to help doctors detect disorders. We use a variety of machine learning methods here, including logistic regression and support vector classifiers (SVC), K-nearest neighbours Classifiers (KNN), Decision Tree Classifiers, Random Forest classifiers and Gradient Boosting classifiers. These algorithms are applied to patient data containing 13 different factors to build a system that predicts heart disease in less time with more accuracy.

Keywords: logistic regression, support vector classifier, k-nearest neighbour, decision tree, random forest and gradient boosting

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Authors: Monica Lia

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This article presents a customer data analysis model using business intelligence tools for data modelling, transforming, data visualization and dynamic reports building. Economic organizational customer’s analysis is made based on the information from the transactional systems of the organization. The paper presents how to develop the data model starting for the data that companies have inside their own operational systems. The owned data can be transformed into useful information about customers using business intelligence tool. For a mature market, knowing the information inside the data and making forecast for strategic decision become more important. Business Intelligence tools are used in business organization as support for decision-making.

Keywords: customer analysis, business intelligence, data warehouse, data mining, decisions, self-service reports, interactive visual analysis, and dynamic dashboards, use cases diagram, process modelling, logical data model, data mart, ETL, star schema, OLAP, data universes

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Authors: Monika Chuchro

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This paper presents a preliminary attempt to apply classification of time series using meta-clusters in order to improve the quality of regression models. In this case, clustering was performed as a method to obtain a subgroups of time series data with normal distribution from inflow into waste water treatment plant data which Composed of several groups differing by mean value. Two simple algorithms: K-mean and EM were chosen as a clustering method. The rand index was used to measure the similarity. After simple meta-clustering, regression model was performed for each subgroups. The final model was a sum of subgroups models. The quality of obtained model was compared with the regression model made using the same explanatory variables but with no clustering of data. Results were compared by determination coefficient (R2), measure of prediction accuracy mean absolute percentage error (MAPE) and comparison on linear chart. Preliminary results allows to foresee the potential of the presented technique.

Keywords: clustering, data analysis, data mining, predictive models

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Authors: Lali Khurtsia, Vano Tsertsvadze

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Georgian drug policy is directed to reduce the supply of drugs. Retrospective analysis has shown that law enforcement activities have been followed by the expulsion of particular injecting drugs. The demand remains unchanged and drugs are substituted by the hand-made, even more dangerous homemade drugs entered the market. To find out expected new trends on the Georgian drug market, qualitative study was conducted with Georgian drug users to determine drug supply routes. It turned out that drug suppliers and consumers for safety reasons and to protect their anonymity, use Skype to make deals. IT in illegal drug trade is even more sophisticated in the worldwide. Trading with Bitcoins in the Darknet ensures high confidentiality of money transactions and the safe circulation of drugs. In 2014 largest Bitcoin mining enterprise in the world was built in Georgia. We argue that the use of Bitcoins and Darknet by Georgian drug consumers and suppliers will be an incentive to response adequately to the government's policy of restricting supply in order to satisfy market demand for drugs.

Keywords: bitcoin, darknet, drugs, policy

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Authors: Rishi Kanth Saripalle

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Medical experts highly recommend to include physical activity in everyone’s daily routine irrespective of gender or age as it helps to improve various medical issues or curb potential issues. Simultaneously, experts are also diligently trying to provide various healthcare services (interventions, plans, exercise routines, etc.) for promoting healthy living and increasing physical activity in one’s ever increasing hectic schedules. With the introduction of wearables, individuals are able to keep track, analyze, and visualize their daily physical activities. However, there seems to be no common agreed standard for representing, gathering, aggregating and analyzing an individual’s physical activity data from disparate multiple sources (exercise pans, multiple wearables, etc.). This issue makes it highly impractical to develop any data-driven physical activity applications and healthcare programs. Further, the inability to integrate the physical activity data into an individual’s Electronic Health Record to provide a wholistic image of that individual’s health is still eluding the experts. This article has identified three primary reasons for this potential issue. First, there is no agreed standard, both structure and semantic, for representing and sharing physical activity data across disparate systems. Second, various organizations (e.g., LA fitness, Gold’s Gym, etc.) and research backed interventions and programs still primarily rely on paper or unstructured format (such as text or notes) to keep track of the data generated from physical activities. Finally, most of the wearable devices operate in silos. This article identifies the underlying problem, explores the idea of reusing existing standards, and identifies the essential modules required to move forward.

Keywords: electronic physical activity record, physical activity in EHR EIM, tracking physical activity data, physical activity data standards

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Authors: Svetlana Gusarova

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The essential role in the recent development of world economy has led to the developing countries, notably to BRICS countries (Brazil, Russia, India, China, South Africa). Over the next 50 years the BRICS countries are expected to be the engines of global trade and economic growth. Trade cooperation of BRICS countries can enhance their economic development. BRICS countries were among Top 10 world exporters of office and telecom equipment, of textiles, of clothing, of iron and steel, of chemicals, of agricultural products, of automotive products, of fuel and mining products. China was one of the main trading partners of all BRICS countries, maintaining close relationship with all BRICS countries in the development of trade. Author analyzed trade complementarity of BRICS countries and revealed the high level of complementarity of their trade flows in connection with availability of specialization in different types of goods. The correlation and regression analysis of communication of Intra-BRICS merchandise turnover and their GDP (PPP) revealed very strong impact on the development of their economies.

Keywords: BRICS countries, trade cooperation, complementarity, regression analysis

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Authors: Muhammad Shahzad Shifa, Amna Shifa, Muhammad Omar, Aamir Shahzad, Rahmat Ali Khan

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Many applications of remote sensing to vegetation and crop response depend on spectral properties of individual leaves and plants. Vegetation indices are usually determined to estimate crop biophysical parameters like crop canopies and crop leaf area indices with the help of remote sensing. Cotton crops assessment is performed with the help of vegetative indices. Remotely sensed images from an optical multispectral radiometer MSR5 are used in this study. The interpretation is based on the fact that different materials reflect and absorb light differently at different wavelengths. Non-normalized and normalized forms of these datasets are analyzed using two complementary data mining algorithms; K-means and K-nearest neighbor (KNN). Our analysis shows that the use of normalized reflectance data and vegetative indices are suitable for an automated assessment and decision making.

Keywords: cotton, condition assessment, KNN algorithm, clustering, MSR5, vegetation indices

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Authors: Max Butler

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Melanoma accounts for 80% of skin cancer-related deaths globally. The poor prognosis and increasing incidence of melanoma impose a significant burden on global healthcare systems. Early detection, precise diagnosis, and preventative strategies are critical to improving patient outcomes. Dermoscopy is the gold standard for specialist assessments of pigmented skin lesions, as it can differentiate between benign and malignant growths with greater accuracy than visual inspection. In the United Kingdom, guidelines from the National Institute of Clinical Excellence (NICE) state dermoscopy should be used in all specialist assessments of pigmented skin lesions. Compliance with this guideline is low, resulting in missed and delayed melanoma diagnoses. To address this problem, a quality improvement project was initiated at Buckinghamshire Healthcare Trust (BHT) within the plastic surgery department. The target group was a trainee and consultant plastic surgeons conducting outpatient skin cancer clinics. Analysis of clinic documentation over a one-month period found that only 62% (38/61) of patients referred with pigmented skin lesions were examined using dermoscopy. To increase dermoscopy rates, teaching was delivered to the department highlighting national guidelines and the evidence base for dermoscopic examination. In addition, clinic paperwork was redesigned to include a text box for dermoscopic examination. Reauditing after the intervention found a significant increase in dermoscopy rates (52/61, p = 0.014). In conclusion, implementing a quality improvement project with targeted teaching and documentation template templates successfully increased dermoscopy rates. This is a promising step toward improving early melanoma detection and patient outcomes.

Keywords: melanoma, dermoscopy, plastic surgery, quality improvement

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Authors: Jafar Ahmadi, Nafiseh Noormohammadi, Sedegeh Fabriki Ourang

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GRF, Growth regulating factor, genes encode a novel class of plant-specific transcription factors. The GRF proteins play a role in the regulation of cell numbers in young and growing tissues and may act as transcription activations in growth and development of plants. Identification of GRF genes and their expression are important in plants to performance of the growth and development of various organs. In this study, to better understanding the structural and functional differences of GRFs family, 45 GRF proteins sequences in A. thaliana, Z. mays, O. sativa, B. napus, B. rapa, H. vulgare, and S. bicolor, have been collected and analyzed through bioinformatics data mining. As a result, in secondary structure of GRFs, the number of alpha helices was more than beta sheets and in all of them QLQ domains were completely in the biggest alpha helix. In all GRFs, QLQ, and WRC domains were completely protected except in AtGRF9. These proteins have no trans-membrane domain and due to have nuclear localization signals act in nuclear and they are component of unstable proteins in the test tube.

Keywords: domain, gene family, GRF, motif

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Authors: Usha Lokala, Srinivas Nowduri, Prabhakar K. Sharma

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Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new framework model which can be trained and adapt itself to new data and make accurate predictions. This work also throws some light on use of SVM’s methodology for text classifiers from the obtained traffic data. Finally, it emphasizes the uniqueness and adaptability of SVMs methodology appropriate for this kind of research work.

Keywords: support vector mechanism (SVM), machine learning (ML), support vector machines (SVM), department of transportation (DFT)

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Authors: Pawel Sowa, Joachim Bargiel, Bogdan Mol, Katarzyna Luszcz

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The article raises the issue of the development of local renewable energy sources and the production of distributed energy in context of improving the reliability of the Polish Power System and the beneficial impact on local and national energy security. The paper refers to the current problems of local governments in the process of investment in the area of distributed energy projects, and discusses the issues of the future role and cooperation within the local power plants and distributed energy. Attention is paid to the local communities the chance to raise their own resources and management of energy fuels (biomass, wind, gas mining) and improving the local energy balance. The material presented takes the issue of the development of the energy potential of municipalities and future cooperation with professional energy. As an example, practical solutions used in one of the communes in Silesia.

Keywords: distributed generation, mini centers energy, renewable energy sources, reliability of supply of rural commune

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Authors: N. S. El-Mougy, M. M. Abdel-Kader, H. M. Abouelnasr

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The efficacy of two essential oils applied as a bean seed dressing followed by seedlings foliar spray with four commercial disinfectants against root rot and wilt incidence was evaluated under field conditions. The essential oils, grape seed and peppermint oils and the disinfectants, Gold, Revarest, Klenva, Malva were applied. Chitosan and the fungicide Topsin-M were used as comparison treatment. Essential oils individually or combined with disinfectants were applied as a bean seed dressing. Furthermore, emerged bean plants were sprayed with the same treatments. Under laboratory conditions, growth inhibition effect was observed for the isolated, tested fungi R. solani and F. oxysporum when exposed to essential oils individually or combined with disinfectants. A high inhibitor effect was recorded for peppermint followed by grape seed oils. Concentrations of 1% and 2% of chitosan as well as Topsin M at 400 ppm showed complete reduction (100%) in the two fungal growths. Under field conditions, the obtained results showed that the applied treatments of chitosan had a superior effect on root rot and wilt disease incidence compared with other tested treatments. It was found that seed coating treatment provides good protection of emerged green bean seeds against the root pathogens attack compared with the fungicide and control treatments. Also, the application of seed dressing with essential oils accompanied by seedling spray demonstrated similar results. It was observed that essential oils had an enhancing effect against disease incidence when combined with disinfectants compared with their application. The obvious yield increase was significantly higher in all applied treatments than in fungicide and control.

Keywords: bean, disinfectants, essential oils, root rot, wilt

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Authors: Jibran Abbasy, Rizwan Sultan, Ammar Humayun, Tabish Chawla

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Background: Hydatid disease caused by Echinococcus Granulosus affects liver in 70-90% of cases. Dogs are the definitive host while humans are the accidental host. Modalities used for its treatment are especially important for our population as the disease is endemic in many Asian countries. The aim of the study was to perform an audit of the various modalities used for treatment of hydatid disease of liver and the response to each modality in tertiary care center of Pakistan. Materials and Methods: Retrospective audit of patients diagnosed and treated for Hydatid disease of the liver at Aga Khan University Hospital from 1st January 2007 to 31st December 2014 was completed. All patients aged 16 and above were included. Patients who had extra hepatic disease and missing records were excluded. Outcome measures were morbidity, mortality and recurrence of the disease. Results: During the study period 56 patients were treated for isolated hepatic hydatid disease and were included. Mean age was 39 years with 48% being females and 52% males. Most common presenting complaint was abdominal pain seen in 53% of patients(n=41). Duration of symptoms was less than 6 months in 74% (n=38). Mostly right lobe was involved in 69% (n=38).Most common treatment modality used was surgery in 34 patients followed by PAIR in 14 patients while 8 patients were treated medically. At a median follow up of 34 months recurrence was seen in 2 patients treated with PAIR while no patient treated with surgery had recurrence with the median follow up of 20 months. While no morbidity and mortality were observed in PAIR, but in surgery 5 patients had morbidity while 1 patient had mortality. Conclusion: Our data is comparative to other studies in terms of morbidity, mortality, and recurrence. We had adequate follow up. In our study PAIR and surgery both are effective and have less complications and recurrence rate. Surgery is still the gold standard in terms of recurrence.

Keywords: echinococcous granulosus, puncture aspiration irrigation reaspiration (PAIR), surgery, hydatid disease

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Authors: Radoslav Židek, Veronika Šidlová, Radovan Kasarda, Birgit Fuerst-Waltl

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Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.

Keywords: genetic data, Pinzgau cattle, supervised learning, machine learning

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Authors: Janusz Kwaśniewski, Ireneusz Dominik, Krzysztof Lalik

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The knowledge about rock layers thickness, especially above drilled mining pavements are crucial for workers safety. The measuring systems used nowadays are generally imperfect and there is a strong demand for improvement. The application of a new type of a measurement system called Self-Excited Acoustical System is presented in the paper. The system was applied until now to monitor stress changes in metal and concrete constructions. The change in measurement methodology resulted in possibility of measuring the thickness of the rocks above the tunnels as well as thickness of a singular rock layer. The idea is to find two resonance frequencies of the self-exited system, which consists of a vibration exciter and vibration receiver placed at a distance, which are coupled with a proper power amplifier, and which operate in a closed loop with a positive feedback. The resonance with the higher amplitude determines thickness of the whole rock, whereas the lower amplitude resonance indicates thickness of a singular layer. The results of the laboratory tests conducted on a group of different rock materials are also presented.

Keywords: auto-oscillator, non-destructive testing, rock thickness measurement, geotechnic

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Authors: Dominika Collett

Abstract:

AI applications, such as machine learning, require access to a vast amount of data in the training phase, which can often be the subject of copyright protection. During later usage, the various content with which the application works can be recorded or made available on the basis of which it produces the resulting output. The EU has recently adopted new legislation to secure machine access to protected works under the DSM Directive; but, the issue of machine use of copyright works is not clearly addressed. However, such clarity is needed regarding the increasing importance of AI and its development. Therefore, this paper provides a basic background of the technology used in the development of applications in the field of computer creativity. The second part of the paper then will focus on a legal analysis of machine use of the authors' works from the perspective of existing European and Czech legislation. The main results of the paper discuss the potential collision of existing legislation in regards to machine use of works with special focus on exceptions and limitations. The legal regulation of machine use of copyright work will impact the development of AI technology.

Keywords: copyright, artificial intelligence, legal use, infringement, Czech law, EU law, text and data mining

Procedia PDF Downloads 117

Authors: J. BalaRaju, M. Govinda Raj, C. S. N. Murthy

Abstract:

Every organization in the competitive world tends to improve its economy by increasing their production and productivity rates. Unequivocally, the production in Indian underground mines over the years is not satisfactory, due to a variety of reasons. There are manifold of avenues for the betterment of production, and one such approach is through enhanced utilization of mechanized equipment such as Load Haul Dumper (LHD). This is used as loading and hauling purpose in underground mines. In view of the aforementioned facts, this paper delves into identification of the key influencing factors such as LHDs maintenance effectiveness, vehicle condition, operator skill and utilization of the machines on performance of LHDs. An attempt has been made for improvement of performance of the equipment through evaluation of Overall Equipment Effectiveness (OEE). Two different approaches for evaluation of OEE have been adopted and compared under various operating conditions. The use of OEE calculation in terms of percentage availability, performance and quality and the hitherto existing situation of the underground mine production is evaluated. Necessary recommendations are suggested to mining industry on the basis of OEE.

Keywords: utilization, maintenance, availability, performance and quality

Procedia PDF Downloads 209