Search results for: negative data

Authors: Omneya M. Helmy, Mona T. Kashef

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Aminoglycosides are used in treating a wide range of infections caused by both Gram-negative and Gram positive bacteria. The presence of 16S rRNA methyl transferases (16S-RMTase) is among the newly discovered resistance mechanisms that confer high resistance to clinically useful aminoglycosides. Cephalosporins are the most commonly used antimicrobials in Egypt; therefore, this study was conducted to determine the isolation frequency of 16S rRNA methyl transferases among third generation cephalosporin-resistant clinical isolates in Egypt. One hundred and twenty three cephalosporin resistant Gram-negative clinical isolates were screened for aminoglycoside resistance by the Kirby Bauer disk diffusion method and tested for possible production of 16S-RMTase. PCR testing and sequencing were used to confirm the presence of 16S-RMTase and the associated antimicrobial resistance determinants, as well as the genetic region surrounding the armA gene. Out of 123 isolates, 66 (53.66%) were resistant to at least one aminoglycoside antibiotic. Only one Escherichia coli isolate (E9ECMO) which was totally resistant to all tested aminoglycosides, was confirmed to have the armA gene in association with blaTEM-1, blaCTX-M-15, blaCTX-M-14 and aac(6)-Ib genes. The armA gene was found to be carried on a large A/C plasmid. Genetic mapping of the armA surrounding region revealed, for the first time, the association of armA with aac(6)-Ib on the same transposon. In Conclusion, the isolation frequency of 16S-RMTase was low among the tested cephalosporin-resistant clinical samples. However, a novel composite transposon has been detected conferring high-level aminoglycosides resistance.

Keywords: aminoglcosides, armA gene, β lactmases, 16S rRNA methyl transferases

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Authors: J. Suneetha, Vijayalaxmi

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Sequential Pattern Mining involves applying data mining methods to large data repositories to extract usage patterns. Sequential pattern mining methodologies used to analyze the data and identify patterns. The patterns have been used to implement efficient systems can recommend on previously observed patterns, in making predictions, improve usability of systems, detecting events, and in general help in making strategic product decisions. In this paper, identified performance of approximate sequential pattern mining defines as identifying patterns approximately shared with many sequences. Approximate sequential patterns can effectively summarize and represent the databases by identifying the underlying trends in the data. Conducting an extensive and systematic performance over synthetic and real data. The results demonstrate that ApproxMAP effective and scalable in mining large sequences databases with long patterns.

Keywords: multiple data, performance analysis, sequential pattern, sequence database scalability

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Authors: Nadjat Zerf Boudjettou, Fahima Nader, Rachid Chalal

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Knowledge management is to acquire and represent knowledge relevant to a domain, a task or a specific organization in order to facilitate access, reuse and evolution. This usually means building, maintaining and evolving an explicit representation of knowledge. The next step is to provide access to that knowledge, that is to say, the spread in order to enable effective use. Knowledge management in the medical field aims to improve the performance of the medical organization by allowing individuals in the care facility (doctors, nurses, paramedics, etc.) to capture, share and apply collective knowledge in order to make optimal decisions in real time. In this paper, we propose a knowledge management approach based on integration technique of heterogeneous data in the medical field by creating a data warehouse, a technique of extracting knowledge from medical data by choosing a technique of data mining, and finally an exploitation technique of that knowledge in a case-based reasoning system.

Keywords: data warehouse, data mining, knowledge discovery in database, KDD, medical knowledge management, Bayesian networks

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Authors: M. Feliciano, F. Rodrigues, A. Gonçalves, J. M. R. C. A. Santos, V. Leite

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With the objective of characterizing the profile and performance of energy use by slaughterhouses, surveys and audits were performed in two different facilities located in the northeastern region of Portugal. Energy consumption from multiple energy sources was assessed monthly, along with production and costs, for the same reference year. Gathered data was analyzed to identify and quantify the main consuming processes and to estimate energy efficiency indicators for benchmarking purposes. Main results show differences between the two slaughterhouses concerning energy sources, consumption by source and sector, and global energy efficiency. Electricity is the most used source in both slaughterhouses with a contribution of around 50%, being essentially used for meat processing and refrigeration. Natural gas, in slaughterhouse A, and pellets, in slaughterhouse B, used for heating water take the second place, with a mean contribution of about 45%. On average, a 62 kgoe/t specific energy consumption (SEC) was found, although with differences between slaughterhouses. A prominent negative correlation between SEC and carcass production was found specially in slaughterhouse A. Estimated Specific Energy Cost and Greenhouse Gases Intensity (GHGI) show mean values of about 50 €/t and 1.8 tCO2e/toe, respectively. Main results show that there is a significant margin for improving energy efficiency and therefore lowering costs in this type of non-energy intensive industries.

Keywords: meat industry, energy intensity, energy efficiency, GHG emissions

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Authors: Nikolaos Vassilas, Theocharis Tsenoglou, Djamchid Ghazanfarpour

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In this work we explore the capability of the mean shift algorithm as a powerful preprocessing tool for improving the quality of spatial data, acquired from airborne scanners, from densely built urban areas. On one hand, high resolution image data corrupted by noise caused by lossy compression techniques are appropriately smoothed while at the same time preserving the optical edges and, on the other, low resolution LiDAR data in the form of normalized Digital Surface Map (nDSM) is upsampled through the joint mean shift algorithm. Experiments on both the edge-preserving smoothing and upsampling capabilities using synthetic RGB-z data show that the mean shift algorithm is superior to bilateral filtering as well as to other classical smoothing and upsampling algorithms. Application of the proposed methodology for 3D reconstruction of buildings of a pilot region of Athens, Greece results in a significant visual improvement of the 3D building block model.

Keywords: 3D buildings reconstruction, data fusion, data upsampling, mean shift

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Authors: Rahma Al Balushi

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Oman Electricity Transmission Company's (OETC) vision is to be a renowned world-class transmission grid by 2025, and one of the indications of achieving the vision is obtaining Asset Management ISO55001 certification, which required setting out a documented Standard Operating Procedures (SOP). Hence, documented SOP for the Geographical information system data process has been established. Also, to effectively manage and improve OETC power transmission, asset data and information need to be governed as such by Asset Information & GIS dept. This paper will describe in detail the GIS data submission process and the journey to develop the current process. The methodology used to develop the process is based on three main pillars, which are system and end-user requirements, Risk evaluation, data availability, and accuracy. The output of this paper shows the dramatic change in the used process, which results subsequently in more efficient, accurate, updated data. Furthermore, due to this process, GIS has been and is ready to be integrated with other systems as well as the source of data for all OETC users. Some decisions related to issuing No objection certificates (NOC) and scheduling asset maintenance plans in Computerized Maintenance Management System (CMMS) have been made consequently upon GIS data availability. On the Other hand, defining agreed and documented procedures for data collection, data systems update, data release/reporting, and data alterations salso aided to reduce the missing attributes of GIS transmission data. A considerable difference in Geodatabase (GDB) completeness percentage was observed between the year 2017 and the year 2021. Overall, concluding that by governance, asset information & GIS department can control GIS data process; collect, properly record, and manage asset data and information within OETC network. This control extends to other applications and systems integrated with/related to GIS systems.

Keywords: asset management ISO55001, standard procedures process, governance, geodatabase, NOC, CMMS

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Authors: Pallavi Malhotra

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This paper examines the importance of ethics in cloud computing. In the modern society, cloud computing is offering individuals and businesses an unlimited space for storing and processing data or information. Most of the data and information stored in the cloud by various users such as banks, doctors, architects, engineers, lawyers, consulting firms, and financial institutions among others require a high level of confidentiality and safeguard. Cloud computing offers centralized storage and processing of data, and this has immensely contributed to the growth of businesses and improved sharing of information over the internet. However, the accessibility and management of data and servers by a third party raise concerns regarding the privacy of clients’ information and the possible manipulations of the data by third parties. This document suggests the approaches various stakeholders should take to address various ethical issues involving cloud-computing services. Ethical education and training is key to all stakeholders involved in the handling of data and information stored or being processed in the cloud.

Keywords: IT ethics, cloud computing technology, cloud privacy and security, ethical education

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Authors: Olayinka Adeniyi, Melissa Omino

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The field of data privacy and data protection in Africa is still an evolving area, with many African countries yet to enact legislation on the subject. While African Governments are bringing their legislation to speed in this field, how patriarchy pervades every sector of African thought and manifests in society needs to be considered. Moreover, the laws enacted ought to be inclusive, especially towards women. This, in a nutshell, is the essence of data feminism. Data feminism is a new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Feminising data privacy and protection will involve thinking women, considering women in the issues of data privacy and protection, particularly in legislation, as is the case in this paper. The line of thought of women inclusion is not uncommon when even international and regional human rights specific for women only came long after the general human rights. The consideration is that these should have been inserted or rather included in the original general instruments in the first instance. Since legislation on data privacy is coming in this century, having seen the rights and shortcomings of earlier instruments, then the cue should be taken to ensure inclusive wholistic legislation for data privacy and protection in the first instance. Data feminism is arguably an area that has been scantily researched, albeit a needful one. With the spate of increase in the violence against women spiraling in the cyber world, compounding the issue of COVID-19 and the needful response of governments, and the effect of these on women and their rights, fast forward, the research on the feminism of data privacy and protection in Africa becomes inevitable. This paper seeks to answer the questions, what is data feminism in the African context, why is it important in the issue of data privacy and protection legislation; what are the laws, if any, existing on data privacy and protection in Africa, are they women inclusive, if not, why; what are the measures put in place for the privacy and protection of women in Africa, and how can this be made possible. The paper aims to investigate the issue of data privacy and protection in Africa, the legal framework, and the protection or provision that it has for women if any. It further aims to research the importance and necessity of feminizing data privacy and protection, the effect of lack of it, the challenges or bottlenecks in attaining this feat and the possibilities of accessing data privacy and protection for African women. The paper also researches the emerging practices of data privacy and protection of women in other jurisprudences. It approaches the research through the methodology of review of papers, analysis of laws, and reports. It seeks to contribute to the existing literature in the field and is explorative in its suggestion. It suggests a draft of some clauses to make any data privacy and protection legislation women inclusive. It would be useful for policymaking, academic, and public enlightenment.

Keywords: feminism, women, law, data, Africa

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Authors: Silene Ten Seldam, Kiara Margarita Lu, Melina Nicole Kyranides

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Psychopathic traits are marked by a lack of empathy and an inability to maintain meaningful relationships. Yet little research has investigated differences in interpersonal relationships between primary and secondary psychopathic traits. Emotional contagion, the tendency to automatically mimic others’ facial expressions and movements, is a type of empathy contributing to relationship quality. Additionally, the motivating and pleasurable aspects of social interaction, social reward is integral to understanding relationships. Therefore, the current research investigated interpersonal relationships through relationship status, the quality of friendships, the susceptibility to positive (happiness, love) and negative (sadness, fear, anger) emotional contagion, and social reward. Recruited online, 389 participants between 18 and 76 years old (M = 33.61; of which 241 were female) completed self-report questionnaires assessing primary and secondary psychopathic traits, friendship, emotional contagion, and social rewards. Hierarchical multiple regression showed relationship status as a protective factor and that individuals with secondary psychopathic traits are less likely to be in a relationship. This study is the first to investigate emotional contagion with primary and secondary psychopathic traits. Emotional contagion for sadness predicted secondary psychopathic traits. Negative social potency (enjoying being cruel and antagonistic to others) predicted both primary and secondary traits. However, admiration and prosocial interactions only predicted primary psychopathic traits. Findings infer differences in maintaining relationships, regulating emotions, empathising with others through emotional contagion, and motivation to socially engage, perhaps due to each dimensions’distinct origins and manifestations.

Keywords: primary psychopathic traits, secondary psychopathic traits, interpersonal relationships, friendship, emotional contagion, social reward

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Authors: Jun-ichi Ochiai, Itsuki Noda, Ryo Kanamori, Keiji Hirata, Hitoshi Matsubara, Hideyuki Nakashima

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We conducted exhaustive simulations for data assimilation and evaluation of service quality for various setting in a new shared transportation system, called SAVS. Computational social simulation is a key technology to design recent social services like SAVS as new transportation service. One open issue in SAVS was to determine the service scale through the social simulation. Using our exhaustive simulation framework, OACIS, we did data-assimilation and evaluation of effects of SAVS based on actual tax-use data at Tajimi city, Japan. Finally, we get the conditions to realize the new service in a reasonable service quality.

Keywords: on-demand bus sytem, social simulation, data assimilation, exhaustive simulation

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Authors: Maaly Jarrah

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There is no doubt that reflective practice has become a stable component in continuous professional development (CPD) courses around the world for the purpose of promoting teacher development, meaningful learning, and deliberate teacher personal and professional growth. However, while there is much research on the benefits of integrating reflective practice in teacher CPD courses, not enough research explores EFL teachers’ experiences with engagement in reflective practice in the CPD from their own perspectives. This research employed a case study approach to explore the experience of 7 EFL teachers with engaging in reflective practice in a CPD course that took place in Kuwait.The participating EFL teachers engaged in collaborative dialogue reflections and completed reflection journal entries as part of the course. Data was collected through semi-structured interviews and analyzed thematically. Findings indicate that the participating teachers’ positive experience with reflective practice is associated with their engagement in collaborative dialogue reflections, while challenges and negative feelings are associated with writing their reflection journal entries. Accordingly, the study offers recommendations for CPD courses to help improve EFL teachers’ experiences with engagement in reflective practice.

Keywords: Collaborative dialogue reflections, continuous professional development, EFL teachers, reflection journals, teacher reflective practice

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Authors: Aqsa Butt, Nida Zafar

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The study assesses the relationship between impulsivity, emotional regulation, problematic mukbang watching, and eating disorders in university students. It was hypothesized there is likely to be a relationship between impulsivity, emotional regulation, problematic mukbang watching, and eating disorders in university students; impulsivity and emotional regulation would predict problematic mukbang watching in university students; problematic mukbang watching would predict eating disorders in university students. A correlational research design was used. A sample of 200 students was taken from different public and private universities in Lahore. Emotional regulation questionnaire (Gross & John, 2003), Abbreviated Barrat Impulsiveness Scale (Christopher et al., 2014), Problematic Mukbang Watching Scale (Kircaburun et al., 2020), and Eating Disorder Diagnostic Scale (Stice et al., 2004) were used for assessment. Results showed a significant positive relationship between impulsivity and expressive suppression with problematic mukbang watching. However, there is a significant negative relationship between cognitive reappraisal and problematic mukbang watching. Problematic mukbang is significantly positively related to bulimia nervosa and binge eating. Furthermore, impulsivity and expressive suppression are significant positive predictors of problematic mukbang watching, and cognitive reappraisal is a significant negative predictor of problematic mukbang watching. Additionally, problematic mukbang watching significantly positively predicts bulimia nervosa and binge eating. The research has important implications for university students to understand that excessive watching of such videos can lead to eating disorders such as bulimia nervosa and binge eating. This research provides an understanding of the effects of Mukbang watching, and it also adds to the existing body of knowledge on eating disorders.

Keywords: impulsivity, emotional regulation, problematic Mukbang watching eating disorders, students

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Authors: Omar Amoudi, Mariya Khalid

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The limitation of the public funds for the infrastructure projects and with the deterioration of international oil prices and the negative consequences on the economies of oil producing and exporting countries, Oman has encouraged the partnership between the public and private sectors. As the private sector has a role in planning, financing, designing, operating and the maintenance of the public services. There is no doubt that, the adoption of Public Private Partnership (PPP) strategy faces many challenges which might affect the project seriously if it is not overcome in earlier time. These challenges depend on the level of understanding of the strategy, the roles and regulations and the availability of resources as well. This research aims at identifying the challenges facing the PPP infrastructure projects in Oman based on the similar previous studies supported by questionnaire survey and semi structured interviews. It also seeks to discuss the rationale for adoption in Oman and uncover the current status of PPP strategy. The identified challenges were ranked according to the importance index of each challenge. After analysis of data, it has observed that, the main challenges facing PPPs projects in Oman are high participation cost, high projects cost and regulation changes. The PPP strategy has to be adopted well and with a high level of experience in order to ensure a successful implementation of PPP projects in Oman.

Keywords: public private partnership (PPP), challenges, infrastructure, Oman

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Authors: Fatemeh Roostaee, Zahra Nikmanesh

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This study was done to investigate the role of religious coping components on occupational stress and the quality of working life of midwives. The method of study was descriptive-correlation. The sample was comprised of all midwives in maternity hospitals in Zahedan during 1393. Participants were selected through applying census method. The instruments of data collection were three questionnaires: the quality of working life, occupational stress, and religious opposition. For statistical analysis, Pearson correlation and step by step regression analysis methods were used. The results showed that there is a significant negative relationship between the component of religious activities (r=-0/454) and occupational stress, and regression analysis was also shown that the variable of religious activities has been explained 45% of occupational stress variable changes. The Pearson correlation test showed that there isn't any significant relationship between religious opposition components and the quality of life. Therefore, it is necessary to present essential trainings on (the field of) strengthening compatibility strategies and religious activities to reduce occupational stress.

Keywords: the quality of working life, occupational stress, religious, midwife

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Authors: Vanessa Kummer, Maik Meusel

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Real estate demand estimates are typically derived from transaction data. However, in regions with excess demand, transactions are driven by supply and therefore do not indicate what people are actually looking for. To estimate the demand for housing in Switzerland, search subscriptions from all important Swiss real estate platforms are used. These data do, however, suffer from missing information—for example, many users do not specify how many rooms they would like or what price they would be willing to pay. In economic analyses, it is often the case that only complete data is used. Usually, however, the proportion of complete data is rather small which leads to most information being neglected. Also, the data might have a strong distortion if it is complete. In addition, the reason that data is missing might itself also contain information, which is however ignored with that approach. An interesting issue is, therefore, if for economic analyses such as the one at hand, there is an added value by using the whole data set with the imputed missing values compared to using the usually small percentage of complete data (baseline). Also, it is interesting to see how different algorithms affect that result. The imputation of the missing data is done using unsupervised learning. Out of the numerous unsupervised learning approaches, the most common ones, such as clustering, principal component analysis, or neural networks techniques are applied. By training the model iteratively on the imputed data and, thereby, including the information of all data into the model, the distortion of the first training set—the complete data—vanishes. In a next step, the performances of the algorithms are measured. This is done by randomly creating missing values in subsets of the data, estimating those values with the relevant algorithms and several parameter combinations, and comparing the estimates to the actual data. After having found the optimal parameter set for each algorithm, the missing values are being imputed. Using the resulting data sets, the next step is to estimate the willingness to pay for real estate. This is done by fitting price distributions for real estate properties with certain characteristics, such as the region or the number of rooms. Based on these distributions, survival functions are computed to obtain the functional relationship between characteristics and selling probabilities. Comparing the survival functions shows that estimates which are based on imputed data sets do not differ significantly from each other; however, the demand estimate that is derived from the baseline data does. This indicates that the baseline data set does not include all available information and is therefore not representative for the entire sample. Also, demand estimates derived from the whole data set are much more accurate than the baseline estimation. Thus, in order to obtain optimal results, it is important to make use of all available data, even though it involves additional procedures such as data imputation.

Keywords: demand estimate, missing-data imputation, real estate, unsupervised learning

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Authors: Imoh Philip

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Basà language is classified as belonging to Kainji family, under the sub-phylum Western-Kainji known as Rubasa (Basa Benue) (Croizier & Blench, 1992:32). Basà is an under-described language spoken in the North-Central Nigeria. The language is characterized by subject-verb-object (henceforth SVO) as its canonical word order. Data for this work is sourced from the researcher’s native intuition of the language corroborated with a careful observation of native speakers. This paper investigates the syntactic derivational strategy of information-structure encoding in Basà language. It emphasizes on a negative operator, as a strategy for focusing a constituent or clause that follows it and negativizes a whole proposition. For items that are not nouns, they have to undergo an obligatory nominalization process, either by affixation, modification or conversion before they are moved to the pre verbal position for these operations. The study discovers and provides evidence of the fact showing that deferent constituents in the sentence such as the subject, direct, indirect object, genitive, verb phrase, prepositional phrase, clause and idiophone, etc. can be focused with the same negativizing operator. The process is characterized by focusing the pre verbal NP constituent alone, whereas the whole proposition is negated. The study can stimulate similar study or be replicated in other languages.

Keywords: negation, focus, Basà, nominalization

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Authors: S. I. Nwadioha, M. S. Odimayo, J. A. Omotayo, A. Olu Taiwo, O. E. Olabiyi

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The current study was conducted to determine the epidemiology and antibiotic sensitivity pattern of bacteria isolated from blood of septicemic patients for improved antibiotic therapy. A three-year descriptive study has been carried out at Microbiology Laboratory, Ekiti State University Teaching Hospital, Ado Ekiti, from April 2012 to April 2015. Information compiled from patients’ records includes age, sex, isolated organisms and antibiotic susceptibility patterns. Three hundred and thirteen blood cultures were collected from neonatology and pediatrics wards, Out Patients’ Department (OPD) and from other adult patients. Forty-one cultures yielded mono microbial growth (no polymicrobial growth), giving an incidence of 13.1% positive blood culture (N=41/313). There were 58.4% Gram-negative bacilli and 41.6% Gram-positive cocci in the microbial growth. Bacteria isolated were Staphylococcus aureus 34%(14/41), Klebsiella species22% (9/41), Enterococci 17%(7/41), Proteus species12%(5/41), Escherichia coli 7%(3/41) and Streptococcal pneumoniae 7%(3/41). There was a (35%) higher occurrence of septicemia in neonates than in any other age groups in the hospital. Bacterial sensitivity to 13 antibiotic agents was determined by antibiotics disc diffusion using modified Kirby Bauer’s method. Gram-positive organisms showed a higher antibiotic sensitivity ranging from 14- 100% than the Gram-negative bacteria (11-80%). Staphylococcus aureus and Klebsiella species are the most prevalent organisms. The third generation Cephalosporins (Ceftriaxone) and Floroquinolone(Levofloxacin, Ofloxacin) have proved reliable for management of these blood infections.

Keywords: blood cultures, septicemia, antibiogram, Nigeria

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Authors: Rajesh Kumar G

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A challenging aspect of any clinical trial is to carefully plan the study design to meet the study objective in optimum way and to validate the assumptions made during protocol designing. And when it is a pediatric study, there is the added challenge of stringent guidelines and difficulty in recruiting the necessary subjects. Unlike adult trials, there is not much historical data available for pediatrics, which is required to validate assumptions for planning pediatric trials. Typically, pediatric studies are initiated as soon as approval is obtained for a drug to be marketed for adults, so with the adult study historical information and with the available pediatric pilot study data or simulated pediatric data, the pediatric study can be well planned. Generalizing the historical adult study for new pediatric study is a tedious task; however, it is possible by integrating various statistical techniques and utilizing the advantage of hybrid study design, which will help to achieve the study objective in a smoother way even with the presence of many constraints. This research paper will explain how well the hybrid study design can be planned along with integrated technique (SEV) to plan the pediatric study; In brief the SEV technique (Simulation, Estimation (using borrowed adult data and applying Bayesian methods)) incorporates the use of simulating the planned study data and getting the desired estimates to Validate the assumptions.This method of validation can be used to improve the accuracy of data analysis, ensuring that results are as valid and reliable as possible, which allow us to make informed decisions well ahead of study initiation. With professional precision, this technique based on the collected data allows to gain insight into best practices when using data from historical study and simulated data alike.

Keywords: adaptive design, simulation, borrowing data, bayesian model

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Authors: Kadio Valere R. Angaman, Birahim Bouna Niang

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Land degradation is a serious problem in developing countries, including Cote d’Ivoire, which has its economy focused on agriculture. It occurs in all kinds of ecosystems over the world. However, the drivers of land degradation vary from one region to another and from one ecosystem to another. Thus, identifying these drivers is an essential prerequisite to developing and implementing appropriate policies to reverse the trend of land degradation in the country, especially in the trays ecosystem. Using the binary logistic model with primary data obtained through 780 farmers surveyed, we analyze and identify the drivers of land degradation in the trays ecosystem. The descriptive statistics show that 52% of farmers interviewed have stated facing land degradation in their farmland. This high rate shows the extent of land degradation in this ecosystem. Also, the results obtained from the binary logit regression reveal that land degradation is significantly influenced by a set of variables such as sex, education, slope, erosion, pesticide, agricultural activity, deforestation, and temperature. The drivers identified are mostly local; as a result, the government must implement some policies and strategies that facilitate and incentive the adoption of sustainable land management practices by farmers to reverse the negative trend of land degradation.

Keywords: drivers, land degradation, trays ecosystem, sustainable land management

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Authors: Arslan Ellahi, Syed Amjad Hussain

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Software Testing is a vital process in software development life cycle. We can attain the quality of software after passing it through software testing phase. We have tried to find out automatic test data generation techniques that are a key research area of software testing to achieve test automation that can eventually decrease testing time. In this paper, we review some of the approaches presented in the literature which use evolutionary search based algorithms like Genetic Algorithm, Particle Swarm Optimization (PSO), etc. to validate the test data generation process. We also look into the quality of test data generation which increases or decreases the efficiency of testing. We have proposed test data generation techniques for model-based testing. We have worked on tuning and fitness function of PSO algorithm.

Keywords: search based, evolutionary algorithm, particle swarm optimization, genetic algorithm, test data generation

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Authors: Claudineia Brazil, Elison Eduardo Jardim Bierhals, Luciane Teresa Salvi, Rafael Haag

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Renewable energy sources are dependent on climatic variability, so for adequate energy planning, observations of the meteorological variables are required, preferably representing long-period series. Despite the scientific and technological advances that meteorological measurement systems have undergone in the last decades, there is still a considerable lack of meteorological observations that form series of long periods. The reanalysis is a system of assimilation of data prepared using general atmospheric circulation models, based on the combination of data collected at surface stations, ocean buoys, satellites and radiosondes, allowing the production of long period data, for a wide gamma. The third generation of reanalysis data emerged in 2010, among them is the Climate Forecast System Reanalysis (CFSR) developed by the National Centers for Environmental Prediction (NCEP), these data have a spatial resolution of 0.50 x 0.50. In order to overcome these difficulties, it aims to evaluate the performance of solar radiation estimation through alternative data bases, such as data from Reanalysis and from meteorological satellites that satisfactorily meet the absence of observations of solar radiation at global and/or regional level. The results of the analysis of the solar radiation data indicated that the reanalysis data of the CFSR model presented a good performance in relation to the observed data, with determination coefficient around 0.90. Therefore, it is concluded that these data have the potential to be used as an alternative source in locations with no seasons or long series of solar radiation, important for the evaluation of solar energy potential.

Keywords: climate, reanalysis, renewable energy, solar radiation

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Authors: Mark De Ste Croix, Jonathan Hughes, Francisco Ayala, Michal Lehnert

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There are well-known challenges to implementing injury prevention training for youth players but no data are available on the knowledge and understanding of deliverers of such programmes at grass root level. To increase adoption and adherence to such programmes coach knowledge and understanding of injury risk and prevention is essential. Therefore, the purpose of this study was to examine grass-root coaches knowledge and understanding of injury risk and prevention in youth players. 68 grass root coaches (18 females and 50 males) who were attending a one-day injury prevention workshop completed a modified validated questionnaire exploring knowledge and understanding of injury risk and prevention in youth players. Only 59% of coaches agreed that youth players are at a high risk of suffering an injury. There were high levels of agreement that injuries can have negative impacts on team performance (75%) and can cause physical problems in later life (85%), however only around half of coaches felt that injuries affect youth players current quality of life (59%). There was strong agreement that it is possible to prevent injuries in youth players (84%), but coaches were generally unaware of programs to help prevent injuries (84%), and only 9% used some form of injury prevention program. Despite this, nearly all coaches felt that their coaching could benefit from a greater understanding of growth and maturation (91%), injury prevention programmes (91%) and specific exercises (93%) for youth athletes. 17% of coaches rated their knowledge of injury prevention as good/very good at the start of the workshop and this increased to 94% at the end of the workshop. 62% of coaches identified their attitude towards injury prevention as indifferent at the start of the workshop compared with only 1% at the end. Only 14% of coaches at the start of the workshop were confident to deliver an injury prevention session but 83% stated they were confident by the end of the workshop. Finally, 98% of coaches felt that the workshop provided them with the confidence and the knowledge to deliver an injury prevention session and 98% suggested that they would implement injury prevention into their coaching. These data suggest that there is a lack of understanding of grass root coaches that children are a high-risk group for injuries, and that such injuries impact on current quality of life. Despite understanding that injuries can be prevented most grass root coaches do not have the knowledge to implement injury prevention into their coaching and very few do. There is a common consensus amongst these coaches that a greater understanding of such programmes will enhance their coaching. The injury prevention workshop appears to have increased the knowledge and changed the attitude of coaches towards injury prevention. All coaches felt that the workshop provided them with the tools to adopt, implement and deliver injury prevention in their coaching. These data highlight that there is a clear need for education regarding injury risk and prevention to be embedded within the coach education pathway, especially at grass root level.

Keywords: coach education, injury prevention, knowledge, and understanding, youth

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Authors: Sevana Daneghian, Leila Chodari, Sahar Mehranfar, Shadi Mohammadpour-Asl, Diman Mahdi

Abstract:

Sarcopenia is an age-related muscle disease. Lack of antioxidant protection, and cumulative oxidative damage to skeletal muscle are recognized mechanisms. Curcumin is a hormetin as it has a stimulating effect in low doses and an inhibitory effect in high doses. The purpose of this study was to examine the effects of four weeks of curcumin supplementation in presarcopenic and sarcopenic rats, and likelihood of potential negative effects while co-exist with sarcopenia. The rats were divided into 7 groups: young sham rats, 18 months old; presarcopenic control, supplemented with 400 and 1500 mg/kg/day, 24 months old; Sarcopenia control, sarcopenia supplemented with 400 and 1500 mg/kg/day. MDA levels were significantly reduced in the low-dose pre-sarcopenic group compared to the control group. Compared to the corresponding control groups, SOD levels decreased in the groups treated with low-dose presarcopenic supplementation and increased in the high-dose sarcopenic supplemented. GPx levels increased at both doses only in the sarcopenic group compared to the control group. SIRT-1 only increased at low doses in the sarcopenic groups and PGC-1α in both pre-sarcopenia groups compared to the corresponding control. IGF-1 increased compared to the control group at both doses in the pre-sarcopenic group and at high doses in sarcopenic group. Considering the hormetic effects of curcumin, it can be argued that, curcumin supplementation has some positive effects not only at low but also at high doses in both groups. This means that the high doses of curcumin have no negative synergistic effects in addition to oxidative stress during sarcopenia and high-dose supplementation in patients already suffering from high oxidative stress due to sarcopenia is safe and could be considered hormetic.

Keywords: curcumin, hormesis, sarcopenia, muscular atrophy, PGC protein, Sirtuins

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Authors: Kateryna Zhdanova, Evelyn Szeinbaum, Michelle Lo, Yeonjae Jo, Abel E. Navarro

Abstract:

Alginate hydrogel beads (AB), spent peppermint leaf (PM), and a hybrid adsorbent of these two materials (ABPM) were studied as potential biosorbents of Cobalt (II) ions from aqueous solutions. Cobalt ion is a commonly underestimated pollutant that is responsible for several health problems. Discontinuous batch experiments were conducted at room temperature to evaluate the effect of solution acidity, mass of adsorbent on the adsorption of Co(II) ions. The interfering effect of salinity, the presence of surfactants, an organic dye, and Pb(II) ions were also studied to resemble the application of these adsorbents in real wastewater. Equilibrium results indicate that Co(II) uptake is maximized at pH values higher than 5, with adsorbent doses of 200 mg, 200 mg, and 120 mg for AB, PM, and ABPM, respectively. Co(II) adsorption followed the trend AB > ABPM > PM with Adsorption percentages of 77%, 71% and 64%, respectively. Salts had a strong negative effect on the adsorption due to the increase of the ionic strength and the competition for adsorption sites. The presence of Pb(II) ions, surfactant, and dye BY57 had a slightly negative effect on the adsorption, apparently due to their interaction with different adsorption sites that do not interfere with the removal of Co(II). A polar-electrostatic adsorption mechanism is proposed based on the experimental results. Scanning electron microscopy indicates that adsorbent has appropriate morphological and textural properties, and also that ABPM encapsulated most of the PM inside of the hydrogel beads. These experimental results revealed that AB, PM, and ABPM are promising adsorbents for the elimination of Co(II) ions from aqueous solutions under different experimental conditions. These biopolymers are proposed as eco-friendly alternatives for the removal of heavy metal ions at lower costs than the conventional techniques.

Keywords: adsorption, Co(II) ions, alginate hydrogel beads, spent peppermint leaf, pH

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Authors: Trinh Bui, A. Cappellieri

Abstract:

The fashion industry has become one of the most polluting industries in the world. In this context, designers can contribute solutions to the problem by applying Design for Sustainability (DfS) criteria, which will enable to promote designing products and services toward Sustainability. Therefore, 'Slow Fashion' movement has been receiving the attention of researchers, designers, and customers who are sensitive to sustainable development. The purpose of this paper is to contribute to a better understanding of DfS in fashion. In particular, how can apply sustainable design principles to the fashion accessory in order to minimize the negative impact on the environment and society? The research method of this study is qualitative, utilising a multi-method case study approach. Grounded theory analysis was applied to analyse the data of the case studies collected and the results obtained. Also, research findings indicate that DfS applied to Fashion Accessory design processes might have great potential and win-win approaches toward a sustainable future. An important implication is that understanding the concepts and applying DfS to fashion accessory design processes can support designers to face challenges and seize opportunities. Furthermore, identify the key concept of sustainability and social responsibility could raise awareness on sustainable fashion for both producers and customers more effectively.

Keywords: design for sustainability, fashion accessory, sustainable fashion, sustainability

Procedia PDF Downloads 275

Authors: Chahrazed Zouaoui

Abstract:

In recent years, the volume of geospatial information is increasing due to the evolution of communication technologies and information, this information is presented often by geographic information systems (GIS) and stored on of spatial databases (BDS). The classical data mining revealed a weakness in knowledge extraction at these enormous amounts of data due to the particularity of these spatial entities, which are characterized by the interdependence between them (1st law of geography). This gave rise to spatial data mining. Spatial data mining is a process of analyzing geographic data, which allows the extraction of knowledge and spatial relationships from geospatial data, including methods of this process we distinguish the monothematic and thematic, geo- Clustering is one of the main tasks of spatial data mining, which is registered in the part of the monothematic method. It includes geo-spatial entities similar in the same class and it affects more dissimilar to the different classes. In other words, maximize intra-class similarity and minimize inter similarity classes. Taking account of the particularity of geo-spatial data. Two approaches to geo-clustering exist, the dynamic processing of data involves applying algorithms designed for the direct treatment of spatial data, and the approach based on the spatial data pre-processing, which consists of applying clustering algorithms classic pre-processed data (by integration of spatial relationships). This approach (based on pre-treatment) is quite complex in different cases, so the search for approximate solutions involves the use of approximation algorithms, including the algorithms we are interested in dedicated approaches (clustering methods for partitioning and methods for density) and approaching bees (biomimetic approach), our study is proposed to design very significant to this problem, using different algorithms for automatically detecting geo-spatial neighborhood in order to implement the method of geo- clustering by pre-treatment, and the application of the bees algorithm to this problem for the first time in the field of geo-spatial.

Keywords: mining, GIS, geo-clustering, neighborhood

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Authors: Marwan Khalil Qader, Arshad Mohammad Abdullah

Abstract:

Antimicrobial resistance is a major cause of significant morbidity and mortality globally. Seven plant extracts (Plantago mediastepposa, Quercusc infectoria, Punic granatum, Thymus lcotschyana, Ginger officeinals, Rhus angustifolia and Cinnamon) were collected from different regions of Kurdistan region of Iraq. These plants’ extracts were dissolved in absolute ethanol and distillate water, after which they were assayed in vitro as an antimicrobial activity against Candida tropicalis, Candida albicanus, Candida dublinensis, Candida krusei and Candida glabrata also against 2 Gram-positive (Bacillus subtilis and Staphylococcus aureus) and 3 Gram-negative bacteria (Escherichia coli, Pseudomonas aeruginosa and Klebsilla pneumonia). The antimicrobial activity was determined in ethanol extracts and distilled water extracts of these plants. The ethanolic extracts of Q. infectoria showed the maximum activity against all species of Candida fungus. The minimum inhibition zone of the Punic granatum ethanol extracts was 0.2 mg/ml for all microorganisms tested. Klebsilla pneumonia was the most sensitive bacterial strain to Quercusc infectoria and Rhus angustifolia ethanol extracts. Among both Gram-positive and Gram-negative bacteria tested with MIC of 0.2 mg/ml, the minimum inhibition zone of Ginger officeinals D. W. extracts was 0.2 mg/mL against Pseudomonas aeruginosa and Klebsilla pneumonia. The most sensitive bacterial strain to Thymus lcotschyana and Plantago mediastepposa D.W. extracts was S. aureus and E. coli.

Keywords: antimicrobial activity, pathogenic bacteria, plant extracts, chemical systems engineering

Procedia PDF Downloads 329

Authors: Amir Mabhout, Toktam Ghafarian, Amirhossein Farzin, Zahra Makki, Sajjad Alizadeh, Amirhossein Ghavi

Abstract:

The high number of Netflix subscribers makes it attractive for data scientists to extract valuable knowledge from the viewers' behavioural analyses. This paper presents a set of statistical insights into viewers' viewing history. After that, a deep learning model is used to predict the future watching behaviour of the users based on previous watching history within the Netflixlatte data pool. Netflixlatte in an aggregated and anonymized data pool of 320 Netflix viewers with a length 250 000 data points recorded between 2008-2022. We observe insightful correlations between the distribution of viewing time and the COVID-19 pandemic outbreak. The presented deep learning model predicts future movie and TV series viewing habits with an average loss of 0.175.

Keywords: data analysis, deep learning, LSTM neural network, netflix

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Authors: Jayesh M. Patel, Bharat P. Modi

Abstract:

The availability of on mobile devices that can invoke the demonstrated that the total data demand from users is far higher than previously articulated by measurements based solely on a cellular-centric view of smart-phone usage. The ratio of Wi-Fi to cellular traffic varies significantly between countries, This paper is shown the compression between the cellular data usage and Wi-Fi data usage by the user. This strategy helps operators to understand the growing importance and application of yield management strategies designed to squeeze maximum returns from their investments into the networks and devices that enable the mobile data ecosystem. The transition from unlimited data plans towards tiered pricing and, in the future, towards more value-centric pricing offers significant revenue upside potential for mobile operators, but, without a complete insight into all aspects of smartphone customer behavior, operators will unlikely be able to capture the maximum return from this billion-dollar market opportunity.

Keywords: cellular, Wi-Fi, mobile, smart phone

Procedia PDF Downloads 361

Authors: Kudzanayi Chiteka, Wellington Makondo

Abstract:

The whole world is on the drive to go green owing to the negative effects of burning fossil fuels. Therefore, there is immediate need to identify and utilise alternative renewable energy sources. Among these energy sources solar energy is one of the most dominant in Zimbabwe. Solar power plants used to generate electricity are entirely dependent on solar radiation. For planning purposes, solar radiation values should be known in advance to make necessary arrangements to minimise the negative effects of the absence of solar radiation due to cloud cover and other naturally occurring phenomena. This research focused on the prediction of Global Horizontal Irradiation values for the sixth day given values for the past five days. Artificial intelligence techniques were used in this research. Three models were developed based on Support Vector Machines, Radial Basis Function, and Feed Forward Back-Propagation Artificial neural network. Results revealed that Support Vector Machines gives the best results compared to the other two with a mean absolute percentage error (MAPE) of 2%, Mean Absolute Error (MAE) of 0.05kWh/m²/day root mean square (RMS) error of 0.15kWh/m²/day and a coefficient of determination of 0.990. The other predictive models had prediction accuracies of MAPEs of 4.5% and 6% respectively for Radial Basis Function and Feed Forward Back-propagation Artificial neural network. These two models also had coefficients of determination of 0.975 and 0.970 respectively. It was found that prediction of GHI values for the future days is possible using artificial intelligence-based predictive models.

Keywords: solar energy, global horizontal irradiation, artificial intelligence, predictive models

Procedia PDF Downloads 268