Search results for: unusual uses task

Authors: Tomas Drevinskas, Ruta Mickiene, Audrius Maruska, Nicola Tiso, Algirdas Salomskas, Raimundas Lelesius, Agneta Karpovaite, Ona Ragazinskiene, Loreta Kubiliene

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Antiviral effect of substances accumulated by plants and natural products is known to ethno-pharmacy and modern day medicine. Antiviral properties are usually assigned to volatile compounds and polyphenols. This research work is divided into several parts and the task of this part was to investigate potential plants, potential substances and potential preparation conditions that can be used for the preparation of antiviral agents. Sixteen different medicinal plants, their parts and two types of propolis were selected for screening. Firstly, extraction conditions of non-volatile compounds were investigated: 3 pre-selected plants were extracted with 5 different ethanol – water mixtures (96%, 75%, 60%, 40%, 20 %, vol.) and bidistilled water. Total phenolic content, total flavonoid content and radical scavenging activity was determined. The results indicated that optimal extrahent is 40%, vol. of ethanol – water mixture. Further investigations were performed with the extrahent of 40%, vol. ethanol – water mixture. All 16 of selected plants, their parts and two types of propolis were extracted using selected extrahent. Determined total phenolic content, total flavonoid content and radical scavenging activity indicated that extracts of Origanum Vulgare L., Mentha piperita L., Geranium macrorrhizum L., Melissa officinalis L. and Desmodium canadence L. contains highest amount of extractable phenolic compounds (7.31, 5.48, 7.88, 8.02 and 7.16 rutin equivalents (mg/ ml) respectively), flavonoid content (2.14, 2.23, 2.49, 0.79 and 1.51 rutin equivalents (mg/ml) respectively) and radical scavenging activity (11.98, 8.72, 13.47, 13.22 and 12.22 rutin equivalents (mg/ml) respectively). Composition of the extracts is analyzed using HPLC.

Keywords: antiviral effect, plants, propolis, phenols

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Authors: B. Sadek, N. Khan, D. Łażewska, K. Kieć-Kononowicz

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The effects of the non-imidazole histamine H3 receptor (H3R) antagonist DL77 in passive avoidance paradigm (PAP) and novel object recognition (NOR) task in MK801-induced cognitive deficits associated with schizophrenia (CDS) in adult male rats, and applying donepezil (DOZ) as a reference drug were investigated. The results show that acute systemic administration of DL77 (2.5, 5, and 10 mg/kg, i.p.) significantly improved MK801-induced (0.1 mg/kg, i.p.) memory deficits in PAP. The ameliorating activity of DL77 (5 mg/kg, i.p.) in MK801-induced deficits was partly reversed when rats were pretreated with the centrally-acting H2R antagonist zolantidine (ZOL, 10 mg/kg, i.p.) or with the antimuscarinic antagonist scopolamine (SCO, 0.1 mg/kg, i.p.), but not with the CNS penetrant H1R antagonist pyrilamine (PYR, 10 mg/kg, i.p.). Moreover, the memory enhancing effect of DL77 (5 mg/kg, i.p.) in MK801-induced memory deficits in PAP was strongly reversed when rats were pretreated with a combination of ZOL (10 mg/kg, i.p.) and SCO (1.0 mg/kg, i.p.). Furthermore, the significant ameliorative effect of DL77 (5 mg/kg, i.p.) on MK801-induced long-term memory (LTM) impairment in NOR test was comparable to the DOZ-provided memory-enhancing effect, and was abrogated when animals were pretreated with the histamine H3R agonist R-(α)-methylhistamine (RAMH, 10 mg/kg, i.p.). However, DL77(5 mg/kg, i.p.) failed to provide procognitive effect on MK801-induced short-term memory (STM) impairment in NOR test. In addition, DL77 (5 mg/kg) did not alter anxiety levels and locomotor activity of animals naive to elevated-plus maze (EPM), demonstrating that improved performances with DL77 (5 mg/kg) in PAP or NOR are unrelated to changes in emotional responding or spontaneous locomotor activity. These results provide evidence for the potential of H3Rs for the treatment of neurodegenerative disorders related to impaired memory function, e.g. CDS.

Keywords: histamine H3 receptor, antagonist, learning, memory impairment, passive avoidance paradigm, novel object recognition

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Authors: T. Holecki, J. Wozniak-Holecka, K. Sobczyk

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Development, implementation, and evaluation of the effects of health policy programs, resulting from the identified health needs and health status of residents, is the own task of all local government units in Poland. This is due to the obligation to provide access to healthcare services to all residents and the implementation of tasks in the field of health promotion based on specific legal acts. Until the end of 2016 local governments financed health policy programs only with their own funds. Currently, there are additional resources available from the public health insurance subsidising up to 80% of health policy programs costs in cities with a population under 5 thousand people and up to 40% in bigger cities. Changes in legal provisions do not translate automatically to increased involvement of local government units in the implementation of public health tasks. The main objective of the study was to assess the actual impact of the new legal regulation on financing local health policy programs on the engagement of local administration in this area of public health activity. To achieve this aim, we analyzed difference in the number of local governments developing and implementing health policy programs before and after the new law came into force. The aim of the study was also to estimate the level of expenditures incurred by self-government units and the National Health Fund to cover the costs of health policy programs. In the first stage of the project, legal acts concerning the subject of research and financial data published by the National Health Fund were analyzed. The material for the second, main stage of the study was the detailed financial data obtained from the National Health Fund and data obtained from local government units. The results present the situation in Poland in territorial terms, divided into 16 voivodships.

Keywords: health care system, health policy programs, local self-governments, public health

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Authors: Jonah Kenei, Elisha Opiyo

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Electronic health records, as a repository of patient information, is nowadays the most commonly used technology to record, store and review patient clinical records and perform other clinical tasks. However, the accurate identification and retrieval of relevant information from clinical records is a difficult task due to the unstructured nature of clinical documents, characterized in particular by a lack of clear structure. Therefore, medical practice is facing a challenge thanks to the rapid growth of health information in electronic health records (EHRs), mostly in narrative text form. As a result, it's becoming important to effectively manage the growing amount of data for a single patient. As a result, there is currently a requirement to visualize electronic health records (EHRs) in a way that aids physicians in clinical tasks and medical decision-making. Leveraging text visualization techniques to unstructured clinical narrative texts is a new area of research that aims to provide better information extraction and retrieval to support clinical decision support in scenarios where data generated continues to grow. Clinical datasets in electronic health records (EHR) offer a lot of potential for training accurate statistical models to classify facets of information which can then be used to improve patient care and outcomes. However, in many clinical note datasets, the unstructured nature of clinical texts is a common problem. This paper examines the very issue of getting raw clinical texts and mapping them into meaningful structures that can support healthcare professionals utilizing narrative texts. Our work is the result of a collaborative design process that was aided by empirical data collected through formal usability testing.

Keywords: classification, electronic health records, narrative texts, visualization

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Authors: Ananya Ananya, Karthik Rao

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Accurate segmentation of knee cartilage in 3-D magnetic resonance (MR) images for quantitative assessment of volume is crucial for studying and diagnosing osteoarthritis (OA) of the knee, one of the major causes of disability in elderly people. Radiologists generally perform this task in slice-by-slice manner taking 15-20 minutes per 3D image, and lead to high inter and intra observer variability. Hence automatic methods for knee cartilage segmentation are desirable and are an active field of research. This paper presents design and experimental evaluation of 2D convolutional neural networks based fully automated methods for knee cartilage segmentation in 3D MRI. The architectures are validated based on 40 test images and 60 training images from SKI10 dataset. The proposed methods segment 2D slices one by one, which are then combined to give segmentation for whole 3D images. Proposed methods are modified versions of U-net and dilated convolutions, consisting of a single step that segments the given image to 5 labels: background, femoral cartilage, tibia cartilage, femoral bone and tibia bone; cartilages being the primary components of interest. U-net consists of a contracting path and an expanding path, to capture context and localization respectively. Dilated convolutions lead to an exponential expansion of receptive field with only a linear increase in a number of parameters. A combination of modified U-net and dilated convolutions has also been explored. These architectures segment one 3D image in 8 – 10 seconds giving average volumetric Dice Score Coefficients (DSC) of 0.950 - 0.962 for femoral cartilage and 0.951 - 0.966 for tibia cartilage, reference being the manual segmentation.

Keywords: convolutional neural networks, dilated convolutions, 3 dimensional, fully automated, knee cartilage, MRI, segmentation, U-net

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Authors: Usha Loganathan, Haresh Pandya

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Analysis and correlation of soil properties represent an important outset for precision agriculture and is currently promoted and implemented in the developed world. Establishing relationships among indices of soil salinity has always been a challenging task in salt affected soils necessitating unique approaches for their reclamation and management to sustain long term productivity of Soil. Soil salinity indices like Electrical Conductivity (EC) and Sodium Adsorption Ratio (SAR) are normally used to characterize soils as either sodic or saline sodic. Currently, Determination of Soil sodium adsorption ratio is a more accepted and reliable measure of soil salinity. However, it involves arduous and protracted laboratory investigations which demand evolving new and economical methods to determine SAR based on simple soil salinity index. A linear regression model to predict soil SAR from soil electrical conductivity has been developed and presented in this paper as per which, soil SAR could very well be worked out as a pedotransfer function of soil EC. The present study was carried out in Orathupalayam (11.09-11.11 N latitude and 74.54-77.59 E longitude) in the vicinity of Orathupalayam Reservoir of Noyyal River Basin, India, over a period of 3 consecutive years from September 2013 through February 2016 in different locations chosen randomly through different seasons. The research findings are discussed in the light of micro level farming practices in India and recommend determination of SAR as a low cost technology aiding in the effective management of salt affected agricultural land.

Keywords: electrical conductivity, orathupalayam, pedotranfer function, sodium adsorption ratio

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Authors: Matthew Heidman, Susan Dallabrida, Analice Costa

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For infant caregivers, choosing an infant formula for their child can be a difficult task in an already stressful environment of caring for a newborn. There exist several channels that influence purchasing decision of infant formula such as, friends and family and their experiences, health care professionals, social media influencers, as well as standard media marketing. This study sought to identify the key channels by which caregivers obtain information regarding infant formula and help them make their purchasing decision. A digital survey was issued for 90 days in the US (n=121) and 30 days in Mexico (n=88) targeting respondents with children ≤4 years of age. Respondents were asked two key questions regarding the influences on their purchasing decisions: 1) “When choosing a formula brand, what do you do to help you make your decision?”, and 2) “When choosing a formula brand, what is most important to you?”. A list of potential answers was provided for each question and respondents were asked to select all that apply to them. Lastly, respondents were provided a 5-point Likert scale and asked to respond to the statement 3) “I am more likely to buy a particular formula brand if my pediatrician recommends it to me”. For question 1, in the US and Mexico, 76% and 95% of respondents respectively, selected “I ask my pediatrician” which represented the top selection. For question 2, 52% and 45% of respondents respectively, selected “On package “Pediatrician Recommended” claim…” which also represented the top selection. For statement 3, 82% and 89% of respondents respectively, stated that they either “somewhat agree” or “strongly agree” with the statement. For infant caregivers, the pediatrician is a very important channel of influence when it comes to purchasing decision of infant formula. Caregivers clearly see the pediatrician as the arbiter of their child’s nutrition and seek their recommendations for infant formula use. For infant formula manufacturers, it is important that they see the pediatrician as the gatekeeper to this market, and they put resources into medical marketing communication to this health care professional group to ensure success.

Keywords: infant formula, pediatrician, purchasing driver, caregiver

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Authors: S. I. Usenko, V. N. Golubeva, I. A. Konopkina, I. V. Astakhova, O. V. Vakhnina, A. A. Korableva, A. A. Kalinina, K. B. Zhogova

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Gold can be determined in natural objects and manufactured articles of different origin. The up-to-date status of research and problems of high gold level determination in alloys and manufactured articles are described in detail in the literature. No less important is the task of this metal determination in minerals, process products and waste pieces. The latters, as objects of gold content chemical analysis, are most hard-to-study for two reasons: Because of high requirements to accuracy of analysis results and because of difference in chemical and phase composition. As a rule, such objects are characterized by compound, variable and very often unknown matrix composition that leads to unpredictable and uncontrolled effect on accuracy and other analytical characteristics of analysis technique. In this paper, the methods for the determination of gold are described, using flame atomic-absorption spectrophotometry and gravimetric analysis technique. The techniques are aimed at gold determination in a solution for gold etching (KJ+J2), in the technological mixture formed after cleaning stainless steel members of vacuum-deposit installation with concentrated nitric and hydrochloric acids as well as in gold-containing powder resulted from liquid wastes reprocessing. Optimal conditions for sample preparation and analysis of liquid and solid waste specimens of compound and variable matrix composition were chosen. The boundaries of relative resultant error were determined for the methods within the range of gold mass concentration from 0.1 to 30g/dm3 in the specimens of liquid wastes and mass fractions from 3 to 80% in the specimens of solid wastes.

Keywords: microelectronics waste pieces, gold, sample preparation, atomic-absorption spectrophotometry, gravimetric analysis technique

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Authors: Maximilian Cedzich, Ingo Dietz Von Bayer, Roland Jochem

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In order for industrial companies to continue to succeed in dynamic, globalized markets, they must be able to train their employees in an agile manner and at short notice in line with the exogenous conditions that arise. For this purpose, it is indispensable to operate a proactive competence management system for employees that recognizes qualification needs timely in order to be able to address them promptly through qualification measures. However, there are hardly any approaches to be found in the literature that includes systematic, proactive competence management. In order to help close this gap, this publication presents a process model that systematically develops bottom-up, future-oriented target competence profiles based on the tasks of the employees. Concretely, in the first step, the tasks of the individual employees are examined for assumed future conditions. In other words, qualitative scenarios are considered for the individual tasks to determine how they are likely to change. In a second step, these scenario-based future tasks are translated into individual future-related target competencies of the employee using a matrix of generic task properties. The final step pursues the goal of validating the target competence profiles formed in this way within the framework of a management workshop. This process model provides industrial companies with a tool that they can use to determine the competencies required by their own employees in the future and compare them with the actual prevailing competencies. If gaps are identified between the target and the actual, these qualification requirements can be closed in the short term by means of qualification measures.

Keywords: dynamic globalized markets, employee competence management, industrial companies, knowledge management

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Authors: T. Abdulkadir

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Most often, marking and assessment of multiple choice kinds of examinations have been opined by many as a cumbersome and herculean task to accomplished manually in Nigeria. Usually this may be in obvious nexus to the fact that mass numbers of candidates were known to take the same examination simultaneously. Eventually, marking such a mammoth number of booklets dared and dread even the fastest paid examiners who often undertake the job with the resulting consequences of stress and boredom. This paper explores the evolution, as well as the set aim to envision and transcend marking the Multiple Choice Objectives- type examination into a thing of creative recreation, or perhaps a more relaxing activity via the use of the mobile phone. A more “pragmatic” dimension method was employed to achieve this work, rather than the formal “in-depth research” based approach due to the “novelty” of the mobile-smartphone e-Marking Scheme discovery. Moreover, being an evolutionary scheme, no recent academic work shares a direct same topic concept with the ‘use of cell phone as an e-marking technique’ was found online; thus, the dearth of even miscellaneous citations in this work. Additional future advancements are what steered the anticipatory motive of this paper which laid the fundamental proposition. However, the paper introduces for the first time the concept of mobile-smart phone e-marking, the steps to achieve it, as well as the merits and demerits of the technique all spelt out in the subsequent pages.

Keywords: cell phone, e-marking scheme (eMS), mobile phone, mobile-smart phone, multiple choice objectives (MCO), smartphone

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Authors: Adegbenga Ashiru, Adebimpe L. Ashiru

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The construction industry is considered to work by diversifying personnel. Hence managing human resource is an issue considered to be a highly challenging task. Nonetheless, HR planning for the construction project is a very critical aspect of managing human resource within an expanding nature of construction industry, and there are rising concerns over the failure of construction planning to achieve its goals in spite of the substantial resources allocated to it and as a result of different planning strategies. To justify the above statement, this research was carried out to examine the sustainability of HR planning for construction project. Based on the researcher’s experience, a quantitative approach was adopted that provided a broader understanding of the research and was analysed using descriptive statistics and inferential statistics. The Statistical Package for the Social Sciences (SPSS) was used to obtain the descriptive and inferential statistical analysis. However, research findings showed that literature sources agreed with varying challenges of HR planning on construction projects which were justified by empirical findings. Also, the paper identified four major factors and the key consideration for Project HR Planning (Organisation’s structure with right individuals at right positions and evaluation current resources) will lead to the efficient utilisation implementation of new HR Planning technique and tools for a construction project. Essentially the main reoccurring theme identified was that management of the construction organisations needs to look into the essential factors needed to be considered at the strategic level. Furthermore, leaders leading a construction project team should consider those essential factors needed at the operational level to clarify the numerous functions of HRM in the construction organisations and avoid inconsistencies among several practices on construction projects. The Sustainability of HR planning for construction project policy was indicated and recommendations were made for further future research.

Keywords: construction industry, HRM planning in construction, SHRM in construction, HR planning in construction

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Authors: Yuan-Jye Tseng, Ching-Yen Chen

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In the design cycle, a main design task is to determine the external shape of the product. The external shape of a product is one of the key factors that can affect the customers’ preferences linking to the motivation to buy the product, especially in the case of a consumer electronic product such as a mobile phone. The relationship between the external shape and the customer preferences needs to be studied to enhance the customer’s purchase desire and action. In this research, a design for customer preferences model is developed for investigating the relationships between the external shape and the customer preferences of a product. In the first stage, the names of the geometric features are collected and evaluated from the data of the specified internet web pages using the developed text miner. The key geometric features can be determined if the number of occurrence on the web pages is relatively high. For each key geometric feature, the numerical values are explored using the text miner to collect the internet data from the web pages. In the second stage, a cluster analysis model is developed to evaluate the numerical values of the key geometric features to divide the external shapes into several groups. Several design suggestion cases can be proposed, for example, large model, mid-size model, and mini model, for designing a mobile phone. A customer preference index is developed by evaluating the numerical data of each of the key geometric features of the design suggestion cases. The design suggestion case with the top ranking of the customer preference index can be selected as the final design of the product. In this paper, an example product of a notebook computer is illustrated. It shows that the external shape of a product can be used to drive customer preferences. The presented design for customer preferences model is useful for determining a suitable external shape of the product to increase customer preferences.

Keywords: cluster analysis, customer preferences, design evaluation, design for customer preferences, product design

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Authors: Paria Saadatjoo

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Given that HVAC systems are the main sources of carbon dioxide producers, developing ways to reduce dependence on these systems and making use of natural resources is too important to achieve environmentally friendly buildings. A major part of building potential in terms of using natural energy resources depends on its physical features. So architectural decisions at the first step of the design process can influence the building's energy efficiency significantly. Implementation of semi-open spaces into solid apartment blocks inspired by the concept of courtyard in ancient buildings as a passive cooling strategy is currently enjoying great popularity. However, the analysis of these features and their effect on wind behavior at initial design steps is a difficult task for architects. The main objective of this research was to investigate the influence of semi-open to closed space ratio on airflow patterns in and around midrise buildings and introduce the best ratio in terms of harnessing natural ventilation. The main strategy of this paper was semi-experimental, and the research methodology was descriptive statistics. At the first step, by changing the terrace area, 6 models with various open to closed space ratios were created. These forms were then transferred to CFD software to calculate the primary indicators of natural ventilation potentials such as wind force coefficient, air flow rate, age of air distribution, etc. Investigations indicated that modifying the terrace area and, in other words, the open to closed space ratio influenced the wind force coefficient, airflow rate, and age of air distribution.

Keywords: natural ventilation, wind, midrise, open space, energy

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Authors: Yuxuan Tang

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Cognitive diagnostic assessment has received much attention in second language education, and assessment for it can provide pedagogically useful feedback for language learners. However, there is a lack of research on how students interpret and use cognitive diagnostic feedback. Thus the present study aims to adopt a mixed-method approach mainly to explore the relationship between the goal-orientation and students' response to cognitive diagnostic feedback. Almost 200 Chinese undergraduates from two universities in Xi'an, China, will be invited to do a cognitive diagnostic reading test, and each student will receive specialized cognitive diagnostic feedback, comprising of students' reading attributes mastery level generated by applying a well-selected cognitive diagnostic model, students' perceived reading ability assessed by a self-assessing questionnaire and students’ level position in the whole class. And a goal-orientation questionnaire and a self-generated questionnaire on the perception of feedback will be given to students the moment they receive feedback. In addition, interviews of students will be conducted on their future plans to see whether they have awareness of carrying out studying plans. The study aims to find a new perspective towards how students use and interpret cognitive diagnostic feedback in terms of their different goal-orientation (self-based, task-based, and other-based goals) by applying the newest goal orientation model, which is an important construct of motivation in psychology, seldom researched under language learning area. And the study is expected to provide evidence on how diagnostic feedback promotes students' learning under the educational belief of assessment for learning. Practically speaking, according to the personalized diagnostic feedback, students can take remedial self-learning more purposefully, and teachers can target students' weaknesses to adjust teaching methods and carry out tailored teaching.

Keywords: assessment for learning, cognitive diagnostic assessment, goal-orientation, personalized feedback

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Authors: Malak M. Shatnawi

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Infrastructure is the fundamental facility that plays an important part in socio-economic development for modern societies, if such sector is well planned, managed by decision makers in a way that is compatible with the population growth, safety, and national security needs; it will enrich progress, prosperity, awareness, social and economic welfare for any country. Infrastructure is the most important aspect of life because it can provide materials, products, and services that will improve and facilitate living conditions and maintain sustainability at the same time, and in order to study critical infrastructure, in general, we must think sustainability. Otherwise there will be a significant gap. The planning processes for sustainability include urban infrastructure and public transportation are considered the most important sectors for economic development for both developed and developing countries as they are linked to the civilizational and urban development, meanwhile, choosing the appropriate transportation mode that will provide a good level of service, and increase the satisfaction of the potential users is a difficult task. This research paper tries to assess where is Jordan located vs. each transportation sustainability dimensions in aspects related to social, economic and environmental dimensions based on (Zietsman et al. 2006) adopted model for sustainability transportation infrastructure. Measures of performance indicators for each dimensional goal were traced and supported with needed data, figures and statistical findings. The study uses analytical, descriptive style and methodology based on different references and previous studies from secondary data sources to support the case. Recommendations for enhancing sustainability were concluded, and future reform directions were proposed which can be applied to Jordan and generalized for other developing countries with similar circumstances.

Keywords: infrastructure transportation sustainability, economic, social, environmental

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Authors: Sitira Williams, Georgina Cherry, Andrea Wright, Kevin Wells, Taran Rai, Richard Brown, Travis Street, Alasdair Cook

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Social media sources (50) were identified, keywords defined by veterinarians and organised into 6 topics known to be indicative of feline pruritus: body areas, behaviors, symptoms, diagnosis, and treatments. These were augmented using academic literature, a cat owner survey, synonyms, and Google Trends. The content was collected using a social intelligence solution, with keywords tagged and filtered. Data were aggregated and de-duplicated. SL content matching body areas, behaviors and symptoms were reviewed manually, and posts were marked relevant if: posted by a pet owner, identifying an itchy cat and not duplicated. A sub-set of 493 posts published from 2009-2022 was used for reflexive thematic analysis in NVIVO (Burlington, MA) to identify themes. Five themes were identified: allergy, pruritus, additional behaviors, unusual or undesirable behaviors, diagnosis, and treatment. Most (258) posts reported the cat was excessively licking, itching, and scratching. The majority were indoor cats and were less playful and friendly when itchy. Half of these posts did not indicate a known cause of pruritus. Bald spots and scabs (123) were reported, often causing swelling and fur loss, and 56 reported bumps, lumps, and dry patches. Other impacts on the cat’s quality of life were ear mites, cat self-trauma and stress. Seven posts reported their cats’ symptoms caused them ongoing anxiety and depression. Cats with food allergies to poultry (often chicken and beef) causing bald spots featured in 23 posts. Veterinarians advised switching to a raw food diet and/or changing their bowls. Some cats got worse after switching, leaving owners’ needs unmet. Allergic reactions to flea bites causing excessive itching, red spots, scabs, and fur loss were reported in 13 posts. Some (3) posts indicated allergic reactions to medication. Cats with seasonal and skin allergies, causing sneezing, scratching, headshaking, watery eyes, and nasal discharge, were reported 17 times. Eighty-five posts identified additional behaviors. Of these, 13 reported their cat’s burst pimple or insect bite. Common behaviors were headshaking, rubbing, pawing at their ears, and aggressively chewing. In some cases, bites or pimples triggered previously unseen itchiness, making the cat irritable. Twenty-four reported their cat had anxiety: overgrooming, itching, losing fur, hiding, freaking out, breathing quickly, sleeplessness, hissing and vocalising. Most reported these cats as having itchy skin, fleas, and bumps. Cats were commonly diagnosed with an ear infection, ringworm, acne, or kidney disease. Acne was diagnosed in cats with an allergy flare-up or overgrooming. Ear infections were diagnosed in itchy cats with mites or other parasites. Of the treatments mentioned, steroids were most frequently used, then anti-parasitics, including flea treatments and oral medication (steroids, antibiotics). Forty-six posts reported distress following poor outcomes after medication or additional vet consultations. SL provides veterinarians with unique insights. Verbatim comments highlight the detrimental effects of pruritus on pets and owner quality of life. This study demonstrates the need for veterinarians to communicate management and treatment options more effectively to relieve owner frustrations. Data analysis could be scaled up using machine learning for topic modeling.

Keywords: content analysis, feline, itch, pruritus, social media, thematic analysis, veterinary dermatology

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Authors: Kamal KC, Zhendong Yin, Dasen Li, Zhilu Wu

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Intra-plant disease classification based on leaf images is a challenging computer vision task due to similarities in texture, color, and shape of leaves with a slight variation of leaf spot; and external environmental changes such as lighting and background noises. Deep convolutional neural network (DCNN) has proven to be an effective tool for binary classification. In this paper, two methods for binary classification of diseased plant leaves using DCNN are presented; model created from scratch and transfer learning. Our main contribution is a thorough evaluation of 4 networks created from scratch and transfer learning of 5 pre-trained models. Training and testing of these models were performed on a plant leaf images dataset belonging to 16 distinct classes, containing a total of 22,265 images from 8 different plants, consisting of a pair of healthy and diseased leaves. We introduce a deep CNN model, Optimized MobileNet. This model with depthwise separable CNN as a building block attained an average test accuracy of 99.77%. We also present a fine-tuning method by introducing the concept of a convolutional block, which is a collection of different deep neural layers. Fine-tuned models proved to be efficient in terms of accuracy and computational cost. Fine-tuned MobileNet achieved an average test accuracy of 99.89% on 8 pairs of [healthy, diseased] leaf ImageSet.

Keywords: deep convolution neural network, depthwise separable convolution, fine-grained classification, MobileNet, plant disease, transfer learning

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Authors: Oyebade K. Oyedotun

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The cardiotocogram is a technical recording of the heartbeat rate and uterine contractions of a fetus during pregnancy. During pregnancy, several complications can occur to both the mother and the fetus; hence it is very crucial that medical experts are able to find technical means to check the healthiness of the mother and especially the fetus. It is very important that the fetus develops as expected in stages during the pregnancy period; however, the task of monitoring the health status of the fetus is not that which is easily achieved as the fetus is not wholly physically available to medical experts for inspection. Hence, doctors have to resort to some other tests that can give an indication of the status of the fetus. One of such diagnostic test is to obtain cardiotocograms of the fetus. From the analysis of the cardiotocograms, medical experts can determine the status of the fetus, and therefore necessary medical interventions. Generally, medical experts classify examined cardiotocograms into ‘normal’, ‘suspect’, or ‘pathological’. This work presents an artificial neural network based decision support system which can filter cardiotocograms data, producing the corresponding statuses of the fetuses. The capability of artificial neural network to explore the cardiotocogram data and learn features that distinguish one class from the others has been exploited in this research. In this research, feedforward and radial basis neural networks were trained on a publicly available database to classify the processed cardiotocogram data into one of the three classes: ‘normal’, ‘suspect’, or ‘pathological’. Classification accuracies of 87.8% and 89.2% were achieved during the test phase of the trained network for the feedforward and radial basis neural networks respectively. It is the hope that while the system described in this work may not be a complete replacement for a medical expert in fetus status evaluation, it can significantly reinforce the confidence in medical diagnosis reached by experts.

Keywords: decision support, cardiotocogram, classification, neural networks

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Authors: Roy. H. A. Lindelauf

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Cyberattacks are complex processes consisting of multiple interwoven tasks conducted by a set of agents. Interdictions and defenses against such attacks often rely on cyber kill chain (CKC) models. A CKC is a framework that tries to capture the actions taken by a cyber attacker. There exists a growing body of literature on CKCs. Most of this work either a) describes the CKC with respect to one or more specific cyberattacks or b) discusses the tools and technologies used by the attacker at each stage of the CKC. Defenders, facing scarce resources, have to decide where to allocate their resources given the CKC and partial knowledge on the tools and techniques attackers use. In this presentation CKCs are analyzed through the lens of covert projects, i.e., interrelated tasks that have to be conducted by agents (human and/or computer) with the aim of going undetected. Various aspects of covert project models have been studied abundantly in the operations research and game theory domain, think of resource-limited interdiction actions that maximally delay completion times of a weapons project for instance. This presentation has investigated both cooperative and non-cooperative game theoretic covert project models and elucidated their relation to CKC modelling. To view a CKC as a covert project each step in the CKC is broken down into tasks and there are players of which each one is capable of executing a subset of the tasks. Additionally, task inter-dependencies are represented by a schedule. Using multi-glove cooperative games it is shown how a defender can optimize the allocation of his scarce resources (what, where and how to monitor) against an attacker scheduling a CKC. This study presents and compares several cooperative game theoretic solution concepts as metrics for assigning resources to the monitoring of agents.

Keywords: cyber defense, cyber kill chain, game theory, information warfare techniques

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Authors: J. S. Saini, P. P. K. Sandhu

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The multi-hop nature of Wireless Mesh Networks and the hasty progression of throughput demands results in multi- channels and multi-radios structures in mesh networks, but the main problem of co-channels interference reduces the total throughput, specifically in multi-hop networks. Quality of Service mentions a vast collection of networking technologies and techniques that guarantee the ability of a network to make available desired services with predictable results. Quality of Service (QoS) can be directed at a network interface, towards a specific server or router's performance, or in specific applications. Due to interference among various transmissions, the QoS routing in multi-hop wireless networks is formidable task. In case of multi-channel wireless network, since two transmissions using the same channel may interfere with each other. This paper has considered the Destination Sequenced Distance Vector (DSDV) routing protocol to locate the secure and optimised path. The proposed technique also utilizes the Lempel–Ziv–Welch (LZW) based lossless data compression and intra cluster data aggregation to enhance the communication between the source and the destination. The use of clustering has the ability to aggregate the multiple packets and locates a single route using the clusters to improve the intra cluster data aggregation. The use of the LZW based lossless data compression has ability to reduce the data packet size and hence it will consume less energy, thus increasing the network QoS. The MATLAB tool has been used to evaluate the effectiveness of the projected technique. The comparative analysis has shown that the proposed technique outperforms over the existing techniques.

Keywords: WMNS, QOS, flooding, collision avoidance, LZW, congestion control

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Authors: Lu Song Ma Li Zhi

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With the rapid advancement of artificial intelligence technology, large language models (LLMs) have become a milestone in the field of natural language processing, demonstrating remarkable capabilities in semantic understanding, intelligent question answering, and text generation. These models are gradually penetrating various industries, particularly showcasing significant application potential in the data analysis domain. However, retraining or fine-tuning these models requires substantial computational resources and ample downstream task datasets, which poses a significant challenge for many enterprises and research institutions. Without modifying the internal parameters of the large models, prompt engineering techniques can rapidly adapt these models to new domains. This paper proposes a prompt design strategy aimed at leveraging the capabilities of large language models to automate the generation of data analysis code. By carefully designing prompts, data analysis requirements can be described in natural language, which the large language model can then understand and convert into executable data analysis code, thereby greatly enhancing the efficiency and convenience of data analysis. This strategy not only lowers the threshold for using large models but also significantly improves the accuracy and efficiency of data analysis. Our approach includes requirements for the precision of natural language descriptions, coverage of diverse data analysis needs, and mechanisms for immediate feedback and adjustment. Experimental results show that with this prompt design strategy, large language models perform exceptionally well in multiple data analysis tasks, generating high-quality code and significantly shortening the data analysis cycle. This method provides an efficient and convenient tool for the data analysis field and demonstrates the enormous potential of large language models in practical applications.

Keywords: large language models, prompt design, data analysis, code generation

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Authors: Kin Yan Ho

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Negative brand news (such as Volkswagen’s faulty carbon emission reports, China’s Luckin Coffee scandal, and bribery in reputable US universities) influence how people perceive a company. Germany’s citizens claimed Volkswagen’s scandal as a national embarrassment and cannot recover their psychological damages through monetary and non-monetary compensation. The main research question is to examine how consumers evaluate and respond to embarrassing brand publicity. The cognitive appraisal theory is used as a theoretical foundation. This study describes the use of scenario-based experiment. The findings suggest that consumers with different levels of embarrassment evaluate brand remedial offers from emotion-focused and task-focused restorative justice perspectives (newly derived from the well-established scales of perceived justice). When consumers face both negative and positive brand information (i.e., negative publicity news and a remedial offer), they change their appraisal criterion. The social situation in the cognitive reappraisal process influences the quality of the customer-brand relationship and the customer’s recovery from brand embarrassment. The results also depict that the components of recovery compensation cause differences in emotion recovery, relationship quality, and repurchase intentions. This study extends embarrassment literature in an embarrassing brand publicity context. The emotional components of brand remedial tactics provide insights to brand managers on how to handle different consumers’ emotions, consumer satisfaction, and foster positive future behavior.

Keywords: brand relationship quality, cognitive appraisal, crisis communications, emotion, justice, social presence

Procedia PDF Downloads 134

Authors: Chih-Ping Chang

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Different from traditional objective evidence, social media evidence has its own characteristics with easily tampering, recoverability, and cannot be read without using other devices (such as a computer). Simply taking a screenshot from social network sites must be questioned its original identity. When the police search and seizure digital information, a common way they use is to directly print out digital data obtained and ask the signature of the parties at the presence, without taking original digital data back. In addition to the issue on its original identity, this conduct to obtain evidence may have another two results. First, it will easily allege that is tampering evidence because the police wanted to frame the suspect and falsified evidence. Second, it is not easy to discovery hidden information. The core evidence associated with crime may not appear in the contents of files. Through discovery the original file, data related to the file, such as the original producer, creation time, modification date, and even GPS location display can be revealed from hidden information. Therefore, how to show this kind of evidence in the courtroom will be arguably the most important task for ruling social media evidence. This article, first, will introduce forensic software, like EnCase, TCT, FTK, and analyze their function to prove the identity with another digital data. Then turning back to the court, the second part of this article will discuss legal standard for authentication of social media evidence and application of that forensic software in the courtroom. As the conclusion, this article will provide a rethinking, that is, what kind of authenticity is this rule of evidence chase for. Does legal system automatically operate the transcription of scientific knowledge? Or furthermore, it wants to better render justice, not only under scientific fact, but through multivariate debating.

Keywords: federal rule of evidence, internet forensic, printouts as evidence, social media evidence, United States v. Vayner

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Authors: Hebe Wong, Luz Fernandez Calventos

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Tandem language learning, a language exchange process based on the principles of autonomy and reciprocity, provides opportunities for interlocutors to learn each other’s language by communicating online or face-to-face. While much attention has been paid to the process and outcomes of tandem learning via email, little has been discussed about the effectiveness of face-to-face tandem learning on language and culture exchange for university students. The LACTS (Language and Culture Tandem Scheme), an 8-week project, was set up to study students’ perceptions of conducting tandem learning to assist their language and culture exchange. Students of both post-graduate and undergraduate programmes (N=103) from a Hong Kong SAR university were put in groups of 4 to 6 according to their availability and language preferences and met for an hour a week. While sample task sheets on a range of topics were provided to assist the language exchange, all groups were encouraged to take charge of their meeting format and choose their own topics. At the end of the project, a 19-item questionnaire, which included both open-and closed-ended questions investigating students’ perceptions of reciprocal teaching and cultural exchange, was administered. Thirty-minute individual interviews were conducted to elicit students’ views and experiences in the LACTS activities. Quantitative and qualitative data analysis showed that most students agreed that the project had enhanced their cultural awareness and helped create an inclusive and participatory learning environment. Significant differences were found in students’ confidence in speaking their targeted language after joining the scheme. The interviews also provided rich data on the variety of formats and leadership patterns in student-led meetings, which could shed light on student autonomy and future tandem language learning projects.

Keywords: autonomy, reciprocity, tandem language learning, university students

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Authors: Houyem Kotti

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The development in societies requires that these societies are continuously changing in various aspects, a change that requires continuous adaptation to the data of the technical age. In order for the individual to perform his/her duty or task in a perfect way, it is necessary to provide all the basic requirements and necessities to increase the efficiency and effectiveness of the personnel working to accomplish their tasks, requirements, and work successfully. The success of the industries and organizations are linked to the need to create individuals in the creative and innovative field. Formation process is considered an economic development and social prosperity, and to improve the quantity and quality of artistic work. Therefore, creativity and innovation play an important role in improving the performance of the artistic organization as it is one of the variables affecting the organization's ability to grow and invest. In order to provide better services to their customers, especially in the face of competition and traditional methods of work, and in an environment that discourages and hinders creativity and impairs any process of development, change or creative behavior. The research methodology that will be performed for this study is described as qualitative by conducting several interviews with artistic people, experts in the artistic field and reviewing the related literature to collect the necessary and required qualitative data from secondary sources such as statistical reports, previous research studies, etc. In this research, we will attempt to clarify the relationship between innovation and its importance in the artistic organization, the conditions of achieving innovation and its constraints, barriers, and challenges. The creativity and innovation and their impacts on the performance of artistic organizations, explaining this mechanism, so as to ensure continuity of these organizations and keeping pace with developments in the global economic environment.

Keywords: artistic work, creativity and innovation, artistic organization, performance

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Authors: Swati Sundararajan, Asit B. Samui, Prashant S. Kulkarni

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Polymeric ionic liquids combine the properties of ionic liquids and polymers into a single material which has gained massive interest in the recent years. These ionic liquids offer several advantages such as high phase change enthalpy, wide temperature range, chemical and thermal stability, non-volatility and the ability to make them task-specific. Separation of CO2 is an area of critical importance due to the concerns over greenhouse gasses leading to global warming. Thermal energy storage materials, also known as phase change materials absorb latent heat during fusion process and release the absorbed energy to the surrounding environment during crystallization. These materials retain this property over a number of cycles and therefore, are useful for bridging the gap between energy requirement and use. In an effort to develop materials, which will help in minimizing the growing energy demand and environmental concerns, a series of dicationic poly(ethylene glycol) based polymeric ionic liquids were synthesized. One part of an acrylate of poly(ethylene glycol) was reacted with imidazolium quarternizing agent and the second part was reacted with triazolium quarternizing agent. These two different monomers were then copolymerized to prepare dicationic polymeric ionic liquid. These materials were characterized for solid-liquid phase transition and the enthalpy by using differential scanning calorimetry. The CO2 capture studies were performed on a fabricated setup with varying pressure range from 1-20 atm. The findings regarding the prepared materials, having potential dual applications in the fields of thermal energy storage and CO2 capture, will be discussed in the presentation.

Keywords: CO2 capture, phase change materials, polyethylene glycol, polymeric ionic liquids, thermal energy storage

Procedia PDF Downloads 254

Authors: Atour Taghipour

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In the real world each manufacturing company is an independent business unit. These business units are linked to each other through upstream and downstream linkages. The management of these linkages is called coordination which, could be considered as a difficult engineering task. The degree of difficulty of coordination depends on the type and the nature of information exchanged between partners as well as the structure of relationship from mutual to the network structure. The literature of manufacturing systems comprises a wide range of varieties of methods and approaches of coordination. In fact, two main streams of research can be distinguished: central coordination versus decentralized coordination. In the centralized systems a high degree of information exchanges is required. The high degree of information exchanges sometimes leads to difficulties when independent members do not want to share information. In order to address these difficulties, decentralized approaches of coordination of operations planning decisions based on some minimal information sharing have been proposed in many academic disciplines. This paper first proposes a framework of analysis in order to analyze the proposed approaches in the literature, based on this framework which includes the similarities between approaches we categorize the existing approaches. This classification can be used as a research map for future researches. The result of our paper highlights several opportunities for future research. First, it is proposed to develop more dynamic and stochastic mechanisms of planning coordination of manufacturing units. Second, in order to exploit the complementarities of approaches proposed by diverse science discipline, we propose to integrate the techniques of coordination. Finally, based on our approach we proposed to develop coordination standards to guaranty both the complementarity of these approaches as well as the freedom of companies to adopt any planning tools.

Keywords: network coordination, manufacturing, operations planning, supply chain

Procedia PDF Downloads 281

Authors: Pieter Conradie, M. Marina Moller

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Web 2.0 Internet-based technologies have intruded all aspects of human life. Presently, this phenomenon is especially evident in the educational context, with increased disruptive Web 2.0 technology infusions dramatically changing educational practice. The most prominent of these Web 2.0 intrusions can be identified as Massive Open Online Courses (Coursera, EdX), video and photo sharing sites (Youtube, Flickr, Instagram), and Web 2.0 online tools utilize to create Personal Learning Environments (PLEs) (Symbaloo (aggregator), Delicious (social bookmarking), PBWorks (collaboration), Google+ (social networks), Wordspress (blogs), Wikispaces (wiki)). These Web 2.0 technologies have supported the realignment from a teacher-based pedagogy (didactic presentation) to a learner-based pedagogy (problem-based learning, project-based learning, blended learning), allowing greater learner autonomy. No longer is the educator the source of knowledge. Instead the educator has become the facilitator and mediator of the learner, involved in developing learner competencies to support life-long learning (continuous learning) in the 21st century. In this study, the self-regulated learning skills of thirty first-year university learners were explored by utilizing the Online Self-regulated Learning Questionnaire. Implementing an action research method, an intervention was affected towards improving the self-regulation skill set of the participants. Statistical significant results were obtained with increased self-regulated learning proficiency, positively impacting learner performance. Goal setting, time management, environment structuring, help seeking, task (learning) strategies and self-evaluation skills were confirmed as determinants of improved learner success.

Keywords: andragogy, online self-regulated learning questionnaire, self-regulated learning, web 2.0

Procedia PDF Downloads 417

Authors: Raghavi C. Janaswamy

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In the healthcare sector, heterogenous data elements such as patients, diagnosis, symptoms, conditions, observation text from physician notes, and prescriptions form the essentials of the Electronic Health Record (EHR). The data in the form of clear text and images are stored or processed in a relational format in most systems. However, the intrinsic structure restrictions and complex joins of relational databases limit the widespread utility. In this regard, the design and development of realistic mapping and deep connections as real-time objects offer unparallel advantages. Herein, a graph neural network-based classification of EHR data has been developed. The patient conditions have been predicted as a node classification task using a graph-based open source EHR data, Synthea Database, stored in Tigergraph. The Synthea DB dataset is leveraged due to its closer representation of the real-time data and being voluminous. The graph model is built from the EHR heterogeneous data using python modules, namely, pyTigerGraph to get nodes and edges from the Tigergraph database, PyTorch to tensorize the nodes and edges, PyTorch-Geometric (PyG) to train the Graph Neural Network (GNN) and adopt the self-supervised learning techniques with the AutoEncoders to generate the node embeddings and eventually perform the node classifications using the node embeddings. The model predicts patient conditions ranging from common to rare situations. The outcome is deemed to open up opportunities for data querying toward better predictions and accuracy.

Keywords: electronic health record, graph neural network, heterogeneous data, prediction

Procedia PDF Downloads 86

Authors: Flavio Cannavo

Abstract:

Continuous evaluation of the status of potentially hazardous volcanos plays a key role for civil protection purposes. The importance of monitoring volcanic activity, especially for energetic paroxysms that usually come with tephra emissions, is crucial not only for exposures to the local population but also for airline traffic. Presently, real-time surveillance of most volcanoes worldwide is essentially delegated to one or more human experts in volcanology, who interpret data coming from different kind of monitoring networks. Unfavorably, the high nonlinearity of the complex and coupled volcanic dynamics leads to a large variety of different volcanic behaviors. Moreover, continuously measured parameters (e.g. seismic, deformation, infrasonic and geochemical signals) are often not able to fully explain the ongoing phenomenon, thus making the fast volcano state assessment a very puzzling task for the personnel on duty at the control rooms. With the aim of aiding the personnel on duty in volcano surveillance, here we introduce a system based on an ensemble of data-driven classifiers to infer automatically the ongoing volcano status from all the available different kind of measurements. The system consists of a heterogeneous set of independent classifiers, each one built with its own data and algorithm. Each classifier gives an output about the volcanic status. The ensemble technique allows weighting the single classifier output to combine all the classifications into a single status that maximizes the performance. We tested the model on the Mt. Etna (Italy) case study by considering a long record of multivariate data from 2011 to 2015 and cross-validated it. Results indicate that the proposed model is effective and of great power for decision-making purposes.

Keywords: Bayesian networks, expert system, mount Etna, volcano monitoring

Procedia PDF Downloads 246