Search results for: extreme ranked set sampling

Authors: Polvasut Mahaiamsiri, Piraphong Foosiri

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The purpose of this research is to explore the model of factors affecting decision making to select successor in family business of Thailand. A Structural Equation Model (SEM) was created from relevant theories and researches. Consequently, examine and analyse, the causal relation factors of Succession Plan, Recruitment Process and Strategic Planning, whether they have direct or indirect effects on Decision Making to Select Successor in family business. Units of analysis are selected from the family business, totalling 300 sampling. Population sampling is current owners or CEO from the percentage of six district areas in Thailand with multi-stage sampling. A set of questionnaires is used to collect data. An analysis of structural equation modelling (SEM) technique using AMOS 21 program is conducted to test the hypotheses and confirmatory factor analysis is performed and shows that these variables can be tested. The finding of this study revealed that these factors are separate constructs that combine to determine decision making to select successors.

Keywords: succession plan, family business, recruitment process, strategic planning, decision making to select successor

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Authors: Benoit Atge, Alice Dhersin, Oscar Da Silva Cacao, Beatrice Martinez, Dominique Ducint, Catherine Verdun-Esquer, Isabelle Baldi, Mathieu Molimard, Antoine Villa, Mireille Canal-Raffin

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Introduction: Healthcare workers' exposure to antineoplastic drugs (AD) is a burning issue for occupational medicine practitioners. Biological monitoring of occupational exposure (BMOE) is an essential tool for assessing AD contamination of healthcare workers. In addition to BMOE, surface sampling is a useful tool in order to understand how workers get contaminated, to identify sources of environmental contamination, to verify the effectiveness of surface decontamination way and to ensure monitoring of these surfaces. The objective of this work was to develop a complete tool including a kit for surface sampling and a quantification analytical method for AD traces detection. The development was realized with the three following criteria: the kit capacity to sample in every professional environment (healthcare services, veterinaries, etc.), the detection of very low AD traces with a validated analytical method and the easiness of the sampling kit use regardless of the person in charge of sampling. Material and method: AD mostly used in term of quantity and frequency have been identified by an analysis of the literature and consumptions of different hospitals, veterinary services, and home care settings. The kind of adsorbent device, surface moistening solution and mix of solvents for the extraction of AD from the adsorbent device have been tested for a maximal yield. The AD quantification was achieved by an ultra high-performance liquid chromatography method coupled with tandem mass spectrometry (UHPLC-MS/MS). Results: With their high frequencies of use and their good reflect of the diverse activities through healthcare, 15 AD (cyclophosphamide, ifosfamide, doxorubicin, daunorubicin, epirubicin, 5-FU, dacarbazin, etoposide, pemetrexed, vincristine, cytarabine, methothrexate, paclitaxel, gemcitabine, mitomycin C) were selected. The analytical method was optimized and adapted to obtain high sensitivity with very low limits of quantification (25 to 5000ng/mL), equivalent or lowest that those previously published (for 13/15 AD). The sampling kit is easy to use, provided with a didactic support (online video and protocol paper). It showed its effectiveness without inter-individual variation (n=5/person; n= 5 persons; p=0,85; ANOVA) regardless of the person in charge of sampling. Conclusion: This validated tool (sampling kit + analytical method) is very sensitive, easy to use and very didactic in order to control the chemical risk brought by AD. Moreover, BMOE permits a focal prevention. Used in routine, this tool is available for every intervention of occupational health.

Keywords: surface contamination, sampling kit, analytical method, sensitivity

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Authors: Danielle M. Barna, Kolbjørn Engeland, Thordis Thorarinsdottir, Chong-Yu Xu

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Design flood values give estimates of flood magnitude within a given return period and are essential to making adaptive decisions around land use planning, infrastructure design, and disaster mitigation. Often design flood values are needed at locations with insufficient data. Additionally, in hydrologic applications where flood retention is important (e.g., floodplain management and reservoir design), design flood values are required at different flood durations. A statistical approach to this problem is a development of a regression model for extremes where some of the parameters are dependent on flood duration in addition to being covariate-dependent. In hydrology, this is called a regional flood-duration-frequency (regional-QDF) model. Typically, the underlying statistical distribution is chosen to be the Generalized Extreme Value (GEV) distribution. However, as the support of the GEV distribution depends on both its parameters and the range of the data, special care must be taken with the development of the regional model. In particular, we find that the GEV is problematic when developing a GAMLSS-type analysis due to the difficulty of proposing a link function that is independent of the unknown parameters and the observed data. We discuss these challenges in the context of developing a regional QDF model for Norway.

Keywords: design flood values, bayesian statistics, regression modeling of extremes, extreme value analysis, GEV

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Authors: E. Izsó, M. Bartalné-Berceli, Sz. Gergely, A. Salgó

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The aims of this investigation is to elaborate near-infrared methods for testing and recognition of chemical components and quality in “Pannon wheat” allied (i.e. true to variety or variety identified) milling fractions as well as to develop spectroscopic methods following the milling processes and evaluate the stability of the milling technology by different types of milling products and according to sampling times, respectively. This wheat categories produced under industrial conditions where samples were collected versus sampling time and maximum or minimum yields. The changes of the main chemical components (such as starch, protein, lipid) and physical properties of fractions (particle size) were analysed by dispersive spectrophotometers using visible (VIS) and near-infrared (NIR) regions of the electromagnetic radiation. Close correlation were obtained between the data of spectroscopic measurement techniques processed by various chemometric methods (e.g. principal component analysis (PCA), cluster analysis (CA) and operation condition of milling technology. Its obvious that NIR methods are able to detect the deviation of the yield parameters and differences of the sampling times by a wide variety of fractions, respectively. NIR technology can be used in the sensitive monitoring of milling technology.

Keywords: near infrared spectroscopy, wheat categories, milling process, monitoring

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Authors: Guangyuan Zhao, Nan Huang, Xuesong Han, Xu Huang

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In order to solve the problem of sample dilution in the traditional particle filter algorithm and achieve accurate state estimation in a nonlinear system, a particle filter method based on an improved artificial bee colony (ABC) algorithm was proposed. The algorithm simulated the process of bee foraging and optimization and made the high likelihood region of the backward probability of particles moving to improve the rationality of particle distribution. The opposition-based learning (OBL) strategy is introduced to optimize the initial population of the artificial bee colony algorithm. The convergence factor is introduced into the neighborhood search strategy to limit the search range and improve the convergence speed. Finally, the crossover and mutation operations of the genetic algorithm are introduced into the search mechanism of the following bee, which makes the algorithm jump out of the local extreme value quickly and continue to search the global extreme value to improve its optimization ability. The simulation results show that the improved method can improve the estimation accuracy of particle filters, ensure the diversity of particles, and improve the rationality of particle distribution.

Keywords: particle filter, impoverishment, state estimation, artificial bee colony algorithm

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Authors: Pranee Tridhoskul

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This research paper is aimed to examine a relationship between the service marketing mix and customers’ frequency of use of service at Mercedes Benz Auto Repair Centres under Thonburi Group, Thailand. Based on 2,267 customers who used the service of Thonburi Group’s Auto Repair Centres as the population, the sampling of this research was a total of 340 samples, by use of Probability Sampling Technique. Systematic Random Sampling was applied by use of questionnaire in collecting the data at Thonburi Group’s Auto Repair Centres. Mean and Pearson’s basic statistical correlations were utilized in analyzing the data. The study discovered a medium level of customers’ perception towards product and service of Thonburi Group’s Auto Repair Centres, price, place or distribution channel and promotion. People who provided service were perceived also at a medium level, whereas the physical evidence and service process were perceived at a high level. Furthermore, there appeared a correlation between the physical evidence and service process, and customers’ frequency of use of automobile service per year.

Keywords: service marketing mix, behavior, Mercedes Auto Service Centre, frequency of use

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Authors: Emily Injete Amondo, Alisher Mirzabaev, Emmanuel Rukundo

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Children in rural farming households are often vulnerable to a multitude of risks, including health risks associated with climate change and variability. Cognizant of this, this study empirically traced the relationship between climate variability and nutritional health outcomes in rural children while identifying the cause-and-effect transmission mechanisms. We combined four waves of the rich Uganda National Panel Survey (UNPS), part of the World Bank Living Standards Measurement Studies (LSMS) for the period 2009-2014, with long-term and high-frequency rainfall and temperature datasets. Self-reported drought and flood shock variables were further used in separate regressions for triangulation purposes and robustness checks. Panel fixed effects regressions were applied in the empirical analysis, accounting for a variety of causal identification issues. The results showed significant negative outcomes for children’s anthropometric measurements due to the impacts of moderate and extreme droughts, extreme wet spells, and heatwaves. On the contrary, moderate wet spells were positively linked with nutritional measures. Agricultural production and child diarrhea were the main transmission channels, with heatwaves, droughts, and high rainfall variability negatively affecting crop output. The probability of diarrhea was positively related to increases in temperature and dry spells. Results further revealed that children in households who engaged in ex-ante or anticipatory risk-reducing strategies such as savings had better health outcomes as opposed to those engaged in ex-post coping such as involuntary change of diet. These results highlight the importance of adaptation in smoothing the harmful effects of climate variability on the health of rural households and children in Uganda.

Keywords: extreme weather events, undernutrition, diarrhea, agricultural production, gridded weather data

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Authors: Charmaine B. Distor

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Collaboration has long been recognized in different fields, but there’s been little research on operationalizing it especially on a multi-sectoral setting as per the author’s best knowledge. Also, communication is one of the factors that is usually overlooked when studying it. Specifically, this study aimed to describe the organizational profile and tasks of collaborators in the visitor management program of Make It Makiling (MIM). It also identified the factors that motivated collaborators to collaborate in MIM while determining the communication dimensions in the collaborative process. It also determined the communication channels used by collaborators in MIM while identifying the outcomes of collaboration in MIM. This study also found out if a relationship exists between collaborators’ motivations for collaboration and their perceived outcomes of collaboration, and collaborators' communication dimensions and their perceived outcomes of collaboration. Lastly, it also provided recommendations to improve the communication in MIM. Data were gathered using a self-administered survey that was patterned after Mattessich and Monsey’s (1992) collaboration experience questionnaire. Interviews and secondary sources mainly provided by the Makiling Center for Mountain Ecosystems (MCME) were also used. From the seven MIM collaborating organizations that were selected through purposive sampling, 86 respondents were chosen. Then, data were analyzed through frequency counts, percentages, measures of central tendencies, and Pearson’s and Spearman rho correlations. Collaborators’ length of collaboration ranged from seven to twenty years. Furthermore, six out of seven of the collaborators were involved in the task of 'emergency, rescue, and communication'. For the other aspect of the antecedents, the history of previous collaboration efforts ranked as the highest rated motivation for collaboration. In line with this, the top communication dimension is the governance while perceived effectiveness garnered the highest overall average among the perceived outcomes of collaboration. Results also showed that the collaborators highly rely on formal communication channels. Meetings and memos were the most commonly used communication channels throughout all tasks under the four phases of MIM. Additionally, although collaborators have a high view towards their co-collaborators, they still rely on MCME to act as their manager in coordinating with one another indirectly. Based on the correlation analysis, antecedent (motivations)-outcome relationship generally had positive relationships. However, for the process (communication dimensions)-outcome relationship, both positive and negative relationships were observed. In conclusion, this study exhibited the same trend with existing literature which also used the same framework. For the antecedent-outcome relationship, it can be deduced that MCME, as the main organizer of MIM, can focus on these variables to achieve their desired outcomes because of the positive relationships. For the process-outcome relationship, MCME should also take note that there were negative relationships where an increase in the said communication dimension may result in a decrease in the desired outcome. Recommendations for further study include a methodology that contains: complete enumeration or any parametric sampling, a researcher-administered survey, and direct observations. These might require additional funding, but all may yield to richer data.

Keywords: antecedent-outcome relationship, carrying capacity, organizational communication, process-outcome relationship

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Authors: Sasitorn Chetanont

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The objectives of this study are to study Chinese tourist’s Behaviors towards travel and shopping in Bangkok. The research methodology was a quantitative research. The sample of this research was 400 Chinese tourists in Bangkok chosen by the accidental sampling and the purposive sampling. Inferential Statistics Analysis by using the Chi-square statistics. As for the results of this study the researcher found that differences between personal, social and cultural information, i.e., gender, age, place of residence, educational level, occupation, income, family, and main objectives of tourism with behaviors of Chinese tourists in Bangkok towards travel and shopping in Bangkok.

Keywords: tourists’ behavior, Chinese tourists, travelling, expenses in travels

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Authors: Omer Cahana, Ofer Levi, Maya Herman

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Magnetic Resonance Imaging (MRI) is a lengthy medical scan that stems from a long acquisition time. Its length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach such as Compress Sensing (CS) or Parallel Imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. To achieve that, two conditions must be satisfied: i) the signal must be sparse under a known transform domain, and ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm must be applied to recover the signal. While the rapid advances in Deep Learning (DL) have had tremendous successes in various computer vision tasks, the field of MRI reconstruction is still in its early stages. In this paper, we present an end-to-end method for MRI reconstruction from k-space to image. Our method contains two parts. The first is sensitivity map estimation (SME), which is a small yet effective network that can easily be extended to a variable number of coils. The second is reconstruction, which is a top-down architecture with lateral connections developed for building high-level refinement at all scales. Our method holds the state-of-art fastMRI benchmark, which is the largest, most diverse benchmark for MRI reconstruction.

Keywords: magnetic resonance imaging, image reconstruction, pyramid network, deep learning

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Authors: Nilesh Bandekar, Sumita Dasgupta, Luis Alberto Allcahuaman Huaya, Souvik Manna

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Cryptobiosis is a dormant state where all measurable metabolic activities are at a halt, allowing an organism to survive in extreme conditions like low temperature (cryobiosis), extreme drought (anhydrobiosis), etc. This phenomenon is observed especially in tardigrades that can retain this state for decades depending on the abiotic environmental conditions. On returning to favorable conditions, tardigrades re-attain a metabolically active state. In this study, cyanobacteria as a model organism are being chosen to induce cryptobiosis for its effective preservation over a long period of time. Preserving cyanobacteria using this strategy will have multiple space applications because of its ability to produce oxygen. In addition, research has shown the survivability of this organism in space for a certain period of time. Few species of cyanobacterial residents of the soil such as Microcoleus, are able to survive in extreme drought as well. This work specifically focuses on Synechococcus elongatus, an endolith cyanobacteria with multiple benefits. It has the capability to produce 25% oxygen in water bodies. It utilizes carbon dioxide to produce oxygen via photosynthesis and also uses carbon dioxide as an energy source to form glucose via the Calvin cycle. There is a fair possibility of initiating cryptobiosis in such an organism by inducing certain proteins extracted from tardigrades such as Heat Shock Proteins (Hsp27 and Hsp30c) and/or hydrophilic Late Embryogenesis Abundant proteins (LEA). Existing methods like cryopreservation are difficult to execute in space keeping in mind their cost and heavy instrumentation. Also, extensive freezing may cause cellular damage. Therefore, cryptobiosis-induced cyanobacteria for its transportation from Earth to Mars as a part of future terraforming missions on Mars will save resources and increase the effectiveness of preservation. Finally, Cyanobacteria species like Synechococcus elongatus can also produce oxygen and glucose on Mars in favorable conditions and holds the key to terraforming Mars.

Keywords: cryptobiosis, cyanobacteria, glucose, mars, Synechococcus elongatus, tardigrades

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Authors: Ahmad Shahin, Fadi Chakik, Walid Moudani

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Our study is about elaborating a potential solution for a search engine that involves semantic technology to retrieve information and display it significantly. Semantic search engines are not used widely over the web as the majorities are still in Beta stage or under construction. Many problems face the current applications in semantic search, the major problem is to analyze and calculate the meaning of query in order to retrieve relevant information. Another problem is the ontology based index and its updates. Ranking results according to concept meaning and its relation with query is another challenge. In this paper, we are offering a light meta-engine (QESM) which uses Google search, and therefore Google’s index, with some adaptations to its returned results by adding multi-query expansion. The mission was to find a reliable ranking algorithm that involves semantics and uses concepts and meanings to rank results. At the beginning, the engine finds synonyms of each query term entered by the user based on a lexical database. Then, query expansion is applied to generate different semantically analogous sentences. These are generated randomly by combining the found synonyms and the original query terms. Our model suggests the use of semantic similarity measures between two sentences. Practically, we used this method to calculate semantic similarity between each query and the description of each page’s content generated by Google. The generated sentences are sent to Google engine one by one, and ranked again all together with the adapted ranking method (QESM). Finally, our system will place Google pages with higher similarities on the top of the results. We have conducted experimentations with 6 different queries. We have observed that most ranked results with QESM were altered with Google’s original generated pages. With our experimented queries, QESM generates frequently better accuracy than Google. In some worst cases, it behaves like Google.

Keywords: semantic search engine, Google indexing, query expansion, similarity measures

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Authors: Oktay Emir

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The objective of this study is to determine how entrepreneurs perceive the economic, social and physical impacts of tourism. The study was conducted in the city of Afyonkarahisar, Turkey, which is rich in thermal tourism resources and investments. A survey was used as the data collection method, and the questionnaire was applied to 472 entrepreneurs. A simple random sampling method was used to identify the sample. Independent sampling t-tests and ANOVA tests were used to analyse the data obtained. Additionally, some statistically significant differences (p<0.05) were found based on the participants’ demographic characteristics regarding their opinions about the social, economic and physical impacts of tourism activities.

Keywords: tourism, perception, entrepreneurship, entrepreneurs, structural equation modelling

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Authors: Sofía Luna, Carla González Salamanca

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The 2008 economic crisis had huge implications in Europe. In this continent, the repercussions of the crisis were not only economic but also political and institutional. The economic stress has generated changes in the perception of the citizens, their attitude and the confidence placed in the political organizations. The lost of confidence is not only present in the debtor countries but it is also present in the European economic powers like Germany and France. This research explains how the economic crisis had an impact in the identity, population’s attitude and how this generated the rise of extreme right parties. In addition, it defines the different types of attitudes and support that exist towards these political and economic institutions. The results of this investigation show that the depression beside of its economic implications, it caused institutional, social and political difficulties for the Union. Moreover, the support and attitudes of the population were severely strained because the confidence in the political organization decreased. Furthermore, a rise in the otherness sentiment was shown. In other words, the distinction between “us” and “them” increased causing repercussions in the collective European identity. Additionally, there was a spread in national identities that caused the rise of the extreme right wing parties. In conclusion, the 2008 economic crisis caused not only economic stress but also it generated a political, social and institutional crisis in Europe.

Keywords: Europe, identity, economic crisis, otherness sentiment

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Authors: Hamza Benyahia, Mostapha Tarfaoui, Ahmed El-Moumen, Djamel Ouinas

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Composite pipes are commonly used in the oil industry, and extreme flow of hot and cold gas fluid can cause degradation of their mechanical performance and properties. Therefore, it is necessary to consider thermomechanical behavior as an important parameter in designing these tubular structures. In this paper, an experimental study is conducted on composite glass/epoxy tubes, with a thickness of 6.2 mm and 86 mm internal diameter made by filament winding of (Փ = ± 55°), to investigate the effects of extreme thermal condition on their mechanical properties b over a temperature range from -40 to 80°C. The climatic chamber is used for the thermal aging and then, combine split disk system is used to perform tensile tests on these composite pies. Thermal aging is carried out for 8hr but each specimen was subjected to various temperature ranges and then, uniaxial tensile test is conducted to evaluate their mechanical performance. Experimental results show degradation in the mechanical properties of composite pipes with an increase in temperature. The rigidity of pipes increases progressively with a decrease in thermal load and results in a radical decrease in their elongation before fracture, thus, decreasing their ductility. However, with an increase in the temperature, there is a decrease in the yield strength and an increase in yield strain, which confirmed an increase in the plasticity of composite pipes.

Keywords: composite pipes, thermal-mechanical properties, filament winding, thermal degradation

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Authors: A. Yamaoka, S. Yukawa

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Mind wandering is a mental phenomenon of drifting away from a current task or external environment toward inner thought. This research examines the feature of mind wandering which people who have high creativity and high mental health engage in because it is expected that mind wandering which such kind of people engage in may not induce negative affect, although it can improve creativity. Sixty-seven participants were required to complete questionnaires which measured their creativity and mental health. After that, researchers conducted experience sampling method and measured the details of their mind wandering and the situation when mind wandering was generated in daily life for three days. The result showed that high creative people and high mental health people more think about positive things during mind wandering and less think about negative things. In further research, researchers will examine how to induce positive thought during mind wandering and how to inhibit negative thought during mind wandering. Doing so will contribute to improve creative problem solving without generation of negative affect.

Keywords: creativity, experience sampling method, mental health, mind wandering

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Authors: Omer Cahana, Maya Herman, Ofer Levi

Abstract:

Magnetic resonance imaging (MRI) is a long medical scan that stems from a long acquisition time. This length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach, such as compress sensing (CS) or parallel imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. In order to achieve that, two properties have to exist: i) the signal must be sparse under a known transform domain, ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm needs to be applied to recover the signal. While the rapid advance in the deep learning (DL) field, which has demonstrated tremendous successes in various computer vision task’s, the field of MRI reconstruction is still in an early stage. In this paper, we present an extension of the state-of-the-art model in MRI reconstruction -VarNet. We utilize VarNet by using dilated convolution in different scales, which extends the receptive field to capture more contextual information. Moreover, we simplified the sensitivity map estimation (SME), for it holds many unnecessary layers for this task. Those improvements have shown significant decreases in computation costs as well as higher accuracy.

Keywords: MRI, deep learning, variational network, computer vision, compress sensing

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Authors: Pinel Sebastien, Bourrin François, De Madron Du Rieu Xavier, Ludwig Wolfgang, Arnau Pedro

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Heavy precipitation constitutes a major meteorological threat in the western Mediterranean. Research has investigated the relationship between the states of the Mediterranean Sea and the atmosphere with the precipitation for short temporal windows. However, at a larger temporal scale, the precursor signals of heavy rainfall in the sea and atmosphere have drawn little attention. Moreover, despite ongoing improvements in numerical weather prediction, the medium-term forecasting of rainfall events remains a difficult task. Here, we aim to investigate the influence of early-spring environmental parameters on the following autumnal heavy precipitations. Hence, we develop a machine learning model to predict extreme autumnal rainfall with a 6-month lead time over the Spanish Catalan coastal area, based on i) the sea pattern (main current-LPC and Sea Surface Temperature-SST) at the mesoscale scale, ii) 4 European weather teleconnection patterns (NAO, WeMo, SCAND, MO) at synoptic scale, and iii) the hydrological regime of the main local river (Rhône River). The accuracy of the developed model classifier is evaluated via statistical analysis based on classification accuracy, logarithmic and confusion matrix by comparing with rainfall estimates from rain gauges and satellite observations (CHIRPS-2.0). Sensitivity tests are carried out by changing the model configuration, such as sea SST, sea LPC, river regime, and synoptic atmosphere configuration. The sensitivity analysis suggests a negligible influence from the hydrological regime, unlike SST, LPC, and specific teleconnection weather patterns. At last, this study illustrates how public datasets can be integrated into a machine learning model for heavy rainfall prediction and can interest local policies for management purposes.

Keywords: extreme hazards, sensitivity analysis, heavy rainfall, machine learning, sea-atmosphere modeling, precipitation forecasting

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Authors: V. Jafari, M. Jafari

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When long-term changes in normal weather patterns happen in a certain area, it generally could be identified as climate change. Concentration of principal's greenhouse gases such as carbon dioxide, nitrous oxide, methane, ozone, and water vapor will cause climate change and perhaps climate variability. Main climate factors are temperature, precipitation, air pressure, and humidity. Extreme events may be the result of the changing of carbon dioxide concentration levels in the atmosphere which cause a change in temperature. Extreme events in some ways will affect the productivity of crop and dairy livestock. In this research, the correlation of milk production and temperature as the main climate factor in ChaharMahal and Bakhtiari province in Iran has been considered. The methodology employed for this study consists, collect reports and published national and provincial data, available recorded data on climate factors and analyzing collected data using statistical software. Milk production in ChaharMahal and Bakhtiari province is in the same pattern as national milk production in Iran. According to the current study results, there is a significant negative correlation between milk production in ChaharMahal and Bakhtiari provinces and temperature as the main climate change factor.

Keywords: Chaharmahal and Bakhtiari, climate change, impacts, Iran, milk production

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Authors: Clara Theresia, Hardianto Iridiastadi

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Drowsiness is one of the main factors that contribute to the occurrence of accidents, particularly in the transportation sector. While the effects of sleep deprivation on cognitive functions have been reported, the exact relationships remain a critical issue. This study aimed at quantifying the effects of extreme sleep deprivation on vigilance, fatigue, and performance during simulated train driving. A total of 12 participants were asked to drive a train simulator continuously for 4 hours, either in a sleep deprived condition (2-hr of sleep) or normal (8-hr of sleep) condition. Dependent variables obtained during the task included Psychomotor Vigilance Task (PVT) parameters, degree of fatigue (assessed via Visual Analogue Scale/VAS) and sleepiness (reported using Karolinska Sleepiness Scale/KSS), and driving performance (the number of speed limit violations). Findings from this study demonstrated substantial decrements in vigilance in the sleep-deprived condition. This condition also resulted in 75% increase in speed violation and a two-fold increase in the degree of fatigue and sleepiness. Extreme sleep deprivation was clearly associated with substantially poorer response. The exact effects, however, were dependent upon the types of responses.

Keywords: cognitive function, psychomotor vigilance task, sleep deprivation, train simulator

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Authors: Kristof Lukas Heidemann

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The adverse effects of climate change, such as rising sea levels, increased temperatures, and extreme weather events are already posing a significant threat to the lives of people living in extreme weather zones all around the globe and could displace more than a billion people worldwide in the upcoming decades, causing a wave of climate-induced migration. Notwithstanding the urgency of the situation, this situation has so far not been addressed in a specific international treaty. Therefore, this paper analyses whether solutions might be found through existing legal framework. Accordingly, the investigation scrutinizes the possibilities of overcoming the conceptual challenge of combining climate law, refugee law, and human rights law. To this end, the study particularly reflects upon the example of Pacific Islanders by assessing the reasoning within the decisions Ioane Teitota v. New Zealand and Daniel Billy and Others v. Australia. The paper concludes that the differences in objective, scope, and enforcement of the three fields are too fundamental to be surmounted by overlapping concepts, e.g. state responsibility or the non-refoulement principle. Consequently, states are urged to tackle the problem with a separate international treaty in which the advantages of the different traditions are incorporated into a new protection mechanism.

Keywords: climate change, climate treaties, forcibly displaced persons, human rights, improving and creating advanced knowledge of concepts, non-refoulement, state responsibility, refugee law, refugee status

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Authors: Veljko Jeremic, Milica Kostic Stankovic, Aleksandar Markovic, Milan Martic

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Evaluating the efficiency of decision making units has been frequently elaborated on in numerous publications. In this paper, the theoretical framework for a novel method of Distance Based Analysis (DBA) is presented. In addition, the method is performed on a sample of the ARWU’s top 54 Universities of the United States, the findings of which clearly demonstrate that the best ranked Universities are far from also being the most efficient.

Keywords: evaluating efficiency, distance based analysis, ranking of universities, ARWU

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Authors: Fatemehsadat Mortazavizadeh, Amin Dehghani, Majid Mirzaei, Nurulhuda Binti Mohammad Ramli, Adnan Dehghani

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Flood is Malaysia's most common and serious natural disaster. Kelantan River Basin is a tropical basin that experiences a rainy season during North-East Monsoon from November to March. It is also one of the hardest hit areas in Peninsular Malaysia during the heavy monsoon rainfall. Considering the consequences of the flood events, it is essential to develop the flood inundation map as part of the mitigation approach. In this study, the delineation of flood inundation zone in the area of Kelantan River basin using a hydrodynamic model is done by HEC-RAS, QGIS and ArcMap. The streamflow data has been generated with the weather generator based on the observation data. Then, the data is statistically analyzed with the Extreme Value (EV1) method for 2-, 5-, 25-, 50- and 100-year return periods. The minimum depth, maximum depth, mean depth, and the standard deviation of all the scenarios, including the OBS, are observed and analyzed. Based on the results, generally, the value of the data increases with the return period for all the scenarios. However, there are certain scenarios that have different results, which not all the data obtained are increasing with the return period. Besides, OBS data resulted in the middle range within Scenario 1 to Scenario 40.

Keywords: flood inundation, kelantan river basin, hydrodynamic model, extreme value analysis

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Authors: Feng Wang, Shencheng Ni, Shuying Han, Shanhong You

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With the development of optical communication, optical performance monitoring (OPM) has received more and more attentions. Since optical signal-to-noise ratio (OSNR) is directly related to bit error rate (BER), it is one of the important parameters in optical networks. Recently, artificial neural network (ANN) has been greatly developed. ANN has strong learning and generalization ability. In this paper, a method of OSNR monitoring based on delay-tap sampling (DTS) and ANN has been proposed. DTS technique is used to extract the eigenvalues of the signal. Then, the eigenvalues are input into the ANN to realize the OSNR monitoring. The experiments of 10 Gb/s non-return-to-zero (NRZ) on–off keying (OOK), 20 Gb/s pulse amplitude modulation (PAM4) and 20 Gb/s return-to-zero (RZ) differential phase-shift keying (DPSK) systems are demonstrated for the OSNR monitoring based on the proposed method. The experimental results show that the range of OSNR monitoring is from 15 to 30 dB and the root-mean-square errors (RMSEs) for 10 Gb/s NRZ-OOK, 20 Gb/s PAM4 and 20 Gb/s RZ-DPSK systems are 0.36 dB, 0.45 dB and 0.48 dB respectively. The impact of chromatic dispersion (CD) on the accuracy of OSNR monitoring is also investigated in the three experimental systems mentioned above.

Keywords: artificial neural network (ANN), chromatic dispersion (CD), delay-tap sampling (DTS), optical signal-to-noise ratio (OSNR)

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Authors: Abdullah Al Mamoon, Ataur Rahman

Abstract:

Due to climate change, future rainfall will change at many locations on earth; however, the spatial and temporal patterns of this change are not easy to predict. One approach of predicting such future changes is to examine the trends in the historical rainfall data at a given region and use the identified trends to make future prediction. For this, a statistical trend test is commonly applied to the historical data. This paper examines the trends of daily extreme rainfall events from 30 rain gauges located in the State of Qatar. Rainfall data covering from 1962 to 2011 were used in the analysis. A combination of four non-parametric and parametric tests was applied to identify trends at 10%, 5%, and 1% significance levels. These tests are Mann-Kendall (MK), Spearman’s Rho (SR), Linear Regression (LR) and CUSUM tests. These tests showed both positive and negative trends throughout the country. Only eight stations showed positive (upward) trend, which were however not statistically significant. In contrast, significant negative (downward) trends were found at the 5% and 10% levels of significance in six stations. The MK, SR and LR tests exhibited very similar results. This finding has important implications in the derivation/upgrade of design rainfall for Qatar, which will affect design and operation of future urban drainage infrastructure in Qatar.

Keywords: trends, extreme rainfall, daily rainfall, Mann-Kendall test, climate change, Qatar

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Authors: Mohammad Obeidat, Ayman Mansour

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In this paper a unique issue rising from feedback control of Atrial Fibrillation monitoring system with embedded communication channels has been investigated. One of the important factors to measure the performance of the feedback control closed loop system is disturbance and noise attenuation factor. It is important that the feedback system can attenuate such disturbances on the atrial fibrillation heart rate signals. Communication channels depend on network traffic conditions and deliver different throughput, implying that the sampling intervals may change. Since signal estimation is updated on the arrival of new data, its dynamics actually change with the sampling interval. Consequently, interaction among sampling, signal estimation, and the controller will introduce new issues in remotely controlled Atrial Fibrillation system. This paper treats a remotely controlled atrial fibrillation system with one communication channel which connects between the heart rate and rhythm measurements to the remote controller. Typical and optimal signal estimation schemes is represented by a signal averaging filter with its time constant derived from the step size of the signal estimation algorithm.

Keywords: atrial fibrillation, communication channels, closed loop, estimation

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Authors: P. V. Pramila , V. Mahesh

Abstract:

Pulmonary Function Tests are important non-invasive diagnostic tests to assess respiratory impairments and provides quantifiable measures of lung function. Spirometry is the most frequently used measure of lung function and plays an essential role in the diagnosis and management of pulmonary diseases. However, the test requires considerable patient effort and cooperation, markedly related to the age of patients esulting in incomplete data sets. This paper presents, a nonlinear model built using Multivariate adaptive regression splines and Random forest regression model to predict the missing spirometric features. Random forest based feature selection is used to enhance both the generalization capability and the model interpretability. In the present study, flow-volume data are recorded for N= 198 subjects. The ranked order of feature importance index calculated by the random forests model shows that the spirometric features FVC, FEF 25, PEF,FEF 25-75, FEF50, and the demographic parameter height are the important descriptors. A comparison of performance assessment of both models prove that, the prediction ability of MARS with the `top two ranked features namely the FVC and FEF 25 is higher, yielding a model fit of R2= 0.96 and R2= 0.99 for normal and abnormal subjects. The Root Mean Square Error analysis of the RF model and the MARS model also shows that the latter is capable of predicting the missing values of FEV1 with a notably lower error value of 0.0191 (normal subjects) and 0.0106 (abnormal subjects). It is concluded that combining feature selection with a prediction model provides a minimum subset of predominant features to train the model, yielding better prediction performance. This analysis can assist clinicians with a intelligence support system in the medical diagnosis and improvement of clinical care.

Keywords: FEV, multivariate adaptive regression splines pulmonary function test, random forest

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Authors: Francesco Pandolfi, Georgios Baltzopoulos, Iunio Iervolino

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The impact of extreme winds on industrial assets and the built environment is gaining increasing attention from stakeholders, including the corporate insurance industry. This has led to a progressively more in-depth study of building vulnerability and fragility to wind. Wind vulnerability models are used in probabilistic risk assessment to relate a loss metric to an intensity measure of the natural event, usually a gust or a mean wind speed. In fact, vulnerability models can be integrated with the wind hazard, which consists of associating a probability to each intensity level in a time interval (e.g., by means of return periods) to provide an assessment of future losses due to extreme wind. This has also given impulse to the world- and regional-scale wind hazard studies.Another approach often adopted for the probabilistic description of building vulnerability to the wind is the use of fragility functions, which provide the conditional probability that selected building components will exceed certain damage states, given wind intensity. In fact, in wind engineering literature, it is more common to find structural system- or component-level fragility functions rather than wind vulnerability models for an entire building. Loss assessment based on component fragilities requires some logical combination rules that define the building’s damage state given the damage state of each component and the availability of a consequence model that provides the losses associated with each damage state. When risk calculations are based on numerical simulation of a structure’s behavior during extreme wind scenarios, the interaction of component fragilities is intertwined with the computational procedure. However, simulation-based approaches are usually computationally demanding and case-specific. In this context, the present work introduces the ExtReMe wind risk assESsment prototype Software, ERMESS, which is being developed at the University of Naples Federico II. ERMESS is a wind risk assessment tool for insurance applications to industrial facilities, collecting a wide assortment of available wind vulnerability models and fragility functions to facilitate their incorporation into risk calculations based on in-built or user-defined wind hazard data. This software implements an alternative method for building-specific risk assessment based on existing component-level fragility functions and on a number of simplifying assumptions for their interactions. The applicability of this alternative procedure is explored by means of an illustrative proof-of-concept example, which considers four main building components, namely: the roof covering, roof structure, envelope wall and envelope openings. The application shows that, despite the simplifying assumptions, the procedure can yield risk evaluations that are comparable to those obtained via more rigorous building-level simulation-based methods, at least in the considered example. The advantage of this approach is shown to lie in the fact that a database of building component fragility curves can be put to use for the development of new wind vulnerability models to cover building typologies not yet adequately covered by existing works and whose rigorous development is usually beyond the budget of portfolio-related industrial applications.

Keywords: component wind fragility, probabilistic risk assessment, vulnerability model, wind-induced losses

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Authors: Renato Morbidelli, Carla Saltalippi, Alessia Flammini, Tommaso Picciafuoco, Corrado Corradini

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A considerable part of rainfall data to be used in the hydrological practice is available in aggregated form within constant time intervals. This can produce undesirable effects, like the underestimate of the annual maximum rainfall depth, Hd, associated with a given duration, d, that is the basic quantity in the development of rainfall depth-duration-frequency relationships and in determining if climate change is producing effects on extreme event intensities and frequencies. The errors in the evaluation of Hd from data characterized by a coarse temporal aggregation, ta, and a procedure to reduce the non-homogeneity of the Hd series are here investigated. Our results indicate that: 1) in the worst conditions, for d=ta, the estimation of a single Hd value can be affected by an underestimation error up to 50%, while the average underestimation error for a series with at least 15-20 Hd values, is less than or equal to 16.7%; 2) the underestimation error values follow an exponential probability density function; 3) each very long time series of Hd contains many underestimated values; 4) relationships between the non-dimensional ratio ta/d and the average underestimate of Hd, derived from continuous rainfall data observed in many stations of Central Italy, may overcome this issue; 5) these equations should allow to improve the Hd estimates and the associated depth-duration-frequency curves at least in areas with similar climatic conditions.

Keywords: central Italy, extreme events, rainfall data, underestimation errors

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Authors: Orpita U. Laz, Ataur Rahman

Abstract:

Climate change will affect various aspects of hydrological cycle such as rainfall. A change in rainfall will affect flood magnitude and frequency in future which will affect the design and operation of hydraulic structures. In this paper, trends in sub-hourly, sub-daily, and daily extreme rainfall events from 18 rainfall stations located in Tasmania, Australia are examined. Two non-parametric tests (Mann-Kendall and Spearman’s Rho) are applied to detect trends at 10%, 5%, and 1% significance levels. Sub-hourly (6, 12, 18, and 30 minutes) annual maximum rainfall events have been found to experience statistically significant upward trends at 10 % level of significance. However, sub-daily durations (1 hour, 3 and 12 hours) exhibit decreasing trends and no trends exists for longer duration rainfall events (e.g. 24 and 72 hours). Some of the durations (e.g. 6 minutes and 6 hours) show similar results (with upward trends) for both the tests. For 12, 18, 60 minutes and 3 hours durations both the tests show similar downward trends. This finding has important implication for Tasmania in the design of urban infrastructure where shorter duration rainfall events are more relevant for smaller urban catchments such as parking lots, roof catchments and smaller sub-divisions.

Keywords: climate change, design rainfall, Mann-Kendall test, trends, Spearman’s Rho, Tasmania

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