Search results for: forecast uncertainty

Authors: Chih-Chieh Yang, Chung-Neng Huang

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In power system, how to improve the fault diagnosis technology of transmission line has always been the primary goal of power grid operators. In recent years, due to the rise of green energy, the addition of all kinds of distributed power also has an impact on the stability of the power system. Because the smart meters are with the function of data recording and bidirectional transmission, the adaptive Fuzzy Neural inference system, ANFIS, as well as the artificial intelligence that has the characteristics of learning and estimation in artificial intelligence. For transmission network, in order to avoid misjudgment of the fault type and location due to the input of these unstable power sources, combined with the above advantages of smart meter and ANFIS, a method for identifying fault types and location of faults is proposed in this study. In ANFIS training, the bus voltage and current information collected by smart meters can be trained through the ANFIS tool in MATLAB to generate fault codes to identify different types of faults and the location of faults. In addition, due to the uncertainty of distributed generation, a wind power system is added to the transmission network to verify the diagnosis correctness of the study. Simulation results show that the method proposed in this study can correctly identify the fault type and location of fault with more efficiency, and can deal with the interference caused by the addition of unstable power sources.

Keywords: ANFIS, fault diagnosis, power system, smart meter

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Authors: Wiem Daoud Ben Amor, Luis Martínez López, Hela Moalla Frikha

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Most of the multi-criteria group decision making (MCGDM) problems dealing with qualitative criteria require consideration of the large background of expert information. It is common that experts have different degrees of knowledge for giving their alternative assessments according to criteria. So, it seems logical that they use different evaluation scales to express their judgment, i.e., multi granular linguistic scales. In this context, we propose the extension of the classical additive ratio assessment (ARAS) method to the case of a hierarchical linguistics term for managing multi granular linguistic scales in uncertain contexts where uncertainty is modeled by means in linguistic information. The proposed approach is called the extended hierarchical linguistics-ARAS method (ARAS-ELH). Within the ARAS-ELH approach, the DM can diagnose the results (the ranking of the alternatives) in a decomposed style, i.e., not only at one level of the hierarchy but also at the intermediate ones. Also, the developed approach allows a feedback transformation i.e the collective final results of all experts able to be transformed at any level of the extended linguistic hierarchy that each expert has previously used. Therefore, the ARAS-ELH technique makes it easier for decision-makers to understand the results. Finally, An MCGDM case study is given to illustrate the proposed approach.

Keywords: additive ratio assessment, extended hierarchical linguistic, multi-criteria group decision making problems, multi granular linguistic contexts

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Authors: Rodrigo Coelho

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We believe that in the majority of the “recent production of public space", the overvaluation of the "image", of the "ephemeral" and of the "objectual", has come to determine the configuration of banal and (more or less) arbitrary "public spaces", mostly linked to a problem of “outdoor decoration”, reflecting a clear sign of uncertainty and arbitrariness about the meaning, the role and shape of public space and public art.This "inconsistency" which is essentially linked to the loss of urban, but also social, cultural and political, vocation of the disciplines that “shape” the urban space (but is also linked to the lack of urban and technical culture of techinicians and policy makers) converted a significant set of the recently built "public space" and “urban art” into diffuse and multi-referenced pieces, which generally shares the inability of confering to the urban space, civic, aesthetic, social and symbolic meanings. In this sense we consider it is essential to undertake a theoretical reflection on the values, the meaning(s) and the shape(s) that open space, and urban art may (or must) take in the current urban and cultural context, in order to redeem for public space its status of significant physical reference, able to embody a spatial and urban identity, and simultaneously enable the collective accession and appropriation of public space. Taking as reference public space interventions built in the last decade on the European context, we will seek to explore and defend the need of considering public space as a true place of exception, an exceptional support where the emphasis is placed on the quality of the experience, especially by the relations public space/urban art can established with the city, with nature and geography in a broad sense, referring us back to a close and inseparable and timeless relationship between nature and culture.

Keywords: art, city, nature, public space

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Authors: Alvarez Magañini, María Victoria-Rubbi, Lautaro Nahuel

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At present, we are facing an unstable international context, conditioned by a relative decline of the US power, primarily in the economic sphere and, to a lesser extent, in the military sphere. This scenario of multipolarity creates tension and uncertainty in the peripheral countries when the issue of their foreign policy arises. This paper presents an analysis of the bilateral relations that were maintained by the Argentine Republic, a peripheral country, along with the United States and China during the period of 2005-2015 in matters of defense in order to identify the empirical consequences resulted from the Argentine actions. Based on the conceptual framework of Peripheral Realism, we analyze indicators related to the weapon trade, defense loans, joint exercises, and personnel training, among others. There will also be a comparative analysis of the conventional military forces of the two powers in question, United States and China. As a conclusion, the cost of having closer relations with China instead of the United States in the defense agenda has been clearly higher than the benefits obtained. The conclusions drawn are empirically aligned with the theoretical paradigm of peripheral realism. Although there are certain conceptual and methodological digressions, these conclusions they could be useful to update and adapt the theory to the current complex international scenario.

Keywords: China, United States, Argentine, peripheral country, peripheral realism

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Authors: R. Hellmuth, F. Wehner

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Factory planning has the task of designing products, plants, processes, organization, areas, and the construction of a factory. The requirements for factory planning and the building of a factory have changed in recent years. Regular restructuring is becoming more important in order to maintain the competitiveness of a factory. Restrictions in new areas, shorter life cycles of product and production technology as well as a VUCA world (Volatility, Uncertainty, Complexity & Ambiguity) lead to more frequent restructuring measures within a factory. A digital factory model is the planning basis for rebuilding measures and becomes an indispensable tool. Short-term rescheduling can no longer be handled by on-site inspections and manual measurements. The tight time schedules require up-to-date planning models. Due to the high adaptation rate of factories described above, a methodology for rescheduling factories on the basis of a modern digital factory twin is conceived and designed for practical application in factory restructuring projects. The focus is on rebuild processes. The aim is to keep the planning basis (digital factory model) for conversions within a factory up to date. This requires the application of a methodology that reduces the deficits of existing approaches. The aim is to show how a digital factory model can be kept up to date during ongoing factory operation. A method based on photogrammetry technology is presented. The focus is on developing a simple and cost-effective solution to track the many changes that occur in a factory building during operation. The method is preceded by a hardware and software comparison to identify the most economical and fastest variant. 

Keywords: digital factory model, photogrammetry, factory planning, restructuring

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Authors: Jiahui Yang, John Quigley, Lesley Walls

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In this paper, the authors develop a stochastic model regarding the investment in supplier delivery performance development from a buyer’s perspective. The authors propose a multivariate model through a Multinomial-Dirichlet distribution within an Empirical Bayesian inference framework, representing both the epistemic and aleatory uncertainties in deliveries. A closed form solution is obtained and the lower and upper bound for both optimal investment level and expected profit under uncertainty are derived. The theoretical properties provide decision makers with useful insights regarding supplier delivery performance improvement problems where multiple delivery statuses are involved. The authors also extend the model from a single supplier investment into a supplier portfolio, using a Lagrangian method to obtain a theoretical expression for an optimal investment level and overall expected profit. The model enables a buyer to know how the marginal expected profit/investment level of each supplier changes with respect to the budget and which supplier should be invested in when additional budget is available. An application of this model is illustrated in a simulation study. Overall, the main contribution of this study is to provide an optimal investment decision making framework for supplier development, taking into account multiple delivery statuses as well as multiple projects.

Keywords: decision making, empirical bayesian, portfolio optimization, supplier development, supply chain management

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Authors: Soham Das

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Oedipus in Oedipus Rex is necessarily a victim of fate and his own psychology. His curiosity brings about his downfall. Ancient Greek plays weren't just portrayals of some obscure tale but were insights into human nature. Oedipus, although a victim of circumstances, digs his own grave by curiously unravelling his past. Jocasta foresees his doom and begs him to stop, but to no avail. The curiosity of Oedipus forces him, almost like a drug, to explore the mystery regarding his birth. This curiosity is not something extraordinary in Oedipus - it is an intrinsic attribute of human nature. Knowledge is not always desired - whether it is Adam or Oedipus, their curiosity caused their eventual downfall. Oedipus was ill-fated since birth. He did not know that Laius was his biological father and therefore killed him. He arrived at Thebes, solved the riddle of the Sphinx, and married Jocasta without knowing that she, in fact, was his biological mother. He begot children and was living happily with his family when a sudden calamity struck Thebes. The calamity, though at first seemed public in nature, but later proved to be very personal for Oedipus. It drives home the fundamental truth about uncertainty of human life. That Laius was slayed by his own son, even after many precautions, proves the helplessness of humans in front of the designs of fate. Oedipus's mutilation of his eyes is also fated. It was committed by him in the heat of the moment and was certainly not a rational decision. It is evident to any modern reader that Oedipus does not have justice. Destiny treats him unfairly. Oedipus, in fact, defends his actions in Oedipus Rex in its sequel Oedipus At Colonus. The research paper discusses the unhappy fate of Oedipus and the role of destiny and his own curiosity in achieving it.

Keywords: ancient Greek drama, Oedipus Rex, Sophocles, destiny

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Authors: Parvin Ghafarian, Mohammadreza Mohammadpur Panchah, Mehri Fallahi

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Synoptic patterns from surface up to tropopause are very important for forecasting the weather and atmospheric conditions. There are many tools to prepare and analyze these maps. Reanalysis data and the outputs of numerical weather prediction models, satellite images, meteorological radar, and weather station data are used in world forecasting centers to predict the weather. The forecasting extreme precipitating on the southern coast of the Caspian Sea (CS) is the main issue due to complex topography. Also, there are different types of climate in these areas. In this research, we used two reanalysis data such as ECMWF Reanalysis 5th Generation Description (ERA5) and National Centers for Environmental Prediction /National Center for Atmospheric Research (NCEP/NCAR) for verification of the numerical model. ERA5 is the latest version of ECMWF. The temporal resolution of ERA5 is hourly, and the NCEP/NCAR is every six hours. Some atmospheric parameters such as mean sea level pressure, geopotential height, relative humidity, wind speed and direction, sea surface temperature, etc. were selected and analyzed. Some different type of precipitation (rain and snow) was selected. The results showed that the NCEP/NCAR has more ability to demonstrate the intensity of the atmospheric system. The ERA5 is suitable for extract the value of parameters for specific point. Also, ERA5 is appropriate to analyze the snowfall events over CS (snow cover and snow depth). Sea surface temperature has the main role to generate instability over CS, especially when the cold air pass from the CS. Sea surface temperature of NCEP/NCAR product has low resolution near coast. However, both data were able to detect meteorological synoptic patterns that led to heavy rainfall over CS. However, due to the time lag, they are not suitable for forecast centers. The application of these two data is for research and verification of meteorological models. Finally, ERA5 has a better resolution, respect to NCEP/NCAR reanalysis data, but NCEP/NCAR data is available from 1948 and appropriate for long term research.

Keywords: synoptic patterns, heavy precipitation, reanalysis data, snow

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Authors: Todd Zhou, Mikhail Yurochkin

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Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

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Authors: Lukman Thalib, Luis Furuya-Kanamori, Rachel Walker, Brigid Gillespie, Suhail Doi

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Background and objectives: There are many topical treatments available for Pressure Injury (PI) treatment, yet there is a lack of evidence with regards to the most effective treatment. The objective of this study was to compare the effect of various topical treatments and identify the best treatment choice(s) for PI healing. Methods: Network meta-analysis of published randomized controlled trials that compared the two or more of the following dressing groups: basic, foam, active, hydroactive, and other wound dressings. The outcome complete healing following treatment and the generalised pair-wise modelling framework was used to generate mixed treatment effects against hydroactive wound dressing, currently the standard of treatment for PIs. All treatments were then ranked by their point estimates. Main Results: 40 studies (1,757 participants) comparing 5 dressing groups were included in the analysis. All dressings groups ranked better than basic (i.e. saline gauze or similar inert dressing). The foam (RR 1.18; 95%CI 0.95-1.48) and active wound dressing (RR 1.16; 95%CI 0.92-1.47) ranked better than hydroactive wound dressing in terms of healing of PIs when the latter was used as the reference group. Conclusion & Recommendations: There was considerable uncertainty around the estimates, yet, the use of hydroactive wound dressings appear to perform better than basic dressings. Foam and active wound dressing groups show promise and need further investigation. High-quality research on clinical effectiveness of the topical treatments are warranted to identify if foam and active wound dressings do provide advantages over hydroactive dressings.

Keywords: Net work Meta Analysis, Pressure Injury, Dresssing, Pressure Ulcer

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Authors: Blair D. Macdonald

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After nearly one hundred years of its origin, foundational quantum mechanics remains one of the greatest unexplained mysteries in physicists today. Within this time, chaos theory and its geometry, the fractal, has developed. In this paper, the propagation behaviour with an iteration of a simple fractal, the Koch Snowflake, was described and analysed. From an arbitrary observation point within the fractal set, the fractal propagates forward by oscillation—the focus of this study and retrospectively behind by exponential growth from a point beginning. It propagates a potentially infinite exponential oscillating sinusoidal wave of discrete triangle bits sharing many characteristics of light and quantum entities. The model's wave speed is potentially constant, offering insights into the perception and a direction of time where, to an observer, when travelling at the frontier of propagation, time may slow to a stop. In isolation, the fractal is a superposition of component bits where position and scale present a problem of location. In reality, this problem is experienced within fractal landscapes or fields where 'position' is only 'known' by the addition of information or markers. The quantum' measurement problem', 'uncertainty principle,' 'entanglement,' and the classical-quantum interface are addressed; these are a problem of scale invariance associated with isolated fractality. Dual forward and retrospective perspectives of the fractal model offer the opportunity for unification between quantum mechanics and cosmological mathematics, observations, and conjectures. Quantum and cosmological problems may be different aspects of the one fractal geometry.

Keywords: measurement problem, observer, entanglement, unification

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Authors: Abu Zafor Sadek

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Pharmaceuticals were one of the least impacted business sectors during the corona pandemic as they are the center point of Covid-19 fight. Emergency use authorization, unproven indication of some commonly used drugs, self-medication, research and production capacity of an individual country, capacity of producing vaccine by many countries, Active Pharmaceutical Ingredients (APIs) related uncertainty, information gap among manufacturer, practitioners and user, export restriction, duration of lock-down, lack of harmony in transportation, disruption in the regulatory approval process, sudden increased demand of hospital items and protective equipment, panic buying, difficulties in in-person product promotion, e-prescription, geo-politics and associated issues added a new dimension to this industry. Although the industry maintains a reasonable growth throughout Covid-19 days; however, it has been characterized by both long- and short-term effects. Short-term effects have already been visible to so many countries, especially those who are import-dependent and have limited research capacity. On the other hand, it will take a few more time to see the long-term effects. Nevertheless, supply chain disruption, changes in strategic planning, new communication model, squeezing of job opportunity, rapid digitalization are the major short-term effects, whereas long-term effects include a shift towards self-sufficiency, growth pattern changes of certain products, special attention towards clinical studies, automation in operations, the increased arena of ethical issues etc. Therefore, this qualitative and exploratory study identifies the post-covid-19 landscape of the global pharmaceutical industry.

Keywords: covid-19, pharmaceutical, businees, landscape

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Authors: Arnab Adhikari, Arnab Bisi

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Since the inception, the fashion apparel supply chain is facing the problem of high demand uncertainty. Often the demand volatility compels the corresponding supply chain member to incur substantial holding cost and opportunity cost in case of the overproduction and the underproduction scenario, respectively. It leads to an uncoordinated fashion apparel supply chain. There exist several scholarly works to achieve coordination in the fashion apparel supply chain by employing the different contracts such as the buyback contract, the revenue sharing contract, the option contract, and so on. Specially, the application of option contract in the apparel industry becomes prevalent with the changing global scenario. Exploration of existing literature related to the option contract reveals that most of the research works concentrate on the one direction demand adjustment i.e. either to match the demand upwards or downwards. Here, we present a holistic approach to coordinate a dyadic fashion apparel supply chain comprising one manufacturer and one retailer with the help of bidirectional option contract. We show a combination of wholesale price contract and bidirectional option contract can coordinate the under expanded supply chain. We also propose a framework that captures the variation of the apparel retailer’s order quantity and the apparel manufacturer’s production quantity with the changing exercise price for the different ranges of the option price. We analytically explore that corresponding cost parameters of the supply chain members along with the nature of demand distribution play an instrumental role in the coordination as well as the retailer’s ordering decision.

Keywords: fashion apparel supply chain, supply chain coordination, wholesale price contract, bidirectional option contract

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Authors: Xiping Wang，Shuran Yao, Liqin Dai

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Many studies report the winter is getting warmer and the minimum air temperature is obviously rising as the important climate warming evidences. The exacerbated air temperature fluctuation tending to bring more severe weather variation is another important consequence of recent climate change which induced more disasters to crop growth in quite a certain regions. Hebei Province is an important winter wheat growing province in North of China that recently endures more winter freezing injury influencing the local winter wheat crop management. A winter wheat freezing injury assessment Bayesian Network framework was established for the objectives of estimating, assessing and predicting winter wheat freezing disasters in Hebei Province. In this framework, the freezing disasters was classified as three severity degrees (SI) among all the three types of freezing, i.e., freezing caused by severe cold in anytime in the winter, long extremely cold duration in the winter and freeze-after-thaw in early season after winter. The factors influencing winter wheat freezing SI include time of freezing occurrence, growth status of seedlings, soil moisture, winter wheat variety, the longitude of target region and, the most variable climate factors. The climate factors included in this framework are daily mean and range of air temperature, extreme minimum temperature and number of days during a severe cold weather process, the number of days with the temperature lower than the critical temperature values, accumulated negative temperature in a potential freezing event. The Bayesian Network model was evaluated using actual weather data and crop records at selected sites in Hebei Province using real data. With the multi-stage influences from the various factors, the forecast and assessment of the event-based target variables, freezing injury occurrence and its damage to winter wheat production, were shown better scoped by Bayesian Network model.

Keywords: bayesian networks, climatic change, freezing Injury, winter wheat

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Authors: Chao Xu

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Selecting ground motion intensity measures reasonably is one of the very important issues to affect the input ground motions selecting and the reliability of vulnerability analysis results. In this paper, inelastic spectral displacement is used as an alternative intensity measure to characterize the ground motion damage potential. The inelastic spectral displacement is calculated based modal pushover analysis and inelastic spectral displacement based incremental dynamic analysis is developed. Probability seismic demand analysis of a six story and an eleven story RC frame are carried out through cloud analysis and advanced incremental dynamic analysis. The sufficiency and efficiency of inelastic spectral displacement are investigated by means of regression and residual analysis, and compared with elastic spectral displacement. Vulnerability curves are developed based on inelastic spectral displacement. The study shows that inelastic spectral displacement reflects the impact of different frequency components with periods larger than fundamental period on inelastic structural response. The damage potential of ground motion on structures with fundamental period prolonging caused by structural soften can be caught by inelastic spectral displacement. To be compared with elastic spectral displacement, inelastic spectral displacement is a more sufficient and efficient intensity measure, which reduces the uncertainty of vulnerability analysis and the impact of input ground motion selection on vulnerability analysis result.

Keywords: vulnerability, probability seismic demand analysis, ground motion intensity measure, sufficiency, efficiency, inelastic time history analysis

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Authors: Heng Ma

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Third-party warehousing logistics has an important role in the development of external logistics. At present, the third-party logistics in our country is still a new industry, the accounting system has not yet been established, the current financial accounting system of third-party warehousing logistics is mainly in the traditional way of thinking, and only able to provide the total cost information of the entire enterprise during the accounting period, unable to reflect operating indirect cost information. In order to solve the problem of third-party logistics industry cost information distortion, improve the level of logistics cost management, the paper combines theoretical research and case analysis method to reflect cost allocation by building third-party logistics costing model using Time-Driven Activity-Based Costing(TDABC), and takes S company as an example to account and control the warehousing logistics cost. Based on the idea of “Products consume activities and activities consume resources”, TDABC put time into the main cost driver and use time-consuming equation resources assigned to cost objects. In S company, the objects focuses on three warehouse, engaged with warehousing and transportation (the second warehouse, transport point) service. These three warehouse respectively including five departments, Business Unit, Production Unit, Settlement Center, Security Department and Equipment Division, the activities in these departments are classified by in-out of storage forecast, in-out of storage or transit and safekeeping work. By computing capacity cost rate, building the time-consuming equation, the paper calculates the final operation cost so as to reveal the real cost. The numerical analysis results show that the TDABC can accurately reflect the cost allocation of service customers and reveal the spare capacity cost of resource center, verifies the feasibility and validity of TDABC in third-party logistics industry cost accounting. It inspires enterprises focus on customer relationship management and reduces idle cost to strengthen the cost management of third-party logistics enterprises.

Keywords: third-party logistics enterprises, TDABC, cost management, S company

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Authors: Ahmed Ali Samejo, Riffat Sultana

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Pakistan has the sandy Thar Desert in the eastern area, which share border line with India and has exotic fauna and flora, the livelihood of native people rely on livestock and rain fed cultivated fields. The climate of Thar Desert is very harsh and stressful due to frequent drought and very little rainfall, which may occur during monsoon season in the months of July to October and temperature is high, and wind speed also increases in April to June. Schistocerca gregaria is a destructive pest of vegetation from Mauritania to the border line of Pakistan and India. Sometimes they produce swarms which consume all plant where ever they land down and cause the loss in agro-economy of the world. During the recent study, we observed that vegetation was not unique throughout the Thar Desert in the year 2015, because the first spell of rainfall showered over all areas of the Thar Desert in July. However, the second and third spell of rain was confined to village Mahandre jo par and surroundings from August to October. Consequently, vegetation and cultivated crops grew up specially bajra crop (Pennistum glaucum). The climate of Mahandre jo par and surroundings became favorable for S.gregaria, and remaining areas of Thar Desert went hostile. Therefore desert locust attracted to the pleasant area (Mahandre jo par and surroundings) and gradually concentrated, increased reproductive activities, but did not gregarize due to the harvest of bajra crop and the onset of the winter season with an immediate decrease in temperature. An outbreak was near to come into existence, and thereupon conditions become stressful for hoppers to continue further development. Afore mentioned was one reason behind hurdle to the outbreak, another reason might be that migration and concentration of desert locust took place at the end of the season, so climate becomes unfavorable for hoppers, due to dryness of vegetation. Soils also become dry, because rainfall was not showered in end of the season, that’s why eggs that were deposited in late summer were desiccated. This data might be proved fruitful to forecast any outbreak update in future.

Keywords: agro-economy, destructive pest, climate, outbreak, vegetation

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Authors: Christian H. Girsang, M. Razi B. Mansoor, Noorizal N. Huang

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An offshore platform was installed in 1977 at about 260km offshore West Malaysia at the water depth of 73.6m. Twelve (12) piles were installed with four (4) are skirt piles. The piles have 1.219m outside diameter and wall thickness of 31mm and were driven to 109m below seabed. Deterministic analyses of the pile capacity under axial loading were conducted using the current API (American Petroleum Institute) method and the four (4) CPT-based methods: the ICP (Imperial College Pile)-method, the NGI (Norwegian Geotechnical Institute)-Method, the UWA (University of Western Australia)-method and the Fugro-method. A statistical analysis of the model uncertainty associated with each pile capacity method was performed. There were two (2) piles analysed: Pile 1 and piles other than Pile 1, where Pile 1 is the pile that was most affected by shallow gas problems. Using the mean estimate of soil properties, the five (5) methods used for deterministic estimation of axial pile capacity in compression predict an axial capacity from 28 to 42MN for Pile 1 and 32 to 49MN for piles other than Pile 1. These values refer to the static capacity shortly after pile installation. They do not include the effects of cyclic loading during the design storm or time after installation on the axial pile capacity. On average, the axial pile capacity is expected to have increased by about 40% because of ageing since the installation of the platform in 1977. On the other hand, the cyclic loading effects during the design storm may reduce the axial capacity of the piles by around 25%. The study concluded that all piles have sufficient safety factor when the pile aging and cyclic loading effect are considered, as all safety factors are above 2.0 for maximum operating and storm loads.

Keywords: axial capacity, cyclic loading, pile ageing, shallow gas

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Authors: Woosik Jang, Seung H. Han, Seung Won Baek, Chan Young Park

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Natural gas (NG) is a local energy resource that exists in certain countries, and most NG producers operate within unstable governments. Moreover, about 90% of the liquefied natural gas (LNG) market is governed by a small number of international oil companies (IOCs) and national oil companies (NOCs), market entry of second movers is extremely limited. To overcome these barriers, project viability should be assessed based on limited information at the project screening perspective. However, there have been difficulties at the early stages of projects as follows: (1) What factors should be considered? (2) How many experts are needed to make a decision? and (3) How to make an optimal decision with limited information? To answer these questions, this research suggests a LNG project viability assessment model based on the Dempster-Shafer theory (DST). Total of 11 indices for the gas field analysis and 23 indices for the market environment analysis are identified that reflect unique characteristics of LNG industry. Moreover, the proposed model evaluates LNG projects based on questionnaire survey and it provides not only quantified results but also uncertainty level of results based on DST. Consequently, the proposed model as a systematic framework can support the decision-making process from the gas field projects using quantitative results, and it is developed to a stand-alone system to enhance the practical usability. It is expected to improve the decision-making quality and opportunity in LNG projects for enterprise through informed decision.

Keywords: project viability, LNG project, enterprise information system, Dempster-Shafer Theory, strategic decision-making

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Authors: Ozgu Hafizoglu

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Analogy is an essential tool of human cognition that enables connecting diffuse and diverse systems with attributional, deep structural, casual relations that are essential to learning, to innovation in artificial worlds, and to discovery in science. Cognitive Model of Analogy (CMA) leads and creates information pattern transfer within and between domains and disciplines in science. This paper demonstrates the Cognitive Model of Analogy (CMA) as an evolutionary approach to scientific research. The model puts forward the challenges of deep uncertainty about the future, emphasizing the need for flexibility of the system in order to enable reasoning methodology to adapt to changing conditions. In this paper, the model of analogical reasoning is created based on brain cells, their fractal, and operational forms within the system itself. Visualization techniques are used to show correspondences. Distinct phases of the problem-solving processes are divided thusly: encoding, mapping, inference, and response. The system is revealed relevant to brain activation considering each of these phases with an emphasis on achieving a better visualization of the brain cells: glial cells, axons, axon terminals, and neurons, relative to matching conditions of analogical reasoning and relational information. It’s found that encoding, mapping, inference, and response processes in four-term analogical reasoning are corresponding with the fractal and operational forms of brain cells: glial, axons, and neurons.

Keywords: analogy, analogical reasoning, cognitive model, brain and glials

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Authors: Ka C. Lam, Oluwafunmibi S. Idowu

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Uncertainty in cost is a major factor affecting performance of construction projects. To our knowledge, several conceptual cost models have been developed with varying degrees of accuracy. Incorporating conceptual quantities into conceptual cost models could improve the accuracy of early predesign cost estimates. Hence, the development of quantity models for estimating conceptual quantities of framed reinforced concrete structures using supervised machine learning is the aim of the current research. Using measured quantities of structural elements and design variables such as live loads and soil bearing pressures, response and predictor variables were defined and used for constructing conceptual quantities models. Twenty-four models were developed for comparison using a combination of non-parametric support vector regression, linear regression, and bootstrap resampling techniques. R programming language was used for data analysis and model implementation. Gross soil bearing pressure and gross floor loading were discovered to have a major influence on the quantities of concrete and reinforcement used for foundations. Building footprint and gross floor loading had a similar influence on beams and slabs. Future research could explore the modelling of other conceptual quantities for walls, finishes, and services using machine learning techniques. Estimation of conceptual quantities would assist construction planners in early resource planning and enable detailed performance evaluation of early cost predictions.

Keywords: bootstrapping, conceptual quantities, modelling, reinforced concrete, support vector regression

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Authors: William Li

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Lane-change path planning is a crucial and yet complex task in autonomous driving. The traditional path planning approach based on a system of carefully-crafted rules to cover various driving scenarios becomes unwieldy as more and more rules are added to deal with exceptions and corner cases. This paper proposes to divide the entire path planning to two stages. In the first stage the ego vehicle travels longitudinally in the source lane to reach a safe state. In the second stage the ego vehicle makes lateral lane-change maneuver to the target lane. The paper derives the safe state conditions based on lateral lane-change maneuver calculation to ensure collision free in the second stage. To determine the acceleration sequence that minimizes the time to reach a safe state in the first stage, the paper proposes three schemes, namely, kinetic model based optimization, deep reinforcement learning, and 5G vehicle-to-vehicle (V2V) communications. The paper investigates these schemes via simulation. The model-based optimization is sensitive to the model assumptions. The deep reinforcement learning is more flexible in handling scenarios beyond the model assumed by the optimization. The 5G V2V eliminates uncertainty in predicting future behaviors of surrounding vehicles by sharing driving intents and enabling cooperative driving.

Keywords: lane change, path planning, autonomous driving, deep reinforcement learning, 5G, V2V communications, connected vehicles

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

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

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

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Authors: Nadia Nisar

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Background: In the temperate regions Influenza activities occur sporadically all year round with peaks coinciding during cold months. Meteorological and environmental conditions play significant role in the transmission of influenza globally. In this study, we assessed the relationship between meteorological parameters and influenza activity in two geographical areas of Pakistan. Methods: Influenza data were collected from Islamabad (north) and Multan (south) regions of national influenza surveillance system during 2010-2015. Meteorological database was obtained from National Climatic Data Center (Pakistan). Logistic regression model with a stepwise approach was used to explore the relationship between meteorological parameters with influenza peaks. In statistical model, we used the weekly proportion of laboratory-confirmed influenza positive samples to represent Influenza activity with metrological parameters as the covariates (temperature, humidity and precipitation). We also evaluate the link between environmental conditions associated with seasonal influenza epidemics: 'cold-dry' and 'humid-rainy'. Results: We found that temperature and humidity was positively associated with influenza in north and south both locations (OR = 0.927 (0.88-0.97)) & (OR = 0.1.078 (1.027-1.132)) and (OR = 1.023 (1.008-1.037)) & (OR = 0.978 (0.964-0.992)) respectively, whilst precipitation was negatively associated with influenza (OR = 1.054 (1.039-1.070)) & (OR = 0.949 (0.935-0.963)). In both regions, temperature and humidity had the highest contribution to the model as compared to the precipitation. We revealed that the p-value for all of climate parameters is <0.05 by Independent-sample t-test. These results demonstrate that there were significant relationships between climate factors and influenza infection with correlation coefficients: 0.52-0.90. The total contribution of these three climatic variables accounted for 89.04%. The reported number of influenza cases increased sharply during the cold-dry season (i.e., winter) when humidity and temperature are at minimal levels. Conclusion: Our findings showed that measures of temperature, humidity and cold-dry season (winter) can be used as indicators to forecast influenza infections. Therefore integrating meteorological parameters for influenza forecasting in the surveillance system may benefit the public health efforts in reducing the burden of seasonal influenza. More studies are necessary to understand the role of these parameters in the viral transmission and host susceptibility process.

Keywords: influenza, climate, metrological, environmental

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Authors: Jian Li

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The uncertainty quantification and inversion problems of subsurface oil-water phase flow usually require extensive repeated forward calculations for new runs with changed conditions. To reduce the computational time, various forms of surrogate models have been built. Related research shows that deep learning has emerged as an effective surrogate model, while most surrogate models with deep learning are purely data-driven, which always leads to poor robustness and abnormal results. To guarantee the model more consistent with the physical laws, a coupled theory-guided convolutional neural network (TgCNN) based surrogate model is built to facilitate computation efficiency under the premise of satisfactory accuracy. The model is a convolutional neural network based on multi-well reservoir simulation. The core notion of this proposed method is to bridge two separate blocks on top of an overall network. They underlie the TgCNN model in a coupled form, which reflects the coupling nature of pressure and water saturation in the two-phase flow equation. The model is driven by not only labeled data but also scientific theories, including governing equations, stochastic parameterization, boundary, and initial conditions, well conditions, and expert knowledge. The results show that the TgCNN-based surrogate model exhibits satisfactory accuracy and efficiency in subsurface oil-water phase flow under multi-well conditions.

Keywords: coupled theory-guided convolutional neural network, multi-well conditions, surrogate model, subsurface oil-water phase

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Authors: Faiz N. K. Yusufi, Aquil Ahmed, Jamal Ahmad

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Diabetes in India is growing at an alarming rate and the complications caused by it need to be controlled. Coronary heart disease (CHD) is one of the complications that will be discussed for prediction in this study. India has the second most number of diabetes patients in the world. To the best of our knowledge, there is no CHD risk score for Indian type 2 diabetes patients. Any form of CHD has been taken as the event of interest. A sample of 750 was determined and randomly collected from the Rajiv Gandhi Centre for Diabetes and Endocrinology, J.N.M.C., A.M.U., Aligarh, India. Collected variables include patients data such as sex, age, height, weight, body mass index (BMI), blood sugar fasting (BSF), post prandial sugar (PP), glycosylated haemoglobin (HbA1c), diastolic blood pressure (DBP), systolic blood pressure (SBP), smoking, alcohol habits, total cholesterol (TC), triglycerides (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), very low density lipoprotein (VLDL), physical activity, duration of diabetes, diet control, history of antihypertensive drug treatment, family history of diabetes, waist circumference, hip circumference, medications, central obesity and history of CHD. Predictive risk scores of CHD events are designed by cox proportional hazard regression. Model calibration and discrimination is assessed from Hosmer Lemeshow and area under receiver operating characteristic (ROC) curve. Overfitting and underfitting of the model is checked by applying regularization techniques and best method is selected between ridge, lasso and elastic net regression. Youden’s index is used to choose the optimal cut off point from the scores. Five year probability of CHD is predicted by both survival function and Markov chain two state model and the better technique is concluded. The risk scores for CHD developed can be calculated by doctors and patients for self-control of diabetes. Furthermore, the five-year probabilities can be implemented as well to forecast and maintain the condition of patients.

Keywords: coronary heart disease, cox proportional hazard regression, ROC curve, type 2 diabetes Mellitus

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Authors: Su Sin Chong, Abdul Aziz Abdul Raman, Sulaiman Wadi Harun, Hamzah Arof

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Chemical Oxygen Demand (COD) plays a vital role determination of an appropriate strategy for wastewater treatment including the control of the quality of an effluent. In this study, a new sensing method was introduced for the first time and developed to investigate chemical oxygen demand (COD) using a Mach-Zehner Interferometer (MZI)-based dye sensor. The sensor is constructed by bridging two single mode fibres (SMF1 and SMF2) with a short section (~20 mm) of multimode fibre (MMF) and was formed by tapering the MMF to generate evanescent field which is sensitive to perturbation of sensing medium. When the COD concentration increase takes effect will induce changes in output intensity and effective refractive index between the microfiber and the sensing medium. The adequacy of decisions based on COD values relies on the quality of the measurements. Therefore, the dual output response can be applied to the analytical procedure enhance measurement quality. This work presents a detailed assessment of the determination of COD values in synthetic wastewaters. Detailed models of the measurement performance, including sensitivity, reversibility, stability, and uncertainty were successfully validated by proficiency tests where supported on sound and objective criteria. Comparison of the standard method with the new proposed method was also conducted. This proposed sensor is compact, reliable and feasible to investigate the COD value.

Keywords: chemical oxygen demand, environmental sensing, Mach-Zehnder interferometer sensor, online monitoring

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Authors: O. O. Aiyelokun, O. A. Agbede

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Numerical modelling of groundwater flow can be susceptible to calibration errors due to lack of adequate ground-based hydro-metrological stations in river basins. Groundwater resources management in Southwest Nigeria is currently challenged by overexploitation, lack of planning and monitoring, urbanization and climate change; hence to adopt models as decision support tools for sustainable management of groundwater; they must be adequately calibrated. Since river basins in Southwest Nigeria are characterized by missing data, and lack of adequate ground-based hydro-meteorological stations; the need for adopting satellite-based data for constructing distributed models is crucial. This study seeks to evaluate the suitability of satellite-based data as substitute for ground-based, for computing boundary conditions; by determining if ground and satellite based meteorological data fit well in Ogun and Oshun River basins. The Climate Forecast System Reanalysis (CFSR) global meteorological dataset was firstly obtained in daily form and converted to monthly form for the period of 432 months (January 1979 to June, 2014). Afterwards, ground-based meteorological data for Ikeja (1981-2010), Abeokuta (1983-2010), and Oshogbo (1981-2010) were compared with CFSR data using Goodness of Fit (GOF) statistics. The study revealed that based on mean absolute error (MEA), coefficient of correlation, (r) and coefficient of determination (R²); all meteorological variables except wind speed fit well. It was further revealed that maximum and minimum temperature, relative humidity and rainfall had high range of index of agreement (d) and ratio of standard deviation (rSD), implying that CFSR dataset could be used to compute boundary conditions such as groundwater recharge and potential evapotranspiration. The study concluded that satellite-based data such as the CFSR should be used as input when constructing groundwater flow models in river basins in Southwest Nigeria, where majority of the river basins are partially gaged and characterized with long missing hydro-metrological data.

Keywords: boundary condition, goodness of fit, groundwater, satellite-based data

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Authors: Uliana N. Tumanova, Viacheslav M. Lyapin, Vladimir G. Bychenko, Alexandr I. Shchegolev, Gennady T. Sukhikh

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All cases of stillbirth undoubtedly subject to postmortem examination, since it is necessary to find out the cause of the stillbirths, as well as a forecast of future pregnancies and their outcomes. Determination of the time of death is an important issue which is addressed during the examination of the body of a stillborn. It is mean the period from the time of death until the birth of the fetus. The time for fetal deaths determination is based on the assessment of the severity of the processes of maceration. To study the possibilities of postmortem magnetic resonance imaging (MRI) for determining the time of intrauterine fetal death based on the evaluation of maceration in the kidney. We have conducted MRI morphological comparisons of 7 dead fetuses (18-21 gestational weeks) and 26 stillbirths (22-39 gestational weeks), and 15 bodies of died newborns at the age of 2 hours – 36 days. Postmortem MRI 3T was performed before the autopsy. The signal intensity of the kidney tissue (SIK), pleural fluid (SIF), external air (SIA) was determined on T1-WI and T2-WI. Macroscopic and histological signs of maceration severity and time of death were evaluated in the autopsy. Based on the results of the morphological study, the degree of maceration varied from 0 to 4. In 13 cases, the time of intrauterine death was up to 6 hours, in 2 cases - 6-12 hours, in 4 -12-24 hours, in 9 -2-3 days, in 3 -1 week, in 2 -1,5-2 weeks. At 15 dead newborns, signs of maceration were absent, naturally. Based on the data from SIK, SIF, SIA on MR-tomograms, we calculated the coefficient of MR-maceration (M). The calculation of the time of intrauterine death (MP-t) (hours) was performed by our formula: МR-t = 16,87+95,38×М²-75,32×М. A direct positive correlation of MR-t and autopsy data from the dead at the gestational ages 22-40 weeks, with a dead time, not more than 1 week, was received. The maceration at the antenatal fetal death is characterized by changes in T1-WI and T2-WI signals at postmortem MRI. The calculation of MP-t allows defining accurately the time of intrauterine death within one week at the stillbirths who died on 22-40 gestational weeks. Thus, our study convincingly demonstrates that radiological methods can be used for postmortem study of the bodies, in particular, the bodies of stillborn to determine the time of intrauterine death. Postmortem MRI allows for an objective and sufficiently accurate analysis of pathological processes with the possibility of their documentation, storage, and analysis after the burial of the body.

Keywords: intrauterine death, maceration, postmortem MRI, stillborn

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Authors: G. Shahzadi, A. Soulaimani

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This research work aims to identify the physical parameters of the constitutive soil model in the design of a rockfill dam by inverse analysis. The best parameters of the constitutive soil model, are those that minimize the objective function, defined as the difference between the measured and numerical results. The Finite Element code (Plaxis) has been utilized for numerical simulation. Polynomial and neural network-based response surfaces have been generated to analyze the relationship between soil parameters and displacements. The performance of surrogate models has been analyzed and compared by evaluating the root mean square error. A comparative study has been done based on objective functions and optimization techniques. Objective functions are categorized by considering measured data with and without uncertainty in instruments, defined by the least square method, which estimates the norm between the predicted displacements and the measured values. Hydro Quebec provided data sets for the measured values of the Romaine-2 dam. Stochastic optimization, an approach that can overcome local minima, and solve non-convex and non-differentiable problems with ease, is used to obtain an optimum value. Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) are compared for the minimization problem, although all these techniques take time to converge to an optimum value; however, PSO provided the better convergence and best soil parameters. Overall, parameter identification analysis could be effectively used for the rockfill dam application and has the potential to become a valuable tool for geotechnical engineers for assessing dam performance and dam safety.

Keywords: Rockfill dam, parameter identification, stochastic analysis, regression, PLAXIS

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