Search results for: cloud-cover radiation model

Authors: Gerald Gwamba, Bo Zhou, Yajun Song, Dong Changyin

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The challenges that sand production presents to the oil and gas industry, particularly while working in poorly consolidated reservoirs, cannot be overstated. From restricting production to blocking production tubing, sand production increases the costs associated with production as it elevates the cost of servicing production equipment over time. Production in reservoirs that present with high viscosities, flow rate, cementation, clay content as well as fine sand contents is even more complex and challenging. As opposed to the one-factor at a-time testing, investigating the interactive effects arising from a combination of several factors offers increased reliability of results as well as representation of actual field conditions. It is thus paramount to investigate the conditions leading to the onset of sanding during production to ensure the future sustainability of hydrocarbon production operations under viscous conditions. We adopt the Design of Experiments (DOE) to analyse, using Taguchi factorial designs, the most significant interactive effects of sanding. We propose an optimized regression model to predict the drawdown time at sand production. The results obtained underscore that reservoirs characterized by varying (high and low) levels of viscosity, flow rate, cementation, clay, and fine sand content have a resulting impact on sand production. The only significant interactive effect recorded arises from the interaction between BD (fine sand content and flow rate), while the main effects included fluid viscosity and cementation, with percentage significances recorded as 31.3%, 37.76%, and 30.94%, respectively. The drawdown time model presented could be useful for predicting the time to reach the maximum drawdown pressure under viscous conditions during the onset of sand production.

Keywords: factorial designs, DOE optimization, sand production prediction, drawdown time, regression model

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Authors: A. Chateauneuf, M. Mostoufi, D. Vyncke

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Monte Carlo (MC) simulation is a technique that provides approximate solutions to a broad range of mathematical problems. A drawback of the method is its high computational cost, especially in a high-dimensional setting, such as estimating the Tail Value-at-Risk for large portfolios or pricing basket options and Asian options. For these types of problems, one can construct an upper bound in the convex order by replacing the copula by the comonotonic copula. This comonotonic upper bound can be computed very quickly, but it gives only a rough approximation. In this paper we introduce the Comonotonic Monte Carlo (CoMC) simulation, by using the comonotonic approximation as a control variate. The CoMC is of broad applicability and numerical results show a remarkable speed improvement. We illustrate the method for estimating Tail Value-at-Risk and pricing basket options and Asian options when the logreturns follow a Black-Scholes model or a variance gamma model.

Keywords: control variate Monte Carlo, comonotonicity, option pricing, scientific computing

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Authors: Diego Roberto Goncalves De Pontes, Sergio Donizetti Zorzo

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Studies show that most users do not read privacy policies from the online services they use. Some authors claim that one of the main causes of this is that policies are long and usually hard to understand, which make users lose interest in reading them. In this scenario, users may agree with terms without knowing what kind of data is being collected and why. Given that, we aimed to develop a model that would present the privacy policies contents in an easy and graphical way for the user to understand. We call it the Privacy Label. Using information recovery techniques, we propose an architecture that is able to extract information about what kind of data is being collected and to what end in the policies and show it to the user in an automated way. To assess our model, we calculated the precision, recall and f-measure metrics on the information extracted by our technique. The results for each metric were 68.53%, 85.61% e 76,13%, respectively, making it possible for the final user to understand which data was being collected without reading the whole policy. Also, our proposal can facilitate the notice-and-choice by presenting privacy policy information in an alternative way for online users.

Keywords: privacy, policies, user behavior, computer human interaction

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Authors: Ian Jones, Jonathan Griffiths

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Laser welding offers the potential for making very precise joints in plastics products, both in terms of the joint location and the amount of heating applied. These methods have allowed the production of complex products such as microfluidic devices where channels and structure resolution below 100 µm is regularly used. However, to date, the dimension of welds made using lasers has been limited by the focus spot size that is achievable from the laser source. Theoretically, the minimum spot size possible from a laser is comparable to the wavelength of the radiation emitted. Practically, with reasonable focal length optics the spot size achievable is a few factors larger than this, and the melt zone in a plastics weld is larger again than this. The narrowest welds feasible to date have therefore been 10-20 µm wide using a near-infrared laser source. The aim of this work was to prepare laser absorber tracks and channels less than 10 µm wide in PMMA thermoplastic using EB lithography followed by sealing of channels using laser welding to carry out welds with widths of the order of 1 µm, below the resolution limit of the near-infrared laser used. Welded joints with a width of 1 µm have been achieved as well as channels with a width of 5 µm. The procedure was based on the principle of transmission laser welding using a thin coating of infrared absorbent material at the joint interface. The coating was patterned using electron-beam lithography to obtain the required resolution in a reproducible manner and that resolution was retained after the transmission laser welding process. The joint strength was ratified using larger scale samples. The results demonstrate that plastics products could be made with a high density of structure with resolution below 1 um, and that welding can be applied without excessively heating regions beyond the weld lines. This may be applied to smaller scale sensor and analysis chips, micro-bio and chemical reactors and to microelectronic packaging.

Keywords: microchannels, polymer, EB lithography, laser welding

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Authors: Samira Karimi-Mansoub, Rahem Abri

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Currently, users expect high quality and personalized information from search results. To satisfy user’s needs, personalized approaches to web search have been proposed. These approaches can provide the most appropriate answer for user’s needs by using user context and incorporating information about query provided by combining search technologies. To carry out personalized web search, there is a need to make different techniques on whole of user search process. There are the number of possible deployment of personalized approaches such as personalized web search, personalized recommendation, personalized summarization and filtering systems and etc. but the common feature of all approaches in various domains is that user modeling is utilized to provide personalized information from the Web. So the most important work in personalized approaches is user model mining. User modeling applications and technologies can be used in various domains depending on how the user collected information may be extracted. In addition to, the used techniques to create user model is also different in each of these applications. Since in the previous studies, there was not a complete survey in this field, our purpose is to present a survey on applications and techniques of user modeling from the viewpoint of improvement in search results by considering the existing literature and researches.

Keywords: filtering systems, personalized web search, user modeling, user search behavior

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Authors: Mrinal Kanti Bhowmik, Usha Rani Gogoi Jr., Anjan Kumar Ghosh, Debotosh Bhattacharjee

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In the last few decades, breast thermography has achieved an average sensitivity and specificity of 90% for breast tumor detection. Breast thermography is a non-invasive, cost-effective, painless and radiation-free breast imaging modality which makes a significant contribution to the evaluation and diagnosis of patients, suspected of having breast cancer. An abnormal breast thermogram may indicate significant biological risk for the existence or the development of breast tumors. Breast thermography can detect a breast tumor, when the tumor is in its early stage or when the tumor is in a dense breast. The infrared breast thermography is very sensitive to environmental changes for which acquisition of breast thermography should be performed under strictly controlled conditions by undergoing some standard protocols. Several factors like air, temperature, humidity, etc. are there to be considered for characterizing thermal images as an imperative tool for detecting breast cancer. A detailed study of various breast thermogram acquisition protocols adopted by different researchers in their research work is provided here in this paper. After going through a rigorous study of different breast thermogram acquisition protocols, a new standard breast thermography acquisition setup is proposed here in this paper for proper and accurate capturing of the breast thermograms. The proposed breast thermogram acquisition setup is being built in the Radiology Department, Agartala Government Medical College (AGMC), Govt. of Tripura, Tripura, India. The breast thermograms are captured using FLIR T650sc thermal camera with the thermal sensitivity of 20 mK at 30 degree C. The paper is an attempt to highlight the importance of different critical parameters of breast thermography like different thermography views, patient preparation protocols, acquisition room requirements, acquisition system requirements, etc. This paper makes an important contribution by providing a detailed survey and a new efficient approach on breast thermogram capturing.

Keywords: acquisition protocol, breast cancer, breast thermography, infrared thermography

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Authors: Isaac Kofi Mensah

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The manual system of voting has been the most widely used system of electing representatives around the globe, particularly in Africa. Due to the known numerous problems and challenges associated with the manual system of voting, many countries are migrating to the electronic voting system as a suitable and credible means of electing representatives over the manual paper-based system. This research paper therefore investigated the factors influencing adoption and use of an electronic voting system in Ghana. A total of 400 Questionnaire Instruments (QI) were administered to potential respondents in Ghana, of which 387 responded representing a response rate of 96.75%. The Technology Acceptance Model was used as the theoretical framework for the study. The research model was tested using a simple linear regression analysis with SPSS. A little of over 71.1% of the respondents recommended the Electoral Commission (EC) of Ghana to adopt an electronic voting system in the conduct of public elections in Ghana. The results indicated that all the six predictors such as perceived usefulness (PU), perceived ease of use (PEOU), perceived free and fair elections (PFFF), perceived credible elections (PCE), perceived system integrity (PSI) and citizens trust in the election management body (CTEM) were all positively significant in predicting the readiness of citizens to adopt and use an electronic voting system in Ghana. However, jointly, the hypotheses tested revealed that apart from Perceived Free and Fair Elections and Perceived Credible and Transparent Elections, all the other factors such as PU, Perceived System Integrity and Security and Citizen Trust in the Election Management Body were found to be significant predictors of the Willingness of Ghanaians to use an electronic voting system. All the six factors considered in this study jointly account for about 53.1% of the reasons determining the readiness to adopt and use an electronic voting system in Ghana. The implications of this research finding on elections in Ghana are discussed.

Keywords: credible elections, Election Management Body (EMB), electronic voting, Ghana, Technology Acceptance Model (TAM)

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Authors: Jingwen Shan

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In order to explore the key factors affecting the robot program learning intention of school students, this study takes the technology acceptance model as the theoretical basis and invites 167 students from Jiading District of Shanghai as the research subjects. In the robot course, the model of school students on their learning behavior is constructed. By verifying the causal path relationship between variables, it is concluded that teachers can enhance students’ perceptual usefulness to robotics courses by enhancing subjective norms, entertainment perception, and reducing technical anxiety, such as focusing on the gradual progress of programming and analyzing learner characteristics. Students can improve perceived ease of use by enhancing self-efficacy. At the same time, robot hardware designers can optimize in terms of entertainment and interactivity, which will directly or indirectly increase the learning intention of the robot course. By changing these factors, the learning behavior of primary and secondary school students can be more sustainable.

Keywords: TAM, learning behavior intentions, robot courses, primary and secondary school students

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Authors: Ebrahim H. Kapeel, Ahmed M. Kamel, Hossam Hendy, Yehia Z. Elhalwagy

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Interest in autopilot design has been raised intensely as a result of recent advancements in Unmanned Aerial vehicles (UAVs). Due to the enormous number of applications that UAVs can achieve, the number of applied control theories used for them has increased in recent years. These small fixed-wing UAVs are suffering high non-linearity, sensitivity to disturbances, and coupling effects between their channels. In this work, the nonlinear dynamic inversion (NDI) control law is designed for a nonlinear small fixed-wing UAV model. The NDI is preferable for varied operating conditions, there is no need for a scheduling controller. Moreover, it’s applicable for high angles of attack. For the designed flight controller validation, a nonlinear Modified PI-D controller is performed with our model. A comparative study between both controllers is achieved to evaluate the NDI performance. Simulation results and analysis are proposed to illustrate the effectiveness of the designed controller based on NDI.

Keywords: attitude control, nonlinear PID, dynamic inversion

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Authors: C. Hall, J. Ramos, V. Ramasamy

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Hydrocarbon-based fuel vehicles are a major contributor to air pollution due to harmful emissions produced, leading to a demand for cleaner fuel types. A leader in this pursuit is hydrogen, with its application in vehicles producing zero harmful emissions and the only by-product being water. To compete with the performance of conventional vehicles, hydrogen gas must be stored on-board of vehicles in cylinders at high pressures (35–70 MPa) and have a short refueling duration (approximately 3 mins). However, the fast-filling of hydrogen cylinders causes a significant rise in temperature due to the combination of the negative Joule-Thompson effect and the compression of the gas. This can lead to structural failure and therefore, a maximum allowable internal temperature of 85°C has been imposed by the International Standards Organization. The technological solution to tackle the issue of rapid temperature rise during the refueling process is to decrease the temperature of the gas entering the cylinder. Pre-cooling of the gas uses a heat exchanger and requires energy for its operation. Thus, it is imperative to determine the least amount of energy input that is required to lower the gas temperature for cost savings. A validated universal thermodynamic model is used to identify an energy-efficient pre-cooling strategy. The model requires negligible computational time and is applied to previously validated experimental cases to optimize pre-cooling requirements. The pre-cooling characteristics include the location within the refueling timeline and its duration. A constant pressure-ramp rate is imposed to eliminate the effects of rapid changes in mass flow rate. A pre-cooled gas temperature of -40°C is applied, which is the lowest allowable temperature. The heat exchanger is assumed to be ideal with no energy losses. The refueling of the cylinders is modeled with the pre-cooling split in ten percent time intervals. Furthermore, varying burst durations are applied in both the early and late stages of the refueling procedure. The model shows that pre-cooling in the later stages of the refuelling process is more energy-efficient than early pre-cooling. In addition, the efficiency of pre-cooling towards the end of the refueling process is independent of the pressure profile at the inlet. This leads to the hypothesis that pre-cooled gas should be applied as late as possible in the refueling timeline and at very low temperatures. The model had shown a 31% reduction in energy demand whilst achieving the same final gas temperature for a refueling scenario when pre-cooling was applied towards the end of the process. The identification of the most energy-efficient refueling approaches whilst adhering to the safety guidelines is imperative to reducing the operating cost of hydrogen refueling stations. Heat exchangers are energy-intensive and thus, reducing the energy requirement would lead to cost reduction. This investigation shows that pre-cooling should be applied as late as possible and for short durations.

Keywords: cylinder, hydrogen, pre-cooling, refueling, thermodynamic model

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Authors: Peng Wang, Tigang Jiang

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The computer based lip reading method of different languages cannot be universal. At present, for the research of Chinese lip reading, whether the work on data sets or recognition algorithms, is far from mature. In this paper, we study the Chinese lipreading method based on machine learning, and propose a Chinese Sentence-level lip-reading network (CNLipNet) model which consists of spatio-temporal convolutional neural network(CNN), recurrent neural network(RNN) and Connectionist Temporal Classification (CTC) loss function. This model can map variable-length sequence of video frames to Chinese Pinyin sequence and is trained end-to-end. More over, We create CNLRS, a Chinese Lipreading Dataset, which contains 5948 samples and can be shared through github. The evaluation of CNLipNet on this dataset yielded a 41% word correct rate and a 70.6% character correct rate. This evaluation result is far superior to the professional human lip readers, indicating that CNLipNet performs well in lipreading.

Keywords: lipreading, machine learning, spatio-temporal, convolutional neural network, recurrent neural network

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Authors: Koyar Rane

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Nano- to submicron size particles of narrow particle size distribution of semi-conducting TiO₂, ZnO, NiO, CuO, Fe₂O₃ have been synthesized by novel hydrazine method and tested for their gas sensing, photocatalytic and bactericidal activities and the behavior found to be enhanced when the oxides in the thin film forms, that obtained in a specially built spray pyrolysis reactor. Hydrazine method is novel in the sense, say, the UV absorption edge of the white pigment grade wide band gap (~3.2eV) TiO₂ and ZnO shifted to the visible region turning into yellowish particles, indicating modification occurring the band structure. The absorption in the visible region makes these oxides visible light sensitive photocatalysis in degrading pollutants, especially the organic dyes which otherwise increase the chemical oxygen demand of the drinking water, enabling the process feasible not under the harsh energetic UV radiation regime. The electromagnetic radiations on irradiation produce electron-hole pairs Semiconductor + hν → e⁻ + h⁺ The electron-hole pairs thus produced form Reactive Oxygen Species, ROS, on the surface of the semiconductors, O₂(adsorbed)+e⁻ → O₂• - superoxide ion OH-(surface)+h⁺ →•OH - Hydroxyl radical The ROS attack the organic material and micro-organisms. Our antibacterial studies indicate the metal oxides control the Biological Oxygen Demand (BOD) of drinking water which had beyond the safe level normally found in the municipal supply. Metal oxides in the thin film form show overall enhanced properties and the films are reusable. The results of the photodegradation and antibactericidal studies are discussed. Gas sensing studies too have been done to find the versatility of the multifunctional metal oxides.

Keywords: hydrazine method, visible light sensitive, photo-degradation of dyes, water/airborne pollutant

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Authors: Ghazal Mortazavi, S. M. J. Mortazavi

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Electricians, power line engineers and power station workers, welders, aluminum reduction workers, MRI operators and railway workers are occupationally exposed to different levels of electromagnetic fields. Mercury is among the most toxic metals. Dental amalgam fillings cause significant exposure to elemental mercury vapour in the general population. Today, substantial evidence indicates that mercury even at low doses may lead to toxicity. Increased release of mercury from dental amalgam fillings after exposure to MRI or microwave radiation emitted by mobile phones has been previously shown by our team. Moreover, our recent studies on the effects of stronger magnetic fields entirely confirmed our previous findings. From the other point of view, we have also shown that papers which reported no increased release of mercury after MRI, may have some methodological flaws. Over the past several years, our lab has focused on the health effects of exposure of laboratory animals and humans to different sources of electromagnetic fields such as mobile phones and their base stations, mobile phone jammers, laptop computers, radars, dentistry cavitrons, and MRI. As a strong association between exposure to electromagnetic fields and mercury level has been found in our studies, our findings lead us to this conclusion that occupational exposure to electromagnetic fields in workers with dental amalgam fillings can lead to elevated levels of mercury. Studies which reported that exposure to mercury can be a risk factor of Alzheimer’s disease (AD) due to the accumulation of amyloid beta protein (Aβ) in the brain and those reported that long-term occupational exposure to high levels of electromagnetic fields can increase the risk of Alzheimer's disease and dementia in male workers support our concept and confirm the significant role of the occupational exposure to electromagnetic fields in increasing the mercury level in workers with amalgam fillings.

Keywords: occupational exposure, electromagnetic fields, workers, mercury release, dental amalgam, restorative dentistry

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Authors: Sukhbir Singh

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This paper describes the modeling of novel solar air collector (NSAC) system by using artificial neural network (ANN) model. The objective of the study is to demonstrate the application of the ANN model to predict the performance of the NSAC with acetamide as a phase change material (PCM) storage. Input data set consist of time, solar intensity and ambient temperature wherever as outlet air temperature of NSAC was considered as output. Experiments were conducted between 9.00 and 24.00 h in June and July 2014 underneath the prevailing atmospheric condition of Kurukshetra (city of the India). After that, experimental results were utilized to train the back propagation neural network (BPNN) to predict the outlet air temperature of NSAC. The results of proposed algorithm show that the BPNN is effective tool for the prediction of responses. The BPNN predicted results are 99% in agreement with the experimental results.

Keywords: Evacuated tube solar air collector, Artificial neural network, Phase change material, solar air collector

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Authors: Ola Hall, Ibrahim Wahab, Thorsteinn Rognvaldsson, Mattias Ohlsson

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The subfield of poverty and welfare estimation that applies machine learning tools and methods on satellite imagery is a nascent but rapidly growing one. This is in part driven by the sustainable development goal, whose overarching principle is that no region is left behind. Among other things, this requires that welfare levels can be accurately and rapidly estimated at different spatial scales and resolutions. Conventional tools of household surveys and interviews do not suffice in this regard. While they are useful for gaining a longitudinal understanding of the welfare levels of populations, they do not offer adequate spatial coverage for the accuracy that is needed, nor are their implementation sufficiently swift to gain an accurate insight into people and places. It is this void that satellite imagery fills. Previously, this was near-impossible to implement due to the sheer volume of data that needed processing. Recent advances in machine learning, especially the deep learning subtype, such as deep neural networks, have made this a rapidly growing area of scholarship. Despite their unprecedented levels of performance, such models lack transparency and explainability and thus have seen limited downstream applications as humans generally are apprehensive of techniques that are not inherently interpretable and trustworthy. While several studies have demonstrated the superhuman performance of AI models, none has directly compared the performance of such models and human readers in the domain of poverty studies. In the present study, we directly compare the performance of human readers and a DL model using different resolutions of satellite imagery to estimate the welfare levels of demographic and health survey clusters in Tanzania, using the wealth quintile ratings from the same survey as the ground truth data. The cluster-level imagery covers all 608 cluster locations, of which 428 were classified as rural. The imagery for the human readers was sourced from the Google Maps Platform at an ultra-high resolution of 0.6m per pixel at zoom level 18, while that of the machine learning model was sourced from the comparatively lower resolution Sentinel-2 10m per pixel data for the same cluster locations. Rank correlation coefficients of between 0.31 and 0.32 achieved by the human readers were much lower when compared to those attained by the machine learning model – 0.69-0.79. This superhuman performance by the model is even more significant given that it was trained on the relatively lower 10-meter resolution satellite data while the human readers estimated welfare levels from the higher 0.6m spatial resolution data from which key markers of poverty and slums – roofing and road quality – are discernible. It is important to note, however, that the human readers did not receive any training before ratings, and had this been done, their performance might have improved. The stellar performance of the model also comes with the inevitable shortfall relating to limited transparency and explainability. The findings have significant implications for attaining the objective of the current frontier of deep learning models in this domain of scholarship – eXplainable Artificial Intelligence through a collaborative rather than a comparative framework.

Keywords: poverty prediction, satellite imagery, human readers, machine learning, Tanzania

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Authors: M. S. Wickramarachchi, L. S. Nawarathna, C. M. B. Dematawewa

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Hatchery performance is critical for the profitability of poultry breeder operations. Some extrinsic parameters of eggs and breeders cause to increase or decrease the hatchability. This study aims to identify the affecting extrinsic parameters on the commercial hatchability of local chicken's eggs and determine the most efficient classification model with a hatchability rate greater than 90%. In this study, seven extrinsic parameters were considered: egg weight, moisture loss, breeders age, number of fertilised eggs, shell width, shell length, and shell thickness. Multiple linear regression was performed to determine the most influencing variable on hatchability. First, the correlation between each parameter and hatchability were checked. Then a multiple regression model was developed, and the accuracy of the fitted model was evaluated. Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), k-Nearest Neighbors (kNN), Support Vector Machines (SVM) with a linear kernel, and Random Forest (RF) algorithms were applied to classify the hatchability. This grouping process was conducted using binary classification techniques. Hatchability was negatively correlated with egg weight, breeders' age, shell width, shell length, and positive correlations were identified with moisture loss, number of fertilised eggs, and shell thickness. Multiple linear regression models were more accurate than single linear models regarding the highest coefficient of determination (R²) with 94% and minimum AIC and BIC values. According to the classification results, RF, CART, and kNN had performed the highest accuracy values 0.99, 0.975, and 0.972, respectively, for the commercial hatchery process. Therefore, the RF is the most appropriate machine learning algorithm for classifying the breeder outcomes, which are economically profitable or not, in a commercial hatchery.

Keywords: classification models, egg weight, fertilised eggs, multiple linear regression

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Authors: Wenzhe Li, Xiaoyu Ying, Grace Ding

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Architecture plane form is an important consideration in the design of green buildings due to its significant impact on energy performance. The most effective method to consider energy performance in the early design stages is parametric modelling. This paper presents a methodology to program plane forms using MATLAB language, generating 16 kinds of plane forms by changing four designed parameters. DesignBuilder (an energy consumption simulation software) was proposed to simulate the energy consumption of the generated planes. A regression mathematical model was established to study the relationship between the plane forms and their energy consumption. The main finding of the study suggested that there was a cubic function relationship between the depth-ratio of U-shaped buildings and energy consumption, and there is also a cubic function relationship between the width-ratio and energy consumption. In the design, the depth-ratio of U-shaped buildings should not be less than 2.5, and the width-ratio should not be less than 2.

Keywords: graphic parameterization, green building design, mathematical model, plane form

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Authors: Catelyn Coyle, Helena Kwakwa

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Objective: As novel and better tolerated therapies become available, effective HCV testing and care models become increasingly necessary to not only identify individuals with active infection but also link them to HCV providers for medical evaluation and treatment. Our aim is to describe an effective HCV testing and linkage to care model piloted in a network of five community health centers located in Philadelphia, PA. Methods: In October 2012, National Nursing Centers Consortium piloted a routine opt-out HCV testing model in a network of community health centers, one of which treats HCV, HIV, and co-infected patients. Key aspects of the model were medical assistant initiated testing, the use of laboratory-based reflex test technology, and electronic medical record modifications to prompt, track, report and facilitate payment of test costs. Universal testing on all adult patients was implemented at health centers serving patients at high-risk for HCV. The other sites integrated high-risk based testing, where patients meeting one or more of the CDC testing recommendation risk factors or had a history of homelessness were eligible for HCV testing. Mid-course adjustments included the integration of dual HIV testing, development of a linkage to care coordinator position to facilitate the transition of HIV and/or HCV-positive patients from primary to specialist care, and the transition to universal HCV testing across all testing sites. Results: From October 2012 to June 2015, the health centers performed 7,730 HCV tests and identified 886 (11.5%) patients with a positive HCV-antibody test. Of those with positive HCV-antibody tests, 838 (94.6%) had an HCV-RNA confirmatory test and 590 (70.4%) progressed to current HCV infection (overall prevalence=7.6%); 524 (88.8%) received their RNA-positive test result; 429 (72.7%) were referred to an HCV care specialist and 271 (45.9%) were seen by the HCV care specialist. The best linkage to care results were seen at the test and treat the site, where of the 333 patients were current HCV infection, 175 (52.6%) were seen by an HCV care specialist. Of the patients with active HCV infection, 349 (59.2%) were unaware of their HCV-positive status at the time of diagnosis. Since the integration of dual HCV/HIV testing in September 2013, 9,506 HIV tests were performed, 85 (0.9%) patients had positive HIV tests, 81 (95.3%) received their confirmed HIV test result and 77 (90.6%) were linked to HIV care. Dual HCV/HIV testing increased the number of HCV tests performed by 362 between the 9 months preceding dual testing and first 9 months after dual testing integration, representing a 23.7% increment. Conclusion: Our HCV testing model shows that integrated routine testing and linkage to care is feasible and improved detection and linkage to care in a primary care setting. We found that prevalence of current HCV infection was higher than that seen in locally in Philadelphia and nationwide. Intensive linkage services can increase the number of patients who successfully navigate the HCV treatment cascade. The linkage to care coordinator position is an important position that acts as a trusted intermediary for patients being linked to care.

Keywords: HCV, routine testing, linkage to care, community health centers

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Authors: Hamed Reza Heshmat Mohajer, Lan Ding, Mattheos Santamouris

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One of the worst environmental dangers for people who live in cities is the Urban Heat Island (UHI) impact which is anticipated to become stronger in the coming years as a result of climate change. Accordingly, the key aim of this paper is to study the interaction between the urban configuration and mitigation strategies including increasing albedo of the urban environment (reflective material), implementation of Urban Green Infrastructure (UGI) and/or a combination thereof. To analyse the microclimate models of different urban categories in the metropolis of Sydney, this study will assess meteorological parameters using a 3D model simulation tool of computational fluid dynamics (CFD) named ENVI-met. In this study, four main parameters are taken into consideration while assessing the effectiveness of UHI mitigation strategies: ambient air temperature, wind speed/direction, and outdoor thermal comfort. Layouts with present condition simulation studies from the basic model (scenario one) are taken as the benchmark. A base model is used to calculate the relative percentage variations between each scenario. The findings showed that maximum cooling potential across different urban layouts can be decreased by 2.15 °C degrees by combining high-albedo material with flora; besides layouts with open arrangements(OT1) present a highly remarkable improvement in ambient air temperature and outdoor thermal comfort when mitigation technologies applied compare to compact counterparts. Besides all layouts present a higher intensity on the maximum ambient air temperature reduction rather than the minimum ambient air temperature. On the other hand, Scenarios associated with an increase in greeneries are anticipated to have a slight cooling effect, especially on high-rise layouts.

Keywords: sustainable urban development, urban green infrastructure, high-albedo materials, heat island effect

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Authors: Jingsi Li, Neil S. Ferguson

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Time pressure could have an influence on the productivity, quality of decision making, and the efficiency of problem-solving. This has been mostly stemmed from cognitive research or psychological literature. However, a salient scarce discussion has been held for transport adjacent fields. It is conceivable that in many activity-travel contexts, time pressure is a potentially important factor since an excessive amount of decision time may incur the risk of late arrival to the next activity. The activity-travel rescheduling behavior is commonly explained by costs and benefits of factors such as activity engagements, personal intentions, social requirements, etc. This paper hypothesizes that an additional factor of perceived time pressure could affect travelers’ rescheduling behavior, thus leading to an impact on travel demand management. Time pressure may arise from different ways and is assumed here to be essentially incurred due to travelers planning their schedules without an expectation of unforeseen elements, e.g., transport disruption. In addition to a linear-additive utility-maximization model, the less computationally compensatory heuristic models are considered as an alternative to simulate travelers’ responses. The paper will contribute to travel behavior modeling research by investigating the following questions: how to measure the time pressure properly in an activity-travel day plan context? How do travelers reschedule their plans to cope with the time pressure? How would the importance of the activity affect travelers’ rescheduling behavior? What will the behavioral model be identified to describe the process of making activity-travel rescheduling decisions? How do these identified coping strategies affect the transport network? In this paper, a Mixed Heuristic Model (MHM) is employed to identify the presence of different choice heuristics through a latent class approach. The data about travelers’ activity-travel rescheduling behavior is collected via a web-based interactive survey where a fictitious scenario is created comprising multiple uncertain events on the activity or travel. The experiments are conducted in order to gain a real picture of activity-travel reschedule, considering the factor of time pressure. The identified behavioral models are then integrated into a multi-agent transport simulation model to investigate the effect of the rescheduling strategy on the transport network. The results show that an increased proportion of travelers use simpler, non-compensatory choice strategies instead of compensatory methods to cope with time pressure. Specifically, satisfying - one of the heuristic decision-making strategies - is adopted commonly since travelers tend to abandon the less important activities and keep the important ones. Furthermore, the importance of the activity is found to increase the weight of negative information when making trip-related decisions, especially route choices. When incorporating the identified non-compensatory decision-making heuristic models into the agent-based transport model, the simulation results imply that neglecting the effect of perceived time pressure may result in an inaccurate forecast of choice probability and overestimate the affectability to the policy changes.

Keywords: activity-travel rescheduling, decision making under uncertainty, mixed heuristic model, perceived time pressure, travel demand management

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Authors: Aspasia Koutsokosta, Stefanos Katsavounis

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This paper reviews the model-based qualitative and quantitative Operations Management research in the context of Construction Supply Chain Management (CSCM). Construction industry has been traditionally blamed for low productivity, cost and time overruns, waste, high fragmentation and adversarial relationships. The construction industry has been slower than other industries to employ the Supply Chain Management (SCM) concept and develop models that support the decision-making and planning. However the last decade there is a distinct shift from a project-based to a supply-based approach of construction management. CSCM comes up as a new promising management tool of construction operations and improves the performance of construction projects in terms of cost, time and quality. Modeling the Construction Supply Chain (CSC) offers the means to reap the benefits of SCM, make informed decisions and gain competitive advantage. Different modeling approaches and methodologies have been applied in the multi-disciplinary and heterogeneous research field of CSCM. The literature review reveals that a considerable percentage of CSC modeling accommodates conceptual or process models which discuss general management frameworks and do not relate to acknowledged soft OR methods. We particularly focus on the model-based quantitative research and categorize the CSCM models depending on their scope, mathematical formulation, structure, objectives, solution approach, software used and decision level. Although over the last few years there has been clearly an increase of research papers on quantitative CSC models, we identify that the relevant literature is very fragmented with limited applications of simulation, mathematical programming and simulation-based optimization. Most applications are project-specific or study only parts of the supply system. Thus, some complex interdependencies within construction are neglected and the implementation of the integrated supply chain management is hindered. We conclude this paper by giving future research directions and emphasizing the need to develop robust mathematical optimization models for the CSC. We stress that CSC modeling needs a multi-dimensional, system-wide and long-term perspective. Finally, prior applications of SCM to other industries have to be taken into account in order to model CSCs, but not without the consequential reform of generic concepts to match the unique characteristics of the construction industry.

Keywords: construction supply chain management, modeling, operations research, optimization, simulation

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Authors: Oluwayemisi O. Alaba, John O. Olaomi

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The 2013 Nigerian Demographic Health Survey (NDHS) data was used to investigate the determinants of family size in Nigeria using the geo-additive model. The fixed effect of categorical covariates were modelled using the diffuse prior, P-spline with second-order random walk for the nonlinear effect of continuous variable, spatial effects followed Markov random field priors while the exchangeable normal priors were used for the random effects of the community and household. The Negative Binomial distribution was used to handle overdispersion of the dependent variable. Inference was fully Bayesian approach. Results showed a declining effect of secondary and higher education of mother, Yoruba tribe, Christianity, family planning, mother giving birth by caesarean section and having a partner who has secondary education on family size. Big family size is positively associated with age at first birth, number of daughters in a household, being gainfully employed, married and living with partner, community and household effects.

Keywords: Bayesian analysis, family size, geo-additive model, negative binomial

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Authors: Chico Horacio Jose Sambo

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Recently neural network has gained popularity when come to solve complex nonlinear problems. Permeability is one of fundamental reservoir characteristics system that are anisotropic distributed and non-linear manner. For this reason, permeability prediction from well log data is well suited by using neural networks and other computer-based techniques. The main goal of this paper is to predict reservoir permeability from well logs data by using neural network approach. A multi-layered perceptron trained by back propagation algorithm was used to build the predictive model. The performance of the model on net results was measured by correlation coefficient. The correlation coefficient from testing, training, validation and all data sets was evaluated. The results show that neural network was capable of reproducing permeability with accuracy in all cases, so that the calculated correlation coefficients for training, testing and validation permeability were 0.96273, 0.89991 and 0.87858, respectively. The generalization of the results to other field can be made after examining new data, and a regional study might be possible to study reservoir properties with cheap and very fast constructed models.

Keywords: neural network, permeability, multilayer perceptron, well log

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Authors: Xiangji Guo, Bo Zhang

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In this paper, the phenomenon of vortex breakdown in a vortex tube was analyzed within the scope of unsteady character in swirl flows. A 3-D Unsteady Reynolds-averaged Navier–Stokes (URANS) closed by the Reynolds Stress Model (RSM) was adopted to simulate the large-scale vortex structure in vortex tube, and the numerical model was verified by the steady results. The swirl number was calculated for the vortex tube and the flow field was classed as strong swirl flow. According to the results, a time-dependent spiral flow field gyrates around a central recirculation zone which is precessing around the axis of the tube, and manifests the flow structure is the spiral type (S-type) vortex breakdown. The vortex breakdown is crucial for the formation of the central recirculation zone (CRZ), a further discussion was about the affection on CRZ with the different external conditions of vortex tube, the study on the unsteady characters was expected to hope to design of vortex tube and analyze the energy separation effect.

Keywords: vortex tube, vortex breakdown, central recirculation zone, unsteady, energy separation

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Authors: Varsha Kheradiya, Ganga Prasad Pandey

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The use of wearable antennas is rising because wireless devices become small. The wearable antenna is part of clothes used in communication applications, including energy harvesting, medical application, navigation, and tracking. In current years, Antennas embroidered on clothes, conducting antennas based on fabric, polymer embedded antennas, and inkjet-printed antennas are all attractive ways. Also shows the analysis required for wearable antennas, such as wearable antennae interacting with the human body. The primary requirements for the antenna are small size, low profile minimizing radiation absorption by the human body, high efficiency, structural integrity to survive worst situations, and good gain. Therefore, research in energy harvesting, biomedicine, and military application design is increasingly favoring flexible wearable antennas. Textile materials that are effectively used for designing and developing wearable antennas for body area networks. The wireless body area network is primarily concerned with creating effective antenna systems. The antenna should reduce their size, be lightweight, and be adaptable when integrated into clothes. When antennas integrate into clothes, it provides a convenient alternative to those fabricated using rigid substrates. This paper presents a study of U slot loaded wearable textile antenna. U slot patch antenna design is illustrated for wideband from 1GHz to 6 GHz using textile material jeans as substrate and pure copper polyester taffeta fabric as conducting material. This antenna design exhibits dual band results for WLAN at 2.4 GHz and 3.6 GHz frequencies. Also, study U slot position horizontal and vertical shifting. Shifting the horizontal positive X-axis position of the U slot produces the third band at 5.8 GHz.

Keywords: microstrip patch antenna, textile material, U slot wearable antenna, wireless body area network

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Authors: Ziwei Chen, Yujiang Gao

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The rapid urbanization has caused the massive physical space in Chinese cities to be in a state of duplication and dislocation through the rapid development, forming many daily spaces that cannot be standardized, typed, and identified, such as illegal construction. This phenomenon is known as urban spatial redundancy and is often excluded from mainstream architectural discussions because of its 'remaining' and 'excessive' derogatory label. In recent years, some practice architects have begun to pay attention to this phenomenon and tried to tap the value behind it. In this context, the author takes the redundancy phenomenon of resident space as the research object and explores the inspiration to the urban architectural renewal and the innovative residential area model, based on the urban survey of redundant living space in Hongkou District of Shanghai. On this basis, it shows that the changes accumulated in the long-term use of the building can be re-applied to the goals before the design, which is an important link and significance of the existence of an architecture.

Keywords: rapid urbanization, living space redundancy, architectural renewal, residential area model

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Authors: Chiou-Fang Liou

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Cancer has been one of the most life-threaten diseases worldwide for 30+ years. The influences of cancer illness include symptoms from cancer itself along with its treatments. The quality of life among patients diagnosed with cancer during cancer treatments has been conceptualized within four domains: Functional Well-Being, Social Well-Being, Physical Well-Being, and Emotional Well-Being. Patients with cancer often need to make adjustments to face all the challenges. The middle-range theory of Resourcefulness and Quality of life has been applied to explore factors contributing to cancer patients’ needs. Resourcefulness is defined as sets of skills that can be learned and consisted of Person and Social Resourcefulness. Empirical evidence also supported a possible relationship between Resourcefulness and Quality of Life. However, little is known about the extent to which the two concepts are related to each other. This study, therefore, applied a multivariate technique, Canonical Correlation Analysis, to identify the relationship between the two sets of variables with multi-dimensional measures, the Resourcefulness and Quality of Life in Cancer patients receiving treatments. After IRB approval, this multi-centered study took place at two medical centers in the Central Region of Taiwan. Sample A total of 186 patients with various cancer diagnoses and either receiving radiation therapy or chemotherapy consented to and answered questionnaires. The Import findings of the Generalized F test identified two typical sets with several linear relations and explained a total of 79.1% of the total variance. The first typical set found Personal Resourcefulness negatively related to Social Well-being, Functional being, Emotional Well-being, and Physical, in that order. The second typical set found Social Resourcefulness negatively related to Functional Well-being and Physical-being yet positively related to Social Well-being and Emotional Well-being. Discussion and Conclusion, The results of this presented study supported the statistically significant relationship between two sets of variables that are consistent with the theory. In addition, the results are considerably important in cancer patients receiving cancer treatments.

Keywords: cancer, canonical correlation analysis, quality of life, resourcefulness

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Authors: Saif Hussein Abdallah Alghazo, Norshima Humaidi

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Adopting cybersecurity protective behaviour among the employees is seriously considered in the organization, especially when the Internet of Things (IoT) is widely used in Industrial Revolution 4.0 (IR 4.0) era. Cybersecurity issues arise due to weaknesses of employees’ behaviour such as carelessness and failure to adopt good practices of information security behaviour. Therefore, this study aims to explore the dimensions that might influence employees’ behaviour to adopt good cybersecurity practices and to develop a new holistic model related to this concept. The study proposed this by reviewing the existing works of literature related to this field extensively, especially by focusing on the existing theory such as Protection Motivation Theory (PMT). Moreover, this study has also explored the role of cybersecurity competency among the security manager in the organization since this construct is essential to enhance the protective behaviour towards cybersecurity among the employees in the organization. The proposed research model is important to be quantitatively tested in the future as the findings will serve as the input to the act that will enhance employee’s cybersecurity protective behaviour in the IR 4.0 environment.

Keywords: cybersecurity protective behaviour, protection motivation theory, IR 4.0, cybersecurity competency

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Authors: Xiaoxiao Li, Shuangcheng Jia, Qian Li

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Keypoint detection has always been a challenge in the field of image recognition. This paper proposes a novelty keypoint detection method which is called Multi-Scale Feature Fusion Convolutional Network with Attention (MFFCNA). We verified that the multi-scale features with the attention mechanism module have better feature expression capability. The feature fusion between different scales makes the information that the network model can express more abundant, and the network is easier to converge. On our self-made street sign corner dataset, we validate the MFFCNA model with an accuracy of 97.8% and a recall of 81%, which are 5 and 8 percentage points higher than the HRNet network, respectively. On the COCO dataset, the AP is 71.9%, and the AR is 75.3%, which are 3 points and 2 points higher than HRNet, respectively. Extensive experiments show that our method has a remarkable improvement in the keypoint recognition tasks, and the recognition effect is better than the existing methods. Moreover, our method can be applied not only to keypoint detection but also to image classification and semantic segmentation with good generality.

Keywords: keypoint detection, feature fusion, attention, semantic segmentation

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Authors: Aleksandar Hatzivelkos

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The term ”compromise” is mostly used casually within the social choice theory. It is usually used as a mere result of the social choice function, and this omits its deeper meaning and ramifications. This paper is based on a mathematical model for the description of a compromise as a version of the Sorites paradox. It introduces a formal definition of d-measure of divergence from a compromise and models a notion of compromise that is often used only colloquially. Such a model for vagueness phenomenon, which lies at the core of the notion of compromise enables the introduction of new mathematical structures. In order to maximize compromise, different methods can be used. In this paper, we explore properties of a social welfare function TdM (from Total d-Measure), which is defined as a function which minimizes the total sum of d-measures of divergence over all possible linear orderings. We prove that TdM satisfy strict Pareto principle and behaves well asymptotically. Furthermore, we show that for certain domain restrictions, TdM satisfy positive responsiveness and IIIA (intense independence of irrelevant alternatives) thus being equivalent to Borda count on such domain restriction. This result gives new opportunities in social choice, especially when there is an emphasis on compromise in the decision-making process.

Keywords: borda count, compromise, measure of divergence, minimization

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