Search results for: measurement models

Authors: Uduak Umoh, Imo Eyoh, Emmauel Nyoho

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This paper compares fuzzy-machine learning algorithms such as Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) for the predicting cases of fire outbreak. The paper uses the fire outbreak dataset with three features (Temperature, Smoke, and Flame). The data is pre-processed using Interval Type-2 Fuzzy Logic (IT2FL) algorithm. Min-Max Normalization and Principal Component Analysis (PCA) are used to predict feature labels in the dataset, normalize the dataset, and select relevant features respectively. The output of the pre-processing is a dataset with two principal components (PC1 and PC2). The pre-processed dataset is then used in the training of the aforementioned machine learning models. K-fold (with K=10) cross-validation method is used to evaluate the performance of the models using the matrices – ROC (Receiver Operating Curve), Specificity, and Sensitivity. The model is also tested with 20% of the dataset. The validation result shows KNN is the better model for fire outbreak detection with an ROC value of 0.99878, followed by SVM with an ROC value of 0.99753.

Keywords: Machine Learning Algorithms , Interval Type-2 Fuzzy Logic, Fire Outbreak, Support Vector Machine, K-Nearest Neighbour, Principal Component Analysis

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Authors: Mohammed Nasser Al-Suqri

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Former information-seeking models are proposed more than two decades ago. These already existed models were given prior to the evolution of digital information era and Artificial Intelligence (AI) technologies. Lack of current information seeking models within Library and Information Studies resulted in fewer advancements for teaching students about information-seeking behaviors, design of library tools and services. In order to better facilitate the aforementioned concerns, this study aims to propose state-of-the-art model while focusing on the information seeking behavior of library users in the Sultanate of Oman. This study aims for the development, designing and contextualizing the real-time user-centric information seeking model capable of enhancing information needs and information usage along with incorporating critical insights for the digital library practices. Another aim is to establish far-sighted and state-of-the-art frame of reference covering Artificial Intelligence (AI) while synthesizing digital resources and information for optimizing information-seeking behavior. The proposed study is empirically designed based on a mix-method process flow, technical surveys, in-depth interviews, focus groups evaluations and stakeholder investigations. The study data pool is consist of users and specialist LIS staff at 4 public libraries and 26 academic libraries in Oman. The designed research model is expected to facilitate LIS by assisting multi-dimensional insights with AI integration for redefining the information-seeking process, and developing a technology rich model.

Keywords: artificial intelligence, information seeking, information behavior, information seeking models, libraries, Sultanate of Oman

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Authors: H. Hruschka

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This paper presents the first comparison of the performance of the restricted Boltzmann machine and the deep belief net on binary market basket data relative to binary factor analysis and the two best-known topic models, namely Dirichlet allocation and the correlated topic model. This comparison shows that the restricted Boltzmann machine and the deep belief net are superior to both binary factor analysis and topic models. Managerial implications that differ between the investigated models are treated as well. The restricted Boltzmann machine is defined as joint Boltzmann distribution of hidden variables and observed variables (purchases). It comprises one layer of observed variables and one layer of hidden variables. Note that variables of the same layer are not connected. The comparison also includes deep belief nets with three layers. The first layer is a restricted Boltzmann machine based on category purchases. Hidden variables of the first layer are used as input variables by the second-layer restricted Boltzmann machine which then generates second-layer hidden variables. Finally, in the third layer hidden variables are related to purchases. A public data set is analyzed which contains one month of real-world point-of-sale transactions in a typical local grocery outlet. It consists of 9,835 market baskets referring to 169 product categories. This data set is randomly split into two halves. One half is used for estimation, the other serves as holdout data. Each model is evaluated by the log likelihood for the holdout data. Performance of the topic models is disappointing as the holdout log likelihood of the correlated topic model – which is better than Dirichlet allocation - is lower by more than 25,000 compared to the best binary factor analysis model. On the other hand, binary factor analysis on its own is clearly surpassed by both the restricted Boltzmann machine and the deep belief net whose holdout log likelihoods are higher by more than 23,000. Overall, the deep belief net performs best. We also interpret hidden variables discovered by binary factor analysis, the restricted Boltzmann machine and the deep belief net. Hidden variables characterized by the product categories to which they are related differ strongly between these three models. To derive managerial implications we assess the effect of promoting each category on total basket size, i.e., the number of purchased product categories, due to each category's interdependence with all the other categories. The investigated models lead to very different implications as they disagree about which categories are associated with higher basket size increases due to a promotion. Of course, recommendations based on better performing models should be preferred. The impressive performance advantages of the restricted Boltzmann machine and the deep belief net suggest continuing research by appropriate extensions. To include predictors, especially marketing variables such as price, seems to be an obvious next step. It might also be feasible to take a more detailed perspective by considering purchases of brands instead of purchases of product categories.

Keywords: binary factor analysis, deep belief net, market basket analysis, restricted Boltzmann machine, topic models

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Authors: Huijuan Liu, Fukun Li, Hao Yuan

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The bolted spherical node is a common type of joint in space steel structures. The bolt-sphere joint portion almost always controls the bearing capacity of the bolted spherical node. The investigation of the bearing performance and progressive failure in service often requires high-fidelity numerical models. This paper focuses on the constitutive models of bolt steel and sphere steel used in China’s space structure construction. The elastoplastic model is determined by a standard tensile test and calibrated Voce saturated hardening rule. The ductile damage is found dominant based on the fractography analysis. Then Rice-Tracey ductile fracture rule is selected and the model parameters are calibrated based on tensile tests of notched specimens. These calibrated material models can benefit research or engineering work in similar fields.

Keywords: bolt-sphere joint, steel, constitutive model, ductile damage, model calibration

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Authors: Avinoam Rabinovich

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CO₂ geological storage is a proven technology for reducing anthropogenic carbon emissions, which is paramount for achieving the ambitious net zero emissions goal. Core flooding experiments are an important step in any CO₂ storage project, allowing us to gain information on the flow of CO₂ and brine in the porous rock extracted from the reservoir. This information is important for understanding basic mechanisms related to CO₂ geological storage as well as for reservoir modeling, which is an integral part of a field project. In this work, a different method for constructing accurate models of CO₂-brine core flooding will be presented. Results for synthetic cases and real experiments will be shown and compared with numerical models to exhibit their predictive capabilities. Furthermore, the various mechanisms which impact the CO₂ distribution and trapping in the rock samples will be discussed, and examples from models and experiments will be provided. The new method entails solving an inverse problem to obtain a three-dimensional permeability distribution which, along with the relative permeability and capillary pressure functions, constitutes a model of the flow experiments. The model is more accurate when data from a number of experiments are combined to solve the inverse problem. This model can then be used to test various other injection flow rates and fluid fractions which have not been tested in experiments. The models can also be used to bridge the gap between small-scale capillary heterogeneity effects (sub-core and core scale) and large-scale (reservoir scale) effects, known as the upscaling problem.

Keywords: CO₂ geological storage, residual trapping, capillary heterogeneity, core flooding, CO₂-brine flow

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Authors: David Lall, Vikram Vishal, P. G. Ranjith

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Numerical modeling of gas production from hydrate-bearing reservoirs requires the solution of various thermal, hydrological, chemical, and mechanical phenomena in a coupled manner. Among the various reservoir properties that influence gas production estimates, the distribution of permeability across the domain is one of the most crucial parameters since it determines both heat transfer and mass transfer. The aspect of permeability in hydrate-bearing reservoirs is particularly complex compared to conventional reservoirs since it depends on the saturation of gas hydrates and hence, is dynamic during production. The dependence of permeability on hydrate saturation is mathematically represented using permeability-reduction models, which are specific to the expected morphology of hydrate accumulations (such as grain-coating or pore-filling hydrates). In this study, we demonstrate the impact of various permeability-reduction models, and consequently, different morphologies of hydrate deposits on the estimates of gas production using depressurization at the reservoir scale. We observe significant differences in produced water volumes and cumulative mass of produced gas between the models, thereby highlighting the uncertainty in production behavior arising from the ambiguity in the prevalent gas hydrate morphology.

Keywords: gas hydrate morphology, multi-scale modeling, THMC, fluid flow in porous media

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Authors: Samuel Ahamefula

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Turbulent motion is a highly nonlinear and complex phenomenon, and its modelling is still very challenging. In this study, we developed a hybrid computational approach to accurately simulate fluid turbulence phenomenon. The focus is coupling and transitioning between Direct Numerical Simulation (DNS) and Large Eddy Simulating Wall Models (LES-WM) regions. In the framework, high-order fidelity fluid dynamical methods are utilized to simulate the unsteady compressible Navier-Stokes equations in the Eulerian format on the unstructured moving grids. The coupling and transitioning of DNS and LES-WM are conducted through the linearly staggered Dirichlet-Neumann coupling scheme. The high-fidelity framework is verified and validated based on namely, DNS ability for capture full range of turbulent scales, giving accurate results and LES-WM efficiency in simulating near-wall turbulent boundary layer by using wall models.

Keywords: computational methods, turbulence modelling, turbulence entropy, navier-stokes equations

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Authors: Fikret Yalcinkaya, Hamza Unsal

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To understand how neurons work, it is required to combine experimental studies on neural science with numerical simulations of neuron models in a computer environment. In this regard, the simplicity and applicability of spiking neuron modeling functions have been of great interest in computational neuron science and numerical neuroscience in recent years. Spiking neuron models can be classified by exhibiting various neuronal behaviors, such as spiking and bursting. These classifications are important for researchers working on theoretical neuroscience. In this paper, three different spiking neuron models; Izhikevich, Adaptive Exponential Integrate Fire (AEIF) and Hindmarsh Rose (HR), which are based on first order differential equations, are discussed and compared. First, the physical meanings, derivatives, and differential equations of each model are provided and simulated in the Matlab environment. Then, by selecting appropriate parameters, the models were visually examined in the Matlab environment and it was aimed to demonstrate which model can simulate well-known biological neuron behaviours such as Tonic Spiking, Tonic Bursting, Mixed Mode Firing, Spike Frequency Adaptation, Resonator and Integrator. As a result, the Izhikevich model has been shown to perform Regular Spiking, Continuous Explosion, Intrinsically Bursting, Thalmo Cortical, Low-Threshold Spiking and Resonator. The Adaptive Exponential Integrate Fire model has been able to produce firing patterns such as Regular Ignition, Adaptive Ignition, Initially Explosive Ignition, Regular Explosive Ignition, Delayed Ignition, Delayed Regular Explosive Ignition, Temporary Ignition and Irregular Ignition. The Hindmarsh Rose model showed three different dynamic neuron behaviours; Spike, Burst and Chaotic. From these results, the Izhikevich cell model may be preferred due to its ability to reflect the true behavior of the nerve cell, the ability to produce different types of spikes, and the suitability for use in larger scale brain models. The most important reason for choosing the Adaptive Exponential Integrate Fire model is that it can create rich ignition patterns with fewer parameters. The chaotic behaviours of the Hindmarsh Rose neuron model, like some chaotic systems, is thought to be used in many scientific and engineering applications such as physics, secure communication and signal processing.

Keywords: Izhikevich, adaptive exponential integrate fire, Hindmarsh Rose, biological neuron behaviours, spiking neuron models

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Authors: Tibor Ajtai, Máté Pintér, Noémi Utry, Gergely Kiss-Albert, Andrea Palágyi, László Manczinger, Csaba Vágvölgyi, Gábor Szabó, Zoltán Bozóki

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According to the best knowledge of the authors, here we experimentally demonstrate first, a quantified correlation between the real-time measured optical feature of the ambient and the off-line measured toxicity data. Finally, using these correlations we are presenting a novel methodology for real time characterisation of ambient toxicity based on the multi wavelength aerosol phase photoacoustic measurement. Ambient carbonaceous particulate matter is one of the most intensively studied atmospheric constituent in climate science nowadays. Beyond their climatic impact, atmospheric soot also plays an important role as an air pollutant that harms human health. Moreover, according to the latest scientific assessments ambient soot is the second most important anthropogenic emission source, while in health aspect its being one of the most harmful atmospheric constituents as well. Despite of its importance, generally accepted standard methodology for the quantitative determination of ambient toxicology is not available yet. Dominantly, ambient toxicology measurement is based on the posterior analysis of filter accumulated aerosol with limited time resolution. Most of the toxicological studies are based on operational definitions using different measurement protocols therefore the comprehensive analysis of the existing data set is really limited in many cases. The situation is further complicated by the fact that even during its relatively short residence time the physicochemical features of the aerosol can be masked significantly by the actual ambient factors. Therefore, decreasing the time resolution of the existing methodology and developing real-time methodology for air quality monitoring are really actual issues in the air pollution research. During the last decades many experimental studies have verified that there is a relation between the chemical composition and the absorption feature quantified by Absorption Angström Exponent (AAE) of the carbonaceous particulate matter. Although the scientific community are in the common platform that the PhotoAcoustic Spectroscopy (PAS) is the only methodology that can measure the light absorption by aerosol with accurate and reliable way so far, the multi-wavelength PAS which are able to selectively characterise the wavelength dependency of absorption has become only available in the last decade. In this study, the first results of the intensive measurement campaign focusing the physicochemical and toxicological characterisation of ambient particulate matter are presented. Here we demonstrate the complete microphysical characterisation of winter time urban ambient including optical absorption and scattering as well as size distribution using our recently developed state of the art multi-wavelength photoacoustic instrument (4λ-PAS), integrating nephelometer (Aurora 3000) as well as single mobility particle sizer and optical particle counter (SMPS+C). Beyond this on-line characterisation of the ambient, we also demonstrate the results of the eco-, cyto- and genotoxicity measurements of ambient aerosol based on the posterior analysis of filter accumulated aerosol with 6h time resolution. We demonstrate a diurnal variation of toxicities and AAE data deduced directly from the multi-wavelength absorption measurement results.

Keywords: photoacoustic spectroscopy, absorption Angström exponent, toxicity, Ames-test

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Authors: Haggay Etkes

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This study provides evidence for the impact of monthly interviews conducted for the Israeli Labor Force Surveys (LFSs) on estimated flows between labor force (LF) statuses and on coefficients in fixed-effects estimations. The study uses the natural experiment of parallel interviews for the quarterly and the monthly LFSs in Israel in 2011 for demonstrating that the Labor Force Participation (LFP) rate of Jewish persons who participated in the monthly LFS increased between interviews, while in the quarterly LFS it decreased. Interestingly, the estimated impact on the LFP rate of self-reporting individuals is 2.6–3.5 percentage points while the impact on the LFP rate of individuals whose data was reported by another member of their household (a proxy), is lower and statistically insignificant. The relative increase of the LFP rate in the monthly survey is a result of a lower rate of exit from the LF and a somewhat higher rate of entry into the LF relative to these flows in the quarterly survey. These differing flows have a bearing on labor search models as the monthly survey portrays a labor market with less friction and a “steady state” LFP rate that is 5.9 percentage points higher than the quarterly survey. The study also demonstrates that monthly interviews affect a specific group (45–64 year-olds); thus the sign of coefficient of age as an explanatory variable in fixed-effects regressions on LFP is negative in the monthly survey and positive in the quarterly survey.

Keywords: measurement error, surveys, search, LFSs

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Authors: Ignatio Madanhire, Charles Mbohwa

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This study looks at the best model of aggregate planning activity in an industrial entity and uses the trial and error method on spreadsheets to solve aggregate production planning problems. Also linear programming model is introduced to optimize the aggregate production planning problem. Application of the models in a furniture production firm is evaluated to demonstrate that practical and beneficial solutions can be obtained from the models. Finally some benchmarking of other furniture manufacturing industries was undertaken to assess relevance and level of use in other furniture firms

Keywords: aggregate production planning, trial and error, linear programming, furniture industry

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Authors: Meida Laely Ramdani, Earmporn Thongkrajai, Dedy Purwito

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Lifestyle is one of the most important factors affecting health. Measurement of lifestyle behaviors is necessary for the identification of causal associations between unhealthy lifestyle and health outcomes. There was many instruments have been measured for lifestyle, but not specific for stroke recurrence. This study aimed to develop a new questionnaire of Lifestyle Adjustment Scale (LSAS) among recurrent stroke patients in Indonesia and to measure the reliability and validity of LSAS. The instrument consist of 33 items was developed from the responses of 30 recurrent stroke patients with the maximum age 60 years. Data was collected during October to November 2015. The properties of the instrument were evaluated by validity assessment and reliability measures. The content validity was judged adequate by a panel of five experts, with the result of I-CVI was 0.97. The Cronbach’s alpha analysis was carried out to measure the reliability of LSAS. The result showed that Cronbach’s alpha coefficient was 0.819. LSAS were classified under the domains of dietary habit, smoking habit, physical activity, and stress management. The results of Cronbach’s alpha coefficient for each subscale were 0.60, 0.39, 0.67, 0.65 and 0.76 respectively. LSAS instrument was valid and reliable therefore can be used as research tool among recurrent stroke patients. The development of this questionnaire has been adapted to the socio-cultural context in Indonesia.

Keywords: LSAS, recurrent stroke patients, lifestyle, Indonesia

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this study is to evaluate the advantages and disadvantages of various machine learning techniques in forecasting ground-motion intensity measures given source characteristics, source-to-site distance, and local site condition. Intensity measures such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Estimating these variables for future earthquake events is a key step in seismic hazard assessment and potentially subsequent risk assessment of different types of structures. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as a statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The algorithms are adjusted to quantify event-to-event and site-to-site variability of the ground motions by implementing them as random effects in the proposed models to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4,528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4 to 500 km in Oklahoma, Kansas, and Texas since 2005. The main reason of the considered database stems from the recent increase in the seismicity rate of these states attributed to petroleum production and wastewater disposal activities, which necessities further investigation in the ground motion models developed for these states. Accuracy of the models in predicting intensity measures, generalization capability of the models for future data, as well as usability of the models are discussed in the evaluation process. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available.

Keywords: artificial neural network, ground-motion models, machine learning, random forest, support vector machine

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Authors: I. V. Pinto, M. R. Sooriyarachchi

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It can be frequently observed that the data arising in our environment have a hierarchical or a nested structure attached with the data. Multilevel modelling is a modern approach to handle this kind of data. When multilevel modelling is combined with a binary response, the estimation methods get complex in nature and the usual techniques are derived from quasi-likelihood method. The estimation methods which are compared in this study are, marginal quasi-likelihood (order 1 & order 2) (MQL1, MQL2) and penalized quasi-likelihood (order 1 & order 2) (PQL1, PQL2). A statistical model is of no use if it does not reflect the given dataset. Therefore, checking the adequacy of the fitted model through a goodness-of-fit (GOF) test is an essential stage in any modelling procedure. However, prior to usage, it is also equally important to confirm that the GOF test performs well and is suitable for the given model. This study assesses the suitability of the GOF test developed for binary response multilevel models with respect to the method used in model estimation. An extensive set of simulations was conducted using MLwiN (v 2.19) with varying number of clusters, cluster sizes and intra cluster correlations. The test maintained the desirable Type-I error for models estimated using PQL2 and it failed for almost all the combinations of MQL. Power of the test was adequate for most of the combinations in all estimation methods except MQL1. Moreover, models were fitted using the four methods to a real-life dataset and performance of the test was compared for each model.

Keywords: goodness-of-fit test, marginal quasi-likelihood, multilevel modelling, penalized quasi-likelihood, power, quasi-likelihood, type-I error

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Authors: Muhammad Waqar Aziz, Syed Abdul Baqi Shah

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The execution time analysis is fundamental to the successful design and execution of real-time embedded software. In such analysis, the Worst-Case Execution Time (WCET) of a program is a key measure, on the basis of which system tasks are scheduled. The WCET analysis of embedded software is also needed for system understanding and to guarantee its behavior. WCET analysis can be performed statically (without executing the program) or dynamically (through measurement). Traditionally, research on the WCET analysis assumes sequential code running on single-core platforms. However, as computation is steadily moving towards using a combination of parallel programs and multi-core hardware, new challenges in WCET analysis need to be addressed. In this article, we report our experiences of performing the WCET analysis of Parallel Embedded Software (PES) running on multi-core platform. The primary purpose was to investigate how WCET estimates of PES can be computed statically, and how they can be derived dynamically. Our experiences, as reported in this article, include the challenges we faced, possible suggestions to these challenges and the workarounds that were developed. This article also provides observations on the benefits and drawbacks of deriving the WCET estimates using the said methods and provides useful recommendations for further research in this area.

Keywords: embedded software, worst-case execution-time analysis, static flow analysis, measurement-based analysis, parallel computing

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Authors: Zachary Pan

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Our general mission is to solve the problem of classifying images into different body part types and deciding if each of them is healthy or not. However, for now, we will determine healthiness for only one-sixth of the body parts, specifically the chest. We will detect pneumonia in X-ray scans of those chest images. With this type of AI, doctors can use it as a second opinion when they are taking CT or X-ray scans of their patients. Another ad-vantage of using this machine learning classifier is that it has no human weaknesses like fatigue. The overall ap-proach to this problem is to split the problem into two parts: first, classify the image, then determine if it is healthy. In order to classify the image into a specific body part class, the body parts dataset must be split into test and training sets. We can then use many models, like neural networks or logistic regression models, and fit them using the training set. Now, using the test set, we can obtain a realistic accuracy the models will have on images in the real world since these testing images have never been seen by the models before. In order to increase this testing accuracy, we can also apply many complex algorithms to the models, like multiplicative weight update. For the second part of the problem, to determine if the body part is healthy, we can have another dataset consisting of healthy and non-healthy images of the specific body part and once again split that into the test and training sets. We then use another neural network to train on those training set images and use the testing set to figure out its accuracy. We will do this process only for the chest images. A major conclusion reached is that convolutional neural networks are the most reliable and accurate at image classification. In classifying the images, the logistic regression model, the neural network, neural networks with multiplicative weight update, neural networks with the black box algorithm, and the convolutional neural network achieved 96.83 percent accuracy, 97.33 percent accuracy, 97.83 percent accuracy, 96.67 percent accuracy, and 98.83 percent accuracy, respectively. On the other hand, the overall accuracy of the model that de-termines if the images are healthy or not is around 78.37 percent accuracy.

Keywords: body part, healthcare, machine learning, neural networks

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Authors: Abdallah Touaibia, Nachida Kasbadji Merzouk, Mustapha Merzouk, Ryma Belarbi

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accuracy assessment and a better understand of the wind resource distribution are the most important tasks for decision making before installing wind energy operating systems in a given region, there where our interest come to the Algerian coastline and its Mediterranean sea area. Despite its large coastline overlooking the border of Mediterranean Sea, there is still no strategy encouraging the development of offshore wind farms in Algerian waters. The present work aims to estimate the offshore wind fields for the Algerian Mediterranean Sea based on wind data measurements ranging from 1995 to 2018 provided of 24 years of measurement by seven observation stations focusing on three coastline cities in Algeria under a different measurement time step recorded from 30 min, 60 min, and 180 min variate from one to each other, two stations in Spain, two other ones in Italy and three in the coast of Algeria from the east Annaba, at the center Algiers, and to Oran taken place at the west of it. The idea behind consists to have multiple measurement points that helping to characterize this area in terms of wind potential by the use of interpolation method of their average wind speed values between these available data to achieve the approximate values of others locations where aren’t any available measurement because of the difficulties against the implementation of masts within the deep depth water. This study is organized as follow: first, a brief description of the studied area and its climatic characteristics were done. After that, the statistical properties of the recorded data were checked by evaluating wind histograms, direction roses, and average speeds using MatLab programs. Finally, ArcGIS and MapInfo soft-wares were used to establish offshore wind maps for better understanding the wind resource distribution, as well as to identify windy sites for wind farm installation and power management. The study pointed out that Cap Carbonara is the windiest site with an average wind speed of 7.26 m/s at 10 m, inducing a power density of 902 W/m², then the site of Cap Caccia with 4.88 m/s inducing a power density of 282 W/m². The average wind speed of 4.83 m/s is occurred for the site of Oran, inducing a power density of 230 W/m². The results indicated also that the dominant wind direction where the frequencies are highest for the site of Cap Carbonara is the West with 34%, an average wind speed of 9.49 m/s, and a power density of 1722 W/m². Then comes the site of Cap Caccia, where the prevailing wind direction is the North-west, about 20% and 5.82 m/s occurring a power density of 452 W/m². The site of Oran comes in third place with the North dominant direction with 32% inducing an average wind speed of 4.59 m/s and power density of 189 W/m². It also shown that the proposed method is either crucial in understanding wind resource distribution for revealing windy sites over a large area and more effective for wind turbines micro-siting.

Keywords: wind ressources, mediterranean sea, offshore, arcGIS, mapInfo, wind maps, wind farms

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Authors: Oluwatosin Olofintoye, Josiah Adeyemo, Gbemileke Shomade

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The relationship between rainfall and runoff is an important issue in surface water hydrology therefore the understanding and development of accurate rainfall-runoff models and their applications in water resources planning, management and operation are of paramount importance in hydrological studies. This paper reviews some of the previous works on the rainfall-runoff process modeling. The hydrologic applications of conceptual models and artificial neural networks (ANNs) for the precipitation-runoff process modeling were studied. Gradient training methods such as error back-propagation (BP) and evolutionary algorithms (EAs) are discussed in relation to the training of artificial neural networks and it is shown that application of EAs to artificial neural networks training could be an alternative to other training methods. Therefore, further research interest to exploit the abundant expert knowledge in the area of artificial intelligence for the solution of hydrologic and water resources planning and management problems is needed.

Keywords: artificial intelligence, artificial neural networks, evolutionary algorithms, gradient training method, rainfall-runoff model

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Authors: Michele Sinico

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The present research investigates the effect of symmetry on the perception of happiness and boredom in design products. Three experiments were carried out in order to verify the degree of the visual expressive value on different models of bookcases, wall clocks, and chairs. 60 participants directly indicated the degree of happiness and boredom using 7-point rating scales. The findings show that the participants acknowledged a different value of expressive quality in the different product models. Results show also that symmetry is not a significant constraint for an emotional design project.

Keywords: product experience, emotional design, symmetry, expressive qualities

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Authors: Pavel Zikmund, Robert Popela

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Extreme formation is a theoretical concept of self-sustain flight when a big Airliner is followed by a small UAV glider flying in airliner’s wake vortex. The paper presents results of climb analysis with a goal to lift the gliding UAV to airliner’s cruise altitude. Wake vortex models, the UAV drag polar and basic parameters and airliner’s climb profile are introduced at first. Then, flight performance of the UAV in the wake vortex is evaluated by analytical methods. Time history of optimal distance between the airliner and the UAV during the climb is determined. The results are encouraging, therefore available UAV drag margin for electricity generation is figured out for different vortex models.

Keywords: flight in formation, self-sustained flight, UAV, wake vortex

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Authors: O. Moussous, T. Medjadj

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The suitability of a Fricke dosimeter for the measurement of a relative depth dose profile and the peak scatter factors was studied. The measurements were carried out in the secondary standard dosimetry laboratory at CRNA Algiers using a collimated 60Co gamma source teletherapy machine. The measurements were performed for different field sizes at the phantom front face, at a fixed source-to-phantom distance of 80 cm. The dose measurements were performed by first placing the dosimeters free-in-air at the distance-source-detector (DSD) of 80.5 cm from the source. Additional measurements were made with the phantom in place. The water phantom type Med-Tec 40x40x40 cm for vertical beam was used in this work as scattering martial. The phantom was placed on the irradiation bench of the cobalt unit at the SSD of 80 cm from the beam focus and the centre of the field coincided with the geometric centre of the dosimeters placed at the depth in water of 5 mm Relative depth dose profile and Peak scatter factors measurements were carried out using our Fricke system. This was intercompared with similar measurements by ionization chamber under identical conditions. There is a good agreement between the relative percentage depth–dose profiles and the PSF values measured by both systems using a water phantom.

Keywords: Fricke dosimeter, depth–dose profiles, peak scatter factors, DSD

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Authors: Chun-Ying Lee, Kuan-Hui Cheng, Mei-Wen Wu

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The nickel-manganese (Ni-Mn) alloy coating prepared from DC electrodeposition process in sulphamate bath was studied. The effects of process parameters, such as current density and electrolyte composition, on the cathodic current efficiency, microstructure, internal stress and mechanical properties were investigated. Because of its crucial effect on the application to the electroforming of microelectronic components, the development of low internal stress coating with high leveling power was emphasized. It was found that both the coating’s manganese content and the cathodic current efficiency increased with the raise in current density. In addition, the internal stress of the deposited coating showed compressive nature at low current densities while changed to tensile one at higher current densities. Moreover, the metallographic observation, X-ray diffraction measurement, transmission electron microscope (TEM) examination, and polarization curve measurement were conducted. It was found that the Ni-Mn coating consisted of nano-sized columnar grains and the maximum hardness of the coating was associated with (111) preferred orientation in the microstructure. The grain size was refined along with the increase in the manganese content of the coating, which accordingly, raised its hardness and mechanical tensile strength. In summary, the Ni-Mn coating prepared at lower current density of 1-2 A/dm2 had low internal stress, high leveling power, and better corrosion resistance.

Keywords: Ni-Mn coating, DC plating, internal stress, leveling power

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Authors: T. Marinaci, C. Venuleo

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Different conceptualizations of disease affect patient care. This study aims to address this gap. It explores how health professionals conceptualize gambling problem, addiction and the goals of recovery process. In-depth, semi-structured, open-ended interviews were conducted with Italian psychologists, psychiatrists, general practitioners, and support staff (N= 114), working within health centres for the treatment of addiction (public health services or therapeutic communities) or medical offices. A Lexical Correspondence Analysis (LCA) was applied to the verbatim transcripts. LCA allowed to identify two main factorial dimensions, which organize similarity and dissimilarity in the discourses of the interviewed. The first dimension labelled 'Models of relationship with the problem', concerns two different models of relationship with the health problem: one related to the request for help and the process of taking charge and the other related to the identification of the psychopathology underlying the disorder. The second dimension, labelled 'Organisers of the intervention' reflects the dialectic between two ways to address the problem. On the one hand, they are the gambling dynamics and its immediate life-consequences to organize the intervention (whatever the request of the user is); on the other hand, they are the procedures and the tools which characterize the health service to organize the way the professionals deal with the user’ s problem (whatever it is and despite the specify of the user’s request). The results highlight how, despite the differences, the respondents share a central assumption: understanding gambling problem implies the reference to the gambler’s identity, more than, for instance, to the relational, social, cultural or political context where the gambler lives. A passive stance is attributed to the user, who does not play any role in the definition of the goal of the intervention. The results will be discussed to highlight the relationship between professional models and users’ ways to understand and deal with the problems related to gambling.

Keywords: cultural models, health professionals, intervention models, problem gambling

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Authors: Bowen Ding, Yihao Kuang

Abstract:

In recent years, with the development of large-scale pre-trained lan-guage models, building vector representations of text through deep neural network models has become a standard practice for natural language processing tasks. From the performance on downstream tasks, we can know that the text representation constructed by these models contains linguistic information, but its encoding mode and extent are unclear. In this work, a structural probe is proposed to detect whether the vector representation produced by a deep neural network is embedded with a syntax tree. The probe is trained with the deep metric learning method, so that the distance between word vectors in the metric space it defines encodes the distance of words on the syntax tree, and the norm of word vectors encodes the depth of words on the syntax tree. The experiment results on ELMo and BERT show that the syntax tree is encoded in their parameters and the word representations they produce.

Keywords: deep metric learning, syntax tree probing, natural language processing, word representations

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Authors: Andreas Aceranti, Guido Bighiani, Francesca Crotto, Marco Colorato, Stefania Zaghi, Marino Zanetti, Simonetta Vernocchi

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The management of eye parameters such as convergence, accommodation, and miosis is very complex; in fact, both the neurovegetative system and the complex Oculocephalgiria system come into play. We have found the effectiveness of the "highvelocity low amplitude" technique directed on C7-T1 (where the cilio-spinal nucleus of the budge is located) in improving the convergence parameter through the measurement of the point of maximum convergence. With this research, we set out to investigate whether the improvement obtained through the High Velocity Low Amplitude maneuver lasts over time, carrying out a pre-manipulation measurement, one immediately after manipulation and one month after manipulation. We took a population of 30 subjects with both refractive and non-refractive problems. Of the 30 patients tested, 27 gave a positive result after the High Velocity Low Amplitude maneuver, giving an improvement in the point of maximum convergence. After a month, we retested all 27 subjects: some further improved the result, others kept, and three subjects slightly lost the gain obtained. None of the re-tested patients returned to the point of maximum convergence starting pre-manipulation. This result opens the door to a multidisciplinary approach between ophthalmologists and osteopaths with the aim of addressing oculomotricity and convergence deficits that increasingly afflict our society due to the massive use of devices and for the conduct of life in closed and restricted environments.

Keywords: point of maximum convergence, HVLA, improvement in PPC, convergence

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Authors: Yalçın Bozkurt

Abstract:

Prediction models were developed for accurate prediction of bodyweight (BW) by using Digital Images of beef cattle body dimensions by Artificial Neural Networks (ANN). For this purpose, the animal data were collected at a private slaughter house and the digital images and the weights of each live animal were taken just before they were slaughtered and the body dimensions such as digital wither height (DJWH), digital body length (DJBL), digital body depth (DJBD), digital hip width (DJHW), digital hip height (DJHH) and digital pin bone length (DJPL) were determined from the images, using the data with 1069 observations for each traits. Then, prediction models were developed by ANN. Digital body measurements were analysed by ANN for body prediction and R2 values of DJBL, DJWH, DJHW, DJBD, DJHH and DJPL were approximately 94.32, 91.31, 80.70, 83.61, 89.45 and 70.56 % respectively. It can be concluded that in management situations where BW cannot be measured it can be predicted accurately by measuring DJBL and DJWH alone or both DJBD and even DJHH and different models may be needed to predict BW in different feeding and environmental conditions and breeds

Keywords: artificial neural networks, bodyweight, cattle, digital body measurements

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Authors: Jonathan Iworiso

Abstract:

Forecasting the equity premium out-of-sample is a major concern to researchers in finance and emerging markets. The quest for a superior model that can forecast the equity premium with significant economic gains has resulted in several controversies on the choice of variables and suitable techniques among scholars. This research focuses mainly on the application of Regression Training (RT) techniques to forecast monthly equity premium out-of-sample recursively with an expanding window method. A broad category of sophisticated regression models involving model complexity was employed. The RT models include Ridge, Forward-Backward (FOBA) Ridge, Least Absolute Shrinkage and Selection Operator (LASSO), Relaxed LASSO, Elastic Net, and Least Angle Regression were trained and used to forecast the equity premium out-of-sample. In this study, the empirical investigation of the RT models demonstrates significant evidence of equity premium predictability both statistically and economically relative to the benchmark historical average, delivering significant utility gains. They seek to provide meaningful economic information on mean-variance portfolio investment for investors who are timing the market to earn future gains at minimal risk. Thus, the forecasting models appeared to guarantee an investor in a market setting who optimally reallocates a monthly portfolio between equities and risk-free treasury bills using equity premium forecasts at minimal risk.

Keywords: regression training, out-of-sample forecasts, expanding window, statistical predictability, economic significance, utility gains

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Authors: Debadi Chakraborty, John E. Sader

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Nanomechanical devices have emerged as a versatile platform for a host of applications due to their extreme sensitivity to environmental conditions. For example, mass measurements with sensitivity at the atomic level have recently been demonstrated. Ultrafast laser spectroscopy coherently excite the vibrational modes of metal nanoparticles and permits precise measurement of the vibration characteristics as a function of nanoparticle shape, size and surrounding environment. This study reports that the vibration of metal nanoparticles in simple liquids, like water and glycerol are not described by conventional fluid mechanics, i.e., Navier Stokes equations. The intrinsic molecular relaxation processes in the surrounding liquid are found to have a profound effect on the fluid-structure interaction of mechanical devices at nanometre scales. Theoretical models have been developed based on the non-Newtonian viscoelastic fluid-structure interaction theory to investigate the vibration of nanoparticles immersed in simple fluids. The utility of this theoretical framework is demonstrated by comparison to measurements on single nanowires and ensembles of metal rods. This study provides a rigorous foundation for the use of metal nanoparticles as ultrasensitive mechanical sensors in fluid and opens a new paradigm for understanding extremely high frequency fluid mechanics, nanoscale sensing technologies, and biophysical processes.

Keywords: fluid-structure interaction, nanoparticle vibration, ultrafast laser spectroscopy, viscoelastic damping

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Authors: Ali Ashtiani, Hamid Shirazi

Abstract:

This paper reviews the status of existing practice and research related to measuring pavement cracking and using crack density as a pavement surface evaluation protocol. Crack density for pavement evaluation is currently not widely used within the airport community and its use by the highway community is limited. However, surface cracking is a distress that is closely monitored by airport staff and significantly influences the development of maintenance, rehabilitation and reconstruction plans for airport pavements. Therefore crack density has the potential to become an important indicator of pavement condition if the type, severity and extent of surface cracking can be accurately measured. A pavement distress survey is an essential component of any pavement assessment. Manual crack surveying has been widely used for decades to measure pavement performance. However, the accuracy and precision of manual surveys can vary depending upon the surveyor and performing surveys may disrupt normal operations. Given the variability of manual surveys, this method has shown inconsistencies in distress classification and measurement. This can potentially impact the planning for pavement maintenance, rehabilitation and reconstruction and the associated funding strategies. A substantial effort has been devoted for the past 20 years to reduce the human intervention and the error associated with it by moving toward automated distress collection methods. The automated methods refer to the systems that identify, classify and quantify pavement distresses through processes that require no or very minimal human intervention. This principally involves the use of a digital recognition software to analyze and characterize pavement distresses. The lack of established protocols for measurement and classification of pavement cracks captured using digital images is a challenge to developing a reliable automated system for distress assessment. Variations in types and severity of distresses, different pavement surface textures and colors and presence of pavement joints and edges all complicate automated image processing and crack measurement and classification. This paper summarizes the commercially available systems and technologies for automated pavement distress evaluation. A comprehensive automated pavement distress survey involves collection, interpretation, and processing of the surface images to identify the type, quantity and severity of the surface distresses. The outputs can be used to quantitatively calculate the crack density. The systems for automated distress survey using digital images reviewed in this paper can assist the airport industry in the development of a pavement evaluation protocol based on crack density. Analysis of automated distress survey data can lead to a crack density index. This index can be used as a means of assessing pavement condition and to predict pavement performance. This can be used by airport owners to determine the type of pavement maintenance and rehabilitation in a more consistent way.

Keywords: airport pavement management, crack density, pavement evaluation, pavement management

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Authors: Dmitry V. Fomichev, Vladimir I. Solonin

Abstract:

In this paper, experimental and numerical study of hydrodynamic characteristics of the air coolant flow in the test wire-wrapped assembly is presented. The test assembly has 37 rods, which are similar to the real fuel pins of the BREST-OD-300 fuel assemblies geometrically. Air open loop test facility installed at the “Nuclear Power Plants and Installations” department of BMSTU was used to obtain the experimental data. The obtaining altitudinal distribution of static pressure in the near-wall test assembly as well as velocity and temperature distribution of coolant flow in the test sections can give us some new knowledge about the mechanism of formation of the turbulence flow structure in the wire wrapped fuel assemblies. Numerical simulations of the turbulence flow has been accomplished using ANSYS Fluent 14.5. Different non-local turbulence models have been considered, such as standard and RNG k-e models and k-w SST model. Results of numerical simulations of the flow based on the considered turbulence models give the best agreement with the experimental data and help us to carry out strong analysis of flow characteristics.

Keywords: wire-spaces fuel assembly, turbulent flow structure, computation fluid dynamics

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