Search results for: compressive strength prediction

Authors: Eugene Y. J. Aw, J. W. Koh, S. H. Chew, K. E. Chua, P. L. Goh, Grace H. B. Foo, M. L. Leong

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This paper presents the evaluation of various soil testing methods such as the four-probe soil electrical resistivity method and cone penetration test (CPT) that can complement a newly developed novel rapid soil classification scheme using computer vision, to improve the accuracy and productivity of on-site classification of excavated soil. In Singapore, excavated soils from the local construction industry are transported to Staging Grounds (SGs) to be reused as fill material for land reclamation. Excavated soils are mainly categorized into two groups (“Good Earth” and “Soft Clay”) based on particle size distribution (PSD) and water content (w) from soil investigation reports and on-site visual survey, such that proper treatment and usage can be exercised. However, this process is time-consuming and labor-intensive. Thus, a rapid classification method is needed at the SGs. Four-probe soil electrical resistivity and CPT were evaluated for their feasibility as suitable additions to the computer vision system to further develop this innovative non-destructive and instantaneous classification method. The computer vision technique comprises soil image acquisition using an industrial-grade camera; image processing and analysis via calculation of Grey Level Co-occurrence Matrix (GLCM) textural parameters; and decision-making using an Artificial Neural Network (ANN). It was found from the previous study that the ANN model coupled with ρ can classify soils into “Good Earth” and “Soft Clay” in less than a minute, with an accuracy of 85% based on selected representative soil images. To further improve the technique, the following three items were targeted to be added onto the computer vision scheme: the apparent electrical resistivity of soil (ρ) measured using a set of four probes arranged in Wenner’s array, the soil strength measured using a modified mini cone penetrometer, and w measured using a set of time-domain reflectometry (TDR) probes. Laboratory proof-of-concept was conducted through a series of seven tests with three types of soils – “Good Earth”, “Soft Clay,” and a mix of the two. Validation was performed against the PSD and w of each soil type obtained from conventional laboratory tests. The results show that ρ, w and CPT measurements can be collectively analyzed to classify soils into “Good Earth” or “Soft Clay” and are feasible as complementing methods to the computer vision system.

Keywords: computer vision technique, cone penetration test, electrical resistivity, rapid and non-destructive, soil classification

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Authors: Fatemeh Yazdanmehr, Iulian Nistor

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The scope of the current study is the prediction of water separation in a two-stage industrial crude oil desalting plant. This research study was focused on developing a desalting operation in an existing production unit of one Iranian heavy oil field with 75 MBPD capacity. Because of some operational issues, such as oil dehydration at high temperatures, the optimization of the desalter operational parameters was essential. The mathematical desalting is modeled based on the population balance method. The existing operational data is used for tuning and validation of the accuracy of the modeling. The inlet oil temperature to desalter used was decreased from 110°C to 80°C, and the desalted electrical field was increased from 0.75 kv to 2.5 kv. The proposed condition for the desalter also meets the water oil specification. Based on these conditions of desalter, the oil recovery is increased by 574 BBL/D, and the gas flaring decrease by 2.8 MMSCF/D. Depending on the oil price, the additional production of oil can increase the annual income by about $15 MM and reduces greenhouse gas production caused by gas flaring.

Keywords: desalter, demulsification, modelling, water-oil separation, crude oil emulsion

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Authors: S. Bouharati, F. Allag, M. Belmahdi, M. Bounechada

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In terms of ecology forecast effects of desertification, the purpose of this study is to develop a predictive model of growth and adaptation of species in arid environment and bioclimatic conditions. The impact of climate change and the desertification phenomena is the result of combined effects in magnitude and frequency of these phenomena. Like the data involved in the phytopathogenic process and bacteria growth in arid soil occur in an uncertain environment because of their complexity, it becomes necessary to have a suitable methodology for the analysis of these variables. The basic principles of fuzzy logic those are perfectly suited to this process. As input variables, we consider the physical parameters, soil type, bacteria nature, and plant species concerned. The result output variable is the adaptability of the species expressed by the growth rate or extinction. As a conclusion, we prevent the possible strategies for adaptation, with or without shifting areas of plantation and nature adequate vegetation.

Keywords: climate changes, dry soil, phytopathogenicity, predictive model, fuzzy logic

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Authors: Samuel Oluwafemi Oyamakin, Angela Unna Chukwu

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Richrads growth equation being a generalized logistic growth equation was improved upon by introducing an allometric parameter using the hyperbolic sine function. The integral solution to this was called hyperbolic Richards growth model having transformed the solution from deterministic to a stochastic growth model. Its ability in model prediction was compared with the classical Richards growth model an approach which mimicked the natural variability of heights/diameter increment with respect to age and therefore provides a more realistic height/diameter predictions using the coefficient of determination (R2), Mean Absolute Error (MAE) and Mean Square Error (MSE) results. The Kolmogorov-Smirnov test and Shapiro-Wilk test was also used to test the behavior of the error term for possible violations. The mean function of top height/Dbh over age using the two models under study predicted closely the observed values of top height/Dbh in the hyperbolic Richards nonlinear growth models better than the classical Richards growth model.

Keywords: height, diameter at breast height, DBH, hyperbolic sine function, Pinus caribaea, Richards' growth model

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Authors: Monika Chuchro

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This paper presents a preliminary attempt to apply classification of time series using meta-clusters in order to improve the quality of regression models. In this case, clustering was performed as a method to obtain a subgroups of time series data with normal distribution from inflow into waste water treatment plant data which Composed of several groups differing by mean value. Two simple algorithms: K-mean and EM were chosen as a clustering method. The rand index was used to measure the similarity. After simple meta-clustering, regression model was performed for each subgroups. The final model was a sum of subgroups models. The quality of obtained model was compared with the regression model made using the same explanatory variables but with no clustering of data. Results were compared by determination coefficient (R2), measure of prediction accuracy mean absolute percentage error (MAPE) and comparison on linear chart. Preliminary results allows to foresee the potential of the presented technique.

Keywords: clustering, data analysis, data mining, predictive models

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Authors: Honggang Qu, Yong Xu, Rongmei Liu, Zhenji Gao, Bin Wang

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Today's mining industry is advancing gradually toward digital and visual direction. The three-dimensional visualization geological modeling of mine is the digital characterization of mineral deposits and is one of the key technology of digital mining. Three-dimensional geological modeling is a technology that combines geological spatial information management, geological interpretation, geological spatial analysis and prediction, geostatistical analysis, entity content analysis and graphic visualization in a three-dimensional environment with computer technology and is used in geological analysis. In this paper, the three-dimensional geological modeling of an iron mine through the use of Surpac is constructed, and the weight difference of the estimation methods between the distance power inverse ratio method and ordinary kriging is studied, and the ore body volume and reserves are simulated and calculated by using these two methods. Compared with the actual mine reserves, its result is relatively accurate, so it provides scientific bases for mine resource assessment, reserve calculation, mining design and so on.

Keywords: three-dimensional geological modeling, geological database, geostatistics, block model

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Authors: Williams S. Ebhota, Freddie L. Inambao

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This study investigates the use of centrifugal casting method to fabricate functionally graded aluminium A356 Alloy and A356-10%SiCp composite for hydro turbine bucket application. The study includes the design and fabrication of a permanent mould. The mould was put into use and the buckets of A356 Alloy and A356-10%SiCp composite were cast, cut and machined into specimens. Some specimens were given T6 heat treatment and the specimens were prepared for different examinations accordingly. The SiCp particles were found to be more at inner periphery of the bucket. The maximum hardness of As-Cast A356 and A356-10%SiCp composite was recorded at the inner periphery to be 60 BRN and 95BRN, respectively. And these values were appreciated to 98BRN and 122BRN for A356 alloy and A356-10%SiCp composite, respectively. It was observed that the ultimate tensile stress and yield tensile stress prediction curves show the same trend.

Keywords: A356 alloy, A356-10%SiCp composite, centrifugal casting, Pelton bucket, turbine blade

Procedia PDF Downloads 274

Authors: Theophilus Adjirackor, Frederic Sam, Irene Opoku-Ntim, David Okoh Kpeglo, Prince K. Gyekye, Frank K. Quashie, Kofi Ofori

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Indoor radon concentrations were collected monthly over a period of one year in 10 different levels in an underground goldmine, and the data was analyzed using a four-moving average time series to determine the relationship between the depths of the underground mine and the indoor radon concentration. The detectors were installed in batches within four quarters. The measurements were carried out using LR115 solid-state nuclear track detectors. Statistical models are applied in the prediction and analysis of the radon concentration at various depths. The time series model predicted a positive relationship between the depth of the underground mine and the indoor radon concentration. Thus, elevated radon concentrations are expected at deeper levels of the underground mine, but the relationship was insignificant at the 5% level of significance with a negative adjusted R2 (R2 = – 0.021) due to an appropriate engineering and adequate ventilation rate in the underground mine.

Keywords: LR115, radon concentration, rime series, underground goldmine

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Authors: Marcelo Müller Beuren, José Luis Duarte Ribeiro

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Studies point that Brazilian large airports are not managing their assets efficiently. Therefore, organizations seek improvements to raise their asset’s productivity. Hence, identification of assets useful life in airports becomes an important subject, since its accuracy leads to better maintenance plans and technological substitution, contribution to airport services management. However, current useful life prediction models do not converge in terms of determinant elements used, as they are particular to the studied situation. For that reason, the main objective of this paper is to identify the determinant elements for a useful life of major assets in airports. With that purpose, a case study was held in the key airport of the south of Brazil trough historical data analysis and specialist interview. This paper concluded that most of the assets useful life are determined by technical elements, maintenance cost, and operational costs, while few presented influence of technological obsolescence. As a highlight, it was possible to identify the determinant elements to be considered by a model which objective is to identify the useful life of airport’s major assets.

Keywords: airports, asset management, asset useful life

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Authors: Agnė Kairyte, Saulius Vaitkus

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The gradual depletion of fossil feedstock and growing environmental concerns attracted extensive attention to natural resources due to their low cost, high abundance, renewability, sustainability, and biodegradability. Lignin is a significant by-product of the pulp and paper industry, having unique functional groups. Recently it became interesting for the manufacturing of high value-added products such as polyurethane and polyisocyanurate foams. This study focuses on the development of high-performance polyurethane foams with various amounts of lignin as a filler. It is determined that the incorporation of lignin as a filler material results in brittle and hard products due to the low molecular mobility of isocyanates and the inherent stiffness of lignin. Therefore, the current study analyses new techniques and possibilities of liquid glass infusion onto the surface of lignin particles to reduce the negative aspects and improve the performance characteristics of the modified foams. The foams modified with sole lignin and liquid glass-infused lignin had an apparent density ranging from 35 kg/m3 to 45 kg/m3 and closed-cell content (80–90%). The incorporation of sole lignin reduced the compressive and tensile strengths and increased dimensional stability and water absorption, while the contrary results were observed for polyurethane foams with liquid glass-infused lignin particles. The effect on rheological parameters of lignin and liquid glass infused lignin modified polyurethane premixes and morphology of polyurethane foam products were monitored to optimize the conditions and reveal the significant influence of the interaction between particles and polymer matrix.

Keywords: filler, lignin waste, liquid glass, polymer matrix, polyurethane foam, sustainability

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Authors: An-Jui. Li, Kelvin Lim, Chien-Kuo Chiu, Benson Hsiung

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This paper details the utilization of artificial intelligence (AI) in the field of slope stability whereby quick and convenient solutions can be obtained using the developed tool. The AI tool used in this study is the artificial neural network (ANN), while the slope stability analysis methods are the finite element limit analysis methods. The developed tool allows for the prompt prediction of the safety factors of fill slopes and their corresponding probability of failure (depending on the degree of variation of the soil parameters), which can give the practicing engineer a reasonable basis in their decision making. In fact, the successful use of the Extreme Learning Machine (ELM) algorithm shows that slope stability analysis is no longer confined to the conventional methods of modeling, which at times may be tedious and repetitive during the preliminary design stage where the focus is more on cost saving options rather than detailed design. Therefore, similar ANN-based tools can be further developed to assist engineers in this aspect.

Keywords: landslide, limit analysis, artificial neural network, soil properties

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Authors: Redouane Lombarkia

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To apply a new material model developed and validated for plain weave fabric CFRP composites usually used in stanchions in sub-cargo section in aircrafts. This work deals with the development of a numerical model of the fuselage section of commercial aircraft based on the pure explicit finite element method FEM within Abaqus/Explicit commercial code. The aim of this work is the evaluation of the energy absorption capabilities of a full-scale composite fuselage section, including sub-cargo stanchions, Drop tests were carried out from a free fall height of about 5 m and impact velocity of about 6 m∕s. To asses, the prediction efficiency of the proposed numerical modeling procedure, a comparison with literature existed experimental results was performed. We demonstrate the efficiency of the proposed methodology to well capture crash damage mechanisms compared to experimental results

Keywords: crashworthiness, fuselage section, finite elements method (FEM), stanchions, specific energy absorption SEA

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Authors: Yulia V. Krasavtseva, Tatiana V. Kornilova

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Risk-taking behaviors are not only dictated by cognitive components but also involve emotional aspects. Anticipatory emotions, involving both cognitive and affective mechanisms, are involved in decision-making in general, and risk-taking in particular. Affective reactions are prompted when an expectation or prediction is either validated or invalidated in the achieved result. This study aimed to combine predictions, anticipatory emotions, affective reactions, and personality traits in the context of risk-taking behaviors. An experimental online method Emotion and Prediction In a Casino (EPIC) was used, based on a casino-like roulette game. In a series of choices, the participant is presented with progressively riskier roulette combinations, where the potential sums of wins and losses increase with each choice and the participant is given a choice: to 'walk away' with the current sum of money or to 'play' the displayed roulette, thus accepting the implicit risk. Before and after the result is displayed, participants also rate their emotions, using the Self-Assessment Mannequin [Bradley, Lang, 1994], picking a picture, representing the intensity of pleasure, arousal, and dominance. The following personality measures were used: 1) Personal Decision-Making Factors [Kornilova, 2003] assessing risk and rationality; 2) I7 – Impulsivity Questionnaire [Kornilova, 1995] assessing impulsiveness, risk readiness, and empathy and 3) Subjective Risk Intelligence Scale [Craparo et al., 2018] assessing negative attitude toward uncertainty, emotional stress vulnerability, imaginative capability, and problem-solving self-efficacy. Two groups of participants took part in the study: 1) 98 university students (Mage=19.71, SD=3.25; 72% female) and 2) 94 online participants (Mage=28.25, SD=8.25; 89% female). Online participants were recruited via social media. Students with high rationality rated their pleasure and dominance before and after choices as lower (ρ from -2.6 to -2.7, p < 0.05). Those with high levels of impulsivity rated their arousal lower before finding out their result (ρ from 2.5 - 3.7, p < 0.05), while also rating their dominance as low (ρ from -3 to -3.7, p < 0.05). Students prone to risk-rated their pleasure and arousal before and after higher (ρ from 2.5 - 3.6, p < 0.05). High empathy was positively correlated with arousal after learning the result. High emotional stress vulnerability positively correlates with arousal and pleasure after the choice (ρ from 3.9 - 5.7, p < 0.05). Negative attitude to uncertainty is correlated with high anticipatory and reactive arousal (ρ from 2.7 - 5.7, p < 0.05). High imaginative capability correlates negatively with anticipatory and reactive dominance (ρ from - 3.4 to - 4.3, p < 0.05). Pleasure (.492), arousal (.590), and dominance (.551) before and after the result were positively correlated. Higher predictions positively correlated with reactive pleasure and arousal. In a riskier scenario (6/8 chances to win), anticipatory arousal was negatively correlated with the pleasure emotion (-.326) and vice versa (-.265). Correlations occur regardless of the roulette outcome. In conclusion, risk-taking behaviors are linked not only to personality traits but also to anticipatory emotions and affect in a modeled casino setting. Acknowledgment: The study was supported by the Russian Foundation for Basic Research, project 19-29-07069.

Keywords: anticipatory emotions, casino game, risk taking, impulsiveness

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Authors: Bharatendra Rai

Abstract:

Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme).

Keywords: housing data, feature selection, random forest, Boruta algorithm, root mean square error

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Authors: Muzaffer Borekci, Murat Serdar Kirçil

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Predicting the collapse potential of a structure during earthquakes is an important issue in earthquake engineering. Many researchers proposed different methods to assess the collapse potential of structures under the effect of strong ground motions. However most of them did not consider degradation and softening effect in hysteretic behavior. In this study, collapse potential of SDOF systems caused by dynamic instability with stiffness and strength degradation has been investigated. An equation was proposed for the estimation of collapse period of SDOF system which is a limit value of period for dynamic instability. If period of the considered SDOF system is shorter than the collapse period then the relevant system exhibits dynamic instability and collapse occurs.

Keywords: collapse, degradation, dynamic instability, seismic response

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Authors: Gaokai Liu

Abstract:

The distribution of material grain sizes reflects the strength, fracture, corrosion and other properties, and the grain size can be acquired via the grain boundary. In recent years, the automatic grain boundary detection is widely required instead of complex experimental operations. In this paper, an effective solution is applied to acquire the grain boundary of material images. First, the initial superpixel segmentation result is obtained via a superpixel approach. Then, a region merging method is employed to merge adjacent regions based on certain similarity criterions, the experimental results show that the merging strategy improves the superpixel segmentation result on material datasets.

Keywords: grain boundary detection, image segmentation, material images, region merging

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Authors: Eun Kyung Kim, Kyehan Rhee

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Viscoelastic wall properties of the arterial plaques change as the disease progresses, and estimation of wall viscoelasticity can provide a valuable assessment tool for plaque rupture prediction. Cross section of the stenotic coronary artery was modeled based on the IVUS image, and the finite element analysis was performed to get wall deformation under pulsatile pressure. The effects of viscoelastic parameters of the plaque on luminal diameter variations were explored. The result showed that decrease of viscous effect reduced the phase angle between the pressure and displacement waveforms, and phase angle was dependent on the viscoelastic properties of the wall. Because viscous effect of tissue components could be identified using the phase angle difference, wall deformation waveform analysis may be applied to predict plaque wall composition change and vascular wall disease progression.

Keywords: atherosclerotic plaque, diameter variation, finite element method, viscoelasticity

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Authors: Mehdi Jalalpour, Mazdak Tootkaboni

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We present a computationally efficient method for reliability-based topology optimization under material properties uncertainty, which is assumed to be lognormally distributed and correlated within the domain. Computational efficiency is achieved through estimating the response statistics with stochastic perturbation of second order, using these statistics to fit an appropriate distribution that follows the empirical distribution of the response, and employing an efficient gradient-based optimizer. The proposed algorithm is utilized for design of new structures and the changes in the optimized topology is discussed for various levels of target reliability and correlation strength. Predictions were verified thorough comparison with results obtained using Monte Carlo simulation.

Keywords: material uncertainty, stochastic perturbation, structural reliability, topology optimization

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Authors: Ju-young Hwang, Hyo-Gyoung Kwak, Hong Jae Yim

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This paper introduces a numerical analysis method for reinforced-concrete (RC) structures exposed to fire and compares the result with experimental results. The proposed analysis method for RC structure under the high temperature consists of two procedures. First step is to decide the temperature distribution across the section through the heat transfer analysis by using the time-temperature curve. After determination of the temperature distribution, the nonlinear analysis is followed. By considering material and geometrical non-linearity with the temperature distribution, nonlinear analysis predicts the behavior of RC structure under the fire by the exposed time. The proposed method is validated by the comparison with the experimental results. Finally, Prediction model to describe the status of after-cooling concrete can also be introduced based on the results of additional experiment. The product of this study is expected to be embedded for smart structure monitoring system against fire in u-City.

Keywords: RC structures, heat transfer analysis, nonlinear analysis, after-cooling concrete model

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Authors: Khashayar Jafari, Mostafa Jafarian Abyaneh, Vahab Toufigh

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Polymer concrete (PC) is a distinct concrete with superior characteristics in comparison to ordinary cement concrete. It has become well-known for its applications in thin overlays, floors and precast components. In this investigation, the mechanical properties of PC with different epoxy resin contents, ordinary cement concrete (OCC) and lightweight concrete (LC) have been studied under uniaxial compression test. The study involves five types of concrete, with each type being tested four times. Their complete elastic-plastic behavior was compared with each other through the measurement of volumetric strain during the tests. According to the results, PC showed higher strength, ductility and energy absorption with respect to OCC and LC.

Keywords: polymer concrete, ordinary cement concrete, lightweight concrete, uniaxial compression test, volumetric strain

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Authors: Li Zhang, Yuehong Su

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Lightpipe as a mature solar light tube technique has been employed worldwide. Accurately assessing the performance of lightpipe and evaluate daylighting available has been a challenging topic. Previous research had used regression model and computational simulation methods to estimate the performance of lightpipe. However, due to the nonlinear nature of solar light transferring in lightpipe, the methods mentioned above express inaccurate and time-costing issues. In the present study, a neural network model as an alternative method is investigated to predict the transmittance of lightpipe. Four types of commercial lightpipe with bended angle 0°, 30°, 45° and 60° are discussed under clear, intermediate and overcast sky conditions respectively. The neural network is generated in MATLAB by using the outcomes of an optical software Photopia simulations as targets for networks training and testing. The coefficient of determination (R²) for each model is higher than 0.98, and the mean square error (MSE) is less than 0.0019, which indicate the neural network strong predictive ability and the use of the neural network method could be an efficient technique for determining the performance of lightpipe.

Keywords: neural network, bended lightpipe, transmittance, Photopia

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Authors: Benjamin Leiby, Darryl Ahner

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This study demonstrates an alternative stochastic imputation approach for large datasets when preferred commercial packages struggle to iterate due to numerical problems. A large country conflict dataset motivates the search to impute missing values well over a common threshold of 20% missingness. The methodology capitalizes on correlation while using model residuals to provide the uncertainty in estimating unknown values. Examination of the methodology provides insight toward choosing linear or nonlinear modeling terms. Static tolerances common in most packages are replaced with tailorable tolerances that exploit residuals to fit each data element. The methodology evaluation includes observing computation time, model fit, and the comparison of known values to replaced values created through imputation. Overall, the country conflict dataset illustrates promise with modeling first-order interactions while presenting a need for further refinement that mimics predictive mean matching.

Keywords: correlation, country conflict, imputation, stochastic regression

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Authors: Lars Laußat, Manfred Helmus, Kamil Szczesny, Markus König

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As one result of the project “Reactive Construction Project Scheduling using Real Time Construction Logistic Data and Simulation”, a procedure for using data about uncertain resource availability assumptions in reactive scheduling processes has been developed. Prediction data about resource availability is generated in a formalized way using real-time monitoring data e.g. from auto-ID systems on the construction site and in the supply chains. The paper focuses on the formalization of the procedure for monitoring construction logistic processes, for the detection of disturbance and for generating of new and uncertain scheduling assumptions for the reactive resource constrained simulation procedure that is and will be further described in other papers.

Keywords: auto-ID, construction logistic, fuzzy, monitoring, RFID, scheduling

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Authors: Derwin K. C. Chan, Alfred S. Y. Lee

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The integrated model of self-determination theory and the theory of planned behaviour has been successfully applied to explain individuals’ adherence to health behaviours, including behavioural adherence toward rehabilitation. This study was a randomised controlled trial that examined the effectiveness of an mHealth intervention (i.e., mobile application) developed based on this integrated model in promoting treatment adherence of patients of anterior cruciate ligament rupture during their post-surgery home-based rehabilitation period. Subjects were 67 outpatients (aged between 18 and 60) who undertook anterior cruciate ligament (ACL) reconstruction surgery for less than 2 months for this study. Participants were randomly assigned either into the treatment group (who received the smartphone application; N = 32) and control group (who receive standard treatment only; N = 35), and completed psychological measures relating to the theories (e.g., motivations, social cognitive factors, and behavioural adherence) and clinical outcome measures (e.g., subjective knee function (IKDC), laxity (KT-1000), muscle strength (Biodex)) relating to ACL recovery at baseline, 2-month, and 4-month. Generalise estimating equation showed the interaction between group and time was significant on intention was only significant for intention (Wald x² = 5.23, p = .02), that of perceived behavioural control (Wald x² = 3.19, p = .07), behavioural adherence (Wald x² = 3.08, p = .08, and subjective knee evaluation (Wald x² = 2.97, p = .09) were marginally significant. Post-hoc between-subject analysis showed that control group had significant drop of perceived behavioural control (p < .01), subjective norm (p < .01) and intention (p < .01), behavioural adherence (p < .01) from baseline to 4-month, but such pattern was not observed in the treatment group. The treatment group had a significant decrease of behavioural adherence (p < .05) in the 2-month, but such a decrease was not observed in 4-month (p > .05). Although the subjective knee evaluation in both group significantly improved at 2-month and 4-month from the baseline (p < .05), and the improvements in the control group (mean improvement at 4-month = 40.18) were slightly stronger than the treatment group (mean improvement at 4-month = 34.52). In conclusion, the findings showed that the theory driven mobile application ameliorated the decline of treatment intention of home-based rehabilitation. Patients in the treatment group also reported better muscle strength than control group at 4-month follow-up. Overall, the mobile application has shown promises on tackling the problem of orthopaedics outpatients’ non-adherence to medical treatment.

Keywords: self-determination theory, theory of planned behaviour, mobile health, orthopaedic patients

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Authors: Nguyen Van Toan, Suguru Sangu, Tetsuro Saito, Naoki Inomata, Takahito Ono

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This paper presents the design and fabrication of an optical window for an optical modulator toward image sensing applications. An optical window consists of micrometer-order SiO2 capillaries (porous solid) that can modulate transmission light intensity by moving the liquid in and out of porous solid. A high optical transmittance of the optical window can be achieved due to refractive index matching when the liquid is penetrated into the porous solid. Otherwise, its light transmittance is lower because of light reflection and scattering by air holes and capillary walls. Silicon capillaries fabricated by deep reactive ion etching (DRIE) process are completely oxidized to form the SiO2 capillaries. Therefore, high aspect ratio SiO2 capillaries can be achieved based on silicon capillaries formed by DRIE technique. Large compressive stress of the oxide causes bending of the capillary structure, which is reduced by optimizing the design of device structure. The large stress of the optical window can be released via thin supporting beams. A 7.2 mm x 9.6 mm optical window area toward a fully integrated with the image sensor format is successfully fabricated and its optical transmittance is evaluated with and without inserting liquids (ethanol and matching oil). The achieved modulation range is approximately 20% to 35% with and without liquid penetration in visible region (wavelength range from 450 nm to 650 nm).

Keywords: thermal oxidation process, SiO2 capillaries, optical window, light transmittance, image sensor, liquid penetration

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Authors: Bothinah Altaf, Gary Montague, Elaine B. Martin

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This study involves the modeling and monitoring of an ammonia synthesis fixed-bed reactor using partial least squares (PLS) and its variants. The process exhibits complex dynamic behavior due to the presence of heat recycling and feed quench. One limitation of static PLS model in this situation is that it does not take account of the process dynamics and hence dynamic PLS was used. Although it showed, superior performance to static PLS in terms of prediction, the monitoring scheme was inappropriate hence adaptive PLS was considered. A limitation of adaptive PLS is that non-conforming observations also contribute to the model, therefore, a new adaptive approach was developed, robust adaptive dynamic PLS. This approach updates a dynamic PLS model and is robust to non-representative data. The developed methodology showed a clear improvement over existing approaches in terms of the modeling of the reactor and the detection of faults.

Keywords: ammonia synthesis fixed-bed reactor, dynamic partial least squares modeling, recursive partial least squares, robust modeling

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Authors: Woon Lam Ng.

Abstract:

This paper explores the basic structures of Chinese calligraphy brushwork, its textures, its characteristic forms, and how its strength can be incorporated into 3d animation. It investigates how these structures could create visual simplification and suggest movement. The conceptual difference between realistic rendering and the Chinese calligraphic concept of simplification is discussed. With the help of the Python programmable environment in Maya, the concept of Chinese calligraphy in 3d space and its idea of visual simplification and abstraction were explored. The work demonstrates how the Chinese calligraphic brushwork could suggest the dynamics of motion in 3d space. Some limitations of the Maya emitting process are also discussed. Possible further explorations through additional mathematical adjustments to the selected Maya shader are also suggested to enhance the presentation.

Keywords: calligraphy, brushwork, dynamics, movements

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Authors: Belkacem Laimouche

Abstract:

With the field of artificial intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions.

Keywords: artificial intelligence, metrology, measurement uncertainty, prediction error, bias, machine learning algorithms, probabilistic models, interlaboratory comparison, data analysis, data reliability, measurement of bias impact on predictions, improvement of model accuracy and reliability

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Authors: Debesh R. Roy

Abstract:

The fundamental issue in understanding the origin and growth mechanism of nanomaterials, from a fundamental unit is a big challenging problem to the scientists. Recently, an immense attention is generated to the researchers for prediction of exceptionally stable atomic cluster units as the building units for future smart materials. The present study is a systematic investigation on the stability and electronic properties of a series of bimetallic (semiconductor-alkaline earth) clusters, viz., BxMg3 (x=1-5) is performed, in search for exceptional and/ or unusual stable motifs. A very popular hybrid exchange-correlation functional, B3LYP as proposed by A. D. Becke along with a higher basis set, viz., 6-31+G[d,p] is employed for this purpose under the density functional formalism. The magic stability among the concerned clusters is explained using the jellium model. It is evident from the present study that the magic stability of B4Mg3 cluster arises due to the jellium shell closure.

Keywords: atomic clusters, density functional theory, jellium model, magic clusters, smart nanomaterials

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Authors: M. Khalfa, H. Khachai, F. Chiker, K. Bougherara, R. Khenata, G. Murtaza, M. Harmel

Abstract:

A theoretical study of structural, elastic, electronic and thermodynamic properties of Fe2VX, (with X = Al and Ga), were studied by means of the full-relativistic version of the full-potential augmented plane wave plus local orbitals method. For exchange and correlation potential we used both generalized-gradient approximation (GGA) and local-density approximation (LDA). Our calculated ground state properties like as lattice constants, bulk modulus and elastic constants appear more accurate when we employed the GGA rather than the LDA approximation, and these results agree very well with the available experimental and theoretical data. Further, prediction of the thermal effects on some macroscopic properties of Fe2VAl and Fe2VGa are given in this paper using the quasi-harmonic Debye model in which the lattice vibrations are taken into account. We have obtained successfully the variations of the primitive cell volume, volume expansion coefficient, heat capacities and Debye temperature with pressure and temperature in the ranges of 0–40 GPa and 0–1500 K.

Keywords: full Heusler, FP-LAPW, electronic properties, thermal properties

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