Search results for: damage prediction models

Authors: Zahra Hamedani

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Many wonderful technological developments in recent years has opened up the possibility of using intelligent buildings for a number of important applications, ranging from minimizing resource usage as well as increasing building efficiency to maximizing comfort, adaption to inhabitants and responsiveness to environmental changes. The concept of an intelligent building refers to the highly embedded, interactive environment within which by exploiting the use of artificial intelligence provides the ability to know its configuration, anticipate the optimum dynamic response to prevailing environmental stimuli, and actuate the appropriate physical reaction to provide comfort and efficiency. This paper contains a general identification of the intelligence paradigm and its impacts on the architecture arena, that with examining the performance of artificial intelligence, a mechanism to analyze and finally for decision-making to control the environment will be described. This mechanism would be a hierarchy of the rational agents which includes decision-making, information, communication and physical layers. This multi-agent system relies upon machine learning techniques for automated discovery, prediction and decision-making. Then, the application of this mechanism regarding adaptation and responsiveness of intelligent building will be provided in two scales of environmental and user. Finally, we review the identifications of sustainability and evaluate the potentials of intelligent building systems in the creation of sustainable architecture and environment.

Keywords: artificial intelligence, intelligent building, responsiveness, adaption, sustainability

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Authors: T. Wresdiyati, S. Sa’diah, A. Winarto

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Diabetes mellitus (DM) is a metabolic disease that can be indicated by the high level of blood glucose. The objective of this study was to observe the antidiabetic properties of ethanolic extract of Indonesian Swietenia mahagoni Jacq. seed on the profile of pancreatic superoxide dismutase and β-cells in the alloxan- experimental diabetic rats. The Swietenia mahagoni seed was obtained from Leuwiliang-Bogor, Indonesia. Extraction of Swietenia mahagoni was done by using ethanol with maceration methods. A total of 25 male Sprague dawley rats were divided into five groups; (a) negative control group, (b) positive control group (DM), (c) DM group that was treated with Swietenia mahagoni seed extract, (d) DM group that was treated with acarbose, and (e) non-DM group that was treated with Swietenia mahagoni seed extract. The DM groups were induced by alloxan (110 mg/kgBW). The extract was orally administrated to diabetic rats 500 mg/kg/BW/day for 28 days. The extract showed hypoglycemic effect, increased body weight, increased the content of superoxide dismutase in the pancreatic tissue, and delayed the rate of β-cells damage of experimental diabetic rats. These results suggested that the ethanolic extract of Indonesian Swietenia mahagoni Jacq. seed could be proposed as a potential anti-diabetic agent.

Keywords: beta cells, diabetes, hypoglycemic, rat, Swietenia mahagoni

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Authors: S. Curteanu, F. Leon, M. Gavrilescu, S. A. Floria

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Optimization algorithms inspired from human behavior were applied in this approach, associated with neural networks models. The algorithms belong to human behaviors of learning and cooperation and human competitive behavior classes. For the first class, the main strategies include: random learning, individual learning, and social learning, and the selected algorithms are: simplified human learning optimization (SHLO), social learning optimization (SLO), and teaching-learning based optimization (TLBO). For the second class, the concept of learning is associated with competitiveness, and the selected algorithms are sports-inspired algorithms (with Football Game Algorithm, FGA and Volleyball Premier League, VPL) and Imperialist Competitive Algorithm (ICA). A real process, the synthesis of polyacrylamide-based multicomponent hydrogels, where some parameters are difficult to obtain experimentally, is considered as a case study. Reaction yield and swelling degree are predicted as a function of reaction conditions (acrylamide concentration, initiator concentration, crosslinking agent concentration, temperature, reaction time, and amount of inclusion polymer, which could be starch, poly(vinyl alcohol) or gelatin). The experimental results contain 175 data. Artificial neural networks are obtained in optimal form with biologically inspired algorithm; the optimization being perform at two level: structural and parametric. Feedforward neural networks with one or two hidden layers and no more than 25 neurons in intermediate layers were obtained with values of correlation coefficient in the validation phase over 0.90. The best results were obtained with TLBO algorithm, correlation coefficient being 0.94 for an MLP(6:9:20:2) – a feedforward neural network with two hidden layers and 9 and 20, respectively, intermediate neurons. Good results obtained prove the efficiency of the optimization algorithms. More than the good results, what is important in this approach is the simulation methodology, including neural networks and optimization biologically inspired algorithms, which provide satisfactory results. In addition, the methodology developed in this approach is general and has flexibility so that it can be easily adapted to other processes in association with different types of models.

Keywords: artificial neural networks, human behaviors of learning and cooperation, human competitive behavior, optimization algorithms

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Authors: Asiful Hossain Seikh, Mohammad Asif Alam, Ubair Abdus Samad, Jabair A. Mohammed, S. M. Al-Zahrani, El-Sayed M. Sherif

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In this current investigation, aliphatic amine-cured diglycidyl ether of bisphenol-A (DGEBA) based epoxy coating was mixed with certain weight % hardener polyaminoamide (1:2) and was coated on carbon steel panels with and without 1% nano crystalline Al powder. The corrosion behavior of the coated samples were investigated by exposing them in the salt spray chamber, for 500 hours. According to ASTM-B-117, the bath was kept at 35 °C and 5% NaCl containing mist was sprayed at 1.3 bars pressure. Composition of coatings was confirmed using Fourier-transform infrared spectroscopy (FTIR). Electrochemical characterization of the coated samples was also performed using potentiodynamic polarization technique and electrochemical impedance spectroscopy (EIS) technique. All the experiments were done in 3.5% NaCl solution. The nano Al coated sample shows good corrosion resistance property compared to bare Al sample. In fact after salt spray exposure no pitting or local damage was observed for nano coated sample and the coating gloss was negligibly affected. The surface morphology of coated and corroded samples was studied using scanning electron microscopy (SEM).

Keywords: epoxy, nano aluminium, potentiodynamic polarization, salt spray, electrochemical impedence spectroscopy

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Authors: Karim Saeidi

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One of the most important pests in the warehouses is the flour beetle, Tribolium confusum Jacquelin du Val (Col.: Tenebrionidae). Regarding the high degree of damage of stored product pests and dangerous effects of the chemical control using plant extracts and their components are some of the best approaches to control these pests. Antifeedant activity of plant extracts from Lavandula angustifolia Mill and Zataria multiflora Boiss using hydro-distillation were tested against the flour beetle, Tribolium confusum Jacquelin du Val. The nutritional indices: relative growth rate (RGR), relative consumption rate (RCR), the efficiency of conversion of ingested food (ECI), and feeding deterrence index (FDI) were measured for adult insects. Treatments were evaluated using a flour disk bioassay in the dark; at 25±1ᵒC and 60±5% R. H. Concentrations of 0, 0.1, 0.5, 0.75, 1, 1.5, and 2 μl/disk were prepared from each essential oil. After 72 h, nutritional indices were calculated. L. angustifolia oils were more effective than Z. multiflora oils by significantly decreasing the RGR, RCR, and ECI. Feeding deterrence index (FDI) of L. angustifolia essential oil was increased significantly as essential oil concentration increased. The essential oil of L. angustifolia was more effective on FDI than Z. multiflora in some concentration.

Keywords: essential oil, nutritional indices, Tribolium confusum

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Authors: Hye Jin Kim, Hwa Sung Shin

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Ischemic stroke means the caused brain damage due to neurological defects, occurring occlusion of cerebral vascular resulting in thrombus or embolism. t-PA (tissue Plasminogen Activator) is the only thrombolytic agent passed the FDA (Food and Drug Administration). However, t-PA directly dissolves the thrombus (direct activity) through fibrinolysis, showing side effects such as re-occlusion. In this study, we evaluated the thrombolytic activities of the serine protease extracted from lugworms inhabiting tidal flats. The new serine protease identified as 38 kDa by SDS-PAGE was not toxic to brain endothelial cells line (hCMEC/D3). Also, the plasmin synthesis inhibition activity (indirect activity) of the new serine protease was confirmed through fibrin zymography assay and fibrin plate assay. It was higher than direct activity as compared to u-PA (urokinase Plasminogen Activator). The activities were found to be maintained at a wide range of temperature (4-70 ℃) and pH 7-10 compared to previous thrombolytic agents from the azocasein assay. In addition, the new serine protease has shown anticoagulant activity from fibrinogenolytic activity assay. In conclusion, the serine protease in lug worms inhabiting the tidal flats could be considered a promising thrombolytic candidate for the treatment of ischemic stroke.

Keywords: alkaline serine protease, bifunctional thrombolytic activity, fibrinolytic activity, ischemic stroke, lug worms

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Authors: Mustafa Aydın, Ali Murat Sunbul

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Hierarchical or nested data structures usually are seen in many research areas. Especially, in the field of education, if we examine most of the studies, we can see the nested structures. Students in classes, classes in schools, schools in cities and cities in regions are similar nested structures. In a hierarchical structure, students being in the same class, sharing the same physical conditions and similar experiences and learning from the same teachers, they demonstrate similar behaviors between them rather than the students in other classes.

Keywords: hierarchical linear modeling, nested data, hierarchical structure, data structure

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Authors: Christos Bouras, Eirini Stergiou, Charitini Karakostaki, Vasileios Tzanos, Vasileios Kokkinos

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Wind power represents a major pathway to curtailing greenhouse gas emissions and thus reducing the rate of climate change. A wind turbine runs practically emission-free for 20 years, representing one of the most environmentally sustainable sources of energy. Nevertheless, environmental and biodiversity concerns can often slow down or halt the deployment of wind farms due to local public opposition. This opposition is often fueled by poor relationships between wind energy stakeholders and civil society, which in many cases led to conflictual protests and property damage. In this context, addressing these concerns is essential in order to facilitate the proliferation of wind farms in Europe and the phase-out of fossil fuels from the energy mix. The aim of this study is to identify a number of good practices and cases to avoid increasing biodiversity protection at all stages of wind farms’ lifecycle in three participating countries, namely Greece, Latvia, and Poland. The results indicate that although available technological solutions are already being exploited worldwide, in these countries, there is still room for improvement. To address this gap, a set of policy recommendations is proposed to accomplish the wind energy targets in the near future while simultaneously mitigating the pertinent biodiversity risks.

Keywords: biodiversity protection, environmental impact, social acceptance, wind energy

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Authors: Amit Kumar, Amanpreet Chander, Ashish Sahani

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This paper presents our recent work on real-time human activity detection based on the media pipe pipeline and machine learning algorithms. The proposed system can detect human activities, including running, jumping, squatting, bending to the left or right, and standing still. This is a robust solution for developing a yoga, dance, metaverse, and fitness application that checks for the correction of the pose without having any additional monitor like a personal trainer. MediaPipe solution offers an open-source cross-platform which utilizes a two-step detector-tracker ML pipeline for live detection of key landmarks on our body which can be used for motion data collection. The prediction of real-time poses uses a variety of machine learning techniques and different types of analysis. Without primarily relying on powerful desktop environments for inference, our method achieves real-time performance on the majority of contemporary mobile phones, desktops/laptops, Python, or even the web. Experimental results show that our method outperforms the existing method in terms of accuracy and real-time capability, achieving an accuracy of 99.92% on testing datasets.

Keywords: human activity detection, media pipe, machine learning, metaverse applications

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Authors: Sina Mahdavi

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Background and Objective: Multiple sclerosis (MS) is an inflammatory autoimmune disease of the CNS that affects the myelination process in the central nervous system (CNS). Complex interactions of various "environmental or infectious" factors may act as triggers in autoimmunity and disease progression. The association between viral infections, especially Human Herpesvirus 6 (HHV-6), and MS is one potential cause that is not well understood. In this study, we aim to summarize the available data on HHV-6 infection in MS disease progression. Materials and Methods: For this study, the keywords "Multiple sclerosis", " Human Herpesvirus 6 ", and "central nervous system" in the databases PubMed and Google Scholar between 2017 and 2022 were searched, and 12 articles were chosen, studied, and analyzed. Results: HHV 6 tends towards TCD 4+ lymphocytes and enters the CNS due to the weakening of the blood-brain barrier due to inflammatory damage. Following the observation that the HHV-6 U24 protein has a seven amino acid sequence with myelin basic protein, which is one of the main components of the myelin sheath, it could cause a molecular mimicry mechanism followed by cross-reactivity. Reactivation of HHV-6 in the CNS can cause the release of proinflammatory cytokines, including TNF-α, leading to immune-mediated demyelination in patients with MS. Conclusion: There is a high expression of endogenous retroviruses during the course of MS, which indicates the relationship between HHV-6 and MS, and that this virus can play a role in the development of MS by creating an inflammatory state. Therefore, measures to modulate the expression of HHV-6 may be effective in reducing inflammatory processes in demyelinated areas of MS patients.

Keywords: multiple sclerosis, human herpesvirus 6, central nervous system, autoimmunity

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Authors: Rezki Akkal, Mohamed Khodja, Slimane Azzi

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Acidification is a technique used in oil reservoirs to improve annual production, reduce the skin and increase the pressure of an oil well while eliminating the formation damage that occurs during the drilling process, completion and, amongst others, to create new channels allowing the easy circulation of oil around a producing well. This is achieved by injecting an acidizing fluid at a relatively low pressure to prevent fracturing formation. The treatment fluid used depends on the type and nature of the reservoir rock traversed as well as its petrophysical properties. In order to understand the interaction mechanisms between the treatment fluids used for the reservoir rock acidizing, several candidate wells for stimulation were selected in the large Hassi Messaoud deposit in southern Algeria. The stimulation of these wells is completed using different fluids composed mainly of HCl acid with other additives such as corrosion inhibitors, clay stabilizers and iron controllers. These treatment fluids are injected over two phases, namely with clean tube (7.5% HCl) and matrix aidizing with HCl (15%). The stimulation results obtained are variable according to the type of rock traversed and its mineralogical composition. These results show that there has been an increase in production flow and head pressure respectively from 1.99 m3 / h to 3.56 m3 / h and from 13 Kgf / cm2 to 20 kgf / cm2 in the sands formation having good petrophysical properties of (porosity = 16%) and low amount of clay (Vsh = 6%).

Keywords: acidizing, Hassi-Messaoud reservoir, tube clean, matrix stimulation

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Authors: Akiode Ayobami, Akiode Akinsewa, Odeku Mojisola, Salako Busola, Odutolu Omobola, Nuhu Khadija

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Purpose: Evidence exists that family planning use can contribute to reduction in infant and maternal mortality in any country. Despite these benefits, contraceptive use in Nigeria still remains very low, only 10% among married women. Understanding factors that predict contraceptive use is very important in order to improve the situation. In this paper, we analysed data from the 2013 Nigerian Demographic and Health Survey (NDHS) to better understand predictors of contraceptive use in Nigeria. The use of logistics regression and other traditional models in this type of situation is not appropriate as they do not account for social structure influence brought about by the hierarchical nature of the data on response variable. We therefore used multilevel modelling to explore the determinants of contraceptive use in order to account for the significant variation in modern contraceptive use by socio-demographic, and other proximate variables across the different Nigerian states. Method: This data has a two-level hierarchical structure. We considered the data of 26, 403 married women of reproductive age at level 1 and nested them within the 36 states and the Federal Capital Territory, Abuja at level 2. We modelled use of modern contraceptive against demographic variables, being told about FP at health facility, heard of FP on TV, Magazine or radio, husband desire for more children nested within the state. Results: Our results showed that the independent variables in the model were significant predictors of modern contraceptive use. The estimated variance component for the null model, random intercept, and random slope models were significant (p=0.00), indicating that the variation in contraceptive use across the Nigerian states is significant, and needs to be accounted for in order to accurately determine the predictors of contraceptive use, hence the data is best fitted by the multilevel model. Only being told about family planning at the health facility and religion have a significant random effect, implying that their predictability of contraceptive use varies across the states. Conclusion and Recommendation: Results showed that providing FP information at the health facility and religion needs to be considered when programming to improve contraceptive use at the state levels.

Keywords: multilevel modelling, family planning, predictors, Nigeria

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Authors: Vasiliki Vlachou, Christos Kontzinos, Ourania Markaki, Panagiotis Kokkinakos, Vagelis Karakolis, John Psarras

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Higher Education is resisting the pull of technology, especially as this concerns the issuance and verification of degrees and certificates. It is widely known that education certificates are largely produced in paper form making them vulnerable to damage while holders of such certificates are dependent on the universities and other issuing organisations. QualiChain is an EU Horizon 2020 (H2020) research project aiming to transform and revolutionise the domain of public education and its ties with the job market by leveraging blockchain, analytics and decision support to develop a platform for the verification and sharing of education certificates. Blockchain plays an integral part in the QualiChain solution in providing a trustworthy environment to store, share and manage such accreditations. Under the context of this paper, three prominent blockchain platforms (Ethereum, Hyperledger Fabric, Hyperledger Iroha) were considered as a means of experimentation for creating a system with the basic functionalities that will be needed for trustworthy degree verification. The methodology and respective system developed and presented in this paper used Hyperledger Iroha and proved that this specific platform can be used to easily develop decentralize applications. Future papers will attempt to further experiment with other blockchain platforms and assess which has the best potential.

Keywords: blockchain, degree verification, higher education certificates, Hyperledger Iroha

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Authors: N. Fuad, M. N. Taib, R. Jailani, M. E. Marwan

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In this paper, the comparison between k-Nearest Neighbor (kNN) algorithms for classifying the 3D EEG model in brain balancing is presented. The EEG signal recording was conducted on 51 healthy subjects. Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram images. Then, maximum PSD values were extracted as features from the model. There are three indexes for the balanced brain; index 3, index 4 and index 5. There are significant different of the EEG signals due to the brain balancing index (BBI). Alpha-α (8–13 Hz) and beta-β (13–30 Hz) were used as input signals for the classification model. The k-NN classification result is 88.46% accuracy. These results proved that k-NN can be used in order to predict the brain balancing application.

Keywords: power spectral density, 3D EEG model, brain balancing, kNN

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Authors: Farrokh Taherkhani, Thomas Pinger, Max Gündel

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The total economic damage caused by corrosion in Germany is estimated to be more than 3% of the GDP per year. Additionally, corrosion and suitable corrosion protection systems are also significant factors in the consideration of life cycle costs for steel hydraulic structures. In addition to classic coating systems (for example, epoxy resin or polyurethane), zinc and its alloys offer effective and very durable corrosion protection for steels. As a protective layer, hot-dip galvanizing prevents the corrosive media from penetrating into the steel matrix and acts as a sacrificial anode, which corrodes in preference to the steel. However, hot-dip galvanizing as a corrosion protection system has not yet been approved by the relevant authority, the Federal Waterways Engineering and Research Institute (BAW) in Germany. In order to make hot-dip galvanizing usable as a corrosion protection system for steel hydraulic structures in the future, different factors must be considered. These factors are (i) corrosion protection type, (ii) resistance to mechanical stress (i.e., abrasion resistance), (iii) combinability with cathodic corrosion protection, (iv) environmental effects and (v) the crack formation and propagation during hot-dip galvanizing. In this work, hot-dip galvanizing as a corrosion protection system for steel hydraulic steel structures, as well as open questions, are discussed. This paper is based on initial long-term exposure tests with corrosion protection systems consisting of hot-dip galvanizing and duplex systems.

Keywords: steel hydraulic structure, hot-dip galvanizing, corrosion, corrosion resistance, zinc coating, organic coating, duplex sytems

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Authors: Farhad Alizadeh, Alireza Faregh Gharamaleki, Mojtaba Jalilzadeh, Houshang Gholami, Ali Akhoundzadeh

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This study presented hybrid pre-processing approach along with a conceptual model to enhance the accuracy of river discharge prediction. In order to achieve this goal, Ensemble Empirical Mode Decomposition algorithm (EEMD), Discrete Wavelet Transform (DWT) and Mutual Information (MI) were employed as a hybrid pre-processing approach conjugated to Least Square Support Vector Machine (LSSVM). A conceptual strategy namely multi-station model was developed to forecast the Souris River discharge more accurately. The strategy used herein was capable of covering uncertainties and complexities of river discharge modeling. DWT and EEMD was coupled, and the feature selection was performed for decomposed sub-series using MI to be employed in multi-station model. In the proposed feature selection method, some useless sub-series were omitted to achieve better performance. Results approved efficiency of the proposed DWT-EEMD-MI approach to improve accuracy of multi-station modeling strategies.

Keywords: river stage-discharge process, LSSVM, discrete wavelet transform, Ensemble Empirical Decomposition Mode, multi-station modeling

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Authors: Novutry Siregar, Afdal, Emilzon Taslim

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Hyaline Membrane Disease (HMD) is the main cause of respiratory failure in preterm neonates caused by surfactant deficiency. Nasal Continuous Positive Airway Pressure (NCPAP) is the therapy for HMD. The success of therapy is determined by gestational age, birth weight, HMD grade, time of NCAP administration, and time of breathing frequency recovery. The aim of this research is to identify the predictive factor of NCPAP therapy success in preterm neonates with HMD. This study used a cross-sectional design by using medical records of patients who were treated in the Perinatology of the Pediatric Department of Dr. M. Djamil Padang Central Hospital from January 2015 to December 2017. The samples were eighty-two neonates that were selected by using the total sampling technique. Data analysis was done by using the Chi-Square Test and the Multiple Logistic Regression Prediction Model. The results showed the success rate of NCPAP therapy reached 53.7%. Birth weight (p = 0.048, OR = 3.34 95% CI 1.01-11.07), HMD grade I (p = 0.018, OR = 4.95 CI 95% 1.31-18.68), HMD grade II (p = 0.044, OR = 5.52 95% CI 1.04-29.15), and time of breathing frequency recovery (p = 0,000, OR = 13.50 95% CI 3.58-50, 83) are the predictive factors of NCPAP therapy success in preterm neonates with HMD. The most significant predictive factor is the time of breathing frequency recovery.

Keywords: predictive factors, the success of therapy, NCPAP, preterm neonates, HMD

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Authors: Hamidreza Saberkari, Mousa Shamsi, Hossein Ahmadi, Saeed Vaali, , MohammadHossein Sedaaghi

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In the recent years, using signal processing tools for accurate identification of the protein coding regions has become a challenge in bioinformatics. Most of the genomic signal processing methods is based on the period-3 characteristics of the nucleoids in DNA strands and consequently, spectral analysis is applied to the numerical sequences of DNA to find the location of periodical components. In this paper, a novel ensemble algorithm for gene selection in DNA sequences has been presented which is based on the combination of Goertzel algorithm and anti-notch filter (ANF). The proposed algorithm has many advantages when compared to other conventional methods. Firstly, it leads to identify the coding protein regions more accurate due to using the Goertzel algorithm which is tuned at the desired frequency. Secondly, faster detection time is achieved. The proposed algorithm is applied on several genes, including genes available in databases BG570 and HMR195 and their results are compared to other methods based on the nucleotide level evaluation criteria. Implementation results show the excellent performance of the proposed algorithm in identifying protein coding regions, specifically in identification of small-scale gene areas.

Keywords: protein coding regions, period-3, anti-notch filter, Goertzel algorithm

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Authors: Mayowa Saheed Sanusi A, Musiliu Olushola Sunmonua, Abdulquadri Alakab Owolabi Raheema, Adeyemi Ikimot Adejokea

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The development of healthy cookies for health-conscious consumers cannot be overemphasized in the present global health crisis. This study was aimed to evaluate and model the influence of ripeness levels of papaya puree (unripe, ripe and overripe), oven temperature (130°C, 150°C and 170°C) and oven rack speed (stationary, 10 and 20 rpm) on physicochemical properties of papaya-based cookies using Response Surface Methodology (RSM). The physicochemical properties (baking time, cookies mass, cookies thickness, spread ratio, proximate composition, Calcium, Vitamin C and Total Phenolic Content) were determined using standard procedures. The data obtained were statistically analysed at p≤0.05 using ANOVA. The polynomial regression model of response surface methodology was used to model the physicochemical properties. The adequacy of the models was determined using the coefficient of determination (R²) and the response optimizer of RSM was used to determine the optimum physicochemical properties for the papaya-based cookies. Cookies produced from overripe papaya puree were observed to have the shortest baking time; ripe papaya puree favors cookies spread ratio, while the unripe papaya puree gives cookies with the highest mass and thickness. The highest crude protein content, fiber content, calcium content, Vitamin C and Total Phenolic Content (TPC) were observed in papaya based-cookies produced from overripe puree. The models for baking time, cookies mass, cookies thickness, spread ratio, moisture content, crude protein and TPC were significant, with R2 ranging from 0.73 – 0.95. The optimum condition for producing papaya based-cookies with desirable physicochemical properties was obtained at 149°C oven temperature, 17 rpm oven rack speed and with the use of overripe papaya puree. The Information on the use of puree from unripe, ripe and overripe papaya can help to increase the use of underutilized unripe or overripe papaya and also serve as a strategic means of obtaining a fat substitute to produce new products with lower production cost and health benefit.

Keywords: papaya based-cookies, modeling, response surface methodology, physicochemical properties

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Authors: Massimiliano Frezza, Sergio Bianchi, Augusto Pianese

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The relation between stock price volatility and trading volume represents a controversial issue which has received a remarkable attention over the past decades. In fact, an extensive literature shows a positive relation between price volatility and trading volume in the financial markets, but the causal relationship which originates such association is an open question, from both a theoretical and empirical point of view. In this regard, various models, which can be considered as complementary rather than competitive, have been introduced to explain this relationship. They include the long debated Mixture of Distributions Hypothesis (MDH); the Sequential Arrival of Information Hypothesis (SAIH); the Dispersion of Beliefs Hypothesis (DBH); the Noise Trader Hypothesis (NTH). In this work, we analyze whether stock market efficiency can explain the diversity of results achieved during the years. For this purpose, we propose an alternative measure of market efficiency, based on the pointwise regularity of a stochastic process, which is the Hurst–H¨older dynamic exponent. In particular, we model the stock market by means of the multifractional Brownian motion (mBm) that displays the property of a time-changing regularity. Mostly, such models have in common the fact that they locally behave as a fractional Brownian motion, in the sense that their local regularity at time t0 (measured by the local Hurst–H¨older exponent in a neighborhood of t0 equals the exponent of a fractional Brownian motion of parameter H(t0)). Assuming that the stock price follows an mBm, we introduce and theoretically justify the Hurst–H¨older dynamical exponent as a measure of market efficiency. This allows to measure, at any time t, markets’ departures from the martingale property, i.e. from efficiency as stated by the Efficient Market Hypothesis. This approach is applied to financial markets; using data for the SP500 index from 1978 to 2017, on the one hand we find that when efficiency is not accounted for, a positive contemporaneous relationship emerges and is stable over time. Conversely, it disappears as soon as efficiency is taken into account. In particular, this association is more pronounced during time frames of high volatility and tends to disappear when market becomes fully efficient.

Keywords: volume–volatility relationship, efficient market hypothesis, martingale model, Hurst–Hölder exponent

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Authors: Yung-Jin Weng, Chia-Chi Chang, Chun-Yu Tsai

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This paper is focused on the research of imprinting and replica molding of quasi-grey scale soft mold curved surface microstructure mold. In this paper, a magnetic photocuring forming system is first developed and built independently, then the magnetic curved surface microstructure soft mode is created; moreover, the magnetic performance of the magnetic curved surface at different heights is tested and recorded, and through experimentation and simulation, the magnetic curved surface microstructure soft mold is used in the research of quasi-grey scale soft mold curved surface microstructure imprinting and replica molding. The experimental results show that, under different surface curvatures and voltage control conditions, different quasi-grey scale array microstructures take shape. In addition, this paper conducts research on the imprinting and replica molding of photoresist composite magnetic powder in order to discuss the forming performance of magnetic photoresist, and finally, the experimental result is compared with the simulation to obtain more accurate prediction and results. This research is predicted to provide microstructure component preparation technology with heterogeneity and controllability, and is a kind of valid shaping quasi-grey scale microstructure manufacturing technology method.

Keywords: soft mold, magnetic, microstructure, curved surface

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Authors: David F. Nettleton, Elodie Bugnicourt, Christian Wasiak, Alejandro Rosales

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In this presentation, preliminary results are given for the modeling and calibration of two different industrial winding MIMO (Multiple Input Multiple Output) processes using machine learning techniques. In contrast to previous approaches which have typically used ‘black-box’ linear statistical methods together with a definition of the mechanical behavior of the process, we use non-linear machine learning algorithms together with a ‘white-box’ rule induction technique to create a supervised model of the fitting error between the expected and real force measures. The final objective is to build a precise model of the winding process in order to control de-tension of the material being wound in the first case, and the friction of the material passing through the die, in the second case. Case 1, Tension Control of a Winding Process. A plastic web is unwound from a first reel, goes over a traction reel and is rewound on a third reel. The objectives are: (i) to train a model to predict the web tension and (ii) calibration to find the input values which result in a given tension. Case 2, Friction Force Control of a Micro-Pullwinding Process. A core+resin passes through a first die, then two winding units wind an outer layer around the core, and a final pass through a second die. The objectives are: (i) to train a model to predict the friction on die2; (ii) calibration to find the input values which result in a given friction on die2. Different machine learning approaches are tested to build models, Kernel Ridge Regression, Support Vector Regression (with a Radial Basis Function Kernel) and MPART (Rule Induction with continuous value as output). As a previous step, the MPART rule induction algorithm was used to build an explicative model of the error (the difference between expected and real friction on die2). The modeling of the error behavior using explicative rules is used to help improve the overall process model. Once the models are built, the inputs are calibrated by generating Gaussian random numbers for each input (taking into account its mean and standard deviation) and comparing the output to a target (desired) output until a closest fit is found. The results of empirical testing show that a high precision is obtained for the trained models and for the calibration process. The learning step is the slowest part of the process (max. 5 minutes for this data), but this can be done offline just once. The calibration step is much faster and in under one minute obtained a precision error of less than 1x10-3 for both outputs. To summarize, in the present work two processes have been modeled and calibrated. A fast processing time and high precision has been achieved, which can be further improved by using heuristics to guide the Gaussian calibration. Error behavior has been modeled to help improve the overall process understanding. This has relevance for the quick optimal set up of many different industrial processes which use a pull-winding type process to manufacture fibre reinforced plastic parts. Acknowledgements to the Openmind project which is funded by Horizon 2020 European Union funding for Research & Innovation, Grant Agreement number 680820

Keywords: data model, machine learning, industrial winding, calibration

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Authors: Inigo Sanz-Pena, Shanika Arachchi, Dilani Dhammika, Sanjaya Mallikarachchi, Jeewantha S. Bandula, Alison H. McGregor, Nicolas Newell

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The use of orthotic braces for adolescent idiopathic scoliosis (AIS) patients is the most common non-surgical treatment to prevent deformity progression. The traditional method to create an orthotic brace involves casting the patient’s torso to obtain a representative geometry, which is then rectified by an orthotist to the desired geometry of the brace. Recent improvements in 3D scanning technologies, rectification software, CNC, and additive manufacturing processes have given the possibility to compliment, or in some cases, replace manual methods with digital approaches. However, the rectification process remains dependent on the orthotist’s skills. Therefore, the rectification process needs to be carefully characterized to ensure that braces designed through a digital workflow are as efficient as those created using a manual process. The aim of this study is to compare 3D scans of patients with AIS against 3D scans of both pre- and post-rectified casts that have been manually shaped by an orthotist. Six AIS patients were recruited from the Ragama Rehabilitation Clinic, Colombo, Sri Lanka. All patients were between 10 and 15 years old, were skeletally immature (Risser grade 0-3), and had Cobb angles between 20-45°. Seven spherical markers were placed at key anatomical locations on each patient’s torso and on the pre- and post-rectified molds so that distances could be reliably measured. 3D scans were obtained of 1) the patient’s torso and pelvis, 2) the patient’s pre-rectification plaster mold, and 3) the patient’s post-rectification plaster mold using a Structure Sensor Mark II 3D scanner (Occipital Inc., USA). 3D stick body models were created for each scan to represent the distances between anatomical landmarks. The 3D stick models were used to analyze the changes in position and orientation of the anatomical landmarks between scans using Blender open-source software. 3D Surface deviation maps represented volume differences between the scans using CloudCompare open-source software. The 3D stick body models showed changes in the position and orientation of thorax anatomical landmarks between the patient and the post-rectification scans for all patients. Anatomical landmark position and volume differences were seen between 3D scans of the patient’s torsos and the pre-rectified molds. Between the pre- and post-rectified molds, material removal was consistently seen on the anterior side of the thorax and the lateral areas below the ribcage. Volume differences were seen in areas where the orthotist planned to place pressure pads (usually at the trochanter on the side to which the lumbar curve was tilted (trochanter pad), at the lumbar apical vertebra (lumbar pad), on the rib connected to the apical vertebrae at the mid-axillary line (thoracic pad), and on the ribs corresponding to the upper thoracic vertebra (axillary extension pad)). The rectification process requires the skill and experience of an orthotist; however, this study demonstrates that the brace shape, location, and volume of material removed from the pre-rectification mold can be characterized and quantified. Results from this study can be fed into software that can accelerate the brace design process and make steps towards the automated digital rectification process.

Keywords: additive manufacturing, orthotics, scoliosis brace design, sculpting software, spinal deformity

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Authors: Zhenyu Yang, Qiunan Chen, Xiaocheng Huang

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Reliable prediction of tunnel collapse remains a prominent challenge in the field of civil engineering. In this study, leveraging the nonlinear Hoek-Brown failure criterion and the upper-bound theorem, an analytical solution for the collapse surface of shallowly buried circular tunnels was derived, taking into account the coupled effects of surface loads and pore water pressures. Initially, surface loads and pore water pressures were introduced as external force factors, equating the energy dissipation rate to the external force, yielding our objective function. Subsequently, the variational method was employed for optimization, and the outcomes were juxtaposed with previous research findings. Furthermore, we utilized the deduced equation set to systematically analyze the influence of various rock mass parameters on collapse shape and extent. To validate our analytical solutions, a comparison with prior studies was executed. The corroboration underscored the efficacy of our proposed methodology, offering invaluable insights for collapse risk assessment in practical engineering applications.

Keywords: tunnel roof stability, analytical solution, hoek–brown failure criterion, limit analysis

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Authors: Ahmad Alwosheel, Ahmed Alqaraawi

Abstract:

This work proposes a new speaker segmentation approach for two speakers. It is an online approach that does not require a prior information about speaker models. It has two phases, a conventional approach such as unsupervised BIC-based is utilized in the first phase to detect speaker changes and train a Neural Network, while in the second phase, the output trained parameters from the Neural Network are used to predict next incoming audio stream. Using this approach, a comparable accuracy to similar BIC-based approaches is achieved with a significant improvement in terms of computation time.

Keywords: artificial neural network, diarization, speaker indexing, speaker segmentation

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Authors: H. Salonen, E. Castagnoli, C. Vornanen-Winqvist, R. Mikkola, C. Duchaine, L. Morawska, J. Kurnitski

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A wide range of health effects among occupants are associated with the exposure to bioaerosols from fungal sources. Although the accurate role of these aerosols in causing the symptoms and diseases is poorly understood, the important effect of bioaerosol exposure on human health is well recognized. Thus, there is a need to determine all of the contributing factors related to the concentration of fungi in indoor air. In this study, we reviewed and summarized the different factors affecting the concentrations of viable fungi in school buildings. The literature research was conducted using Pubmed and Google Scholar. In addition, we searched the lists of references of selected articles. According to the literature, the main factors influencing the concentration of viable fungi in the school buildings are moisture damage in building structures, the season (temperature and humidity conditions), the type and rate of ventilation, the number and activities of occupants and diurnal variations. This study offers valuable information that can be used in the interpretation of the fungal analysis and to decrease microbial exposure by reducing known sources and/or contributing factors. However, more studies of different local factors contributing to the human microbial exposure in school buildings—as well as other type of buildings and different indoor environments—are needed.

Keywords: fungi, concentration, indoor, school, contributing factor

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Authors: Ismael Zamora, Elisabeth Ortega, Tatiana Radchenko, Guillem Plasencia

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The structure elucidation based on Mass Spectra (MS) data of unknown substances is an unresolved problem that affects many different fields of application. The recent overview of software available for structure elucidation of small molecules has shown the demand for efficient computational tool that will be able to perform structure elucidation of unknown small molecules and peptides. We developed an algorithm for De-Novo fragment analysis based on MS data that proposes a set of scored and ranked structures that are compatible with the MS and MSMS spectra. Several different algorithms were developed depending on the initial set of fragments and the structure building processes. Also, in all cases, several scores for the final molecule ranking were computed. They were validated with small and middle databases (DB) with the eleven test set compounds. Similar results were obtained from any of the databases that contained the fragments of the expected compound. We presented an algorithm. Or De-Novo fragment analysis based on only mass spectrometry (MS) data only that proposed a set of scored/ranked structures that was validated on different types of databases and showed good results as proof of concept. Moreover, the solutions proposed by Mass Spectrometry were submitted to the prediction of NMR spectra in order to elucidate which of the proposed structures was compatible with the NMR spectra collected.

Keywords: De Novo, structure elucidation, mass spectrometry, NMR

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Authors: Naci Büyükkaraciğan, Yildirim Akyol

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Large plants that covering the back and accumulate water of a river valley for energy production, drinking, irrigation water supply, economic benefits that serve many purposes, such as regulation of flood protection, are called dams. Place, in which unites in order to achieve an optimum balance between manpower for Lowest cost and economic as belonging to that structure to create machines, materials and construction of the project, is called as the site. Dam construction sites are combined sites in together in many businesses. Therefore, there can be found in the many workers and machines are many accidents in this type of construction sites. The necessity of systematic and scientific studies due to various reasons arises in order to be protected from conditions that could damage the health, During the execution of the work on construction sites. Occupational health and safety of the study, called the case, also in the European Union has begun to be addressed by weight since the 1980s. In particular, issued in 1989 89/391/EEC on occupational health and safety directive, occupational health and adopted the Directive within the framework of the security field, and then exposed to a large number of individual directive within this framework on the basis of the directive. Turkey's Law No. 6331 entered into force in June 2012 on the subject. In this study, measures related to the construction site of the dam should be taken with occupational safety and health have been examined and tried to put forward recommendations on the subject.

Keywords: civil engineering, dam, occupational safety and health, site organizations

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Authors: Esther Cabezas-Rivas

Abstract:

Ordinary differential equations (ODE) are a fundamental part of the curriculum for most engineering degrees, and students typically have difficulties in the subsequent abstract mathematical calculations. To enhance their motivation and profit that they are digital natives, we propose a teamwork project that includes the creation of a video. It should explain how to model mathematically a real-world problem transforming it into an ODE, which should then be solved using the tools learned in the lectures. This idea was indeed implemented with first-year students of a BSc in Engineering and Management during the period of online learning caused by the outbreak of COVID-19 in Spain. Each group of 4 students was assigned a different topic: model a hot water heater, search for the shortest path, design the quickest route for delivery, cooling a computer chip, the shape of the hanging cables of the Golden Gate, detecting land mines, rocket trajectories, etc. These topics should be worked out through two complementary channels: a written report describing the problem and a 10-15 min video on the subject. The report includes the following items: description of the problem to be modeled, detailed obtention of the ODE that models the problem, its complete solution, and interpretation in the context of the original problem. We report the outcomes of this teaching in context and active learning experience, including the feedback received by the students. They highlighted the encouragement of creativity and originality, which are skills that they do not typically relate to mathematics. Additionally, the video format (unlike a common presentation) has the advantage of allowing them to critically review and self-assess the recording, repeating some parts until the result is satisfactory. As a side effect, they felt more confident about their oral abilities. In short, students agreed that they had fun preparing the video. They recognized that it was tricky to combine deep mathematical contents with entertainment since, without the latter, it is impossible to engage people to view the video till the end. Despite this difficulty, after the activity, they claimed to understand better the material, and they enjoyed showing the videos to family and friends during and after the project.

Keywords: active learning, contextual teaching, models in differential equations, student-produced videos

Procedia PDF Downloads 135

Authors: S. Delgado, C. Cerrada, R. S. Gómez

Abstract:

This research introduces an approach to voxelizing the surfaces of triangular meshes with efficiency and accuracy. Our method leverages parallel equidistant scan-lines and introduces a Gap Detection technique to address the limitations of existing approaches. We present a comprehensive study showcasing the method's effectiveness, scalability, and versatility in different scenarios. Voxelization is a fundamental process in computer graphics and simulations, playing a pivotal role in applications ranging from scientific visualization to virtual reality. Our algorithm focuses on enhancing the voxelization process, especially for complex models and high resolutions. One of the major challenges in voxelization in the Graphics Processing Unit (GPU) is the high cost of discovering the same voxels multiple times. These repeated voxels incur in costly memory operations with no useful information. Our scan-line-based method ensures that each voxel is detected exactly once when processing the triangle, enhancing performance without compromising the quality of the voxelization. The heart of our approach lies in the use of parallel, equidistant scan-lines to traverse the interiors of triangles. This minimizes redundant memory operations and avoids revisiting the same voxels, resulting in a significant performance boost. Moreover, our method's computational efficiency is complemented by its simplicity and portability. Written as a single compute shader in Graphics Library Shader Language (GLSL), it is highly adaptable to various rendering pipelines and hardware configurations. To validate our method, we conducted extensive experiments on a diverse set of models from the Stanford repository. Our results demonstrate not only the algorithm's efficiency, but also its ability to produce 26 tunnel free accurate voxelizations. The Gap Detection technique successfully identifies and addresses gaps, ensuring consistent and visually pleasing voxelized surfaces. Furthermore, we introduce the Slope Consistency Value metric, quantifying the alignment of each triangle with its primary axis. This metric provides insights into the impact of triangle orientation on scan-line based voxelization methods. It also aids in understanding how the Gap Detection technique effectively improves results by targeting specific areas where simple scan-line-based methods might fail. Our research contributes to the field of voxelization by offering a robust and efficient approach that overcomes the limitations of existing methods. The Gap Detection technique fills a critical gap in the voxelization process. By addressing these gaps, our algorithm enhances the visual quality and accuracy of voxelized models, making it valuable for a wide range of applications. In conclusion, "Closing the Gap: Efficient Voxelization with Equidistant Scan-lines and Gap Detection" presents an effective solution to the challenges of voxelization. Our research combines computational efficiency, accuracy, and innovative techniques to elevate the quality of voxelized surfaces. With its adaptable nature and valuable innovations, this technique could have a positive influence on computer graphics and visualization.

Keywords: voxelization, GPU acceleration, computer graphics, compute shaders

Procedia PDF Downloads 52