Search results for: compressive strength prediction

Authors: Sandra Mitrovic, Gaurav Singh

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In many industries, including telecommunications, churn prediction has been a topic of active research. A lot of attention has been drawn on devising the most informative features, and this area of research has gained even more focus with spread of (social) network analytics. The call detail records (CDRs) have been used to construct customer networks and extract potentially useful features. However, to the best of our knowledge, no studies including network features have yet proposed a generic way of representing network information. Instead, ad-hoc and dataset dependent solutions have been suggested. In this work, we build upon a recently presented method (node2vec) to obtain representations for nodes in observed network. The proposed approach is generic and applicable to any network and domain. Unlike node2vec, which assumes a static network, we consider a dynamic and time-evolving network. To account for this, we propose an approach that constructs the feature representation of each node by generating its node2vec representations at different timestamps, concatenating them and finally compressing using an auto-encoder-like method in order to retain reasonably long and informative feature vectors. We test the proposed method on churn prediction task in telco domain. To predict churners at timestamp ts+1, we construct training and testing datasets consisting of feature vectors from time intervals [t1, ts-1] and [t2, ts] respectively, and use traditional supervised classification models like SVM and Logistic Regression. Observed results show the effectiveness of proposed approach as compared to ad-hoc feature selection based approaches and static node2vec.

Keywords: churn prediction, dynamic networks, node2vec, auto-encoders

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Authors: Emad A. Mohammed

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Utilizing the capabilities of Data Mining and Artificial Intelligence in the prediction of the minimum miscibility pressure (MMP) required for multi-contact miscible (MCM) displacement of reservoir petroleum by hydrocarbon gas flooding using Fuzzy Logic models and Artificial Neural Network models will help a lot in giving accurate results. The factors affecting the (MMP) as it is proved from the literature and from the dataset are as follows: XC2-6: Intermediate composition in the oil-containing C2-6, CO2 and H2S, in mole %, XC1: Amount of methane in the oil (%),T: Temperature (°C), MwC7+: Molecular weight of C7+ (g/mol), YC2+: Mole percent of C2+ composition in injected gas (%), MwC2+: Molecular weight of C2+ in injected gas. Fuzzy Logic and Neural Networks have been used widely in prediction and classification, with relatively high accuracy, in different fields of study. It is well known that the Fuzzy Inference system can handle uncertainty within the inputs such as in our case. The results of this work showed that our proposed models perform better with higher performance indices than other emprical correlations.

Keywords: MMP, gas flooding, artificial intelligence, correlation

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Authors: Minsu Kim, Hae-Chang Cho, Jae Hoon Chung, Inwook Heo, Kang Su Kim

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For safe design of the UHPC flexural members, accurate estimations of their shear strengths are very important. However, since the shear strengths are significantly affected by various factors such as tensile strength of concrete, shear span to depth ratio, volume ratio of steel fiber, and steel fiber factor, the accurate estimations of their shear strengths are very challenging. In this study, therefore, the Adaptive Neuro-Fuzzy System (ANFIS), which has been widely used to solve many complex problems in engineering fields, was introduced to estimate the shear strengths of UHPC flexural members. A total of 32 experimental results has been collected from previous studies for training of the ANFIS algorithm, and the well-trained ANFIS algorithm provided good estimations on the shear strengths of the UHPC test specimens. Acknowledgement: This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning(NRF-2016R1A2B2010277).

Keywords: ultra-high-performance concrete, ANFIS, shear strength, flexural member

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Authors: Sofina Faith C. Navarro, Luke V. Subala, Rica H. Gatus, Alfonzo Ramon DG. Burguete

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Roof tiles, as integral components of buildings, play a crucial role in protecting structures from many things. The study focuses on the production of sustainable roof tiles that address the waste disposal challenges associated with Manila clam shells and mitigate the environmental impact of conventional roof tile materials. Various concentrations of roof tiles are developed, incorporating different proportions of powdered clam shell that contains calcium carbonate and shredded raffia palm fiber. Subsequently, the roof tiles are cast using standard methods and transported to the University of the Philippines Institute of Civil Engineering (UP-ICE) for flexural strength testing. In conclusion, the research aimed to assess the flexural durability of concrete roof tiles with varying concentrations of Raffia Palm Fiber and Manila Clam Shells additives. The findings indicate notable differences in maximum load capacities among the specimens, with C3.1 emerging as the concentration with the highest load-bearing capacity at 313.59729 N. This concentration, with a flexural strength of 2.15214, is identified as the most durable option, with a slightly heavier weight of 1.10 kg. On the other hand, C2.2, with a flexural strength of 0.366 and a weight of 0.80 kg, is highlighted for its impressive durability performance while maintaining a lighter composition. Therefore, for the production of concrete roof tile, C3.1 is recommended for optimal durability, while C2.2 is suggested as a preferable option considering both durability and lightweight characteristics.

Keywords: raffia palm fiber, flexural strength, lightweightness, Manila Clam Shells

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Authors: Alfonzo Ramon Burguete, Rica Gatus, Sofina Faith Navarro, Luke Subala

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Roof tiles, as integral components of buildings, play a crucial role in protecting structures from many things. The study focuses on the production of sustainable roof tiles that address the waste disposal challenges associated with Manila clam shells and mitigate the environmental impact of conventional roof tile materials. Various concentrations of roof tiles are developed, incorporating different proportions of powdered clam shell that contains calcium carbonate and shredded raffia palm fiber. Subsequently, the roof tiles are cast using standard methods and transported to the University of the Philippines Institute of Civil Engineering (UP-ICE) for flexural strength testing. In conclusion, the research aimed to assess the flexural durability of concrete roof tiles with varying concentrations of Raffia Palm Fiber and Manila Clam Shells additives. The findings indicate notable differences in maximum load capacities among the specimens, with C3.1 emerging as the concentration with the highest load-bearing capacity at 313.59729 N. This concentration, with a flexural strength of 2.15214, is identified as the most durable option, with a slightly heavier weight of 1.10 kg. On the other hand, C2.2, with a flexural strength of 0.366 and a weight of 0.80 kg, is highlighted for its impressive durability performance while maintaining a lighter composition. Therefore, for the production of concrete roof tile C3.1 is recommended for optimal durability, while C2.2 is suggested as a preferable option considering both durability and lightweight characteristics.

Keywords: manila clam shells, raffia palm fiber, flexural strength, lightweightness

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Authors: H. Unal, A. Mimaroglu, I. Ozsoy

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In this study, the mechanical properties of micro filled epoxy composites were investigated. The matrix material is epoxy. Micro fillers are Al2O3 and TiO2 added in 10-30 wt% by weight ratio. Test samples were prepared using an open mould type die. Tensile, three point bending and hardness tests were carried out. The tensile strength, elastic modulus, elongation at break, flexural strength, flexural modulus and the hardness of the composite materials were obtained and evaluated. It was seen from the results that the level of the mechanical properties of the epoxy composites is highly influenced by micro filler content.

Keywords: composites, epoxy, fillers, mechanical properties

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Authors: Karim Hamidi Machekposhti, Hossein Sedghi, Abdolrasoul Telvari, Hossein Babazadeh

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Rainfall and runoff phenomenon is a chaotic and complex outcome of nature which requires sophisticated modelling and simulation methods for explanation and use. Time Series modelling allows runoff data analysis and can be used as forecasting tool. In the paper attempt is made to model river runoff data and predict the future behavioural pattern of river based on annual past observations of annual river runoff. The river runoff analysis and predict are done using ARIMA model. For evaluating the efficiency of prediction to hydrological events such as rainfall, runoff and etc., we use the statistical formulae applicable. The good agreement between predicted and observation river runoff coefficient of determination (R²) display that the ARIMA (4,1,1) is the suitable model for predicting Karkheh River runoff at Iran.

Keywords: time series modelling, ARIMA model, river runoff, Karkheh River, CLS method

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Authors: S. Y. Park, S. M. Lee, S. H. Lee, K. M. Lim

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In this research analyzed the effects that phase formation reaction change in the grey cast iron makes on characteristics of microstructures, liquidity, and mechanical properties through cooling curve when adding rare earth elements (R.E). This research was analyzed with comparison between the case of not adding the rare earth elements (R.E) into the grey cast iron with the standard composition (as 3.3%C-2.1%Si-0.7%Mn-0.1%S) and the case of adding 0.3% rare earth elements (R.E). The thermal analysis parameters have been drawn through eutectic temperature theoretically calculated, recalescence temperature, and undercooling temperature measured from start of eutectic reaction to end of solidification in the cooling curve obtained by thermal analysis to analyze formation behavior of graphite, and the effects by addition of rare earth elements on this have been reviewed. When adding rare earth elements (R.E), the cause of liquidity slowdown was analyzed trough the solidification starting temperature and change of solidification ending temperature. The strength and hardness have been measured to evaluate the mechanical properties, and the sound tensile strength has been evaluated through quality coefficient after measuring relative hardness and normality degree of tensile strength by calculating theoretical tensile strength and theoretical hardness. The change of Pearlite Inter-lamellar Spacing of matrix microstructure and eutectic cell count of macrostructure was measured to analyze the effects of the rare earth elements on the sound tensile strength. The change of eutectic cell count has been clarified through activation of the eutectic reaction, and the cause of pearlite inter-lamellar spacing clarified through eutectoid reaction temperature.

Keywords: cooling curve, element, grey cast iron, thermal analysis, rare earth element

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Authors: Sondos M. Hammad, Sherin M. ElGokhy, Mahmoud M. Fahmy, Elsayed A. Sallam

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In this paper, an ensemble-based Support Vector Machine (SVM) classification approach is proposed. It is used for miRNA prediction. Three problems, commonly associated with previous approaches, are alleviated. These problems arise due to impose assumptions on the secondary structural of premiRNA, imbalance between the numbers of the laboratory checked miRNAs and the pseudo-hairpins, and finally using a training data set that does not consider all the varieties of samples in different species. We aggregate the predicted outputs of three well-known SVM classifiers; namely, Triplet-SVM, Virgo and Mirident, weighted by their variant features without any structural assumptions. An additional SVM layer is used in aggregating the final output. The proposed approach is trained and then tested with balanced data sets. The results of the proposed approach outperform the three base classifiers. Improved values for the metrics of 88.88% f-score, 92.73% accuracy, 90.64% precision, 96.64% specificity, 87.2% sensitivity, and the area under the ROC curve is 0.91 are achieved.

Keywords: MiRNAs, SVM classification, ensemble algorithm, assumption problem, imbalance data

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Authors: H. K. Shivanand, Ranjith R. Hombal, Paraveej Shirahatti, Gujjalla Anil Babu, S. ShivaPrakash

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When it comes to the selection of materials the knowledge of materials science plays a vital role in selection and enhancements of materials properties. In the world of material science a composite material has the significant role based on its application. The composite materials are those in which two or more components having different physical and chemical properties are combined to create a new enhanced property substance. In this study three different materials (Kenaf, Kevlar and Graphene) been chosen based on their properties and a composite material is developed with help of vacuum bagging process. The fibers (Kenaf and Kevlar) and Resin(vinyl ester) ratio was maintained at 70:30 during the process and 0.5% 1% and 1.5% of Graphene was added during fabrication process. The material was machined to thedimension ofASTM standards(300×300mm and thickness 3mm)with help of water jet cutting machine. The composite materials were tested for Mechanical properties such as Interlaminar shear strength(ILSS) and Flexural strength. It is found that there is significant increase in material properties in the developed composite material.

Keywords: Kevlar, Kenaf, graphene, vacuum bagging process, Interlaminar shear strength test, flexural test

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Authors: Nagarjuna Remalli, Robert Brandt

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Down-sizing of combustion engines in automobiles are prevailed owing to required increase in efficiency. This leads to a stress increment on valve springs, which affects their intended function due to an increase in relaxation. High strength martensitic steels are used for valve spring applications. Recent investigations unveiled that low temperature creep (LTC) in martensitic steels obey a logarithmic creep law. The exhaustion theory links the logarithmic creep behavior to an activation energy which is characteristic for any given time during creep. This activation energy increases with creep strain due to barriers of low activation energies exhausted during creep. The assumption of the exhaustion theory is that the material is inhomogeneous in microscopic scale. According to these assumptions it is anticipated that small obstacles (e. g. ε–carbides) having a wide range of size distribution are non-uniformly distributed in the materials. X-ray diffraction studies revealed the presence of ε–carbides in high strength martensitic steels. In this study, high strength martensitic steels that are crept in the temperature range of 75 – 150 °C were investigated with the aid of a transmission electron microscope for the evidence of an inhomogeneous distribution of obstacles having different size to examine the validation of exhaustion theory.

Keywords: creep mechanisms, exhaustion theory, low temperature creep, martensitic steels

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Authors: Omar A. Farghal

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This research aimed to experimentally and analytically investigate the contribution of bonded web carbon fiber reinforced polymer (CFRP) sheets to the shear strength of reinforced concrete (RC) T-beams. Two strengthening techniques using CFRP strips were applied along the shear-span zone: the first one is vertical U-jacket and the later is vertical strips bonded to the beam sides only. Fibers of both U-jacket and side sheets were vertically oriented (θ = 90°). Test results showed that the strengthening technique with U-jacket CFRP sheets improved the shear strength particularly. Three mechanisms of failure were recognized for the tested beams depending upon the end condition of the bonded CFRP sheet. Although the failure mode for the different beams was a brittle one, the strengthened beams provided with U-jacket CFRP sheets showed more or less a ductile behavior at a higher loading level up to a load level just before failure. As a consequence, these beams approved an acceptable enhancement in the structural ductility. Moreover, the obtained results concerning both the strains induced in the CFRP sheets and the maximum loads are used to study the applicability of the analytical models proposed in this study (ACI code) to predict: the nominal shear strength of the strengthened beams.

Keywords: carbon fiber reinforced polymer, wrapping, ductility, shear strengthening

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Authors: Kaoutar Abbahaddou, Mohammed Salah Chiadmi

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The need to find a relevant way to predict the next-day price of a stock index is a real concern for many financial stakeholders and researchers. We have known across years the proliferation of several methods. Nevertheless, among all these methods, the most controversial one is a machine learning algorithm that claims to be reliable, namely neural networks. Thus, the purpose of this article is to study the prediction power of neural networks in the particular case of Islamic finance as it is an under-looked area. In this article, we will first briefly present a review of the literature regarding neural networks and Islamic finance. Next, we present the architecture and principles of artificial neural networks most commonly used in finance. Then, we will show its empirical application on two Islamic stock indexes. The accuracy rate would be used to measure the performance of the algorithm in predicting the right price the next day. As a result, we can conclude that artificial neural networks are a reliable method to predict the next-day price for Islamic indices as it is claimed for conventional ones.

Keywords: Islamic finance, stock price prediction, artificial neural networks, machine learning

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Authors: Ognen Kostovski, Svetozar Antovic, Rubens Jovanovic, Irena Kostovska, Nikola Jankulovski

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Introduction:Colorectal carcinoma (CRC) is one of the most common malignancies in the world. The cancer stem cell (CSC) markers are associated with aggressive cancer types and poor prognosis. The aim of study was to determine whether the expression of colorectal cancer stem cell markers CD133 and CD44 could be significant in prediction of clinically aggressive form of CRC. Materials and methods: Our study included ninety patients (n=90) with CRC. Patients were divided into two subgroups: with metatstatic CRC and non-metastatic CRC. Tumor samples were analyzed with standard histopathological methods, than was performed immunohistochemical analysis with monoclonal antibodies against CD133 and CD44 stem cell markers. Results: High coexpression of CD133 and CD44 was observed in 71.4% of patients with metastatic disease, compared to 37.9% in patients without metastases. Discordant expression of both markers was found in 8% of the subgroup with metastatic CRC, and in 13.4% of the subgroup without metastatic CRC. Statistical analyses showed a significant association of increased expression of CD133 and CD44 with the disease stage, T - category and N - nodal status. With multiple regression analysis the stage of disease was designate as a factor with the greatest statistically significant influence on expression of CD133 (p <0.0001) and CD44 (p <0.0001). Conclusion: Our results suggest that the coexpression of CD133 and CD44 have an important role in prediction of clinically aggressive form of CRC. Both stem cell markers can be routinely implemented in standard pathohistological diagnostics and can be useful markers for pre-therapeutic oncology screening.

Keywords: colorectal carcinoma, stem cells, CD133+, CD44+

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Authors: Helida T. G. Soares, Antonio J. P. da Silva

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As the society develops, the accumulation of solid waste in landfills, in the environment, and the depletion of the raw material increases. In this way, there is relevance in researching the interaction between the environmental management and civil construction; therefore, this project has for scope the analysis and the effects of the rubber microparticles use as a small aggregate added to the sand, producing an ecological mortar for the pavement constitution, from the mixture of a paste, composed of Portland cement and water, and its application in sports courts. It was used the detailed reutilization of micro rubber in its most primordial, micro form, highlighting the powder pattern as the additional balancing of the mortar, analyzing the evolution of the mechanical properties. Percentages of 5, 10 and 15% rubber were used based on the total mass of the trace, where there is no removal of aggregates or cement, only increment of the rubber. The results obtained through the mechanical test of simple compression showed that the rubber, added to the mortar, presents low mechanical resistance compared to the reference trait, the study of this subject is vast of possibilities to be explored. In this sense, we seek sustainability and innovation from the use of an ecological material, thus adding value and reducing the impact of this material on the environment. The manufacturing process takes place from the direct mixing of cement paste and rubber, whether manually, mechanically or industrially. It results in the production of a low-cost mortar, through the use of recycled rubber, with high efficiency in general properties, such as compressive strength and friction coefficient, allowing its use for the construction of floors for sports courts with high durability. Thus, it is possible to reuse this micro rubber residue in other applications in simple concrete artifacts.

Keywords: civil construction, ecological mortar, high efficiency, rubber

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Authors: S. P. Pradhan, Amulya Ratna Roul

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A number of landslides are occurring during the rainy season along Rayagada-Koraput Railway track for past three years. The track was constructed about 20 years ago. However, the protection measures are not able to control the recurring slope failures now. It leads to a loss to Indian Railway and its passengers ultimately leading to wastage of time and money. The slopes along Rayagada-Koraput track include both rock and soil slopes. The rock types include mainly Khondalite and Charnockite whereas soil slopes are mainly composed of laterite ranging from less weathered to highly weathered laterite. The field studies were carried out in one of the critical slope. Field study was followed by the kinematic analysis to assess the type of failure. Slake Durability test, Uniaxial Compression test, specific gravity test and triaxial test were done on rock samples to calculate and assess properties such as weathering index, unconfined compressive strength, density, cohesion, and friction angle. Following all the laboratory tests, rock mass rating was calculated. Further, from Kinematic analysis and Rock Mass Ratingbasic, Slope Mass Rating was proposed for each slope. The properties obtained were used to do the slope stability simulations using finite element method based modelling. After all the results, suitable protection measures, to prevent the loss due to slope failure, were suggested using the relation between Slope Mass Rating and protection measures.

Keywords: landslides, slope stability, rock mass rating, slope mass rating, numerical simulation

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Authors: Li Chen, Alex Skvortsov, Chris Norwood

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Gas releasing into water can be found in for many industrial situations. This process results in the formation of bubbles and acoustic emission which depends upon the bubble characteristics. If the bubble creation rates (bubble volume flow rate) are of interest, an inverse method has to be used based on the measurement of acoustic emission. However, there will be sound attenuation through the bubbly plume which will influence the measurement and should be taken into consideration in the model. The sound transmission through the bubbly plume depends on the characteristics of the bubbly plume, such as the shape and the bubble distributions. In this study, the bubbly plume shape is modelled using a self-similarity model, which has been normally applied for a single phase buoyant plume. The prediction is compared with the experimental data. It has been found the model can be applied to a buoyant plume of gas-liquid mixture. The influence of the gas flow rate and discharge nozzle size is studied.

Keywords: bubbly plume, buoyant plume, bubble acoustics, self-similarity model

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Authors: Wahab Ali, Oyebade K. Oyedotun, Adnan Khashman

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Breast cancer remains a threat to the woman’s world in view of survival rates, it early diagnosis and mortality statistics. So far, research has shown that many survivors of breast cancer cases are in the ones with early diagnosis. Breast cancer is usually categorized into stages which indicates its severity and corresponding survival rates for patients. Investigations show that the farther into the stages before diagnosis the lesser the chance of survival; hence the early diagnosis of breast cancer becomes imperative, and consequently the application of novel technologies to achieving this. Over the year, mammograms have used in the diagnosis of breast cancer, but the inconclusive deductions made from such scans lead to either false negative cases where cancer patients may be left untreated or false positive where unnecessary biopsies are carried out. This paper presents the application of artificial neural networks in the prediction of severity of breast tumour (whether benign or malignant) using mammography reports and other factors that are related to breast cancer.

Keywords: breast cancer, intelligent classification, neural networks, mammography

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Authors: Karuna Tuchinda, Sasithon Bland

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This work investigates the wear of a steam turbine blade coated with titanium nitride (TiN), and compares to the wear of uncoated blades. The coating is deposited on by physical vapor deposition (PVD) method. The working conditions of the blade were simulated and surface temperature and pressure values as well as flow velocity and flow direction were obtained. This data was used in the finite element wear model developed here in order to predict the wear of the blade. The wear mechanisms considered are erosive wear due to particle impingement and fluid jet, and fatigue wear due to repeated impingement of particles and fluid jet. Results show that the life of the TiN-coated blade is approximately 1.76 times longer than the life of the uncoated one.

Keywords: physical vapour deposition, steam turbine blade, titanium-based coating, wear prediction

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Authors: Ahsan R. Khokhar, Fizza Hassan

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Alkali-Silica Reaction (ASR) has been recognized as a potential cause of concrete degradation in the world since the 1940s. In Pakistan, mega hydropower structures like dams, weirs constructed from aggregates extracted from a local riverbed exhibited different levels of alkali-silica reactivity over an extended service period. The concrete expansion potential due to such aggregates has been categorized as slow-reactive. Apart from hydropower structures, ASR existence has been identified in the concrete structural elements of a Textile Mill building which used aggregates extracted from the nearby riverbed. The original structure of the Textile Mill was erected in the 80s with the addition of a textile ‘sizing and wrapping’ hall constructed in the 90s. In the years to follow, intensive spalling was observed in the structural members of the subject hall; enough to threat to the overall stability of the building. Limitations such as incomplete building data posed hurdles during the detailed structural investigation. The paper lists observations made while assessing the extent of damage and its effect on the building hall structure. Core testing and Petrographic tests were carried out as per the ASTM standards for strength degradation analysis followed by the identifying its root cause. Results confirmed significant structural strength reduction because of ASR which necessitated the formulation of an immediate re-strengthening solution. The paper also discusses the possible tracks of rehabilitative measures which are being adapted to stabilize the structure and seize further concrete expansion.

Keywords: Alkali-Silica Reaction (ASR), concrete strength degradation, damage assessment, damage evaluation

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Authors: Salihu Takuma

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Neem seed oil has high contents of unsaturated fatty acids which can be converted to epoxy fatty acids. The vegetable oil – based epoxy material are sustainable, renewable and biodegradable materials replacing petrochemical – based epoxy materials in some applications. Polystyrene is highly brittle with limited mechanical applications. Raw neem seed oil was obtained from National Research Institute for Chemical Technology (NARICT), Zaria, Nigeria. The oil was epoxidized at 60 0C for three (3) hours using formic acid generated in situ. The epoxidized oil was characterized using Fourier Transform Infrared spectroscopy (FTIR). The disappearance of C = C stretching peak around 3011.7 cm-1and formation of a new absorption peak around 943 cm-1 indicate the success of epoxidation. The epoxidized oil was blended with pure polystyrene in different weight percent compositions using solution casting in chloroform. The tensile properties of the blends demonstrated that the addition of 5 wt % ENO to PS led to an increase in elongation at break, but a decrease in tensile strength and modulus. This is in accordance with the common rule that plasticizers can decrease the tensile strength of the polymer.

Keywords: biodegradable, elongation at break, epoxidation, epoxy fatty acids, sustainable, tensile strength and modulus

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Authors: Muhammad Shahzad Iqbal, Zobia Sarwar, Salah-ud-Din

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Tomato spotted wilt virus (TSWV) belongs to the genus Tospoviruses (family Bunyaviridae). It is one of the most devastating pathogens of tomato (Solanum Lycopersicum) and heavily damages the crop yield each year around the globe. In this study, we retrieved 329 mature miRNA sequences from two microRNA databases (miRBase and miRSoldb) and checked the putative target sites in the downloaded-genome sequence of TSWV. A consensus of three miRNA target prediction tools (RNA22, miRanda and psRNATarget) was used to screen the false-positive microRNAs targeting sites in the TSWV genome. These tools calculated different target sites by calculating minimum free energy (mfe), site-complementarity, minimum folding energy and other microRNA-mRNA binding factors. R language was used to plot the predicted target-site data. All the genes having possible target sites for different miRNAs were screened by building a consensus table. Out of these 329 mature miRNAs predicted by three algorithms, only eight miRNAs met all the criteria/threshold specifications. MC-Fold and MC-Sym were used to predict three-dimensional structures of miRNAs and further analyzed in USCF chimera to visualize the structural and conformational changes before and after microRNA-mRNA interactions. The results of the current study show that the predicted eight miRNAs could further be evaluated by in vitro experiments to develop TSWV-resistant transgenic tomato plants in the future.

Keywords: tomato spotted wild virus (TSWV), Solanum lycopersicum, plant virus, miRNAs, microRNA target prediction, mRNA

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Authors: Abdelmessih Malak Sadek Labib

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Ceramic materials are known for their stability in harsh environments and excellent electrical, mechanical, and thermal properties. They have been widely used in various applications despite the emergence of new materials such as plastics and composites. However, ceramics are often brittle, which can lead to catastrophic failure. The fragility of ceramics and the mechanisms behind their failure have been a topic of extensive research, particularly in load-bearing applications like veneers. Porcelain, a type of traditional pottery, is commonly used in such applications. Traditional pottery consists of clay, silica, and feldspar, and the presence of quartz in the ceramic body can lead to microcracks and stress concentrations. The mullite hypothesis suggests that the strength of porcelain can be improved by increasing the interlocking of mullite needles in the ceramic body. However, there is a lack of reports on Young's moduli in the literature, leading to erroneous conclusions about the mechanical behavior of porcelain. This project aims to investigate the role of quartz and mullite on the mechanical strength of various porcelains while considering factors such as particle size, flexural strength, and fractographic forces. Research Aim: The aim of this research project is to assess the role of quartz and mullite in enhancing the mechanical strength of different porcelains. The project will also explore the effect of reducing particle size on the properties of porcelain, as well as investigate flexural strength and fractographic techniques. Methodology: The methodology for this project involves using scientific expressions and a mix of modern English to ensure the understanding of all attendees. It will include the measurement of Young's modulus and the evaluation of the mechanical behavior of porcelains through various experimental techniques. Findings: The findings of this study will provide a realistic assessment of the role of quartz and mullite in strengthening and reducing the fragility of porcelain. The research will also contribute to a better understanding of the mechanical behavior of ceramics, specifically in load-bearing applications. Theoretical Importance: The theoretical importance of this research lies in its contribution to the understanding of the factors influencing the mechanical strength and fragility of ceramics, particularly porcelain. By investigating the interplay between quartz, mullite, and other variables, this study will enhance our knowledge of the properties and behavior of traditional ceramics. Data Collection and Analysis Procedures: Data for this research will be collected through experiments involving the measurement of Young's modulus and other mechanical properties of porcelains. The effects of quartz, mullite, particle size, flexural strength, and fractographic forces will be examined and analyzed using appropriate statistical techniques and fractographic analysis. Questions Addressed: This research project aims to address the following questions: (1) How does the presence of quartz and mullite affect the mechanical strength of porcelain? (2) What is the impact of reducing particle size on the properties of porcelain? (3) How do flexural strength and fractographic forces influence the behavior of porcelains? Conclusion: In conclusion, this research project aims to enhance the understanding of the role of quartz and mullite in strengthening and reducing the fragility of porcelain. By investigating the mechanical properties of porcelains and considering factors such as particle size, flexural strength, and fractographic forces, this study will contribute to the knowledge of traditional ceramics and their potential applications. The findings will have practical implications for the use of ceramics in various fields.

Keywords: stability, harsh environments, electrical, techniques, mechanical disadvantages, materials

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Authors: Shreemoyee Sarkar, Vikhyat Chadha

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In this paper, the local fractal properties and chaotic properties of financial time series are investigated by calculating two exponents, the Local Hurst Exponent: LHE and Lyapunov Exponent in a moving time window of a financial series.y. For the purpose of this paper, the Dow Jones Industrial Average (DIJA) and S&P 500, two of the major indices of United States have been considered. The behaviour of the above-mentioned exponents prior to some major crashes (1998 and 2008 crashes in S&P 500 and 2002 and 2008 crashes in DIJA) is discussed. Also, the optimal length of the window for obtaining the best possible results is decided. Based on the outcomes of the above, an attempt is made to predict the crashes and accuracy of such an algorithm is decided.

Keywords: local hurst exponent, lyapunov exponent, market crash prediction, time series chaos, time series local fractal properties

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Authors: Ali Nadi, Ali Edrissi

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Relief demand and transportation links availability is the essential information that is needed for every natural disaster operation. This information is not in hand once a disaster strikes. Relief demand and network condition has been evaluated based on prediction method in related works. Nevertheless, prediction seems to be over or under estimated due to uncertainties and may lead to a failure operation. Therefore, in this paper a stochastic programming model is proposed to evaluate real-time relief demand and network condition at the onset of a natural disaster. To address the time sensitivity of the emergency response, the proposed model uses reinforcement learning for optimization of the total relief assessment time. The proposed model is tested on a real size network problem. The simulation results indicate that the proposed model performs well in the case of collecting real-time information.

Keywords: disaster management, real-time demand, reinforcement learning, relief demand

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Authors: Robert Pečenko, Karin Tomažič, Igor Planinc, Sabina Huč, Tomaž Hozjan

Abstract:

Timber is favourable structural material due to high strength to weight ratio, recycling possibilities, and green credentials. Despite being flammable material, it has relatively high fire resistance. Everyday engineering practice around the word is based on an outdated design of timber structures considering standard fire exposure, while modern principles of performance-based design enable use of advanced non-standard fire curves. In Europe, standard for fire design of timber structures EN 1995-1-2 (Eurocode 5) gives two methods, reduced material properties method and reduced cross-section method. In the latter, fire resistance of structural elements depends on the effective cross-section that is a residual cross-section of uncharred timber reduced additionally by so called zero strength layer. In case of standard fire exposure, Eurocode 5 gives a fixed value of zero strength layer, i.e. 7 mm, while for non-standard parametric fires no additional comments or recommendations for zero strength layer are given. Thus designers often implement adopted 7 mm rule also for parametric fire exposure. Since the latest scientific evidence suggests that proposed value of zero strength layer can be on unsafe side for standard fire exposure, its use in the case of a parametric fire is also highly questionable and more numerical and experimental research in this field is needed. Therefore, the purpose of the presented study is to use advanced calculation methods to investigate the thickness of zero strength layer and parametric charring rates used in effective cross-section method in case of parametric fire. Parametric studies are carried out on a simple solid timber beam that is exposed to a larger number of parametric fire curves Zero strength layer and charring rates are determined based on the numerical simulations which are performed by the recently developed advanced two step computational model. The first step comprises of hygro-thermal model which predicts the temperature, moisture and char depth development and takes into account different initial moisture states of timber. In the second step, the response of timber beam simultaneously exposed to mechanical and fire load is determined. The mechanical model is based on the Reissner’s kinematically exact beam model and accounts for the membrane, shear and flexural deformations of the beam. Further on, material non-linear and temperature dependent behaviour is considered. In the two step model, the char front temperature is, according to Eurocode 5, assumed to have a fixed temperature of around 300°C. Based on performed study and observations, improved levels of charring rates and new thickness of zero strength layer in case of parametric fires are determined. Thus, the reduced cross section method is substantially improved to offer practical recommendations for designing fire resistance of timber structures. Furthermore, correlations between zero strength layer thickness and key input parameters of the parametric fire curve (for instance, opening factor, fire load, etc.) are given, representing a guideline for a more detailed numerical and also experimental research in the future.

Keywords: advanced numerical modelling, parametric fire exposure, timber structures, zero strength layer

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Authors: Mehrnoosh Abouzari, Shahrokh Sahraei

Abstract:

Today, with the increase in quantity and quality and variety of crimes, the discussion of crime prevention has faced a serious challenge that human resources alone and with traditional methods will not be effective. One of the developments in the modern world is the presence of artificial intelligence in various fields, including criminal law. In fact, the use of artificial intelligence in criminal investigations and fighting crime is a necessity in today's world. The use of artificial intelligence is far beyond and even separate from other technologies in the struggle against crime. Second, its application in criminal science is different from the discussion of prevention and it comes to the prediction of crime. Crime prevention in terms of the three factors of the offender, the offender and the victim, following a change in the conditions of the three factors, based on the perception of the criminal being wise, and therefore increasing the cost and risk of crime for him in order to desist from delinquency or to make the victim aware of self-care and possibility of exposing him to danger or making it difficult to commit crimes. While the presence of artificial intelligence in the field of combating crime and social damage and dangers, like an all-seeing eye, regardless of time and place, it sees the future and predicts the occurrence of a possible crime, thus prevent the occurrence of crimes. The purpose of this article is to collect and analyze the studies conducted on the use of artificial intelligence in predicting and preventing crime. How capable is this technology in predicting crime and preventing it? The results have shown that the artificial intelligence technologies in use are capable of predicting and preventing crime and can find patterns in the data set. find large ones in a much more efficient way than humans. In crime prediction and prevention, the term artificial intelligence can be used to refer to the increasing use of technologies that apply algorithms to large sets of data to assist or replace police. The use of artificial intelligence in our debate is in predicting and preventing crime, including predicting the time and place of future criminal activities, effective identification of patterns and accurate prediction of future behavior through data mining, machine learning and deep learning, and data analysis, and also the use of neural networks. Because the knowledge of criminologists can provide insight into risk factors for criminal behavior, among other issues, computer scientists can match this knowledge with the datasets that artificial intelligence uses to inform them.

Keywords: artificial intelligence, criminology, crime, prevention, prediction

Procedia PDF Downloads 71

Authors: Hyun Ah Yoon, Ji Yeon Kang, Hee Sun Kim, Yeong Soo Shin

Abstract:

Reinforced concrete (RC) shear wall system of residential buildings is popular in South Korea. RC walls are subjected to axial forces in common and the effect of axial forces on the strength loss of the fire damaged walls has not been investigated. This paper aims at investigating temperature distribution on fire damaged concrete walls having different axial loads. In the experiments, a variable of specimens is axial force ratio. RC walls are fabricated with 150mm of wall thicknesses, 750mm of lengths and 1,300mm of heights having concrete strength of 24MPa. After curing, specimens are heated on one surface with ISO-834 standard time-temperature curve for 2 hours and temperature distributions during the test are measured using thermocouples inside the walls. The experimental results show that the temperature of the RC walls exposed to fire increases as axial force ratio increases. To verify the experiments, finite element (FE) models are generated for coupled temperature-structure analyses. The analytical results of thermal behaviors are in good agreement with the experimental results. The predicted displacement of the walls decreases when the axial force increases. 

Keywords: axial force ratio, fire, reinforced concrete wall, residual strength

Procedia PDF Downloads 456

Authors: M. Zielinska, B. Daniels, J. Gabel, A. Paletko

Abstract:

A common nickel based superalloy Inconel625 was brazed with Ni-base braze filler material (AMS4777) containing melting-point-depressants such as B and Si. Different braze gaps, brazing times and forms of braze filler material were tested. It was determined that the melting point depressants B and Si tend to form hard and brittle phases in the joint during the braze cycle. Brittle phases significantly reduce mechanical properties (e. g. tensile strength) of the joint. Therefore, it is important to define optimal process parameters to achieve high strength joints, free of brittle phases. High ultimate tensile strength (UTS) values can be obtained if the joint area is free of brittle phases, which is equivalent to a complete isothermal solidification of the joint. Isothermal solidification takes place only if the concentration of the melting point depressant in the braze filler material of the joint is continuously reduced by diffusion into the base material. For a given brazing temperature, long brazing times and small braze filler material volumes (small braze gaps) are beneficial for isothermal solidification. On the base of the obtained results it can be stated that the form of the braze filler material has an additional influence on the joint quality. Better properties can be achieved by the use of braze-filler-material in form of foil instead of braze-filler-material in form of paste due to a reduced amount of voids and a more homogeneous braze-filler-material-composition in the braze-gap by using foil.

Keywords: diffusion brazing, microstructure, superalloy, tensile strength

Procedia PDF Downloads 358

Authors: Yiea Funk Te, Daniel Mueller, Irena Pletikosa Cvijikj

Abstract:

Small and medium enterprises (SMEs) play an important role in the economy of many countries. When the overall world economy is considered, SMEs represent 95% of all businesses in the world, accounting for 66% of the total employment. Existing studies show that the current business environment is characterized as highly turbulent and strongly influenced by modern information and communication technologies, thus forcing SMEs to experience more severe challenges in maintaining their existence and expanding their business. To support SMEs at improving their competitiveness, researchers recently turned their focus on applying data mining techniques to build risk and growth prediction models. However, data used to assess risk and growth indicators is primarily obtained via questionnaires, which is very laborious and time-consuming, or is provided by financial institutes, thus highly sensitive to privacy issues. Recently, web mining (WM) has emerged as a new approach towards obtaining valuable insights in the business world. WM enables automatic and large scale collection and analysis of potentially valuable data from various online platforms, including companies’ websites. While WM methods have been frequently studied to anticipate growth of sales volume for e-commerce platforms, their application for assessment of SME risk and growth indicators is still scarce. Considering that a vast proportion of SMEs own a website, WM bears a great potential in revealing valuable information hidden in SME websites, which can further be used to understand SME risk and growth indicators, as well as to enhance current SME risk and growth prediction models. This study aims at developing an automated system to collect business-relevant data from the Web and predict future growth trends of SMEs by means of WM and data mining techniques. The envisioned system should serve as an 'early recognition system' for future growth opportunities. In an initial step, we examine how structured and semi-structured Web data in governmental or SME websites can be used to explain the success of SMEs. WM methods are applied to extract Web data in a form of additional input features for the growth prediction model. The data on SMEs provided by a large Swiss insurance company is used as ground truth data (i.e. growth-labeled data) to train the growth prediction model. Different machine learning classification algorithms such as the Support Vector Machine, Random Forest and Artificial Neural Network are applied and compared, with the goal to optimize the prediction performance. The results are compared to those from previous studies, in order to assess the contribution of growth indicators retrieved from the Web for increasing the predictive power of the model.

Keywords: data mining, SME growth, success factors, web mining

Procedia PDF Downloads 261