Search results for: gel strength prediction

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: 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: 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: 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: 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: Luis E. Zapata, Sergio Ruiz, María F. Mantilla, Jhon A. Villamizar

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Sand as fine aggregate for concrete production needs a feasible substitute due to several environmental issues. In this work, a study of the behavior of self-compacting concrete mixtures under replacement of sand by iron slag from 0.0% to 50.0% of weight and variations of water/cementitious material ratio between 0.3 and 0.5 is presented. Control fresh state tests of Slump flow, T500, J-ring and L-box were determined. In the hardened state, compressive strength was determined and optimization from response surface analysis was performed. The study of the variables in the hardened state was developed based on inferential statistical analyses using central composite design methodology and posterior analyses of variance (ANOVA). An increase in the compressive strength up to 50% higher than control mixtures at 7, 14, and 28 days of maturity was the most relevant result regarding the presence of iron slag as replacement of natural sand. Considering the obtained result, it is possible to infer that iron slag is an acceptable alternative replacement material of the natural fine aggregate to be used in structural concrete.

Keywords: ANOVA, iron slag, response surface analysis, self-compacting concrete

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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: 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: 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: Bingjie Wang, Qianqian Qang, Nan Lu, Xiubing Liang, Baolong Shen

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Novel composites of HfNbTaTiZrV refractory high-entropy alloy (RHEA) reinforced with 0-5 vol.% Al₂O₃ particles have been synthesized by vacuum arc melting. The microstructure evolution, compressive mechanical properties at room and elevated temperatures, as well as strengthening mechanism of the composites, are analyzed. The HfNbTaTiZrV RHEA reinforced with 4 vol.% Al₂O₃ displays excellent phase stability at elevated temperatures. A superior compressive yield strength of 2700 MPa at room temperature, 1392 MPa at 800 °C, and 693 MPa at 1000 °C has been obtained for this composite. The improved yield strength results from multiple strengthening mechanisms caused by Al₂O₃ addition, including interstitial strengthening, grain boundary strengthening, and dispersion strengthening. Besides, the effects of interstitial strengthening increase with the temperature and is the main strengthening mechanism at elevated temperatures. These findings not only promote the development of oxide-reinforced RHEAs for challenging engineering applications but also provide guidelines for the design of light refractory materials with multiple strengthening mechanisms.

Keywords: Al₂O₃-reinforcement, HfNbTaTiZrV, refractory high-entropy alloy, interstitial strengthening

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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: 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: T. Manzur, S. Iffat, M. A. Noor

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In this paper, relationship between different properties of IC concrete and water cement ratio, obtained from a comprehensive experiment conducted on IC using local materials (Burnt clay chips- BC) is presented. In addition, saturated SAP was used as an IC material in some cases. Relationships have been developed through regression analysis. The focus of this analysis is on developing relationship between a dependent variable and an independent variable. Different percent replacements of BC and water cement ratios were used. Compressive strength, modulus of elasticity, water permeability and chloride permeability were tested and variations of these parameters were analyzed with respect to water cement ratio.

Keywords: compressive strength, concrete, curing, lightweight, aggregate, superabsorbent polymer, internal curing

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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: Yousef Zandi, Hoseyn Leka, Mahin Ganadi

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This paper presents the results of a laboratory study carried out on effect of using the simultaneous of microsilis, super plasticizer and air entrain additives on compressive strength of light weight perlite concrete. In this study, 63 test specimens with different percentage and mixtures including microsilis, super plasticizer and air entrain were used. 63 test specimens with different mixtures including microsilis and air entrain were also prepared for comparison purposes. In the mixtures, lightweight perlite aggregate, microsilis, super plasticizer, air entrain, cement type I, sand and water were used. Laboratory test results showed that workability of lightweight perlite concrete was increased and compressive strength was released by the use of super plasticizer, without any change in water/cement ratio. We know that compressive strength of concrete is depends on water/cement ratio. Since, it was expected that the use of air entrain and super plasticizer lower water/cement ratio and raised strengths, considerably. It was concluded that use of simultaneous of air entrains and super plasticizer additive were not economical and use of air entrain and microsilis is better than use of air entrain, super plasticizer and microsilis. It was concluded that the best results were obtained by using 10% microsilis and 0.5% air entrain.

Keywords: perlite, microsilis, air entrain, super plasticizer

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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: Mehrnoosh Abouzari, Shahrokh Sahraei

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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

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

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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

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Authors: Abdelmessih Malak Sadek Labib

Abstract:

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: Sorayya Malek, Mogeeb Mosleh, Sharifah M. Syed

Abstract:

In this study, Support Vector Machine (SVM) technique was applied to predict the dichotomized value of Dissolved oxygen (DO) from two freshwater lakes namely Chini and Bera Lake (Malaysia). Data sample contained 11 parameters for water quality features from year 2005 until 2009. All data parameters were used to predicate the dissolved oxygen concentration which was dichotomized into 3 different levels (High, Medium, and Low). The input parameters were ranked, and forward selection method was applied to determine the optimum parameters that yield the lowest errors, and highest accuracy. Initial results showed that pH, water temperature, and conductivity are the most important parameters that significantly affect the predication of DO. Then, SVM model was applied using the Anova kernel with those parameters yielded 74% accuracy rate. We concluded that using SVM models to predicate the DO is feasible, and using dichotomized value of DO yields higher prediction accuracy than using precise DO value.

Keywords: dissolved oxygen, water quality, predication DO, support vector machine

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Authors: Nikhil Gopalkrishnan, Praveen Bhaskaran, Aditya Bhargava, Gyandeep Bhumarkar

Abstract:

The present paper is an attempt to perform various Non-Destructive Tests on concrete structure as NDT is gaining a wide importance in the branch of civil engineering these days. Various tests that are performed under NDT not only enable us to determine the strength of concrete structure, but also provide us in-hand information regarding the durability, in-situ properties of the concrete structure. Keeping these points in our mind, we have focused our views on performing a case study to show the comparison between the NDT test results performed on a particular concrete structure and another structure at the same site which is subjected to a continuous fire of say 48-72 hours. The mix design and concrete grade of both the structures were same before the one was affected by fire. The variations in the compressive strength, concrete quality and in-situ properties of the two structures have been discussed in this paper. NDT tests namely Ultrasonic Pulse Velocity Test, Rebound Hammer Test, Core-Cutter Test was performed at both the sites. The main objective of this research is to analyze the variations in the strength and quality of the concrete structure which is subjected to a high temperature fire and the one which isn’t exposed to it.

Keywords: core-cutter test, non-destructive test, rebound hammer test, ultrasonic pulse velocity test

Procedia PDF Downloads 349

Authors: Darren Zou

Abstract:

Forecasting the stock market is a very challenging task. Various economic indicators such as GDP, exchange rates, interest rates, and unemployment have a substantial impact on the stock market. Time series models are the traditional methods used to predict stock market changes. In this paper, a machine learning method, Bayesian Additive Regression Tree (BART) is used in predicting stock market indexes based on multiple economic indicators. BART can be used to model heterogeneous treatment effects, and thereby works well when models are misspecified. It also has the capability to handle non-linear main effects and multi-way interactions without much input from financial analysts. In this research, BART is proposed to provide a reliable prediction on day-to-day stock market activities. By comparing the analysis results from BART and with time series method, BART can perform well and has better prediction capability than the traditional methods.

Keywords: BART, Bayesian, predict, stock

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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: Shakhawan K. Mawlood, Catriona Pickard, Benjamin Pickard

Abstract:

In summer 2015 the remains of 800 children are among 1,967 bodies were exhumed by archaeologists at St Peter's Burial Ground in Blackburn, Lancashire. One hundred samples from these 19th century ancient bones were selected for DNA analysis. These comprised samples biased for those which prior osteological evidence indicated a potential for microbial infection by Mycobacterium tuberculosis (causing tuberculosis, TB) or Treponema pallidum (causing Syphilis) species, as well a random selection of other bones for which visual inspection suggested good preservation (and, therefore, likely DNA retrieval).They were subject to polymerase chain reaction (PCR) assays aimed at detecting traces of DNA from infecting mycobacteria, with the purpose both of confirming the palaeopathological diagnosis of tuberculosis and determining in individual cases whether disease and death was due to M. tuberculosis or other reasons. Our secondary goal was to determine sex determination and age prediction. The results demonstrated that extraction of vast majority ancient bones DNA samples succeeded.

Keywords: ancient bone, DNA, tuberculosis, age prediction

Procedia PDF Downloads 103