Search results for: strength prediction models

Authors: Yehjune Heo

Abstract:

As biometric systems become widely deployed, the security of identification systems can be easily attacked by various spoof materials. This paper contributes to finding a reliable and practical anti-spoofing method using Convolutional Neural Networks (CNNs) based on the types of loss functions and optimizers. The types of CNNs used in this paper include AlexNet, VGGNet, and ResNet. By using various loss functions including Cross-Entropy, Center Loss, Cosine Proximity, and Hinge Loss, and various loss optimizers which include Adam, SGD, RMSProp, Adadelta, Adagrad, and Nadam, we obtained significant performance changes. We realize that choosing the correct loss function for each model is crucial since different loss functions lead to different errors on the same evaluation. By using a subset of the Livdet 2017 database, we validate our approach to compare the generalization power. It is important to note that we use a subset of LiveDet and the database is the same across all training and testing for each model. This way, we can compare the performance, in terms of generalization, for the unseen data across all different models. The best CNN (AlexNet) with the appropriate loss function and optimizers result in more than 3% of performance gain over the other CNN models with the default loss function and optimizer. In addition to the highest generalization performance, this paper also contains the models with high accuracy associated with parameters and mean average error rates to find the model that consumes the least memory and computation time for training and testing. Although AlexNet has less complexity over other CNN models, it is proven to be very efficient. For practical anti-spoofing systems, the deployed version should use a small amount of memory and should run very fast with high anti-spoofing performance. For our deployed version on smartphones, additional processing steps, such as quantization and pruning algorithms, have been applied in our final model.

Keywords: anti-spoofing, CNN, fingerprint recognition, loss function, optimizer

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Authors: Maciej Szelag

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The discovery of the carbon nanotubes (CNT), has led to a breakthrough in the material engineering. The CNT is characterized by very large surface area, very high Young's modulus (about 2 TPa), unmatched durability, high tensile strength (about 50 GPa) and bending strength. Their diameter usually oscillates in the range from 1 to 100 nm, and the length from 10 nm to 10-2 m. The relatively new approach is the CNT’s application in the concrete technology. The biggest problem in the use of the CNT to cement composites is their uneven dispersion and low adhesion to the cement paste. Putting the nanotubes alone into the cement matrix does not produce any effect because they tend to agglomerate, due to their large surface area. Most often, the CNT is used as an aqueous suspension in the presence of a surfactant that has previously been sonicated. The paper presents the results of investigations of the basic physical properties (apparent density, shrinkage) and mechanical properties (compression and tensile strength) of cement paste with the addition of the multiwall carbon nanotubes (MWCNT). The studies were carried out on four series of specimens (made of two different Portland Cement). Within each series, samples were made with three w/c ratios – 0.4, 0.5, 0.6 (water/cement). Two series were an unmodified cement matrix. In the remaining two series, the MWCNT was added in amount of 0.1% by cement’s weight. The MWCNT was used as an aqueous dispersion in the presence of a surfactant – SDS – sodium dodecyl sulfate (C₁₂H₂₅OSO₂ONa). So prepared aqueous solution was sonicated for 30 minutes. Then the MWCNT aqueous dispersion and cement were mixed using a mechanical stirrer. The parameters were tested after 28 days of maturation. Additionally, the change of these parameters was determined after samples temperature loading at 250°C for 4 hours (thermal shock). Measurement of the apparent density indicated that cement paste with the MWCNT addition was about 30% lighter than conventional cement matrix. This is due to the fact that the use of the MWCNT water dispersion in the presence of surfactant in the form of SDS resulted in the formation of air pores, which were trapped in the volume of the material. SDS as an anionic surfactant exhibits characteristics specific to blowing agents – gaseous and foaming substances. Because of the increased porosity of the cement paste with the MWCNT, they have obtained lower compressive and tensile strengths compared to the cement paste without additive. It has been observed, however, that the smallest decreases in the compressive and tensile strength after exposure to the elevated temperature achieved samples with the MWCNT. The MWCNT (well dispersed in the cement matrix) can form bridges between hydrates in a nanoscale of the material’s structure. Thus, this may result in an increase in the coherent cohesion of the cement material subjected to a thermal shock. The obtained material could be used for the production of an aerated concrete or using lightweight aggregates for the production of a lightweight concrete.

Keywords: cement paste, elevated temperature, mechanical parameters, multiwall carbon nanotubes, physical parameters, SDS

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Authors: Liam Fassam, Pouria Liravi, Jacquie Bridgman

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Introduction: The way we travel is rapidly changing, car ownership and use are declining among young people and those residents in urban areas. Also, the increasing role and popularity of sharing economy companies like Uber highlight a movement towards consuming transportation solutions as a service [Mobility of a Service]. This research looks to bridge the knowledge gap that exists between city mobility, smart cities, sharing economy and social entrepreneurship business models. Understanding of this subject is crucial for smart city design, as access to affordable transport has been identified as a contributing factor to social isolation leading to issues around health and wellbeing. Methodology: To explore the current fit vis-a-vis transportation business models and social impact this research undertook a comparative analysis between a systematic literature review and a Delphi study. The systematic literature review was undertaken to gain an appreciation of the current academic thinking on ‘social entrepreneurship and smart city mobility’. The second phase of the research initiated a Delphi study across a group of 22 participants to review future opinion on ‘how social entrepreneurship can assist city mobility sharing models?’. The Delphi delivered an initial 220 results, which once cross-checked for duplication resulted in 130. These 130 answers were sent back to participants to score importance against a 5-point LIKERT scale, enabling a top 10 listing of areas for shared user transports in society to be gleaned. One further round (4) identified no change in the coefficient of variant thus no further rounds were required. Findings: Initial results of the literature review returned 1,021 journals using the search criteria ‘social entrepreneurship and smart city mobility’. Filtering allied to ‘peer review’, ‘date’, ‘region’ and ‘Chartered associated of business school’ ranking proffered a resultant journal list of 75. Of these, 58 focused on smart city design, 9 on social enterprise in cityscapes, 6 relating to smart city network design and 3 on social impact, with no journals purporting the need for social entrepreneurship to be allied to city mobility. The future inclusion factors from the Delphi expert panel indicated that smart cities needed to include shared economy models in their strategies. Furthermore, social isolation born by costs of infrastructure needed addressing through holistic A-political social enterprise models, and a better understanding of social benefit measurement is needed. Conclusion: In investigating the collaboration between key public transportation stakeholders, a theoretical model of social enterprise transportation models that positively impact upon the smart city needs of reduced transport poverty and social isolation was formed. As such, the research has identified how a revised business model of Mobility of a Service allied to a social entrepreneurship can deliver impactful measured social benefits associated to smart city design existent research.

Keywords: social enterprise, collaborative transportation, new models of ownership, transport social impact

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Authors: Marc Solé, Francesc Giné, Magda Valls, Nina Bijedic

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What people say on social media has turned into a rich source of information to understand social behavior. Specifically, the growing use of Twitter social media for political communication has arisen high opportunities to know the opinion of large numbers of politically active individuals in real time and predict the global political tendencies of a specific country. It has led to an increasing body of research on this topic. The majority of these studies have been focused on polarized political contexts characterized by only two alternatives. Unlike them, this paper tackles the challenge of forecasting Spanish political trends, characterized by multiple political parties, by means of analyzing the Twitters Users political tendency. According to this, a new strategy, named Tweets Analysis Strategy (TAS), is proposed. This is based on analyzing the users tweets by means of discovering its sentiment (positive, negative or neutral) and classifying them according to the political party they support. From this individual political tendency, the global political prediction for each political party is calculated. In order to do this, two different strategies for analyzing the sentiment analysis are proposed: one is based on Positive and Negative words Matching (PNM) and the second one is based on a Neural Networks Strategy (NNS). The complete TAS strategy has been performed in a Big-Data environment. The experimental results presented in this paper reveal that NNS strategy performs much better than PNM strategy to analyze the tweet sentiment. In addition, this research analyzes the viability of the TAS strategy to obtain the global trend in a political context make up by multiple parties with an error lower than 23%.

Keywords: political tendency, prediction, sentiment analysis, Twitter

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Authors: Mathias B. Michael, Esther T. Akinlabi, Tien-Chien Jen

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Thermal energy consumption in palm oil production plant comprises mainly of steam, hot water and hot air. In most efficient plants, hot water and air are generated from the steam supply system. Research has shown that thermal energy utilize in palm oil production plants is about 70 percent of the total energy consumption of the plant. In order to manage the plants’ energy efficiently, the energy systems are modelled and optimized. This paper aimed to present the model of steam supply systems of a typical palm oil production plant in Ghana. The models include exergy and energy models of steam boiler, steam turbine and the palm oil mill. The paper further simulates the virtual plant model to obtain the thermal energy performance of the plant under study. The simulation results show that, under normal operating condition, the boiler energy performance is considerably below the expected level as a result of several factors including intermittent biomass fuel supply, significant moisture content of the biomass fuel and significant heat losses. The total thermal energy performance of the virtual plant is set as a baseline. The study finally recommends number of energy efficiency measures to improve the plant’s energy performance.

Keywords: palm biomass, steam supply, exergy and energy models, energy performance benchmark

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Authors: Syed Asif Hassan, Syed Atif Hassan

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Multidrug-resistant Tuberculosis (MDR-TB) is an infection caused by the resistant strains of Mycobacterium tuberculosis that do not respond either to isoniazid or rifampicin, which are the most important anti-TB drugs. The increase in the occurrence of a drug-resistance strain of MTB calls for an intensive search of novel target-based therapeutics. In this context LprG (Rv1411c) a lipoprotein from MTB plays a pivotal role in the immune evasion of Mtb leading to survival and propagation of the bacterium within the host cell. Therefore, a machine learning method will be developed for generating a computational model that could predict for a potential anti LprG activity of the novel antituberculosis compound. The present study will utilize dataset from PubChem database maintained by National Center for Biotechnology Information (NCBI). The dataset involves compounds screened against MTB were categorized as active and inactive based upon PubChem activity score. PowerMV, a molecular descriptor generator, and visualization tool will be used to generate the 2D molecular descriptors for the actives and inactive compounds present in the dataset. The 2D molecular descriptors generated from PowerMV will be used as features. We feed these features into three different classifiers, namely, random forest, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model based on the accuracy of predicting novel antituberculosis compound with an anti LprG activity. Additionally, the efficacy of predicted active compounds will be screened using SMARTS filter to choose molecule with drug-like features.

Keywords: antituberculosis drug, classifier, machine learning, molecular descriptors, prediction

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Authors: Magdalena Idzikowska, Katarzyna Pająk, Kamil Kowalczyk

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Seafloor topography is a fundamental issue in geological, geophysical, and oceanographic studies. Single-beam or multibeam sonars attached to the hulls of ships are used to emit a hydroacoustic signal from transducers and reproduce the topography of the seabed. This solution provides relevant accuracy and spatial resolution. Bathymetric data from ships surveys provides National Centers for Environmental Information – National Oceanic and Atmospheric Administration. Unfortunately, most of the seabed is still unidentified, as there are still many gaps to be explored between ship survey tracks. Moreover, such measurements are very expensive and time-consuming. The solution is raster bathymetric models shared by The General Bathymetric Chart of the Oceans. The offered products are a compilation of different sets of data - raw or processed. Indirect data for the development of bathymetric models are also measurements of gravity anomalies. Some forms of seafloor relief (e.g. seamounts) increase the force of the Earth's pull, leading to changes in the sea surface. Based on satellite altimetry data, Sea Surface Height and marine gravity anomalies can be estimated, and based on the anomalies, it’s possible to infer the structure of the seabed. The main goal of the work is to create regional bathymetric models and models of the sea surface in the area of the east coast of North America – a region of seamounts and undulating seafloor. The research includes an analysis of the methods and techniques used, an evaluation of the interpolation algorithms used, model thickening, and the creation of grid models. Obtained data are raster bathymetric models in NetCDF format, survey data from multibeam soundings in MB-System format, and satellite altimetry data from Copernicus Marine Environment Monitoring Service. The methodology includes data extraction, processing, mapping, and spatial analysis. Visualization of the obtained results was carried out with Geographic Information System tools. The result is an extension of the state of the knowledge of the quality and usefulness of the data used for seabed and sea surface modeling and knowledge of the accuracy of the generated models. Sea level is averaged over time and space (excluding waves, tides, etc.). Its changes, along with knowledge of the topography of the ocean floor - inform us indirectly about the volume of the entire water ocean. The true shape of the ocean surface is further varied by such phenomena as tides, differences in atmospheric pressure, wind systems, thermal expansion of water, or phases of ocean circulation. Depending on the location of the point, the higher the depth, the lower the trend of sea level change. Studies show that combining data sets, from different sources, with different accuracies can affect the quality of sea surface and seafloor topography models.

Keywords: seafloor, sea surface height, bathymetry, satellite altimetry

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Authors: C. Jouhar, B. Rajesh Kamath, M. K. Veeraiah, M. Z. Kurian

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Since long time ago, petroleum-based mineral oils have been used for liquid insulation in high voltage equipments. Mineral oils are widely used as insulation for transmission and distribution power transformers, capacitors and other high voltage equipment. Petroleum-based insulating oils have excellent dielectric properties such as high electric field strength, low dielectric losses and good long-term performance. Due to environmental consideration, an attempt to search the alternate liquid insulation is required. The influence of particles on the voltage breakdown in insulating oil and other liquids has been recognized for many years. Particles influence both AC and DC voltage breakdown in insulating oil. Experiments are conducted under AC voltage. The breakdown process starts with a microscopic bubble, an area of large distance where ions or electrons initiate avalanches. Insulating liquids drive their dielectric strength from the much higher density compare to gases. Experiments are carried out under High Voltage AC (HVAC) in different types of oils namely castor oil, vegetable oil and mineral oil. The Breakdown Voltage (BDV) with presence of moisture and particle contamination in different types of oils is studied. The BDV of vegetable oil is better when compared to other oils without contamination. The BDV of mineral oil is better when compared to other types of oils in presence of contamination.

Keywords: breakdown voltage, high voltage AC, insulating oil, oil breakdown

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Authors: Yuexin Liu, Gordon B. Mills, Russell R. Broaddus, John N. Weinstein

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The lethality of endometrioid endometrial cancer (EEC) is primarily attributable to the high-stage diseases. However, there are no available biomarkers that predict EEC patient staging at the time of diagnosis. We aim to develop a predictive scheme to help in this regards. Using reverse-phase protein array expression profiles for 210 EEC cases from The Cancer Genome Atlas (TCGA), we constructed a Protein Scoring of EEC Staging (PSES) scheme for surgical stage prediction. We validated and evaluated its diagnostic potential in an independent cohort of 184 EEC cases obtained at MD Anderson Cancer Center (MDACC) using receiver operating characteristic curve analyses. Kaplan-Meier survival analysis was used to examine the association of PSES score with patient outcome, and Ingenuity pathway analysis was used to identify relevant signaling pathways. Two-sided statistical tests were used. PSES robustly distinguished high- from low-stage tumors in the TCGA cohort (area under the ROC curve [AUC]=0.74; 95% confidence interval [CI], 0.68 to 0.82) and in the validation cohort (AUC=0.67; 95% CI, 0.58 to 0.76). Even among grade 1 or 2 tumors, PSES was significantly higher in high- than in low-stage tumors in both the TCGA (P = 0.005) and MDACC (P = 0.006) cohorts. Patients with positive PSES score had significantly shorter progression-free survival than those with negative PSES in the TCGA (hazard ratio [HR], 2.033; 95% CI, 1.031 to 3.809; P = 0.04) and validation (HR, 3.306; 95% CI, 1.836 to 9.436; P = 0.0007) cohorts. The ErbB signaling pathway was most significantly enriched in the PSES proteins and downregulated in high-stage tumors. PSES may provide clinically useful prediction of high-risk tumors and offer new insights into tumor biology in EEC.

Keywords: endometrial carcinoma, protein, protein scoring of EEC staging (PSES), stage

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Authors: Chandra Prayaga, Aaron Wade, Lakshmi Prayaga, Gopi Shankar Mallu

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This paper presents the use of machine learning algorithms to predict the success of students in an introductory physics course. Data having 140 rows pertaining to the performance of two batches of students was used. The lack of sufficient data to train robust machine learning models was compensated for by generating synthetic data similar to the real data. CTGAN and CTGAN with Gaussian Copula (Gaussian) were used to generate synthetic data, with the real data as input. To check the similarity between the real data and each synthetic dataset, pair plots were made. The synthetic data was used to train machine learning models using the PyCaret package. For the CTGAN data, the Ada Boost Classifier (ADA) was found to be the ML model with the best fit, whereas the CTGAN with Gaussian Copula yielded Logistic Regression (LR) as the best model. Both models were then tested for accuracy with the real data. ROC-AUC analysis was performed for all the ten classes of the target variable (Grades A, A-, B+, B, B-, C+, C, C-, D, F). The ADA model with CTGAN data showed a mean AUC score of 0.4377, but the LR model with the Gaussian data showed a mean AUC score of 0.6149. ROC-AUC plots were obtained for each Grade value separately. The LR model with Gaussian data showed consistently better AUC scores compared to the ADA model with CTGAN data, except in two cases of the Grade value, C- and A-.

Keywords: machine learning, student success, physics course, grades, synthetic data, CTGAN, gaussian copula CTGAN

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Authors: Ugur Buyuk, Sevda Engin

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Among the various alloy systems being considered as lead-free solder candidates, Sn-Ag-Cu alloys have been recognized as the most promising because of their excellent reliability and compatibility with current components. Thus, Sn-Ag-Cu alloys have recently attracted considerable attention and have been proposed by the Japanese, the EU and the US consortiums to replace conventional Sn-Pb eutectic solder. However, many problems or unknown characteristics of the Sn-Ag-Cu alloy system such as the best composition, the large undercooling in solidification, and the formation of large intermetallics still exist. It is expected that the addition of some solidification nuclei for Sn-Ag-Cu alloys will refine the solidification microstructure and will suppress undercooling.In the present work, the effects of the fourth elements, i.e., Zn, Ni, Bi, In and Co, on microstructural and mechanical properties of Sn-3.5Ag-0.9Cu lead-free solder were investigated. Sn-3.5Ag-0.9Cu-0.5X (X= Zn, Ni, Bi, In, Co (wt.)) alloys were prepared in a graphite crucible under vacuum atmosphere. The samples were directionally solidified upward at a constant temperature gradient and growth rates by using a Bridgman type directional solidification furnace. The microstructure, microhardness and ultimate tensile strength of alloys were measured. The effects of fourth elements on the microstructure and mechanical properties of Sn–Ag-Cu eutectic alloys were investigated. The results obtained in the present work were compared with the previous experimental results.

Keywords: lead-free solders, microhardness, microstructure, tensile strength

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Authors: Stefano Frassinelli, Alessandro Niccolai, Riccardo E. Zich

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Diving is an important Olympic sport. In this sport, the effective performance of the athlete is related to his capability to interact correctly with the springboard. In fact, the elevation of the jump and the correctness of the dive are influenced by the vibrations of the board. In this paper, the vibrations of the springboard will be analyzed by means of typical tools for vibration analysis: Firstly, a modal analysis will be done on two different models of the springboard, then, these two model and another one will be analyzed with a time analysis, done integrating the equations of motion od deformable bodies. All these analyses will be compared with experimental data measured on a real springboard by means of a 6-axis accelerometer; these measurements are aimed to assess the models proposed. The acquired data will be analyzed both in frequency domain and in time domain.

Keywords: springboard analysis, modal analysis, time domain analysis, vibrations

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Authors: Harika Dasari, Eric Eisenbraun

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The present study models the role of electrode structural characteristics on the thermal behavior of lithium-ion batteries. Preliminary modeling runs have employed a 1D lithium-ion battery coupled to a two-dimensional axisymmetric model using silicon as the battery anode material. The two models are coupled by the heat generated and the average temperature. Our study is focused on the silicon anode particle sizes and it is observed that silicon anodes with nano-sized particles reduced the temperature of the battery in comparison to anodes with larger particles. These results are discussed in the context of the relationship between particle size and thermal transport properties in the electrode.

Keywords: particle size, NMC, silicon, heat generation, separator

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Authors: Alexandra Papagianni, George Filis, Panagiotis Papadopoulos

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The liberalization of the energy market and the increasing penetration of fluctuating renewables (e.g., wind and solar power) have heightened the importance of the spot market for ensuring efficient electricity supply. This is further emphasized by the EU’s goal of achieving net-zero emissions by 2050. The day-ahead market (DAM) plays a key role in European energy trading, accounting for 80-90% of spot transactions and providing critical insights for next-day pricing. Therefore, short-term electricity price forecasting (EPF) within the DAM is crucial for market participants to make informed decisions and improve their market positioning. Existing literature highlights out-of-sample performance as a key factor in assessing EPF accuracy, with influencing factors such as predictors, forecast horizon, model selection, and strategy. Several studies indicate that electricity demand is a primary price determinant, while renewable energy sources (RES) like wind and solar significantly impact price dynamics, often lowering prices. Additionally, incorporating data from neighboring countries, due to market coupling, further improves forecast accuracy. Most studies predict up to 24 steps ahead using hourly data, while some extend forecasts using higher-frequency data (e.g., half-hourly or quarter-hourly). Short-term EPF methods fall into two main categories: statistical and computational intelligence (CI) methods, with hybrid models combining both. While many studies use advanced statistical methods, particularly through different versions of traditional AR-type models, others apply computational techniques such as artificial neural networks (ANNs) and support vector machines (SVMs). Recent research combines multiple methods to enhance forecasting performance. Despite extensive research on EPF accuracy, a gap remains in understanding how forecasting strategy affects prediction outcomes. While iterated strategies are commonly used, they are often chosen without justification. This paper contributes by examining whether the choice of forecasting strategy impacts the quality of day-ahead price predictions, especially for multi-step forecasts. We evaluate both iterated and direct methods, exploring alternative ways of conducting iterated forecasts on benchmark and state-of-the-art forecasting frameworks. The goal is to assess whether these factors should be considered by end-users to improve forecast quality. We focus on the Greek DAM using data from July 1, 2021, to March 31, 2022. This period is chosen due to significant price volatility in Greece, driven by its dependence on natural gas and limited interconnection capacity with larger European grids. The analysis covers two phases: pre-conflict (January 1, 2022, to February 23, 2022) and post-conflict (February 24, 2022, to March 31, 2022), following the Russian-Ukraine conflict that initiated an energy crisis. We use the mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (sMAPE) for evaluation, as well as the Direction of Change (DoC) measure to assess the accuracy of price movement predictions. Our findings suggest that forecasters need to apply all strategies across different horizons and models. Different strategies may be required for different horizons to optimize both accuracy and directional predictions, ensuring more reliable forecasts.

Keywords: short-term electricity price forecast, forecast strategies, forecast horizons, recursive strategy, direct strategy

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Authors: M. A. Sohaly, M. A. Elfouly

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Parkinson's disease (PD) is a heterogeneous movement disorder that often appears in the elderly. PD is induced by a loss of dopamine secretion. Some drugs increase the secretion of dopamine. In this paper, we will simply study the stability of PD models as a nonlinear delay differential equation. After a period of taking drugs, these act as positive feedback and increase the tremors of patients, and then, the differential equation has positive coefficients and the system is unstable under these conditions. We will present a set of suggested modifications to make the system more compatible with the biodynamic system. When giving a set of numerical examples, this research paper is concerned with the mathematical analysis, and no clinical data have been used.

Keywords: Parkinson's disease, stability, simulation, two delay differential equation

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Authors: Sheikh Samsuzzhan Alam

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The present study investigates the selection of variogram model in analyzing spatial variations of environmental variables with the trend. Sometimes, the autofitted theoretical variogram does not really capture the true nature of the empirical semivariogram. So proper exploration and analysis are needed to select the best variogram model. For this study, an open source data collected from California Soil Resource Lab1 is used to explain the problems when fitting a theoretical variogram. Five most commonly used variogram models: Linear, Gaussian, Exponential, Matern, and Spherical were fitted to the experimental semivariogram. Ordinary kriging methods were considered to evaluate the accuracy of the selected variograms through cross-validation. This study is beneficial for selecting an appropriate theoretical variogram model for environmental variables.

Keywords: anisotropy, cross-validation, environmental variables, kriging, variogram models

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Authors: Md. R. Shattique, Md. T. Zaki, Md. G. Kibria

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Fly-ash bricks give a comprehensive solution towards recycling of fly-ash and since there is no requirement of firing to produce them, they are also eco-friendly bricks; little or no carbon-dioxide is emitted during their entire production cycle. As bricks are the most essential and widely utilized building materials in the construction industry, the significance of developing an alternate eco-friendly brick is substantial in modern times. In this paper, manufacturing and potential utilization of Fly-ash made building bricks have been studied and was found to be a prospective substitute for fired clay bricks that contribute greatly to polluting the environment. Also, a comparison between sand made and ceramic dust made Fly-ash bricks have been carried out experimentally. The ceramic dust made bricks seem to show higher compressive strength at lower unit volume weight compared to sand made Fly-ash bricks. Moreover, the water absorption capacity of ceramic dust Fly-ash bricks was lower than sand made bricks. Then finally a statistical comparison between fired clay bricks and fly-ash bricks were carried out. All the requirements for good quality building bricks are matched by the fly-ash bricks. All the facts from this study pointed out that these bricks give a new opportunity for being an alternate building material.

Keywords: coal fly-ash, ceramic dust, burnt clay bricks, sand, gypsum, absorption capacity, unit volume weight, compressive strength

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Authors: Sanja Podunavac-Kuzmanović, Denis Kučević, Strahinja Kovačević, Milica Karadžić, Lidija Jevrić

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The present study describes the application of chemometric methods in analysis of milk samples which were collected in a conventional dairy farm and an organic dairy farm in AP Vojvodina, Republic of Serbia. The chemometric analysis included the application of univariate regression modeling and Analysis of Variance (ANOVA) method. The ANOVA was used in order to determine the differences in fatty acids content in the milk samples from conventional and organic farm. The results of the ANOVA testing indicate that there is a highly statistically significant difference between the content of fatty acid (saturated fatty acid vs. unsaturated fatty acids) in different dairy farming. Besides, the linear univariate models have been obtained as a result of modeling the linear relationships between the milk fat content and saturated fatty acids content, and the linear relationships between the milk fat content and unsaturated fatty acids content. The models obtained on the basis of the milk samples which originate from the organic farming are statistically better than the models based on the milk samples from conventional farming.

Keywords: hemometrics, milk, organic farming, quality control

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Authors: Kwangwon Seo, Haksoo Han

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Al-Si was synthesized and introduced in poly 2,2’-m-(phenylene)-5,5’-bibenzimidazole (PBI). As a result, a series of five Al-Si/PBI composite (ASPBI) membranes (0, 3, 6, 9, and 12 wt.%) were developed and characterized for application in high temperature polymer electrolyte membrane fuel cells (HT-PEMFCs). The chemical and morphological structure of ASPBI membranes were analyzed by Fourier transform infrared spectroscopy, X-ray diffractometer and scanning electron microscopy. According to the doping level test and thermogravimetric analysis, as the concentration of Al-Si increased, the doping level increased up to 475%. Moreover, the proton conductivity, current density at 0.6V, and maximum power density of ASPBI membranes increased up to 0.31 Scm-1, 0.320 Acm-2, and 0.370 Wcm-2, respectively, because the increased concentration of Al-Si allows the membranes to hold more PA. Alternatively, as the amount of Al-Si increased, the tensile strength of PA-doped and -undoped membranes decreased. This was resulted by both excess PA and aggregation, which can cause serious degradation of the membrane and induce cracks. Moreover, the PA-doped and -undoped ASPBI12 had the lowest tensile strength. The improved performances of ASPBI membranes imply that ASPBI membranes are possible candidates for HT-PEMFC applications. However, further studies searching to improve the compatibility between PBI matrix and inorganic and optimize the loading of Al-Si should be performed.

Keywords: composite membrane, high temperature polymer electrolyte membrane fuel cell, membrane electrode assembly, polybenzimidazole, polymer electrolyte membrane, proton conductivity

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Authors: Yina F. Muñoz, Alexander Paz, Hanns De La Fuente-Mella, Joaquin V. Fariña, Guilherme M. Sales

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The primary approach for estimating bridge deterioration uses Markov-chain models and regression analysis. Traditional Markov models have problems in estimating the required transition probabilities when a small sample size is used. Often, reliable bridge data have not been taken over large periods, thus large data sets may not be available. This study presents an important change to the traditional approach by using the Small Data Method to estimate transition probabilities. The results illustrate that the Small Data Method and traditional approach both provide similar estimates; however, the former method provides results that are more conservative. That is, Small Data Method provided slightly lower than expected bridge condition ratings compared with the traditional approach. Considering that bridges are critical infrastructures, the Small Data Method, which uses more information and provides more conservative estimates, may be more appropriate when the available sample size is small. In addition, regression analysis was used to calculate bridge deterioration. Condition ratings were determined for bridge groups, and the best regression model was selected for each group. The results obtained were very similar to those obtained when using Markov chains; however, it is desirable to use more data for better results.

Keywords: concrete bridges, deterioration, Markov chains, probability matrix

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Authors: Gurmeet Singh Cheema, Gurjinder Singh, Amardeep Singh Kang

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Aluminium alloy 6061 is a medium to high strength heat-treatable alloy which has very good corrosion resistance and very good weldability. Friction Stir Welding was developed and this technique has attracted considerable interest from the aerospace and automotive industries since it is able to produce defect free joints particularly for light metals i.e aluminum alloy and magnesium alloy. In the friction stir welding process, welding parameters such as tool rotational speed, welding speed and tool shoulder diameter play a major role in deciding the weld quality. In this research work, an attempt has been made to understand the effect of tool rotational speed, welding speed and tool shoulder diameter on friction stir welded AA6061 aluminium alloy joints. Statistical tool such as central composite design is used to develop the mathematical relationships. The mathematical model was developed to predict mechanical properties of friction stir welded aluminium alloy joints at the 95% confidence level.

Keywords: aluminium alloy, friction stir welding, central composite design, mathematical relationship

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Authors: Jie Lin, Yiwen Pan, Li Zhang, Zhangyong Xia

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Atherosclerosis is a chronic inflammatory disease characterized by the accumulation of lipids, immune cells, and extracellular matrix in the arterial walls. This pathological process can lead to the formation of plaques that can obstruct blood flow and trigger various cardiovascular diseases such as heart attack and stroke. The underlying molecular mechanisms still remain unclear, although many studies revealed the dysfunction of endothelial cells, recruitment and activation of monocytes and macrophages, and the production of pro-inflammatory cytokines and chemokines in atherosclerosis. This study aimed to identify hub genes involved in the progression of atherosclerosis and to analyze their biological function in silico, thereby enhancing our understanding of the disease’s molecular mechanisms. Through the analysis of microarray data, we examined the gene expression in media and neo-intima from plaques, as well as distant macroscopically intact tissue, across a cohort of 32 hypertensive patients. Initially, 112 differentially expressed genes (DEGs) were identified. Subsequent immune infiltration analysis indicated a predominant presence of 27 immune cell types in the atherosclerosis group, particularly noting an increase in monocytes and macrophages. In the Weighted gene co-expression network analysis (WGCNA), 10 modules with a minimum of 30 genes were defined as key modules, with blue, dark, Oliver green and sky-blue modules being the most significant. These modules corresponded respectively to monocyte, activated B cell, and activated CD4 T cell gene patterns, revealing a strong morphological-genetic correlation. From these three gene patterns (modules morphology), a total of 2509 key genes (Gene Significance >0.2, module membership>0.8) were extracted. Six hub genes (CD36, DPP4, HMOX1, PLA2G7, PLN2, and ACADL) were then identified by intersecting 2509 key genes, 102 DEGs with lipid-related genes from the Genecard database. The bio-functional analysis of six hub genes was estimated by a robust classifier with an area under the curve (AUC) of 0.873 in the ROC plot, indicating excellent efficacy in differentiating between the disease and control group. Moreover, PCA visualization demonstrated clear separation between the groups based on these six hub genes, suggesting their potential utility as classification features in predictive models. Protein-protein interaction (PPI) analysis highlighted DPP4 as the most interconnected gene. Within the constructed key gene-drug network, 462 drugs were predicted, with ursodeoxycholic acid (UDCA) being identified as a potential therapeutic agent for modulating DPP4 expression. In summary, our study identified critical hub genes implicated in the progression of atherosclerosis through comprehensive bioinformatic analyses. These findings not only advance our understanding of the disease but also pave the way for applying similar analytical frameworks and predictive models to other diseases, thereby broadening the potential for clinical applications and therapeutic discoveries.

Keywords: atherosclerosis, hub genes, drug prediction, bioinformatics

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Authors: Nessreen Y. Ibrahim

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Designing the future according to realistic analytical study for the futuristic market needs can be a milestone strategy to make a huge improvement in developing countries economics. In developing countries, access to high technology and latest science approaches is very limited. The financial problems in low and medium income countries have negative effects on the kind and quality of imported new technologies and application for their markets. Thus, there is a strong need for shifting paradigm thinking in the design process to improve and evolve their development strategy. This paper discusses future possibilities in developing countries, and how they can design their own future according to specific future models FDM (Future Design Models), which established to solve certain economical problems, as well as political and cultural conflicts. FDM is strategic thinking framework provides an improvement in both content and process. The content includes; beliefs, values, mission, purpose, conceptual frameworks, research, and practice, while the process includes; design methodology, design systems, and design managements tools. In this paper the main objective was building an innovative economic model to design a chosen possible futuristic scenario; by understanding the market future needs, analyze real world setting, solve the model questions by future driven design, and finally interpret the results, to discuss to what extent the results can be transferred to the real world. The paper discusses Egypt as a potential case study. Since, Egypt has highly complex economical problems, extra-dynamic political factors, and very rich cultural aspects; we considered Egypt is a very challenging example for applying FDM. The paper results recommended using FDM numerical modeling as a starting point to design the future.

Keywords: developing countries, economic models, future design, possible futures

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Authors: Meor Othman Hamzah, Lillian Gungat, Nur Izzi Md. Yusoff, Jan Valentin

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The recent hike in raw materials costs and the quest for preservation of the environment has prompted asphalt industries to adopt greener road construction technology. This paper presents a study on such technology by means of asphalt recycling and use of warm mix asphalt (WMA) additive. It evaluates the effects of a WMA named RH-WMA on binder rheological properties and asphalt mixture performance. The recycled asphalt, obtained from local roads, was processed, fractionated, and incorporated with virgin aggregate and binder. For binder testing, the recycled asphalt was extracted and blended with virgin binder. The binder and mixtures specimen containing 30 % and 50 % recycled asphalt contents were mixed with 3 % RH-WMA. The rheological properties of the binder were evaluated based on fundamental, viscosity, and frequency sweep tests. Indirect tensile strength and resilient modulus tests were carried out to assess the mixture’s performances. The rheological properties and strength performance results showed that the addition of RH-WMA slightly reduced the binder and mixtures stiffness. The percentage of recycled asphalt increased the stiffness of binder and mixture, and thus improves the resistance to rutting. Therefore, the integration of recycled asphalt and RH-WMA can be an alternative material for road sustainable construction for countries in the tropics.

Keywords: recycled asphalt, warm mix additive, rheological, mixture performance

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Authors: Shahroz Khan, Erkan Gunpinar, Kemal Mart

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Recently parametric design techniques became a vital concept in the field of Computer Aided Design (CAD), which helps to provide sophisticated platform to the designer in order to automate the design process in efficient time. In these techniques, design process starts by parameterizing the important features of design models (typically the key dimensions), with the implementation of design constraints. The design constraints help to retain the overall shape of the model while modifying its parameters. However, the process of initializing an appropriate number of design parameters and constraints is the crucial part of parametric design techniques, especially for complex surface models such as yacht hull. This paper introduces a method to create complex surface models in favor of parametric design techniques, a method to define the right number of parameters and respective design constraints, and a system to implement design parameters in contract to design constraints schema. For this, in our proposed approach the design process starts by dividing the yacht hull into three sections. Each section consists of different shape lines, which form the overall shape of yacht hull. The shape lines are created using Cubic Bezier Curves, which allow larger design flexibility. Design parameters and constraints are defined on the shape lines in 3D design space to facilitate the designers for better and individual handling of parameters. Afterwards, shape modifiers are developed, which allow the modification of each parameter while satisfying the respective set of criteria and design constraints. Such as, geometric continuities should be maintained between the shape lines of the three sections, fairness of the hull surfaces should be preserved after modification and while design modification, effect of a single parameter should be negligible on other parameters. The constraints are defined individually on shape lines of each section and mutually between the shape lines of two connecting sections. In order to validate and visualize design results of our shape modifiers, a real time graphic interface is created.

Keywords: design parameter, design constraints, shape modifies, yacht hull

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Authors: Gelacio JuáRez-Luna, Daniel Enrique GonzáLez-RamíRez, Enrique Tenorio-Montero

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Frame elements coupled with springs elements are used for modelling the development of hinges in segmented tunnels, the spring elements modelled the rotational, transversal and axial failure. These spring elements are equipped with constitutive models to include independently the moment, shear force and axial force, respectively. These constitutive models are formulated based on damage mechanics and experimental test reported in the literature review. The mesh of the segmented tunnels was discretized in the software GID, and the nonlinear analyses were carried out in the finite element software ANSYS. These analyses provide the capacity curve of the primary and secondary lining of a segmented tunnel. Two numerical examples of segmented tunnels show the capability of the spring elements to release energy by the development of hinges. The first example is a segmental concrete lining discretized with frame elements loaded until hinges occurred in the lining. The second example is a tunnel with primary and secondary lining, discretized with a double ring frame model. The outer ring simulates the segmental concrete lining and the inner ring simulates the secondary cast-in-place concrete lining. Spring elements also modelled the joints between the segments in the circumferential direction and the ring joints, which connect parallel adjacent rings. The computed load vs displacement curves are congruent with numerical and experimental results reported in the literature review. It is shown that the modelling of a tunnel with primary and secondary lining with frame elements and springs provides reasonable results and save computational cost, comparing with 2D or 3D models equipped with smeared crack models.

Keywords: damage, hinges, lining, tunnel

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Authors: Pratiksha Dubey, Praveen Singh, Shantanu Sen Gupta, Anand K. Bachhawat

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5-oxoproline (pyroglutamic acid) is a cyclic lactam of glutamic acid. In the cell, it can be produced by several different pathways and is metabolized into glutamate with the help of the 5-oxoprolinase enzyme (OPLAH or OXP1). The inhibition of 5-oxoprolinase enzyme in mammals was found to result in heart failure and is thought to be a consequence of oxidative stress [1]. To analyze the consequences of 5-oxoproline accumulation more clearly, we are generating models for 5-oxoproline accumulation in yeast. The 5-oxoproline accumulation model in yeast is being developed by two different strategies. The first one is by overexpression of the mouse  -glutamylcyclotransferase enzyme. It degrades -glu-met dipeptide into 5-oxoproline and methionine taken by the cell from the medium. The second strategy is by providing high concentration of 5-oxoproline externally to the yeast cells. The intracellular 5-oxoproline levels in both models are being evaluated. In addition, the metabolic and cellular consequences are being investigated.

Keywords: 5-oxoproline, pyroglutamic acid, yeast, genetics

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Authors: T. Aldeeb, M. Abduelmula

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This study examines the fatigue life of S275 mild steel at room temperature. Mechanical components can fail under cyclic loading during period of time, known as the fatigue phenomenon. In order to prevent fatigue induced failures, material behavior should be investigated to determine the endurance limit of the material for safe design and infinite life, thus leading to reducing the economic cost and loss in human lives. The fatigue behavior of S275 mild steel was studied and investigated. Specimens were prepared in accordance with ASTM E3-11, and fatigue tests of the specimen were conducted in accordance with ASTM E466-07 on a smooth plate, with a continuous radius between ends (hourglass-shaped plate). The method of fatigue testing was applied with constant load amplitude and constant frequency of 4 Hz with load ratio (Fully Reversal R= -1). Surface fractures of specimens were investigated using Scanning Electron Microscope (SEM). The experimental results were compared with the results of a Finite Element Analysis (FEA), using simulation software. The experiment results indicated that the endurance fatigue limit of S275 mild steel was 195.47 MPa.

Keywords: fatigue strength, fatigue life, finite element analysis(FEA), S275 mild steel, scanning electron microscope (SEM)

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Authors: M. S. Belashov, A. V. Benin, Lin Hong, Sh. Sh. Nazarova, O. B. Sabirova, A. M. Uzdin, Lin Hong

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The features of calculating structures for the action of weak earthquakes are analyzed. Earthquakes with a recurrence of 30 years and 50 years are considered. In the first case, the structure is to operate normally without damage after the earthquake. In the second case, damages are allowed that do not affect the possibility of the structure operation. Three issues are emphasized: setting elastic and damping characteristics of reinforced concrete, formalization of limit states, and combinations of loads. The dependence of damping on the reinforcement coefficient is estimated. When evaluating limit states, in addition to calculations for crack resistance and strength, a human factor, i.e., the possibility of panic among people, was considered. To avoid it, it is proposed to limit a floor-by-floor speed level in certain octave ranges. Proposals have been developed for estimating the coefficients of the combination of various loads with the seismic one. As an example, coefficients of combinations of seismic and ice loads are estimated. It is shown that for strong actions, the combination coefficients for different regions turn out to be close, while for weak actions, they may differ.

Keywords: weak earthquake, frequent earthquake, damage, limit state, reinforcement, crack resistance, strength resistance, a floor-by-floor velocity, combination coefficients

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Authors: Daleesha M. Viswanathan, Sumam Mary Idicula

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Dynamic hand gestures are an intrinsic component in sign language communication. Extracting spatial temporal features of the hand gesture trajectory plays an important role in a dynamic gesture recognition system. Finding a discrete feature descriptor for the motion trajectory based on the orientation feature is the main concern of this paper. Kalman filter algorithm and Hidden Markov Models (HMM) models are incorporated with this recognition system for hand trajectory tracking and for spatial temporal classification, respectively.

Keywords: orientation features, discrete feature vector, HMM., Indian sign language

Procedia PDF Downloads 371