Search results for: deep composite kernel

Authors: Neritan Shkodrani, Klearta Rrushi, Anxhela Shaha

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Generally, the design of axial load capacity of deep foundations is based on the data provided from field tests, such as SPT (Standard Penetration Test) and CPT (Cone Penetration Test) tests. This paper reports the results of axial load capacity analysis of drilled shafts at a construction site at Semani, in Fier county, Fier prefecture in Albania. In this case, the axial load capacity analyses are based on the data of 416 SPT tests and 12 CPTU tests, which are carried out in this site construction using 12 boreholes (10 borings of a depth 30.0 m and 2 borings of a depth of 80.0m). The considered foundation widths range from 0.5m to 2.5 m and foundation embedment lengths is fixed at a value of 25m. SPT – based analytical methods from the Japanese practice of design (Building Standard Law of Japan) and CPT – based analytical Eslami and Fellenius methods are used for obtaining axial ultimate load capacity of drilled shafts. The considered drilled shaft (25m long and 0.5m - 2.5m in diameter) is analyzed for the soil conditions of each borehole. The values obtained from sets of calculations are shown in different charts. Then the reported axial load capacity values acquired from SPT and CPTU data are compared and some conclusions are found related to the mentioned methods of calculations.

Keywords: deep foundations, drilled shafts, axial load capacity, ultimate load capacity, allowable load capacity, SPT test, CPTU test

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Authors: Menonjyoti Kalita, Pradip Baishya

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In recent times, the world has been facing serious environmental concerns and issues, such as sustainability and cost, due to the overproduction of synthetic materials and their participation in degrading the environment by means of industrial waste and non-biodegradable characteristics. As such, biocomposites come in handy to ease such troubles. Bio-based composites are promising materials for future applications for substituting synthetic packaging materials. The challenge of making packaging materials lighter, safer and cheaper leads to investigating advanced materials with desired properties. Also, awareness of environmental issues forces researchers and manufacturers to spend effort on composite and bio-composite materials fields. This paper explores and tests some nature-friendly materials has been done which can replace low-density plastics. The materials selected included sugarcane bagasse, areca palm, and bamboo leaves. Sugarcane bagasse bamboo leaves and areca palm sheath are the primary material or natural fibre for testing. These products were processed, and the tensile strength of the processed parts was tested in Micro UTM; it was found that areca palm can be used as a good building material in replacement to polypropylene and even could be used in the production of furniture with the help of epoxy resin. And for bamboo leaves, it was found that bamboo and cotton, when blended in a 50:50 ratio, it has great tensile strength. For areca, it was found that areca fibres can be a good substitute for polypropylene, which can be used in building construction as binding material and also other products.

Keywords: biodegradable characteristics, bio-composites, areca palm sheath, polypropylene, micro UTM

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Ibrahim A. Elsayed, Mohamed H. Elalfy, Mostafa M. Abdalla

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A strain-based failure criterion for composite notched laminates is introduced where the most critical stress concentration factor for the anisotropic notched laminates could be related to the failure of the corresponding quasi-isotropic laminate and the anisotropy ratio of the laminate. The proposed criterion will simplify the design of composites to meet notched failure requirements by eliminating the need for the detailed specifications of the stacking sequence at the preliminary design stage. The designer will be able to design based on the stiffness of the laminate, then at a later stage, select an appropriate stacking sequence to meet the stiffness requirements. The failure strains for the notched laminates are computed using the material’s Omni-strain envelope. The concept of Omni-strain envelope concerns the region of average strain where the laminate is safe regardless of ply orientation. In this work, we use Hashin’s failure criteria and the strains around the hole are computed using Savin’s analytic solution. A progressive damage analysis study has been conducted where the failure loads for the notched laminates are computed using finite element analysis. The failure strains are computed and used to estimate the concentration factor. It is found that the correlation found using Savin’s analytic solution predicts the same ratio of concentration factors between anisotropic and quasi-isotropic laminates as the more expensive progressive failure analysis.

Keywords: anisotropy ratio, failure criteria, notched laminates, Omni-strain envelope, savin’s solution

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Authors: Afnan T. Bagasi, Donia M. Bettaieb, Abeer Alsobahi

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The wayfinding system is related to the course of the museum journey for visitors directly and indirectly. The design aspects of this system play an important role, making it an effective and communication system within the museum space. However, translating the concepts that pertain to its design, such as Intelligibility that is based on integration and connectivity in museum space design, needs more customization in the form of specific design considerations with reference to the most important approaches. Those approaches link the organizational and practical aspects to the semiotic and semantic aspects related to the space syntax by targeting the visual and perceived consistency of visitors. In this context, the study aims to identify how to apply the concept of intelligibility and clarity by employing integration and connectivity to design a wayfinding system in museums as a kind of composite interior space. Using the available plans and images to extrapolate the design considerations used to design the wayfinding system in the Saudi National Museum as a case study, a descriptive-analytical method was used to understand the basic organizational and morphological principles of the museum space through four main aspects in space design: morphological, semantic, semiotic, and pragmatic. The study's findings will assist designers, professionals, and researchers in the field of museum design in understanding the significance of the wayfinding system by delving into it through museum spaces by highlighting the essential aspects using a clear analytical method.

Keywords: wayfinding system, museum journey, intelligibility, integration, connectivity

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Authors: Rajesh Kumar Gautam, Debabrata Seth

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Thioflavin-T (ThT) play a key role of an important biologically active fluorescent sensor for amyloid fibrils. ThT molecule has been developed a method to detect the analysis of different type of diseases such as neurodegenerative disorders, Alzheimer’s, Parkinson’s, and type II diabetes. ThT was used as a fluorescent marker to detect the formation of amyloid fibril. In the presence of amyloid fibril, ThT becomes highly fluorescent. ThT undergoes twisting motion around C-C bonds of the two adjacent benzothiazole and dimethylaniline aromatic rings, which is predominantly affected by the micro-viscosity of the local environment. The present study articulates photophysics and torsional dynamics of biologically active molecule ThT in the presence of deep-eutectic solvents (DESs). DESs are environment-friendly, low cost and biodegradable alternatives to the ionic liquids. DES resembles ionic liquids, but the constituents of a DES include a hydrogen bond donor and acceptor species, in addition to ions. Due to the presence of the H-bonding network within a DES, it exhibits structural heterogeneity. Herein, we have prepared two different DESs by mixing urea with choline chloride and N, N-diethyl ethanol ammonium chloride at ~ 340 K. It was reported that deep eutectic mixture of choline chloride with urea gave a liquid with a freezing point of 12°C. We have experimented by taking two different concentrations of ThT. It was observed that at higher concentration of ThT (50 µM) it forms aggregates in DES. The photophysics of ThT as a function of temperature have been explored by using steady-state, and picoseconds time-resolved fluorescence emission spectroscopic techniques. From the spectroscopic analysis, we have observed that with rising temperature the fluorescence quantum yields and lifetime values of ThT molecule gradually decreases; this is the cumulative effect of thermal quenching and increase in the rate of the torsional rate constant. The fluorescence quantum yield and fluorescence lifetime decay values were always higher for DES-II (urea & N, N-diethyl ethanol ammonium chloride) than those for DES-I (urea & choline chloride). This was mainly due to the presence of structural heterogeneity of the medium. This was further confirmed by comparison with the activation energy of viscous flow with the activation energy of non-radiative decay. ThT molecule in less viscous media undergoes a very fast twisting process and leads to deactivation from the photoexcited state. In this system, the torsional motion increases with increasing temperature. We have concluded that beside bulk viscosity of the media, structural heterogeneity of the medium play crucial role to guide the photophysics of ThT in DESs. The analysis of the experimental data was carried out in the temperature range 288 ≤ T = 333K. The present articulate is to obtain an insight into the DESs as media for studying various photophysical processes of amyloid fibrils sensing molecule of ThT.

Keywords: deep eutectic solvent, photophysics, Thioflavin T, the torsional rate constant

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Authors: Hamed Nazeri, Pierre Mertiny, Yongsheng Ma, Kajsa Duke

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The background of the present study is the use of environment-friendly biopolymer and biocomposite materials. Among the recently introduced biopolymers, poly (propylene carbonate) (PPC) has been gaining attention. This study focuses on the size of representative volume elements (RVE) in order to simulate PPC composites reinforced by cellulose nanocrystals (CNCs) as a bio-nanocomposite. Before manufacturing nanocomposites, numerical modeling should be implemented to explore and predict mechanical properties, which may be accomplished by creating and studying a suitable RVE. In other studies, modeling of composites with rod shaped fillers has been reported assuming that fillers are unidirectionally aligned. But, modeling of non-aligned filler dispersions is considerably more difficult. This study investigates the minimum RVE size to enable subsequent FEA modeling. The matrix and nano-fillers were modeled using the finite element software ABAQUS, assuming randomly dispersed fillers with a filler mass fraction of 1.5%. To simulate filler dispersion, a Monte Carlo technique was employed. The numerical simulation was implemented to find composite elastic moduli. After commencing the simulation with a single filler particle, the number of particles was increased to assess the minimum number of filler particles that satisfies the requirements for an RVE, providing the composite elastic modulus in a reliable fashion.

Keywords: biocomposite, Monte Carlo method, nanocomposite, representative volume element

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Authors: Salwa M. Elmesallamy, Ahmed A. Farag, Magd M. Badr, Dalia S. Fathy, Ahmed Bakry, Mona A. El-Etre

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The term green environment is an international trend. It is become imperative to treat the corrosion of steel with a green coating to protect the environment. From the potential adverse effects of the traditional materials.A series of polybenzoxazine/henna composites (PBZ/henna), with different weight percent (3,5, and 7 wt % (of henna), were prepared for corrosion protection of carbon steel. The structures of the prepared composites were verified using FTIR analysis. The mechanical properties of the resins, such as adhesion, hardness, binding, and tensile strength, were also measured. It was found that the tensile strength increases by henna loading up to 25% higher than the tidy resin. The thermal stability was investigated by thermogravimetric analysis (TGA) the loading of lawsone (henna) molecules into the PBZ matrix increases the thermal stability of the composite. UV stability was tested by the UV weathering accelerator to examine the possibility that henna can also act as an aging UV stabilizer. The effect of henna content on the corrosion resistance of composite coatings was tested using potentiostatic polarization and electrochemical spectroscopy. The presence of henna in the coating matrix enhances the protection efficiency of polybenzoxazine coats. Increasing henna concentration increases the protection efficiency of composites. The quantum chemical calculations for polybenzoxazine/henna composites have resulted that the highest corrosion inhibition efficiency, has the highest EHOMO and lowest ELUMO; which is in good agreement with results obtained from experiments.

Keywords: polybenzoxazine, corrosion, green chemistry, carbon steel

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Authors: Noboru Wakamoto, Kiyotaka Obunai, Kazuya Okubo, Toru Fujii

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The aim of this study is to moderate the dependence of counter frictional coefficient on temperature between counter surfaces and to reduce the wear of C/C composites at low temperature. To modify the C/C composites, Silica (SiO₂) powders were added into phenolic resin for carbon precursor. The preform plate of the precursor of C/C composites was prepared by conventional filament winding method. The C/C composites plates were obtained by carbonizing preform plate at 2200 °C under an argon atmosphere. At that time, the silicon carbides (SiC) were synthesized around the surfaces and the internal vacancies of the C/C composites. The frictional coefficient on the counter surfaces and specific wear volumes of the C/C composites were measured by our developed frictional test machine like pin-on disk type. The XRD indicated that SiC was synthesized in the body of C/C composite fabricated by current method. The results of friction test showed that coefficient of friction of unmodified C/C composites have temperature dependence when the test condition was changed. In contrast, frictional coefficient of the C/C composite modified with SiO₂ powders was almost constant at about 0.27 when the temperature condition was changed from Room Temperature (RT) to 300 °C. The specific wear rate decreased from 25×10^-6 mm²/N to 0.1×10^-6 mm²/N. The observations of the surfaces after friction tests showed that the frictional surface of the modified C/C composites was covered with a film produced by the friction. This study found that synthesizing SiC around surface and internal vacancies of C/C composites was effective to moderate the dependence on the frictional coefficient and reduce to the abrasion of C/C composites.

Keywords: C/C composites, friction coefficient, wear, SiC

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Authors: Pitigalage Chamath Chandira Peiris

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A widely researched domain in the field of image processing in recent times has been single image super-resolution, which tries to restore a high-resolution image from a single low-resolution image. Many more single image super-resolution efforts have been completed utilizing equally traditional and deep learning methodologies, as well as a variety of other methodologies. Deep learning-based super-resolution methods, in particular, have received significant interest. As of now, the most advanced image restoration approaches are based on convolutional neural networks; nevertheless, only a few efforts have been performed using Transformers, which have demonstrated excellent performance on high-level vision tasks. The effectiveness of CNN-based algorithms in image super-resolution has been impressive. However, these methods cannot completely capture the non-local features of the data. Enhancer is a simple yet powerful Transformer-based approach for enhancing the resolution of images. A method for single image super-resolution was developed in this study, which utilized an efficient and effective transformer design. This proposed architecture makes use of a locally enhanced window transformer block to alleviate the enormous computational load associated with non-overlapping window-based self-attention. Additionally, it incorporates depth-wise convolution in the feed-forward network to enhance its ability to capture local context. This study is assessed by comparing the results obtained for popular datasets to those obtained by other techniques in the domain.

Keywords: single image super resolution, computer vision, vision transformers, image restoration

Procedia PDF Downloads 91

Authors: M. P. Rajeeshkumar, K. V. Aneesh Kumar, J. L. Otero-Ferrer, A. Lombarte, M. Hashim, N. Saravanane, V. N.Sanjeevan, V. M. Tuset

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The anglerfishes are widely distributed from shallow to deep-water habitats and are highly diverse in morphology, behaviour, and niche occupancy patterns. To understand this interspecific variability and degree of niche overlap, we performed a functional analysis of five species inhabiting Indian waters where diversity of deep-sea anglerfishes is very high. The sensory capacities (otolith shape and eye size) were also studied to improve the understanding of coexistence of species. The analyses of fish body and otolith shape clustered species in two morphotypes related to phylogenetic lineages: i) Malthopsis lutea, Lophiodes lugubri and Halieutea coccinea were characterized by a dorso-ventrally flattened body with high swimming ability and relative small otoliths, and ii) Chaunax spp. were distinguished by their higher body depth, lower swimming efficiency, and relative big otoliths. The sensory organs did not show a pattern linked to depth distribution of species. However, the larger eye size in M. lutea suggested a nocturnal feeding activity, whereas Chaunax spp. had a large mouth and deeper body in response to different ecological niches. Therefore, the present study supports the hypothesis of spatial and temporal segregation of anglerfishes in the Indian waters, which can be explained from a functional approach and understanding from sensory capabilities.

Keywords: functional traits, otoliths, niche overlap, fishes, Indian waters

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Authors: Gyu-Dong Kim, Wuk-Jae Yoo, Sang-Youl Lee

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Composite structures offer numerous advantages over conventional structural systems in the form of higher specific stiffness and strength, lower life-cycle costs, and benefits such as easy installation and improved safety. Recently, there has been a considerable increase in the use of composites in engineering applications and as wraps for seismic upgrading and repairs. However, these composites deteriorate with time because of outdated materials, excessive use, repetitive loading, climatic conditions, manufacturing errors, and deficiencies in inspection methods. In particular, damaged fibers in a composite result in significant degradation of structural performance. In order to reduce the failure probability of composites in service, techniques to assess the condition of the composites to prevent continual growth of fiber damage are required. Condition assessment technology and nondestructive evaluation (NDE) techniques have provided various solutions for the safety of structures by means of detecting damage or defects from static or dynamic responses induced by external loading. A variety of techniques based on detecting the changes in static or dynamic behavior of isotropic structures has been developed in the last two decades. These methods, based on analytical approaches, are limited in their capabilities in dealing with complex systems, primarily because of their limitations in handling different loading and boundary conditions. Recently, investigators have introduced direct search methods based on metaheuristics techniques and artificial intelligence, such as genetic algorithms (GA), simulated annealing (SA) methods, and neural networks (NN), and have promisingly applied these methods to the field of structural identification. Among them, GAs attract our attention because they do not require a considerable amount of data in advance in dealing with complex problems and can make a global solution search possible as opposed to classical gradient-based optimization techniques. In this study, we propose an alternative damage-detection technique that can determine the degraded stiffness distribution of vibrating laminated composites made of Glass Fiber-reinforced Polymer (GFRP). The proposed method uses a modified form of the bivariate Gaussian distribution function to detect degraded stiffness characteristics. In addition, this study presents a method to detect the fiber property variation of laminated composite plates from the micromechanical point of view. The finite element model is used to study free vibrations of laminated composite plates for fiber stiffness degradation. In order to solve the inverse problem using the combined method, this study uses only first mode shapes in a structure for the measured frequency data. In particular, this study focuses on the effect of the interaction among various parameters, such as fiber angles, layup sequences, and damage distributions, on fiber-stiffness damage detection.

Keywords: stiffness detection, fiber damage, genetic algorithm, layup sequences

Procedia PDF Downloads 257

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

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

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

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Authors: Chia-Yi Liu, Bo-Bin Xiao, Wen-Bin Lin, Hsiang-Ning Wu, Liang-Hsun Huang

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In order to reduce the risk of radiation damage that personnel may suffer during operations in the radiation environment, the use of automated guided vehicles to assist or replace on-site personnel in the radiation environment has become a key technology and has become an important trend. In this paper, we demonstrate our proof of concept for autonomous self-learning radiation source searcher in an unknown environment without a map. The research uses GPU version of Asynchronous Advantage Actor-Critic network (GA3C) of deep reinforcement learning to search for radiation sources. The searcher network, based on GA3C architecture, has self-directed learned and improved how search the anomalous radiation source by training 1 million episodes under three simulation environments. In each episode of training, the radiation source position, the radiation source intensity, starting position, are all set randomly in one simulation environment. The input for searcher network is the fused data from a 2D laser scanner and a RGB-D camera as well as the value of the radiation detector. The output actions are the linear and angular velocities. The searcher network is trained in a simulation environment to accelerate the learning process. The well-performance searcher network is deployed to the real unmanned vehicle, Dashgo E2, which mounts LIDAR of YDLIDAR G4, RGB-D camera of Intel D455, and radiation detector made by Institute of Nuclear Energy Research. In the field experiment, the unmanned vehicle is enable to search out the radiation source of the 18.5MBq Na-22 by itself and avoid obstacles simultaneously without human interference.

Keywords: deep reinforcement learning, GA3C, source searching, source detection

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Authors: Levent Taylan Ozgur Yildirim, Qihao Qian, John Yilin Wang

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A critical review of assessments of geological carbon dioxide (CO2) storage resources in Pennsylvania and the surrounding region was completed with a focus on the studies of Midwest Regional Carbon Sequestration Partnership (MRCSP), United States Department of Energy (US-DOE), and United States Geological Survey (USGS). Pennsylvania Geological Survey participated in the MRCSP Phase I research to characterize potential storage formations in Pennsylvania. The MRCSP’s volumetric method estimated ~89 gigatonnes (Gt) of total CO2 storage resources in deep saline formations, depleted oil and gas reservoirs, coals, and shales in Pennsylvania. Meanwhile, the US-DOE calculated storage efficiency factors using log-odds normal distribution and Monte Carlo sampling, revealing contingent storage resources of ~18 Gt to ~20 Gt in deep saline formations, depleted oil and gas reservoirs, and coals in Pennsylvania. Additionally, the USGS employed Beta-PERT distribution and Monte Carlo sampling to determine buoyant and residual storage efficiency factors, resulting in 20 Gt of contingent storage resources across four storage assessment units in Appalachian Basin. However, few studies have explored CO2 storage resources in shales in the region, yielding inconclusive findings. This article provides a critical and most up to date review and analysis of geological CO2 storage resources in Pennsylvania and the region.

Keywords: carbon capture and storage, geological CO2 storage, pennsylvania, appalachian basin

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Authors: Barnabás Körmöndi

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The recent crises have affected societies worldwide, resulting in the irresponsible exploitation of natural resources and the unattainability of sustainability. Regions that are economically underdeveloped, such as the Bodrogköz in Eastern Hungary and Slovakia, experience these issues more severely. The aim of this study is to analyze the natural and social resources of the Bodrogköz area for the planning of complex development based on ecotourism. The objective is to develop ecotourism opportunities in this least developed area of the borderland of Hungary and Slovakia. The study utilizes desk research, deep interviews, focus group meetings, and remote sensing methods. Desk research is aimed at providing a comprehensive understanding of the area, while deep interviews and focus group meetings were conducted to understand the stakeholders' perspectives on the potential for ecotourism. Remote sensing methods were used to better understand changes in the natural environment. The study identified the potential for ecotourism development in the Bodrogköz area due to its near-natural habitats along its bordering rivers and rich cultural heritage. The analysis revealed that ecotourism could promote the region's sustainable development, which is essential for its economic growth. Additionally, the study identified the possible threats to the natural environment during ecotourism development and suggested strategies to mitigate these threats. This study highlights the significance of ecotourism in promoting sustainable development in underdeveloped areas such as the Bodrogköz. It provides a basis for future research on ecotourism development and sustainable planning in similar regions. The analysis is based on the data collected through desk research, deep interviews, focus group meetings, and remote sensing. The assessment was conducted through content analysis, which allowed for the identification of themes and patterns in the data. The study addressed the question of how to develop ecotourism in the least developed area of the borderland of Hungary and Slovakia and promote sustainable development in the region. In conclusion, the study highlights the potential for ecotourism development in Bodrogköz and identifies the natural and social resources that contribute to its development. The study emphasizes the need for sustainable development to promote economic growth and mitigate any environmental threats. The findings can inform the development of future strategic plans for ecotourism, promoting sustainable development in underdeveloped regions.

Keywords: ecotourism, natural resources, remote sensing, social development

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Authors: Virgo Sulakatko

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The reduction of energy consumption of the built environment has been one of the topics tackled by European Commission during the last decade. Increased energy efficiency requirements have increased the renovation rate of apartment buildings covered with External Thermal Insulation Composite System (ETICS). Due to fast and optimized application process, a large extent of quality assurance is depending on the specific activities of artisans and are often not controlled. The on-site degradation factors (DF) have the technical influence to the façade and cause future costs to the owner. Besides the thermal conductivity, the building envelope needs to ensure the mechanical resistance and stability, fire-, noise-, corrosion and weather protection, and long-term durability. As the shortcomings of the construction phase become problematic after some years, the common value of the renovation is reduced. Previous work on the subject has identified and rated the relevance of DF to the technical requirements and developed a method to reveal the economic value of repair works. The future costs can be traded off to increased the quality assurance during the construction process. The proposed framework is describing the joint simulation of the technical importance and economic value of the on-site DFs of ETICS. The model is providing new knowledge to improve the resource allocation during the construction process by enabling to identify and diminish the most relevant degradation factors and increase economic value to the owner.

Keywords: ETICS, construction technology, construction management, life cycle costing

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Authors: V. Manush Nandan, K. Lokdeep, R. Vimal, K. Hari Hara Subramanyan, C. Aswin, V. Logeswaran

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Natural fibers have attained the potential market in the composite industry because of the huge environmental impact caused by synthetic fibers. Among the natural fibers, jute fibers are the most abundant plant fibers which are manufactured mainly in countries like India. Even though there is a good motive to utilize the natural supplement, the strength of the natural fiber composites is still a topic of discussion. In recent days, many researchers are showing interest in the chemical modification of the natural fibers to increase various mechanical and thermal properties. In the present study, jute fibers have been modified chemically using glutaric anhydride at different concentrations of 5%, 10%, 20%, and 30%. The glutaric anhydride solution is prepared by dissolving the different quantity of glutaric anhydride in benzene and dimethyl-sulfoxide using sodium formate catalyst. The jute fiber mats have been treated by the method of retting at various time intervals of 3, 6, 12, 24, and 36 hours. The modification structure of the treated fibers has been confirmed with infrared spectroscopy. The degree of modification increases with an increase in retention time, but higher retention time has damaged the fiber structure. The unmodified fibers and glutarylated fibers at different retention times are reinforced with epoxy matrix under room temperature. The tensile strength and flexural strength of the composites are analyzed in detail. Among these, the composite made with glutarylated fiber has shown good mechanical properties when compared to those made of unmodified fiber.

Keywords: flexural properties, glutarylation, glutaric anhydride, tensile properties

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Authors: B. Elshafei, X. Mao

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The demand for renewable energy is significantly increasing, major investments are being supplied to the wind power generation industry as a leading source of clean energy. The wind energy sector is entirely dependable and driven by the prediction of wind speed, which by the nature of wind is very stochastic and widely random. This s0tudy employs deep multi-fidelity Gaussian process regression, used to predict wind speeds for medium term time horizons. Data of the RUNE experiment in the west coast of Denmark were provided by the Technical University of Denmark, which represent the wind speed across the study area from the period between December 2015 and March 2016. The study aims to investigate the effect of pre-processing the data by denoising the signal using empirical wavelet transform (EWT) and engaging the vector components of wind speed to increase the number of input data layers for data fusion using deep multi-fidelity Gaussian process regression (GPR). The outcomes were compared using root mean square error (RMSE) and the results demonstrated a significant increase in the accuracy of predictions which demonstrated that using vector components of the wind speed as additional predictors exhibits more accurate predictions than strategies that ignore them, reflecting the importance of the inclusion of all sub data and pre-processing signals for wind speed forecasting models.

Keywords: data fusion, Gaussian process regression, signal denoise, temporal extrapolation

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Authors: Lin Po-Hsi, Sheu Ming-Thau

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Urea cycle disorders (UCD) are rare genetic metabolic disorders that compromise the body's urea cycle. Sodium phenylbutyrate (SPB) is a medication commonly administered in tablet or powder form to lower ammonia levels. Nonetheless, its high sodium content poses risks to sodium-sensitive UCD patients. This necessitates the creation of an alternative drug formulation to mitigate sodium load and optimize drug delivery for UCD patients. This study focused on crafting a novel oral drug formulation for UCD, leveraging choline bicarbonate and phenylbutyric acid. The active pharmaceutical ingredient-ionic liquids (API-ILs) and therapeutic deep eutectic systems (THEDES) were formed by combining these with choline chloride. These systems display characteristics like maintaining a liquid state at room temperature and exhibiting enhanced solubility. This in turn amplifies drug dissolution rate, permeability, and ultimately oral bioavailability. Incorporating choline-based phenylbutyric acid as a substitute for traditional SPB can effectively curtail the sodium load in UCD patients. Our in vitro dissolution experiments revealed that the ILs and DESs, synthesized using choline bicarbonate and choline chloride with phenylbutyric acid, surpassed commercial tablets in dissolution speed. Pharmacokinetic evaluations in SD rats indicated a notable uptick in the oral bioavailability of phenylbutyric acid, underscoring the efficacy of choline salt ILs in augmenting its bioavailability. Additional in vitro intestinal permeability tests on SD rats authenticated that the ILs, formulated with choline bicarbonate and phenylbutyric acid, demonstrate superior permeability compared to their sodium and acid counterparts. To conclude, choline salt ILs developed from choline bicarbonate and phenylbutyric acid present a promising avenue for UCD treatment, with the added benefit of reduced sodium load. They also hold merit in formulation engineering. The sustained-release capabilities of DESs position them favorably for drug delivery, while the low toxicity and cost-effectiveness of choline chloride signal potential in formulation engineering. Overall, this drug formulation heralds a prospective therapeutic avenue for UCD patients.

Keywords: phenylbutyric acid, sodium phenylbutyrate, choline salt, ionic liquids, deep eutectic systems, oral bioavailability

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Authors: Sara ElElimy, Samir Moustafa

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Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model).

Keywords: big data analytics, machine learning, CDRs, 5G

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Authors: Padmalochan Panda, R. Ramaseshan

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Aluminum nitride (AlN) is a potential candidate for semiconductor industry due to its wide band gap (6.2 eV), high thermal conductivity and low thermal coefficient of expansion. A-plane oriented AlN film finds an important role in deep UV-LED with higher isotropic light extraction efficiency. Also, Cr-doped AlN films exhibit dilute magnetic semiconductor property with high Curie temperature (300 K), and thus compatible with modern day microelectronics. In this work, highly a-axis oriented wurtzite AlN and Al1-xMxN (M = Cr, Ti) films have synthesized by reactive co-sputtering technique at different concentration. Crystal structure of these films is studied by Grazing incidence X-ray diffraction (GIXRD) and Transmission electron microscopy (TEM). Identification of binding energy and concentration (x) in these films is carried out by X-ray photoelectron spectroscopy (XPS). Local crystal structure around the Cr and Ti atom of these films are investigated by X-ray absorption spectroscopy (XAS). It is found that Cr and Ti replace the Al atom in AlN lattice and the bond lengths in first and second coordination sphere with N and Al, respectively, decrease concerning doping concentration due to strong p-d hybridization. The nano-indentation hardness of Cr and Ti-doped AlN films seems to increase from 17.5 GPa (AlN) to around 23 and 27.5 GPa, respectively. An-isotropic optical properties of these films are studied by the Spectroscopic Ellipsometry technique. Refractive index and extinction coefficient of these films are enhanced in normal dispersion region as compared to the parent AlN film. The optical band gap energies also seem to vary between deep UV to UV regions with the addition of Cr, thus by bringing out the usefulness of these films in the area of optoelectronic device applications.

Keywords: ellipsometry, GIXRD, hardness, XAS

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Authors: Zihua Wu, Yueming He, Xiaoxiao Yu, Yuanyuan Wang, Huaqing Xie

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The match of Solar thermoelectric generator (STEG) and phase change materials (PCM) can enhance the solar energy storage and reduce environmental impact from the day-and-night transformation and weather changes. This work utilizes D-mannitol (DM) matrix as the suitable PCM for coupling with thermoelectric generator to achieve the middle-temperature solar energy storage performance at 165℃-167℃. DM/MWCNT composite phase change materials prepared by ball milling not only can keep a high phase change enthalpy of DM material but also have great photo-thermal conversion efficiency of 82%. Based on the self-made storage device container, the effect of PCM thickness on the solar energy storage performance is further discussed and analyzed. The experimental results prove that PCM-STEG coupling system can output more electric energy than pure STEG system because PCM can decline the heat transfer and storage thermal energy to further generate the electric energy through thermal-to-electric conversion when the light is removed. The increase of PCM thickness can reduce the heat transfer and enhance thermal storage, and then the power generation performance of PCM-STEG coupling system can be improved. As the increase of light intensity, the output electric energy of the coupling system rises accordingly, and the maximum amount of electrical energy can reach by 113.85 J at 1.6 W/cm2. The study of the PCM-STEG coupling system has certain reference for the development of solar energy storage and application.

Keywords: solar energy, solar thermoelectric generator, phase change materials, solar-to-electric energy, DM/MWCNT

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Authors: Sharmili Routray, Kishor Chandra Biswal

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The use of corrosion resistant fiber reinforced polymer (FRP) reinforcement is beneficial in structures particularly those exposed to deicing salts, and/or located in highly corrosive environment. Generally Glass, Carbon and Aramid fibers are used for the strengthening purpose of the structures. Due to the necessities of low weight and high strength materials, it is required to find out the suitable substitute with low cost. Recent developments in fiber production technology allow the strengthening of structures using Basalt fiber which is made from basalt rock. Basalt fiber has good range of thermal performance, high tensile strength, resistance to acids, good electro‐magnetic properties, inert nature, resistance to corrosion, radiation and UV light, vibration and impact loading. This investigation focuses on the effect of fibre content and fiber orientation of basalt fibre on mechanical properties of the fabricated composites. Specimen prepared with unidirectional Basalt fabric as reinforcing materials and epoxy resin as a matrix in polymer composite. In this investigation different fiber orientation are taken and the fabrication is done by hand lay-up process. The variation of the properties with the increasing number of plies of fiber in the composites is also studied. Specimens are subjected to tensile strength test and the failure of the composite is examined with the help of INSTRON universal testing Machine (SATEC) of 600 kN capacities. The average tensile strength and modulus of elasticity of BFRP plates are determined from the test Program.

Keywords: BFRP, fabrication, Fiber Reinforced Polymer (FRP), strengthening

Procedia PDF Downloads 276

Authors: Erick C. Valverde, Wansu Lim

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Facial emotion recognition (FER) system has been recently developed for more advanced computer vision applications. The ability to identify human emotions would enable smart healthcare facility to diagnose mental health illnesses (e.g., depression and stress) as well as better human social interactions with smart technologies. The FER system involves two steps: 1) face detection task and 2) facial emotion recognition task. It classifies the human expression in various categories such as angry, disgust, fear, happy, sad, surprise, and neutral. This system requires intensive research to address issues with human diversity, various unique human expressions, and variety of human facial features due to age differences. These issues generally affect the ability of the FER system to detect human emotions with high accuracy. Early stage of FER systems used simple supervised classification task algorithms like K-nearest neighbors (KNN) and artificial neural networks (ANN). These conventional FER systems have issues with low accuracy due to its inefficiency to extract significant features of several human emotions. To increase the accuracy of FER systems, deep learning (DL)-based methods, like convolutional neural networks (CNN), are proposed. These methods can find more complex features in the human face by means of the deeper connections within its architectures. However, the inference speed and computational costs of a DL-based FER system is often disregarded in exchange for higher accuracy results. To cope with this drawback, an optimized DL-based FER system is proposed in this study.An extreme version of Inception V3, known as Xception model, is leveraged by applying different network optimization methods. Specifically, network pruning and quantization are used to enable lower computational costs and reduce memory usage, respectively. To support low resource requirements, a 68-landmark face detector from Dlib is used in the early step of the FER system.Furthermore, a DL compiler is utilized to incorporate advanced optimization techniques to the Xception model to improve the inference speed of the FER system. In comparison to VGG-Net and ResNet50, the proposed optimized DL-based FER system experimentally demonstrates the objectives of the network optimization methods used. As a result, the proposed approach can be used to create an efficient and real-time FER system.

Keywords: deep learning, face detection, facial emotion recognition, network optimization methods

Procedia PDF Downloads 109

Authors: Anne Schattel, Mitja Echim, Christof Büskens

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Trajectory planning for deep space missions has become a recent topic of great interest. Flying to space objects like asteroids provides two main challenges. One is to find rare earth elements, the other to gain scientific knowledge of the origin of the world. Due to the enormous spatial distances such explorer missions have to be performed unmanned and autonomously. The mathematical field of optimization and optimal control can be used to realize autonomous missions while protecting recourses and making them safer. The resulting algorithms may be applied to other, earth-bound applications like e.g. deep sea navigation and autonomous driving as well. The project KaNaRiA ('Kognitionsbasierte, autonome Navigation am Beispiel des Ressourcenabbaus im All') investigates the possibilities of cognitive autonomous navigation on the example of an asteroid mining mission, including the cruise phase and approach as well as the asteroid rendezvous, landing and surface exploration. To verify and test all methods an interactive, real-time capable simulation using virtual reality is developed under KaNaRiA. This paper focuses on the specific challenge of the guidance during the cruise phase of the spacecraft, i.e. trajectory optimization and optimal control, including first solutions and results. In principle there exist two ways to solve optimal control problems (OCPs), the so called indirect and direct methods. The indirect methods are being studied since several decades and their usage needs advanced skills regarding optimal control theory. The main idea of direct approaches, also known as transcription techniques, is to transform the infinite-dimensional OCP into a finite-dimensional non-linear optimization problem (NLP) via discretization of states and controls. These direct methods are applied in this paper. The resulting high dimensional NLP with constraints can be solved efficiently by special NLP methods, e.g. sequential quadratic programming (SQP) or interior point methods (IP). The movement of the spacecraft due to gravitational influences of the sun and other planets, as well as the thrust commands, is described through ordinary differential equations (ODEs). The competitive mission aims like short flight times and low energy consumption are considered by using a multi-criteria objective function. The resulting non-linear high-dimensional optimization problems are solved by using the software package WORHP ('We Optimize Really Huge Problems'), a software routine combining SQP at an outer level and IP to solve underlying quadratic subproblems. An application-adapted model of impulsive thrusting, as well as a model of an electrically powered spacecraft propulsion system, is introduced. Different priorities and possibilities of a space mission regarding energy cost and flight time duration are investigated by choosing different weighting factors for the multi-criteria objective function. Varying mission trajectories are analyzed and compared, both aiming at different destination asteroids and using different propulsion systems. For the transcription, the robust method of full discretization is used. The results strengthen the need for trajectory optimization as a foundation for autonomous decision making during deep space missions. Simultaneously they show the enormous increase in possibilities for flight maneuvers by being able to consider different and opposite mission objectives.

Keywords: deep space navigation, guidance, multi-objective, non-linear optimization, optimal control, trajectory planning.

Procedia PDF Downloads 400

Authors: A. Gopala Krishna, M. Lakshmi Chaitanya, V. Kalyana Manohar

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In the existent analysis, laser micro machining (LMM) of Silicon carbide (SiCp) reinforced Aluminum 7075 Metal Matrix Composite (Al7075/SiCp MMC) was studied. While machining, Because of the intense heat generated, A layer gets formed on the work piece surface which is called recast layer and this layer is detrimental to the surface quality of the component. The recast layer needs to be as small as possible for precise applications. Therefore, The height of recast layer and the depth of groove which are conflicting in nature were considered as the significant manufacturing criteria, Which determines the pursuit of a machining process obtained in LMM of Al7075/10%SiCp composite. The present work formulates the depth of groove and height of recast layer in relation to the machining parameters using the Response Surface Methodology (RSM) and correspondingly, The formulated mathematical models were put to use for optimization. Since the effect of machining parameters on the depth of groove and height of recast layer was contradictory, The problem was explicated as a multi objective optimization problem. Moreover, An evolutionary Non-dominated sorting genetic algorithm (NSGA-II) was employed to optimize the model established by RSM. Subsequently this algorithm was also adapted to achieve the Pareto optimal set of solutions that provide a detailed illustration for making the optimal solutions. Eventually experiments were conducted to affirm the results obtained from RSM and NSGA-II.

Keywords: Laser Micro Machining (LMM), depth of groove, Height of recast layer, Response Surface Methodology (RSM), non-dominated sorting genetic algorithm

Procedia PDF Downloads 335

Authors: K. Ravi, Sabu Subhash

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Densely compacted bentonite or bentonite–sand mixture has been identified as a suitable buffer in the deep geological repository (DGR) for the safe disposal of high-level nuclear waste (HLW) due to its favourable physicochemical and hydro-mechanical properties. The addition of sand to the bentonite enhances the thermal conductivity and compaction properties and reduces the drying shrinkage of the buffer material. The buffer material may undergo cyclic wetting and drying upon ingress of groundwater from the surrounding rock mass and from evaporation due to high temperature (50–210 °C) derived from the waste canister. The cycles of changes in temperature may result in thermal history, and the hydro-mechanical properties of the buffer material may be affected. This paper examines the influence of thermal history on the undrained shear strength of bentonite and bentonite-sand mixture. Bentonite from Rajasthan state and sand from the Assam state of India are used in this study. The undrained shear strength values are obtained by conducting unconfined compressive strength (UCS) tests on cylindrical specimens (dry densities 1.30 and 1.5 Mg/m3) of bentonite and bentonite-sand mixture consisting of 30 % bentonite+ 70 % sand. The specimens are preheated at temperatures varying from 50-150 °C for one, two and four hours in hot air oven. The results indicate that the undrained shear strength is increased by the thermal history of the buffer material. The specimens of bentonite-sand mixture exhibited more increase in strength compared to the pure bentonite specimens. This indicates that the sand content of the mixture plays a vital role in taking the thermal stresses of the bentonite buffer in DGR conditions.

Keywords: bentonite, deep geological repository, thermal history, undrained shear strength

Procedia PDF Downloads 333

Authors: M. Cabuk, M. Yavuz, T. A. Yesil, H. I. Unal

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Electrorheological (ER) fluids are a class of smart materials exhibiting reversible changes in their rheological and mechanical properties under an applied electric field (E). ER fluids generally are composed of polarisable solid particles dispersed in non-conducting oil. ER fluids are fluids which exhibit. The resistance to motion of the ER fluid can be controlled by adjusting the applied E, due to their fast and reversible changes in their rheological properties presence of E. In this study, a series of chitosan/expanded perlite (CS/EP) composites with different chitosan mass fractions (10%, 20%, and 50%) was used. Characterizations of the composites were carried out by Fourier Transform Infrared (FTIR), X-ray diffraction (XRD) and Scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX) techniques. Antisedimentation stability and dielectric properties of the composites were also determined. The effects of volume fraction, electric field strength, shear rate, shear stress, and temperature onto ER properties of the CS/EP composite particles dispersed in silicone oil (SO) were investigated in detail. Vibration damping behavior of the CS/EP composites were determined as a function of frequence, storage (Gʹ) and loss (Gʹ ʹ) moduli. It was observed that ER response of the CS/EP/SO ER fluids increased with increasing electric field strength and exhibited the typical shear thinning non-Newtonian viscoelastic behaviors with increasing shear rate. The maximum yield stress was obtained with 1250 Pa under E = 3 kV/mm. Further, the CS/EP/SO ER fluids were observed to sensitive to vibration control by showing reversible viscosity enhancements (Gʹ > Gʹ ʹ). Acknowledgements: The authors thank the TÜBİTAK (214Z199) for the financial support of this work.

Keywords: chitosan, electrorheology, perlite, vibration control

Procedia PDF Downloads 224

Authors: Ozgu Hafizoglu

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Analogy is an essential tool of human cognition that enables connecting diffuse and diverse systems with attributional, deep structural, casual relations that are essential to learning, to innovation in artificial worlds, and to discovery in science. Cognitive Model of Analogy (CMA) leads and creates information pattern transfer within and between domains and disciplines in science. This paper demonstrates the Cognitive Model of Analogy (CMA) as an evolutionary approach to scientific research. The model puts forward the challenges of deep uncertainty about the future, emphasizing the need for flexibility of the system in order to enable reasoning methodology to adapt to changing conditions. In this paper, the model of analogical reasoning is created based on brain cells, their fractal, and operational forms within the system itself. Visualization techniques are used to show correspondences. Distinct phases of the problem-solving processes are divided thusly: encoding, mapping, inference, and response. The system is revealed relevant to brain activation considering each of these phases with an emphasis on achieving a better visualization of the brain cells: glial cells, axons, axon terminals, and neurons, relative to matching conditions of analogical reasoning and relational information. It’s found that encoding, mapping, inference, and response processes in four-term analogical reasoning are corresponding with the fractal and operational forms of brain cells: glial, axons, and neurons.

Keywords: analogy, analogical reasoning, cognitive model, brain and glials

Procedia PDF Downloads 172