Search results for: dimensional accuracy

Authors: Oumaima Khlifati, Khadija Baba

Abstract:

Pavement distress is the main factor responsible for the deterioration of road structure durability, damage vehicles, and driver comfort. Transportation agencies spend a high proportion of their funds on pavement monitoring and maintenance. The auscultation of pavement distress was based on the manual survey, which was extremely time consuming, labor intensive, and required domain expertise. Therefore, the automatic distress detection is needed to reduce the cost of manual inspection and avoid more serious damage by implementing the appropriate remediation actions at the right time. Inspired by recent deep learning applications, this paper proposes an algorithm for automatic road distress detection and classification using on the Deep Convolutional Neural Network (DCNN). In this study, the types of pavement distress are classified as transverse or longitudinal cracking, alligator, pothole, and intact pavement. The dataset used in this work is composed of public asphalt pavement images. In order to learn the structure of the different type of distress, the DCNN models are trained and tested as a multi-label classification task. In addition, to get the highest accuracy for our model, we adjust the structural optimization hyper parameters such as the number of convolutions and max pooling, filers, size of filters, loss functions, activation functions, and optimizer and fine-tuning hyper parameters that conclude batch size and learning rate. The optimization of the model is executed by checking all feasible combinations and selecting the best performing one. The model, after being optimized, performance metrics is calculated, which describe the training and validation accuracies, precision, recall, and F1 score.

Keywords: distress pavement, hyperparameters, automatic classification, deep learning

Procedia PDF Downloads 94

Authors: Divyans Mahansaria

Abstract:

User authentication is an important security measure for protecting confidential data and systems. However, the vulnerability while authenticating into a system has significantly increased. Thus, necessary mechanisms must be deployed during the process of authenticating a user to safeguard him/her from the vulnerable attacks. The proposed solution implements a novel authentication mechanism to counter various forms of security breach attacks including phishing, Trojan horse, replay, key logging, Asterisk logging, shoulder surfing, brute force search and others. QR code (Quick Response Code) is a type of matrix barcode or two-dimensional barcode that can be used for storing URLs, text, images and other information. In the proposed solution, during each new authentication request, a QR code is dynamically generated and presented to the user. A piece of generic information is mapped to plurality of elements and stored within the QR code. The mapping of generic information with plurality of elements, randomizes in each new login, and thus the QR code generated for each new authentication request is for one-time use only. In order to authenticate into the system, the user needs to decode the QR code using any QR code decoding software. The QR code decoding software needs to be installed on handheld mobile devices such as smartphones, personal digital assistant (PDA), etc. On decoding the QR code, the user will be presented a mapping between the generic piece of information and plurality of elements using which the user needs to derive cipher secret information corresponding to his/her actual password. Now, in place of the actual password, the user will use this cipher secret information to authenticate into the system. The authentication terminal will receive the cipher secret information and use a validation engine that will decipher the cipher secret information. If the entered secret information is correct, the user will be provided access to the system. Usability study has been carried out on the proposed solution, and the new authentication mechanism was found to be easy to learn and adapt. Mathematical analysis of the time taken to carry out brute force attack on the proposed solution has been carried out. The result of mathematical analysis showed that the solution is almost completely resistant to brute force attack. Today’s standard methods for authentication are subject to a wide variety of software, hardware, and human attacks. The proposed scheme can be very useful in controlling the various types of authentication related attacks especially in a networked computer environment where the use of username and password for authentication is common.

Keywords: authentication, QR code, cipher / decipher text, one time password, secret information

Procedia PDF Downloads 269

Authors: Hadjer Sadoune, Liza Lamini, Scherazade Krim, Amel Djouadi, Rachida Rihani

Abstract:

Deep neural networks are highly regarded for their accuracy in predicting intricate fermentation processes. Their ability to learn from a large amount of datasets through artificial intelligence makes them particularly effective models. The primary obstacle in improving the performance of these models is to carefully choose the suitable hyperparameters, including the neural network architecture (number of hidden layers and hidden units), activation function, optimizer, learning rate, and other relevant factors. This study predicts biogas production from real wastewater treatment plant data using a sophisticated approach: hybrid Bayesian optimization with a tree-structured Parzen estimator (BO-TPE) for an optimised deep neural network (DNN) model. The plant utilizes an Upflow Anaerobic Sludge Blanket (UASB) digester that treats industrial wastewater from soft drinks and breweries. The digester has a working volume of 1574 m3 and a total volume of 1914 m3. Its internal diameter and height were 19 and 7.14 m, respectively. The data preprocessing was conducted with meticulous attention to preserving data quality while avoiding data reduction. Three normalization techniques were applied to the pre-processed data (MinMaxScaler, RobustScaler and StandardScaler) and compared with the Non-Normalized data. The RobustScaler approach has strong predictive ability for estimating the volume of biogas produced. The highest predicted biogas volume was 2236.105 Nm³/d, with coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values of 0.712, 164.610, and 223.429, respectively.

Keywords: anaerobic digestion, biogas production, deep neural network, hybrid bo-tpe, hyperparameters tuning

Procedia PDF Downloads 39

Authors: Galal Omer, Onisimo Mutanga, Elfatih M. Abdel-Rahman, Elhadi Adam

Abstract:

Threatened tree species (TTS) play a significant role in ecosystem functioning and services, land use dynamics, and other socio-economic aspects. Such aspects include ecological, economic, livelihood, security-based, and well-being benefits. The development of techniques for mapping and monitoring TTS is thus critical for understanding the functioning of ecosystems. The advent of advanced imaging systems and supervised learning algorithms has provided an opportunity to classify TTS over fragmenting landscape. Recently, vegetation maps have been produced using advanced imaging systems such as WorldView-2 (WV-2) and robust classification algorithms such as support vectors machines (SVM) and artificial neural network (ANN). However, delineation of TTS in a fragmenting landscape using high resolution imagery has widely remained elusive due to the complexity of the species structure and their distribution. Therefore, the objective of the current study was to examine the utility of the advanced WV-2 data for mapping TTS in the fragmenting Dukuduku indigenous forest of South Africa using SVM and ANN classification algorithms. The results showed the robustness of the two machine learning algorithms with an overall accuracy (OA) of 77.00% (total disagreement = 23.00%) for SVM and 75.00% (total disagreement = 25.00%) for ANN using all eight bands of WV-2 (8B). This study concludes that SVM and ANN classification algorithms with WV-2 8B have the potential to classify TTS in the Dukuduku indigenous forest. This study offers relatively accurate information that is important for forest managers to make informed decisions regarding management and conservation protocols of TTS.

Keywords: artificial neural network, threatened tree species, indigenous forest, support vector machines

Procedia PDF Downloads 515

Authors: Omojokun G. Aju, Adedayo O. Sule

Abstract:

ZigBee wireless sensor and control network is one of the most popularly deployed wireless technologies in recent years. This is because ZigBee is an open standard lightweight, low-cost, low-speed, low-power protocol that allows true operability between systems. It is built on existing IEEE 802.15.4 protocol and therefore combines the IEEE 802.15.4 features and newly added features to meet required functionalities thereby finding applications in wide variety of wireless networked systems. ZigBee‘s current focus is on embedded applications of general-purpose, inexpensive, self-organising networks which requires low to medium data rates, high number of nodes and very low power consumption such as home/industrial automation, embedded sensing, medical data collection, smart lighting, safety and security sensor networks, and monitoring systems. Although the ZigBee design specification includes security features to protect data communication confidentiality and integrity, however, when simplicity and low-cost are the goals, security is normally traded-off. A lot of researches have been carried out on ZigBee technology in which emphasis has mainly been placed on ZigBee network performance characteristics such as energy efficiency, throughput, robustness, packet delay and delivery ratio in different scenarios and applications. This paper investigate and analyse the data accuracy, network implementation difficulties and security challenges of ZigBee network applications in star-based and mesh-based topologies with emphases on its home monitoring application using the ZigBee ProBee ZE-10 development boards for the network setup. The paper also expose some factors that need to be considered when designing ZigBee network applications and suggest ways in which ZigBee network can be designed to provide more resilient to network attacks.

Keywords: home monitoring, IEEE 802.14.5, topology, wireless security, wireless sensor network (WSN), ZigBee

Procedia PDF Downloads 385

Authors: Huan-Chieh Chiu, Hung-Shuo Wu, Chien-Hao Wang, Yu-Cheng Yang, Ching-Ya Tseng, Joe-Air Jiang

Abstract:

Power failure resulting from tower collapse due to violent seismic events might bring enormous and inestimable losses. The Chi-Chi earthquake, for example, strongly struck Taiwan and caused huge damage to the power system on September 21, 1999. Nearly 10% of extra high voltage (EHV) transmission towers were damaged in the earthquake. Therefore, seismic hazards of EHV transmission towers should be monitored and evaluated. The ultimate goal of this study is to establish a damage level assessment model for EHV transmission towers. The data of earthquakes provided by Taiwan Central Weather Bureau serve as a reference and then lay the foundation for earthquake simulations and analyses afterward. Some parameters related to the damage level of each point of an EHV tower are simulated and analyzed by the data from monitoring stations once an earthquake occurs. Through the Fourier transform, the seismic wave is then analyzed and transformed into different wave frequencies, and the data would be shown through a response spectrum. With this method, the seismic frequency which damages EHV towers the most is clearly identified. An estimation model is built to determine the damage level caused by a future seismic event. Finally, instead of relying on visual observation done by inspectors, the proposed model can provide a power company with the damage information of a transmission tower. Using the model, manpower required by visual observation can be reduced, and the accuracy of the damage level estimation can be substantially improved. Such a model is greatly useful for health and construction monitoring because of the advantages of long-term evaluation of structural characteristics and long-term damage detection.

Keywords: damage level monitoring, drift ratio, fragility curve, smart grid, transmission tower

Procedia PDF Downloads 299

Authors: Wenjiang Deng, Tian Mou, Yudi Pawitan, Trung Nghia Vu

Abstract:

Estimation of isoform-level gene expression from RNA-seq data depends on simplifying assumptions, such as uniform reads distribution, that are easily violated in real data. Such violations typically lead to biased estimates. Most existing methods provide a bias correction step(s), which is based on biological considerations, such as GC content–and applied in single samples separately. The main problem is that not all biases are known. For example, new technologies such as single-cell RNA-seq (scRNA-seq) may introduce new sources of bias not seen in bulk-cell data. This study introduces a method called XAEM based on a more flexible and robust statistical model. Existing methods are essentially based on a linear model Xβ, where the design matrix X is known and derived based on the simplifying assumptions. In contrast, XAEM considers Xβ as a bilinear model with both X and β unknown. Joint estimation of X and β is made possible by simultaneous analysis of multi-sample RNA-seq data. Compared to existing methods, XAEM automatically performs empirical correction of potentially unknown biases. XAEM implements an alternating expectation-maximization (AEM) algorithm, alternating between estimation of X and β. For speed XAEM utilizes quasi-mapping for read alignment, thus leading to a fast algorithm. Overall XAEM performs favorably compared to other recent advanced methods. For simulated datasets, XAEM obtains higher accuracy for multiple-isoform genes, particularly for paralogs. In a differential-expression analysis of a real scRNA-seq dataset, XAEM achieves substantially greater rediscovery rates in an independent validation set.

Keywords: alternating EM algorithm, bias correction, bilinear model, gene expression, RNA-seq

Procedia PDF Downloads 143

Authors: Anvesh Gaddam, Amit Agrawal, Suhas Joshi, Mark Thompson

Abstract:

An increase in surface area to volume ratio with a decrease in characteristic length scale, leads to a rapid increase in pressure drop across the microchannel. Texturing the microchannel surfaces reduce the effective surface area, thereby decreasing the pressured drop. Surface texturing introduces two wetting states: a metastable Cassie-Baxter state and stable Wenzel state. Predicting wetting transition in textured microchannels is essential for identifying optimal parameters leading to maximum drag reduction. Optical methods allow visualization only in confined areas, therefore, obtaining whole-field information on wetting transition is challenging. In this work, we propose a non-invasive method to capture wetting transitions in textured microchannels under flow conditions. To this end, we tracked the behavior of the Poiseuille number Po = f.Re, (with f the friction factor and Re the Reynolds number), for a range of flow rates (5 < Re < 50), and different wetting states were qualitatively demarcated by observing the inflection points in the f.Re curve. Microchannels with both longitudinal and transverse ribs with a fixed gas fraction (δ, a ratio of shear-free area to total area) and at a different confinement ratios (ε, a ratio of rib height to channel height) were fabricated. The measured pressure drop values for all the flow rates across the textured microchannels were converted into Poiseuille number. Transient behavior of the pressure drop across the textured microchannels revealed the collapse of liquid-gas interface into the gas cavities. Three wetting states were observed at ε = 0.65 for both longitudinal and transverse ribs, whereas, an early transition occurred at Re ~ 35 for longitudinal ribs at ε = 0.5, due to spontaneous flooding of the gas cavities as the liquid-gas interface ruptured at the inlet. In addition, the pressure drop in the Wenzel state was found to be less than the Cassie-Baxter state. Three-dimensional numerical simulations confirmed the initiation of the completely wetted Wenzel state in the textured microchannels. Furthermore, laser confocal microscopy was employed to identify the location of the liquid-gas interface in the Cassie-Baxter state. In conclusion, the present method can overcome the limitations posed by existing techniques, to conveniently capture wetting transition in textured microchannels.

Keywords: drag reduction, Poiseuille number, textured surfaces, wetting transition

Procedia PDF Downloads 161

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

Procedia PDF Downloads 137

Authors: Atefeh Abedini, Fatemeh Razavi, Mihan Pourabdollah Toutkaboni, Hossein Mehravaran, Arda Kiani

Abstract:

In countries with the highest prevalence of tuberculosis, such as Iran, the differentiation of malignant tumors from non-malignant is very important. In this study, which was conducted for the first time among the Iranian population, the utility of the ultrasonographic morphological characteristics in patients undergoing EBUS was used to distinguish the non-malignant versus malignant lymph nodes. The morphological characteristics of lymph nodes, which consist of size, shape, vascular pattern, echogenicity, margin, coagulation necrosis sign, calcification, and central hilar structure, were obtained during Endobronchial Ultrasound-Guided Trans-Bronchial Needle Aspiration and were compared with the final pathology results. During this study period, a total of 253 lymph nodes were evaluated in 93 cases. Round shape, non-hilar vascular pattern, heterogeneous echogenicity, hyperechogenicity, distinct margin, and the presence of necrosis sign were significantly higher in malignant nodes. On the other hand, the presence of calcification and also central hilar structure were significantly higher in the benign nodes (p-value ˂ 0.05). Multivariate logistic regression showed that size>1 cm, heterogeneous echogenicity, hyperechogenicity, the presence of necrosis signs and, the absence of central hilar structure are independent predictive factors for malignancy. The accuracy of each of the aforementioned factors is 42.29 %, 71.54 %, 71.90 %, 73.51 %, and 65.61 %, respectively. Of 74 malignant lymph nodes, 100% had at least one of these independent factors. According to our results, the morphological characteristics of lymph nodes based on Endobronchial Ultrasound-Guided Trans-Bronchial Needle Aspiration can play a role in the prediction of malignancy.

Keywords: EBUS-TBNA, malignancy, nodal characteristics, pathology

Procedia PDF Downloads 136

Authors: K. Al Ammari, B. G. Clarke

Abstract:

Soil improvement using vibro stone column techniques consists of two main parts: (1) the installed load bearing columns of well-compacted, coarse-grained material and (2) the improvements to the surrounding soil due to vibro compaction. Extensive research work has been carried out over the last 20 years to understand the improvement in the composite foundation performance due to the second part mentioned above. Nevertheless, few of these studies have tried to quantify some of the key design parameters, namely the changes in the stiffness and stress state of the treated soil, or have consider these parameters in the design and calculation process. Consequently, empirical and conservative design methods are still being used by ground improvement companies with a significant variety of results in engineering practice. Two-dimensional finite element study to develop an axisymmetric model of a single stone column reinforced foundation was performed using PLAXIS 2D AE to quantify the effect of the vibro installation of this column in soft saturated clay. Settlement and bearing performance were studied as an essential part of the design and calculation of the stone column foundation. Particular attention was paid to the large deformation in the soft clay around the installed column caused by the lateral expansion. So updated mesh advanced option was taken in the analysis. In this analysis, different degrees of stone column lateral expansions were simulated and numerically analyzed, and then the changes in the stress state, stiffness, settlement performance and bearing capacity were quantified. It was found that application of radial expansion will produce a horizontal stress in the soft clay mass that gradually decrease as the distance from the stone column axis increases. The excess pore pressure due to the undrained conditions starts to dissipate immediately after finishing the column installation, allowing the horizontal stress to relax. Changes in the coefficient of the lateral earth pressure K ٭, which is very important in representing the stress state, and the new stiffness distribution in the reinforced clay mass, were estimated. More encouraging results showed that increasing the expansion during column installation has a noticeable effect on improving the bearing capacity and reducing the settlement of reinforced ground, So, a design method should include this significant effect of the applied lateral displacement during the stone column instillation in simulation and numerical analysis design.

Keywords: bearing capacity, design, installation, numerical analysis, settlement, stone column

Procedia PDF Downloads 375

Authors: Arkadiusz Zurek

Abstract:

The potential of using drones is visible in many areas of logistics, especially in terms of their use for monitoring and control of many processes. The technologies implemented in the last decade concern new possibilities for companies that until now have not even considered them, such as warehouse inventories. Unmanned aerial vehicles are no longer seen as a revolutionary tool for Industry 4.0, but rather as tools in the daily work of factories and logistics operators. The research problem is to develop a method for measuring the weight of goods in a selected link of the clothing supply chain by drones. However, the purpose of this article is to analyze the causes of errors in traditional measurements, and then to identify adverse events related to the use of drones for the inventory of a heap of textiles intended for production purposes. On this basis, it will be possible to develop guidelines to eliminate the causes of these events in the measurement process using drones. In a real environment, work was carried out to determine the volume and weight of textiles, including, among others, weighing a textile sample to determine the average density of the assortment, establishing a local geodetic network, terrestrial laser scanning and photogrammetric raid using an unmanned aerial vehicle. As a result of the analysis of measurement data obtained in the facility, the volume and weight of the assortment and the accuracy of their determination were determined. In this article, this work presents how such heaps are currently being tested, what adverse events occur, indicate and describes the current use of photogrammetric techniques of this type of measurements so far performed by external drones for the inventory of wind farms or construction of the station and compare them with the measurement system of the aforementioned textile heap inside a large-format facility.

Keywords: drones, unmanned aerial system, UAS, indoor system, security, process automation, cost optimization, photogrammetry, risk elimination, industry 4.0

Procedia PDF Downloads 87

Authors: I. Wardana Ketut, I. Suparwa Nyoman

Abstract:

This study investigated the form and characteristic of speech sound produced by three Balinese subjects who have recovered from aphasia as well as intervened their language impairment on side of linguistic and neuronal aspects of views. The failure of judging the speech sound was caused by impairment of motor cortex that indicated there were lesions in left hemispheric language zone. Sound articulation phenomena were in the forms of phonemes deletion, replacement or assimilation in individual words and meaning building for anomic aphasia. Therefore, the Balinese sound patterns were stimulated by showing pictures to the subjects and recorded to recognize what individual consonants or vowels they unclearly produced and to find out how the sound disorder occurred. The physiology of sound production by subject’s speech organs could not only show the accuracy of articulation but also any level of severity the lesion they suffered from. The subjects’ speech sounds were investigated, classified and analyzed to know how poor the lingual units were and observed to clarify weaknesses of sound characters occurred either for place or manner of articulation. Many fricative and stopped consonants were replaced by glottal or palatal sounds because the cranial nerve, such as facial, trigeminal, and hypoglossal underwent impairment after the stroke. The phonological intervention was applied through a technique called phonemic articulation drill and the examination was conducted to know any change has been obtained. The finding informed that some weak articulation turned into clearer sound and simple meaning of language has been conveyed. The hierarchy of functional parts of brain played important role of language formulation and processing. From this finding, it can be clearly emphasized that this study supports the role of right hemisphere in recovery from aphasia is associated with functional brain reorganization.

Keywords: aphasia, intervention, phonology, stroke

Procedia PDF Downloads 196

Authors: G. Barański, P. Kacejko, M. Wendeker

Abstract:

The impact of the ignition advance control algorithms of the ASz-62IR-16X aircraft piston engine on a combustion process has been presented in this paper. This aircraft engine is a nine-cylinder 1000 hp engine with a special electronic control ignition system. This engine has two spark plugs per cylinder with an ignition advance angle dependent on load and the rotational speed of the crankshaft. Accordingly, in most cases, these angles are not optimal for power generated. The scope of this paper is focused on developing algorithms to control the ignition advance angle in an electronic ignition control system of an engine. For this type of engine, i.e. radial engine, an ignition advance angle should be controlled independently for each cylinder because of the design of such an engine and its crankshaft system. The ignition advance angle is controlled in an open-loop way, which means that the control signal (i.e. ignition advance angle) is determined according to the previously developed maps, i.e. recorded tables of the correlation between the ignition advance angle and engine speed and load. Load can be measured by engine crankshaft speed or intake manifold pressure. Due to a limited memory of a controller, the impact of other independent variables (such as cylinder head temperature or knock) on the ignition advance angle is given as a series of one-dimensional arrays known as corrective characteristics. The value of the ignition advance angle specified combines the value calculated from the primary characteristics and several correction factors calculated from correction characteristics. Individual cylinder control can proceed in line with certain indicators determined from pressure registered in a combustion chamber. Control is assumed to be based on the following indicators: maximum pressure, maximum pressure angle, indicated mean effective pressure. Additionally, a knocking combustion indicator was defined. Individual control can be applied to a single set of spark plugs only, which results from two fundamental ideas behind designing a control system. Independent operation of two ignition control systems – if two control systems operate simultaneously. It is assumed that the entire individual control should be performed for a front spark plug only and a rear spark plug shall be controlled with a fixed (or specific) offset relative to the front one or from a reference map. The developed algorithms will be verified by simulation and engine test sand experiments. This work has been financed by the Polish National Centre for Research and Development, INNOLOT, under Grant Agreement No. INNOLOT/I/1/NCBR/2013.

Keywords: algorithm, combustion process, radial engine, spark plug

Procedia PDF Downloads 295

Authors: Lu Ren, You-Liang Fang, Yan-Gang Zhao

Abstract:

In recent years, the application of BIM (Building Information Modelling) to construction schedule plan has been the focus of more and more researchers. In order to assess the reasonable level of the BIM-based construction schedule plan, that is whether the schedule can be completed on time, some researchers have introduced reliability theory to evaluate. In the process of evaluation, the uncertain factors affecting the construction schedule plan are regarded as random variables, and probability distributions of the random variables are assumed to be normal distribution, which is determined using two parameters evaluated from the mean and standard deviation of statistical data. However, in practical engineering, most of the uncertain influence factors are not normal random variables. So the evaluation results of the construction schedule plan will be unreasonable under the assumption that probability distributions of random variables submitted to the normal distribution. Therefore, in order to get a more reasonable evaluation result, it is necessary to describe the distribution of random variables more comprehensively. For this purpose, cubic normal distribution is introduced in this paper to describe the distribution of arbitrary random variables, which is determined by the first four moments (mean, standard deviation, skewness and kurtosis). In this paper, building the BIM model firstly according to the design messages of the structure and making the construction schedule plan based on BIM, then the cubic normal distribution is used to describe the distribution of the random variables due to the collecting statistical data of the random factors influencing construction schedule plan. Next the reliability analysis of the construction schedule plan based on BIM can be carried out more reasonably. Finally, the more accurate evaluation results can be given providing reference for the implementation of the actual construction schedule plan. In the last part of this paper, the more efficiency and accuracy of the proposed methodology for the reliability analysis of the construction schedule plan based on BIM are conducted through practical engineering case.

Keywords: BIM, construction schedule plan, cubic normal distribution, reliability analysis

Procedia PDF Downloads 149

Authors: L. Angiuli, L. Trizio, R. Giua, A. Digilio, M. Tutino, P. Dambruoso, F. Mazzone, C. M. Placentino

Abstract:

In the winter of 2014 a series of measurements were performed to evaluate the behavior of real-time PAHs and black carbon analyzers in a coke oven plant located in Taranto, a city of Southern Italy. Data were collected both insides than outside the plant, at air quality monitoring sites. Contemporary measures of PM2.5 and PM1 were performed. Particle-bound PAHs were measured by two methods: (1) aerosol photoionization using an Ecochem PAS 2000 analyzer, (2) PM2.5 and PM1 quartz filter collection and analysis by gas chromatography/mass spectrometry (GC/MS). Black carbon was determined both in real-time by Magee Aethalometer AE22 analyzer than by semi-continuous Sunset Lab EC/OC instrument. Detected PM2.5 and PM1 levels were higher inside than outside the plant while PAHs real-time values were higher outside than inside. As regards PAHs, inside the plant Ecochem PAS 2000 revealed concentrations not significantly different from those determined on the filter during low polluted days, but at increasing concentrations the automated instrument underestimated PAHs levels. At the external site, Ecochem PAS 2000 real-time concentrations were steadily higher than those on the filter. In the same way, real-time black carbon values were constantly lower than EC concentrations obtained by Sunset EC/OC in the inner site, while outside the plant real-time values were comparable to Sunset EC values. Results showed that in a coke plant real-time analyzers of PAHs and black carbon in the factory configuration provide qualitative information, with no accuracy and leading to the underestimation of the concentration. A site specific calibration is needed for these instruments before their installation in high polluted sites.

Keywords: black carbon, coke oven plant, PAH, PAS, aethalometer

Procedia PDF Downloads 345

Authors: Shyaka Eugene

Abstract:

The efficient and safe demolition of structures is a critical challenge in civil engineering and construction. This study focuses on the development of optimal demolition strategies by investigating the crack propagation behavior in beams induced by soundless cracking agents. It is commonly used in controlled demolition and has gained prominence due to its non-explosive and environmentally friendly nature. This research employs a comprehensive experimental and computational approach to analyze the crack initiation, propagation, and eventual failure in beams subjected to soundless cracking agents. Experimental testing involves the application of various cracking agents under controlled conditions to understand their effects on the structural integrity of beams. High-resolution imaging and strain measurements are used to capture the crack propagation process. In parallel, numerical simulations are conducted using advanced finite element analysis (FEA) techniques to model crack propagation in beams, considering various parameters such as cracking agent composition, loading conditions, and beam properties. The FEA models are validated against experimental results, ensuring their accuracy in predicting crack propagation patterns. The findings of this study provide valuable insights into optimizing demolition strategies, allowing engineers and demolition experts to make informed decisions regarding the selection of cracking agents, their application techniques, and structural reinforcement methods. Ultimately, this research contributes to enhancing the safety, efficiency, and sustainability of demolition practices in the construction industry, reducing environmental impact and ensuring the protection of adjacent structures and the surrounding environment.

Keywords: expansion pressure, energy release rate, soundless chemical demolition agent, crack propagation

Procedia PDF Downloads 63

Authors: Sajjad Izadpanah, Seyed Hadi Ghaderi, Morteza Sayah Irani, Mahdi Gerdooei

Abstract:

In this research work, a sophisticated yield criterion known as BBC2003, capable of describing planar anisotropic behaviors of aluminum alloy sheets, was integrated into the commercial finite element code ABAQUS/Standard via a user subroutine. The complete formulation of the implementation process using a fully implicit integration scheme, i.e., the classic backward Euler method, is presented, and relevant aspects of the yield criterion are introduced. In order to solve nonlinear differential and algebraic equations, the line-search algorithm was adopted in the user-defined material subroutine (UMAT) to expand the convergence domain of the iterative Newton-Raphson method. The developed subroutine was used to simulate a challenging computational problem with complex stress states, i.e., deep drawing of an anisotropic aluminum alloy AA3105. The accuracy and stability of the developed subroutine were confirmed by comparing the numerically predicted earing and thickness variation profiles with the experimental results, which showed an excellent agreement between numerical and experimental earing and thickness profiles. The integration of the BBC2003 yield criterion into ABAQUS/Standard represents a significant contribution to the field of computational mechanics and provides a useful tool for analyzing the mechanical behavior of anisotropic materials subjected to complex loading conditions.

Keywords: BBC2003 yield function, plastic anisotropy, fully implicit integration scheme, line search algorithm, explicit and implicit integration schemes

Procedia PDF Downloads 75

Authors: P. Moskal, T. Bednarski, P. Bialas, E. Czerwinski, A. Gajos, A. Gruntowski, D. Kaminska, L. Kaplon, G. Korcyl, P. Kowalski, T. Kozik, W. Krzemien, E. Kubicz, Sz. Niedzwiecki, M. Palka, L. Raczynski, Z. Rudy, P. Salabura, N. G. Sharma, M. Silarski, A. Slomski, J. Smyrski, A. Strzelecki, A. Wieczorek, W. Wislicki, M. Zielinski, N. Zon

Abstract:

A new concept and results of the performance tests of the TOF-PET detection system developed at the Jagiellonian University will be presented. The novelty of the concept lies in employing long strips of polymer scintillators instead of crystals as detectors of annihilation quanta, and in using predominantly the timing of signals instead of their amplitudes for the reconstruction of Lines-of-Response. The diagnostic chamber consists of plastic scintillator strips readout by pairs of photo multipliers arranged axially around a cylindrical surface. To take advantage of the superior timing properties of plastic scintillators the signals are probed in the voltage domain with the accuracy of 20 ps by a newly developed electronics, and the data are collected by the novel trigger-less and reconfigurable data acquisition system. The hit-position and hit-time are reconstructed by the dedicated reconstruction methods based on the compressing sensing theory and the library of synchronized model signals. The solutions are subject to twelve patent applications. So far a time-of-flight resolution of ~120 ps (sigma) was achieved for a double-strip prototype with 30 cm field-of-view (FOV). It is by more than a factor of two better than TOF resolution achievable in current TOF-PET modalities and at the same time the FOV of 30 cm long prototype is significantly larger with respect to typical commercial PET devices. The Jagiellonian PET (J-PET) detector with plastic scintillators arranged axially possesses also another advantage. Its diagnostic chamber is free of any electronic devices and magnetic materials thus giving unique possibilities of combining J-PET with CT and J-PET with MRI for scanning the same part of a patient at the same time with both methods.

Keywords: PET-CT, PET-MRI, TOF-PET, scintillator

Procedia PDF Downloads 497

Authors: Sangita Pokhrel, Nalinda Somasiri, Rebecca Jeyavadhanam, Swathi Ganesan

Abstract:

Tourism is a booming industry with huge future potential for global wealth and employment. There are countless data generated over social media sites every day, creating numerous opportunities to bring more insights to decision-makers. The integration of Big Data Technology into the tourism industry will allow companies to conclude where their customers have been and what they like. This information can then be used by businesses, such as those in charge of managing visitor centers or hotels, etc., and the tourist can get a clear idea of places before visiting. The technical perspective of natural language is processed by analysing the sentiment features of online reviews from tourists, and we then supply an enhanced long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We have constructed a web review database using a crawler and web scraping technique for experimental validation to evaluate the effectiveness of our methodology. The text form of sentences was first classified through Vader and Roberta model to get the polarity of the reviews. In this paper, we have conducted study methods for feature extraction, such as Count Vectorization and TFIDF Vectorization, and implemented Convolutional Neural Network (CNN) classifier algorithm for the sentiment analysis to decide the tourist’s attitude towards the destinations is positive, negative, or simply neutral based on the review text that they posted online. The results demonstrated that from the CNN algorithm, after pre-processing and cleaning the dataset, we received an accuracy of 96.12% for the positive and negative sentiment analysis.

Keywords: counter vectorization, convolutional neural network, crawler, data technology, long short-term memory, web scraping, sentiment analysis

Procedia PDF Downloads 88

Authors: Apostolos Kalatzis, Ashish Teotia, Vishnunarayan Girishan Prabhu, Laura Stanley

Abstract:

Emotions are functional behaviors influenced by thoughts, stimuli, and other factors that induce neurophysiological changes in the human body. Understanding and classifying emotions are challenging as individuals have varying perceptions of their environments. Therefore, it is crucial that there are publicly available databases and virtual reality (VR) based environments that have been scientifically validated for assessing emotional classification. This study utilized two commercially available VR applications (Guided Meditation VR™ and Richie’s Plank Experience™) to induce acute stress and calm state among participants. Subjective and objective measures were collected to create a validated multimodal dataset and classification scheme for affective state classification. Participants’ subjective measures included the use of the Self-Assessment Manikin, emotional cards and 9 point Visual Analogue Scale for perceived stress, collected using a Virtual Reality Assessment Tool developed by our team. Participants’ objective measures included Electrocardiogram and Respiration data that were collected from 25 participants (15 M, 10 F, Mean = 22.28  4.92). The features extracted from these data included heart rate variability components and respiration rate, both of which were used to train two machine learning models. Subjective responses validated the efficacy of the VR applications in eliciting the two desired affective states; for classifying the affective states, a logistic regression (LR) and a support vector machine (SVM) with a linear kernel algorithm were developed. The LR outperformed the SVM and achieved 93.8%, 96.2%, 93.8% leave one subject out cross-validation accuracy, precision and recall, respectively. The VR assessment tool and data collected in this study are publicly available for other researchers.

Keywords: affective computing, biosignals, machine learning, stress database

Procedia PDF Downloads 144

Authors: Chittana Phompila

Abstract:

The current increased population pushes increasing demands for natural resources and living space. In Laos, urban areas have been expanding rapidly in recent years. The rapid urbanization can have negative impacts on landscapes, including forest and agriculture lands. The primary objective of this research were to map current urban areas in a large city in Savannakhet province, in Laos, 2) to compare changes in urbanization between 1990 and 2018, and 3) to estimate forest and agriculture areas lost due to expansions of urban areas during the last over twenty years within study area. Landsat 8 data was used and existing GIS data was collected including spatial data on rivers, lakes, roads, vegetated areas and other land use/land covers). GIS data was obtained from the government sectors. Object based classification (OBC) approach was applied in ECognition for image processing and analysis of urban area using. Historical data from other Landsat instruments (Landsat 5 and 7) were used to allow us comparing changes in urbanization in 1990, 2000, 2010 and 2018 in this study area. Only three main land cover classes were focused and classified, namely forest, agriculture and urban areas. Change detection approach was applied to illustrate changes in built-up areas in these periods. Our study shows that the overall accuracy of map was 95% assessed, kappa~ 0.8. It is found that that there is an ineffective control over forest and land-use conversions from forests and agriculture to urban areas in many main cities across the province. A large area of agriculture and forest has been decreased due to this conversion. Uncontrolled urban expansion and inappropriate land use planning can lead to creating a pressure in our resource utilisation. As consequence, it can lead to food insecurity and national economic downturn in a long term.

Keywords: urbanisation, forest cover, agriculture areas, Landsat 8 imagery

Procedia PDF Downloads 160

Authors: F. Lazzeri, I. Reiter

Abstract:

Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: time-series, features engineering methods for forecasting, energy demand forecasting, Azure Machine Learning

Procedia PDF Downloads 299

Authors: A. F. Momin, D. V. Khakhar

Abstract:

Granular materials consist of particulate particles found in nature and various industries that, due to gravity flow, behave macroscopically like liquids. A fundamental industrial unit operation is a hopper with inclined walls or a converging channel in which material flows downward under gravity and exits the storage bin through the bottom outlet. The simplest form of the flow corresponds to a wedge-shaped, quasi-two-dimensional geometry with smooth walls and radially directed gravitational force toward the apex of the wedge. These flows were examined using the Mohr-Coulomb criterion in the classic work of Savage (1965), while Ravi Prakash and Rao used the critical state theory (1988). The smooth-wall radial gravity (SWRG) wedge-shaped hopper is simulated using the discrete element method (DEM) to test existing theories. DEM simulations involve the solution of Newton's equations, taking particle-particle interactions into account to compute stress and velocity fields for the flow in the SWRG system. Our computational results are consistent with the predictions of Savage (1965) and Ravi Prakash and Rao (1988), except for the region near the exit, where both viscous and frictional effects are present. To further comprehend this behaviour, a parametric analysis is carried out to analyze the rheology of wedge-shaped hoppers by varying the orifice diameter, wedge angle, friction coefficient, and stiffness. The conclusion is that velocity increases as the flow rate increases but decreases as the wedge angle and friction coefficient increase. We observed no substantial changes in velocity due to varying stiffness. It is anticipated that stresses at the exit result from the transfer of momentum during particle collisions; for this reason, relationships between viscosity and shear rate are shown, and all data are collapsed into a single curve. In addition, it is demonstrated that viscosity and volume fraction exhibit power law correlations with the inertial number and that all the data collapse into a single curve. A continuum model for determining granular flows is presented using empirical correlations.

Keywords: discrete element method, gravity flow, smooth-wall, wedge-shaped hoppers

Procedia PDF Downloads 91

Authors: Max Butler

Abstract:

Melanoma accounts for 80% of skin cancer-related deaths globally. The poor prognosis and increasing incidence of melanoma impose a significant burden on global healthcare systems. Early detection, precise diagnosis, and preventative strategies are critical to improving patient outcomes. Dermoscopy is the gold standard for specialist assessments of pigmented skin lesions, as it can differentiate between benign and malignant growths with greater accuracy than visual inspection. In the United Kingdom, guidelines from the National Institute of Clinical Excellence (NICE) state dermoscopy should be used in all specialist assessments of pigmented skin lesions. Compliance with this guideline is low, resulting in missed and delayed melanoma diagnoses. To address this problem, a quality improvement project was initiated at Buckinghamshire Healthcare Trust (BHT) within the plastic surgery department. The target group was a trainee and consultant plastic surgeons conducting outpatient skin cancer clinics. Analysis of clinic documentation over a one-month period found that only 62% (38/61) of patients referred with pigmented skin lesions were examined using dermoscopy. To increase dermoscopy rates, teaching was delivered to the department highlighting national guidelines and the evidence base for dermoscopic examination. In addition, clinic paperwork was redesigned to include a text box for dermoscopic examination. Reauditing after the intervention found a significant increase in dermoscopy rates (52/61, p = 0.014). In conclusion, implementing a quality improvement project with targeted teaching and documentation template templates successfully increased dermoscopy rates. This is a promising step toward improving early melanoma detection and patient outcomes.

Keywords: melanoma, dermoscopy, plastic surgery, quality improvement

Procedia PDF Downloads 71

Authors: Magdalena Kowalska, Weronika Rupik

Abstract:

The pancreas is an important organ present in all vertebrate species. It contains two different tissues, exocrine and endocrine, that act as two glands in one. The development and differentiation of the pancreas in reptiles is poorly known in comparison to other vertebrates. Therefore, the aim of this study was to investigate the particular steps concerning the differentiation of the pancreas in the grass snake (Natrix natrix) embryos. For this, histological methods (including hematoxylin and eosin, and Heidenhain's AZAN staining), transmission electron microscopy and three-dimensional (3D) reconstructions from serial paraffin sections were used. The results of this study indicated that the first step of pancreas development in Natrix was the connection of the two pancreatic buds: dorsal and ventral one. Then, duct walls in both buds started to be remodeled from the multilayered to single-layered epithelium. This remodeling started in the dorsal bud and was simultaneously with the differentiation of the duct lumens which occurred by the cavition. During this process, the cells that had no contact with the mesenchyme underwent cell death named anoikis. These findings indicated that the walls of ducts in the embryonic pancreas of the grass snake were initially formed by the abundant principal and single endocrine cells. Later the basal and goblet cells differentiated. Among the endocrine cells, as the first the B and A cells differentiated, then the D and PP cells. The next step of the pancreatic development was the withdrawing of the endocrine cells from the duct walls to form the pancreatic islets. The endocrine cells and islets were found only in the dorsal part of the pancreas in Natrix embryos what is different than in other vertebrate species. The islets were formed mainly by the A cells. Simultaneously, with the differentiation of the endocrine pancreas, the acinar tissue started to differentiate. The source of the acinar cells were pancreatic ducts similar as in other vertebrates. The acini formation began at the proximal part of the pancreas and went towards the caudal direction. Differentiating pancreatic ducts developed into the branched system that can be divided into extralobular, intralobular, and intercalated ducts, similarly as in other vertebrate species. However, the pattern of branching was different. In conclusions, particular steps of the pancreas differentiation in the grass snake were different than in other vertebrates. It can be supposed that these differences are related to the specific topography of the snake’s internal organs and their taxonomy position. All specimens used in the study were captured according to the Polish regulations concerning the protection of wild species. Permission was granted by the Local Ethics Commission in Katowice (41/2010; 87/2015) and the Regional Directorate for Environmental Protection in Katowice (WPN.6401.257.2015.DC).

Keywords: embryogenesis, organogenesis, pancreas, Squamata

Procedia PDF Downloads 172

Authors: Pengfei Meng, Shuangcheng Jia, Qian Li

Abstract:

We proposed a real-time high-precision semantic segmentation network based on a multi-resolution feature fusion module, the auxiliary feature extracting module, upsampling module, and atrous spatial pyramid pooling (ASPP) module. We designed a feature fusion structure, which is integrated with sufficient features of different resolutions. We also studied the effect of side-branch structure on the network and made discoveries. Based on the discoveries about the side-branch of the network structure, we used a side-branch auxiliary feature extraction layer in the network to improve the effectiveness of the network. We also designed upsampling module, which has better results than the original upsampling module. In addition, we also re-considered the locations and number of atrous spatial pyramid pooling (ASPP) modules and modified the network structure according to the experimental results to further improve the effectiveness of the network. The network presented in this paper takes the backbone network of Bisenetv2 as a basic network, based on which we constructed a network structure on which we made improvements. We named this network deep multiple-resolution bilateral networks for real-time, referred to as DMBR-Net. After experimental testing, our proposed DMBR-Net network achieved 81.2% mIoU at 119FPS on the Cityscapes validation dataset, 80.7% mIoU at 109FPS on the CamVid test dataset, 29.9% mIoU at 78FPS on the COCOStuff test dataset. Compared with all lightweight real-time semantic segmentation networks, our network achieves the highest accuracy at an appropriate speed.

Keywords: multi-resolution feature fusion, atrous convolutional, bilateral networks, pyramid pooling

Procedia PDF Downloads 154

Authors: Mohamed Boullouz, Mohamed Louay Metougui

Abstract:

Accurate modeling of greenhouse gas (GHG) emissions, crop growth, soil productivity, and biochemical processes is crucial considering escalating global concerns about climate change and the urgent need to improve agricultural sustainability. The application of the denitrification-disintegration (DNDC) model in the context of Morocco's unique agro-climate is thoroughly investigated in this study. Our main research hypothesis is that the DNDC model offers an effective and powerful tool for precisely simulating a wide range of significant parameters, including greenhouse gas emissions, crop growth, yield potential, and complex soil biogeochemical processes, all consistent with the intricate features of environmental Moroccan agriculture. In order to verify these hypotheses, a vast amount of field data covering Morocco's various agricultural regions and encompassing a range of soil types, climatic factors, and crop varieties had to be gathered. These experimental data sets will serve as the foundation for careful model calibration and subsequent validation, ensuring the accuracy of simulation results. In conclusion, the prospective research findings add to the global conversation on climate-resilient agricultural practices while encouraging the promotion of sustainable agricultural models in Morocco. A policy architect's and an agricultural actor's ability to make informed decisions that not only advance food security but also environmental stability may be strengthened by the impending recognition of the DNDC model as a potent simulation tool tailored to Moroccan conditions.

Keywords: greenhouse gas emissions, DNDC model, sustainable agriculture, Moroccan cropping systems

Procedia PDF Downloads 66

Authors: M. Bilal Ishfaq, Adnan N. Qureshi

Abstract:

COVID-19 is the most infectious disease these days, it was first reported in Wuhan, the capital city of Hubei in China then it spread rapidly throughout the whole world. Later on 11 March 2020, the World Health Organisation (WHO) declared it a pandemic. Since COVID-19 is highly contagious, it has affected approximately 219M people worldwide and caused 4.55M deaths. It has brought the importance of accurate diagnosis of respiratory diseases such as pneumonia and COVID-19 to the forefront. In this paper, we propose a hybrid approach for the automated detection of COVID-19 using medical imaging. We have presented the hybridization of manually extracted and convolutional features. Our approach combines Haralick texture features and convolutional features extracted from chest X-rays and CT scans. We also employ a minimum redundancy maximum relevance (MRMR) feature selection algorithm to reduce computational complexity and enhance classification performance. The proposed model is evaluated on four publicly available datasets, including Chest X-ray Pneumonia, COVID-19 Pneumonia, COVID-19 CTMaster, and VinBig data. The results demonstrate high accuracy and effectiveness, with 0.9925 on the Chest X-ray pneumonia dataset, 0.9895 on the COVID-19, Pneumonia and Normal Chest X-ray dataset, 0.9806 on the Covid CTMaster dataset, and 0.9398 on the VinBig dataset. We further evaluate the effectiveness of the proposed model using ROC curves, where the AUC for the best-performing model reaches 0.96. Our proposed model provides a promising tool for the early detection and accurate diagnosis of COVID-19, which can assist healthcare professionals in making informed treatment decisions and improving patient outcomes. The results of the proposed model are quite plausible and the system can be deployed in a clinical or research setting to assist in the diagnosis of COVID-19.

Keywords: COVID-19, feature engineering, artificial neural networks, radiology images

Procedia PDF Downloads 76

Authors: B. S Kaith, K. Sharma, V. Kumar, S. Kalia

Abstract:

Superabsorbent are three-dimensional networks of linear or branched polymeric chains which can uptake large volume of biological fluids. The ability is due to the presence of functional groups like –NH2, -COOH and –OH. Such cross-linked products based on natural materials, such as cellulose, starch, dextran, gum and chitosan, because of their easy availability, low production cost, non-toxicity and biodegradability have attracted the attention of Scientists and Technologists all over the world. Since natural polymers have better biocompatibility and are non-toxic than most synthetic one, therefore, such materials can be applied in the preparation of controlled drug delivery devices, biosensors, tissue engineering, contact lenses, soil conditioning, removal of heavy metal ions and dyes. Gums are natural potential antioxidants and are used as food additives. They have excellent properties like high solubility, pH stability, non-toxicity and gelling characteristics. Till date lot of methods have been applied for the synthesis and modifications of cross-linked materials with improved properties suitable for different applications. It is well known that ion beam irradiation can play a crucial role to synthesize, modify, crosslink or degrade polymeric materials. High energetic heavy ions irradiation on polymer film induces significant changes like chain scission, cross-linking, structural changes, amorphization and degradation in bulk. Various researchers reported the effects of low and heavy ion irradiation on the properties of polymeric materials and observed significant improvement in optical, electrical, chemical, thermal and dielectric properties. Moreover, modifications induced in the materials mainly depend on the structure, the ion beam parameters like energy, linear energy transfer, fluence, mass, charge and the nature of the target material. Ion-beam irradiation is a useful technique for improving the surface properties of biodegradable polymers without missing the bulk properties. Therefore, a considerable interest has been grown to study the effects of SHIs irradiation on the properties of synthesized semi-IPNs and IPNs. The present work deals with the preparation of semi-IPNs and IPNs and impact of SHI like O7+ and Ni9+ irradiation on optical, chemical, structural, morphological and thermal properties along with impact on different applications. The results have been discussed on the basis of Linear Energy Transfer (LET) of the ions.

Keywords: adsorbent, gel, IPNs, semi-IPNs

Procedia PDF Downloads 373