Search results for: damage prediction models

Authors: C. V. Pietreanu, S. E. Zaharia, C. Dinu

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The present research is built on three major pillars, commencing by making some considerations on accident investigation methods and pointing out both defining aspects and differences between linear and non-linear analysis. The traditional linear focus on accident analysis describes accidents as a sequence of events, while the latest systemic models outline interdependencies between different factors and define the processes evolution related to a specific (normal) situation. Linear and non-linear accident analysis methods have specific limitations, so the second point of interest is mirrored by the aim to discover the drawbacks of systemic models which becomes a starting point for developing new directions to identify risks or data closer to the cause of incidents/accidents. Since communication represents a critical issue in the interaction of human factor and has been proved to be the answer of the problems made by possible breakdowns in different communication procedures, from this focus point, on the third pylon a new error-modeling instrument suitable for risk assessment/accident analysis will be elaborated.

Keywords: accident analysis, multi-factorial error modeling, risk, systemic methods

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Authors: Arya Pirooz

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The effects of different fuels (DME, RME B100, and SME B100) on barrel engines were studied as a general, single dimensional investigation for characterization of these types of engines. A base computational model was created as reference point to be used as a point of comparison with different cases. The models were computed using the commercial computational fluid dynamics program, Diesel-RK. The base model was created using basic dimensions of the PAMAR-3 engine with inline unit injectors. Four fuel cases were considered. Optimized models were also considered for diesel and DME cases with respect to injection duration, fuel, injection timing, exhaust and intake port opening, CR, angular offset. These factors were optimized for highest BMEP, combined PM and NOx emissions, and highest SFC. Results included mechanical efficiency (eta_m), efficiency and power, emission characteristics, combustion characteristics. DME proved to have the highest performing characteristics in relation to diesel and RME fuels for this type of barrel engine.

Keywords: DME, RME, Diesel-RK, characterization, inline unit injector

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Authors: Xiaoli Zhang, Xiongfei Li, Yuncong Feng

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ROC curves, as a widely used evaluating tool in machine learning field, are the tradeoff of true positive rate and negative rate. However, they are blamed for ignoring some vital information in the evaluation process, such as the amount of information about the target that each instance carries, predicted score given by each classification model to each instance. Hence, in this paper, a new classification performance method is proposed by extending the Receiver Operator Characteristic (ROC) curves to 3D space, which is denoted as 3D ROC Histogram. In the histogram, the

Keywords: classification, performance evaluation, receiver operating characteristic histogram, hardness prediction

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Authors: Liliana Rusu

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The goal of the proposed work is to provide a more comprehensive picture of the wind and wave climate in the Romanian nearshore, using the results provided by numerical models. The Romanian coastal environment is located in the western side of the Black Sea, the more energetic part of the sea, an area with heavy maritime traffic and various offshore operations. Information about the wind and wave climate in the Romanian waters is mainly based on observations at Gloria drilling platform (70 km from the coast). As regards the waves, the measurements of the wave characteristics are not so accurate due to the method used, being also available for a limited period. For this reason, the wave simulations that cover large temporal and spatial scales represent an option to describe better the wave climate. To assess the wind climate in the target area spanning 1992–2016, data provided by the NCEP-CFSR (U.S. National Centers for Environmental Prediction - Climate Forecast System Reanalysis) and consisting in wind fields at 10m above the sea level are used. The high spatial and temporal resolution of the wind fields is good enough to represent the wind variability over the area. For the same 25-year period, as considered for the wind climate, this study characterizes the wave climate from a wave hindcast data set that uses NCEP-CFSR winds as input for a model system SWAN (Simulating WAves Nearshore) based. The wave simulation results with a two-level modelling scale have been validated against both in situ measurements and remotely sensed data. The second level of the system, with a higher resolution in the geographical space (0.02°×0.02°), is focused on the Romanian coastal environment. The main wave parameters simulated at this level are used to analyse the wave climate. The spatial distributions of the wind speed, wind direction and the mean significant wave height have been computed as the average of the total data. As resulted from the amount of data, the target area presents a generally moderate wave climate that is affected by the storm events developed in the Black Sea basin. Both wind and wave climate presents high seasonal variability. All the results are computed as maps that help to find the more dangerous areas. A local analysis has been also employed in some key locations corresponding to highly sensitive areas, as for example the main Romanian harbors.

Keywords: numerical simulations, Romanian nearshore, waves, wind

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Authors: Hager Elsheikh, Juliane Glaubitz, Frank Ulrich Weiss, Matthias Sendler

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Aim: Acute pancreatitis (AP) and chronic pancreatitis (CP) are both accompanied by a prominent immune response which influences the course of disease. Whereas during AP the pro-inflammatory immune response dominates, during CP a fibroinflammatory response regulates organ remodeling. The transcription factor signal transducer and activator of transcription 6 (STAT6) is a crucial part of the Type 2 immune response. Here we investigate the role of STAT6 in a mouse model of AP and CP. Material and Methods: AP was induced by hourly repetitive i.p. injections of caerulein (50µg/kg/bodyweight) in C57Bl/6 J and STAT6-/- mice. CP was induced by repetitive caerulein injections 6 times a day, 3 days a week over 4 weeks. Disease severity was evaluated by serum amylase/lipase measurement, H&E staining of pancreas. Pancreatic infiltrate was characterized by immunofluorescent labeling of CD68, CD206, CCR2, CD4 and CD8. Pancreas fibrosis was evaluated by Azan blue staining. qRT-PCR was performed of Arg1, Nos2, Il6, Il1b, Col3a, Socs3 and Ym1. Affymetrix chip array analyses were done to illustrate the IL4/IL13/STAT6 signaling in bone marrow derived macrophages. Results: AP severity is mitigated in STAT6-/- mice, as shown by decreased serum amylase and lipase, as well as histological damage. CP mice surprisingly showed only slightly reduced fibrosis of the pancreas. Also staining of CD206 a classical marker of alternatively activated macrophages showed no decrease of M2-like polarization in the absence of STAT6. In contrast, transcription profile analysis in BMDM showed complete blockade of the IL4/IL13 pathway in STAT6-/- animals. Conclusion: STAT6 signaling pathway is protective during AP and mitigates the pancreatic damage. During chronic pancreatitis the IL4/IL13 – STAT6 axisis involved in organ fibrogenesis. Notably, fibrosis is not dependent on a single signaling pathway, and alternative macrophage activation is also complex and involves different subclasses (M2a, M2b, M2c and M2d) which could be independent of the IL4/IL13 STAT6 axis.

Keywords: chronic pancreatitis, macrophages, IL4/IL13, Type immune response

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Authors: A. A. Okafor, C. H. Achebe, J. L. Chukwuneke, C. G. Ozoegwu

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The performance of an engine whose basic design parameters are known can be predicted with the assistance of simulation programs into the less time, cost and near value of actual. This paper presents a comprehensive mathematical model of the performance parameters of four stroke spark ignition engine. The essence of this research work is to develop a mathematical model for the analysis of engine performance parameters of four stroke spark ignition engine before embarking on full scale construction, this will ensure that only optimal parameters are in the design and development of an engine and also allow to check and develop the design of the engine and it’s operation alternatives in an inexpensive way and less time, instead of using experimental method which requires costly research test beds. To achieve this, equations were derived which describe the performance parameters (sfc, thermal efficiency, mep and A/F). The equations were used to simulate and optimize the engine performance of the model for various engine speeds. The optimal values obtained for the developed bivariate mathematical models are: sfc is 0.2833kg/kwh, efficiency is 28.77% and a/f is 20.75.

Keywords: bivariate models, engine performance, injector engine, optimization, performance parameters, simulation, spark ignition

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Authors: Mariangela Viviani, Daniele Germano, Simone Colace, Agostino Forestiero, Giuseppe Papuzzo, Sara Laurita

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Nowadays, people spend most of their time in closed environments, in offices, or at home. Therefore, secure and highly livable environmental conditions are needed to reduce the probability of aerial viruses spreading. Also, to lower the human impact on the planet, it is important to reduce energy consumption. Heating, Ventilation, and Air Conditioning (HVAC) systems account for the major part of energy consumption in buildings [1]. Devising systems to control and regulate the airflow is, therefore, essential for energy efficiency. Moreover, an optimal setting for thermal comfort and air quality is essential for people’s well-being, at home or in offices, and increases productivity. Thanks to the features of Artificial Intelligence (AI) tools and techniques, it is possible to design innovative systems with: (i) Improved monitoring and prediction accuracy; (ii) Enhanced decision-making and mitigation strategies; (iii) Real-time air quality information; (iv) Increased efficiency in data analysis and processing; (v) Advanced early warning systems for air pollution events; (vi) Automated and cost-effective m onitoring network; and (vii) A better understanding of air quality patterns and trends. We propose AIR SAFE, an IoT-based infrastructure designed to optimize air quality and thermal comfort in indoor environments leveraging AI tools. AIR SAFE employs a network of smart sensors collecting indoor and outdoor data to be analyzed in order to take any corrective measures to ensure the occupants’ wellness. The data are analyzed through AI algorithms able to predict the future levels of temperature, relative humidity, and CO₂ concentration [2]. Based on these predictions, AIR SAFE takes actions, such as opening/closing the window or the air conditioner, to guarantee a high level of thermal comfort and air quality in the environment. In this contribution, we present the results from the AI algorithm we have implemented on the first s et o f d ata c ollected i n a real environment. The results were compared with other models from the literature to validate our approach.

Keywords: air quality, internet of things, artificial intelligence, smart home

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Authors: Oleg Kabantsev, Karomatullo Umarov

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The engineering analysis of earthquake consequences demonstrates a significantly different level of damage to load-bearing systems of different types. Buildings with reinforced concrete columns and separate shear-walls receive the highest level of damage. Traditional methods for predicting damage under earthquake excitations do not provide an answer to the question about the reasons for the increased vulnerability of reinforced concrete frames with shear-walls bearing systems. Thus, the study of the problem of formation and accumulation of damages in the structures reinforced concrete frame with shear-walls requires the use of new methods of assessment of the stress-strain state, as well as new approaches to the calculation of the distribution of forces and stresses in the load-bearing system based on account of various mechanisms of elastic-plastic deformation of reinforced concrete columns and walls. The results of research into the processes of non-linear deformation of structures with a transition to destruction (collapse) will allow to substantiate the characteristics of limit states of various structures forming an earthquake-resistant load-bearing system. The research of elastic-plastic deformation processes of reinforced concrete structures of frames with shear-walls is carried out on the basis of experimentally established parameters of limit deformations of concrete and reinforcement under dynamic excitations. Limit values of deformations are defined for conditions under which local damages of the maximum permissible level are formed in constructions. The research is performed by numerical methods using ETABS software. The research results indicate that under earthquake excitations, plastic deformations of various levels are formed in various groups of elements of the frame with the shear-wall load-bearing system. During the main period of seismic effects in the shear-wall elements of the load-bearing system, there are insignificant volumes of plastic deformations, which are significantly lower than the permissible level. At the same time, plastic deformations are formed in the columns and do not exceed the permissible value. At the final stage of seismic excitations in shear-walls, the level of plastic deformations reaches values corresponding to the plasticity coefficient of concrete , which is less than the maximum permissible value. Such volume of plastic deformations leads to an increase in general deformations of the bearing system. With the specified parameters of the deformation of the shear-walls in concrete columns, plastic deformations exceeding the limiting values develop, which leads to the collapse of such columns. Based on the results presented in this study, it can be concluded that the application seismic-force-reduction factor, common for the all load-bearing system, does not correspond to the real conditions of formation and accumulation of damages in elements of the load-bearing system. Using a single coefficient of seismic-force-reduction factor leads to errors in predicting the seismic resistance of reinforced concrete load-bearing systems. In order to provide the required level of seismic resistance buildings with reinforced concrete columns and separate shear-walls, it is necessary to use values of the coefficient of seismic-force-reduction factor differentiated by types of structural groups.1

Keywords: reinforced concrete structures, earthquake excitation, plasticity coefficients, seismic-force-reduction factor, nonlinear dynamic analysis

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Authors: Ammarah Irum, Muhammad Ali Tahir

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Document-level sentiment analysis in Urdu is a challenging Natural Language Processing (NLP) task due to the difficulty of working with lengthy texts in a language with constrained resources. Deep learning models, which are complex neural network architectures, are well-suited to text-based applications in addition to data formats like audio, image, and video. To investigate the potential of deep learning for Urdu sentiment analysis, we implemented five different deep learning models, including Bidirectional Long Short Term Memory (BiLSTM), Convolutional Neural Network (CNN), Convolutional Neural Network with Bidirectional Long Short Term Memory (CNN-BiLSTM), and Bidirectional Encoder Representation from Transformer (BERT). In this study, we developed a hybrid deep learning model called BiLSTM-Single Layer Multi Filter Convolutional Neural Network (BiLSTM-SLMFCNN) by fusing BiLSTM and CNN architecture. The proposed and baseline techniques are applied on Urdu Customer Support data set and IMDB Urdu movie review data set by using pre-trained Urdu word embedding that are suitable for sentiment analysis at the document level. Results of these techniques are evaluated and our proposed model outperforms all other deep learning techniques for Urdu sentiment analysis. BiLSTM-SLMFCNN outperformed the baseline deep learning models and achieved 83%, 79%, 83% and 94% accuracy on small, medium and large sized IMDB Urdu movie review data set and Urdu Customer Support data set respectively.

Keywords: urdu sentiment analysis, deep learning, natural language processing, opinion mining, low-resource language

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Authors: Tanu Khanuja, Harikrishnan N. Unni

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Head is the most vulnerable part of human body and may cause severe life threatening injuries. As the in vivo brain response cannot be recorded during injury, computational investigation of the head model could be really helpful to understand the injury mechanism. Majority of the physical damage to living tissues are caused by relative motion within the tissue due to tensile and shearing structural failures. The present Finite Element study focuses on investigating intracranial pressure and stress/strain distributions resulting from impact loads on various sites of human head. This is performed by the development of the 3D model of a human head with major segments like cerebrum, cerebellum, brain stem, CSF (cerebrospinal fluid), and skull from patient specific MRI (magnetic resonance imaging). The semi-automatic segmentation of head is performed using AMIRA software to extract finer grooves of the brain. To maintain the accuracy high number of mesh elements are required followed by high computational time. Therefore, the mesh optimization has also been performed using tetrahedral elements. In addition, model validation with experimental literature is performed as well. Hard tissues like skull is modeled as elastic whereas soft tissues like brain is modeled with viscoelastic prony series material model. This paper intends to obtain insights into the severity of brain injury by analyzing impacts on frontal, top, back, and temporal sites of the head. Yield stress (based on von Mises stress criterion for tissues) and intracranial pressure distribution due to impact on different sites (frontal, parietal, etc.) are compared and the extent of damage to cerebral tissues is discussed in detail. This paper finds that how the back impact is more injurious to overall head than the other. The present work would be helpful to understand the injury mechanism of traumatic brain injury more effectively.

Keywords: dynamic impact analysis, finite element analysis, intracranial pressure, MRI, traumatic brain injury, von Misses stress

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Authors: Molakala Mourya Vardhan Reddy, Kandimala Revanth, Koduru Sumanth, Beena B. M.

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Emergency response and recovery operations are severely hampered by natural catastrophes, especially floods. Understanding post-flood scenarios is essential to disaster management because it facilitates quick evaluation and decision-making. To this end, we introduce FloodNet, a brand-new high-resolution aerial picture collection created especially for comprehending post-flood scenes. A varied collection of excellent aerial photos taken during and after flood occurrences make up FloodNet, which offers comprehensive representations of flooded landscapes, damaged infrastructure, and changed topographies. The dataset provides a thorough resource for training and assessing computer vision models designed to handle the complexity of post-flood scenarios, including a variety of environmental conditions and geographic regions. Pixel-level semantic segmentation masks are used to label the pictures in FloodNet, allowing for a more detailed examination of flood-related characteristics, including debris, water bodies, and damaged structures. Furthermore, temporal and positional metadata improve the dataset's usefulness for longitudinal research and spatiotemporal analysis. For activities like flood extent mapping, damage assessment, and infrastructure recovery projection, we provide baseline standards and evaluation metrics to promote research and development in the field of post-flood scene comprehension. By integrating FloodNet into machine learning pipelines, it will be easier to create reliable algorithms that will help politicians, urban planners, and first responders make choices both before and after floods. The goal of the FloodNet dataset is to support advances in computer vision, remote sensing, and disaster response technologies by providing a useful resource for researchers. FloodNet helps to create creative solutions for boosting communities' resilience in the face of natural catastrophes by tackling the particular problems presented by post-flood situations.

Keywords: image classification, segmentation, computer vision, nature disaster, unmanned arial vehicle(UAV), machine learning.

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Authors: Hermann Kuehnle

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Developments in ICT totally reshape manufacturing as machines, objects and equipment on the shop floors will be smart and online. Interactions with virtualizations and models of a manufacturing unit will appear exactly as interactions with the unit itself. These virtualizations may be driven by providers with novel ICT services on demand that might jeopardize even well established business models. Context aware equipment, autonomous orders, scalable machine capacity or networkable manufacturing unit will be the terminology to get familiar with in manufacturing and manufacturing management. Such newly appearing smart abilities with impact on network behavior, collaboration procedures and human resource development will make distributed manufacturing a preferred model to produce. Computing miniaturization and smart devices revolutionize manufacturing set ups, as virtualizations and atomization of resources unwrap novel manufacturing principles. Processes and resources obey novel specific laws and have strategic impact on manufacturing and major operational implications. Mechanisms from distributed manufacturing engaging interacting smart manufacturing units and decentralized planning and decision procedures already demonstrate important effects from this shift of focus towards collaboration and interoperability.

Keywords: autonomous unit, networkability, smart manufacturing unit, virtualization

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Authors: Mario P. Walker, Masaaki Okuma

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Ships are subjected to motions which can disrupt on-board operations and damage equipment. Roll motion, in particular, is of great interest due to low damping conditions which may lead to capsizing. Therefore finding ways to reduce this motion is important in ship designs. Several techniques have been investigated to reduce rolling. These include the commonly used anti-roll tanks, fin stabilizers and bilge keels. However, these systems are not without their challenges. For example, water-flow in anti-roll tanks creates complications, and for fin stabilizers and bilge keels, an extremely large size is required to produce any significant damping creating operational challenges. Additionally, among these measures presented above only anti-roll tanks are effective in zero forward motion of the vessels. This paper proposes and investigates a method to reduce rolling by inducing Coriolis effect using water-flow in the radial direction. Motion in the radial direction of a rolling structure will induce Coriolis force and, depending on the direction of flow will either amplify or attenuate the structure. The system is modelled with two degrees of freedom, having rotational motion for parametric rolling and radial motion of the water-flow. Equations of motion are derived and investigated. Numerical examples are analyzed in detail. To demonstrate applicability parameters from a Ro-Ro vessel are used as extensive research have been conducted on these over the years. The vessel is investigated under free and forced roll conditions. Several models are created using various masses, heights, and velocities of water-flow at a given time. The proposed system was found to produce substantial roll reduction which increases with increase in any of the parameters varied as stated above, with velocity having the most significant effect. The proposed system provides a simple approach to reduce ship rolling. Water-flow control is very simple as the water flows in only one direction with constant velocity. Only needing to control the time at which the system should be turned on or off. Furthermore, the proposed system is effective in both forward and zero forward motion of the ship, and provides no hydrodynamic drag. This is a starting point for designing an effective and practical system. For this to be a viable approach further investigations are needed to address challenges that present themselves.

Keywords: Coriolis effect, damping, rolling, water-flow

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Authors: Hayder H. Kareem, Shunqi Pan

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The geological environment where the groundwater is collected represents the most important element that affects the behaviour of groundwater aquifer. As groundwater is a worldwide vital resource, it requires knowing the parameters that affect this source accurately so that the conceptualized mathematical models would be acceptable to the broadest ranges. Therefore, groundwater models have recently become an effective and efficient tool to investigate groundwater aquifer behaviours. Groundwater aquifer may contain aquitards, aquicludes, or interfaces within its geological formations. Aquitards and aquicludes have geological formations that forced the modellers to include those formations within the conceptualized groundwater models, while interfaces are commonly neglected from the conceptualization process because the modellers believe that the interface has no effect on aquifer behaviour. The current research highlights the impact of an interface existing in a real unconfined groundwater aquifer called Dibdibba, located in Al-Najaf City, Iraq where it has a river called the Euphrates River that passes through the eastern part of this city. Dibdibba groundwater aquifer consists of two types of soil layers separated by an interface soil layer. A groundwater model is built for Al-Najaf City to explore the impact of this interface. Calibration process is done using PEST 'Parameter ESTimation' approach and the best Dibdibba groundwater model is obtained. When the soil interface is conceptualized, results show that the groundwater tables are significantly affected by that interface through appearing dry areas of 56.24 km² and 6.16 km² in the upper and lower layers of the aquifer, respectively. The Euphrates River will also leak water into the groundwater aquifer of 7359 m³/day. While these results are changed when the soil interface is neglected where the dry area became 0.16 km², the Euphrates River leakage became 6334 m³/day. In addition, the conceptualized models (with and without interface) reveal different responses for the change in the recharge rates applied on the aquifer through the uncertainty analysis test. The aquifer of Dibdibba in Al-Najaf City shows a slight deficit in the amount of water supplied by the current pumping scheme and also notices that the Euphrates River suffers from stresses applied to the aquifer. Ultimately, this study shows a crucial need to represent the interface soil layer in model conceptualization to be the intended and future predicted behaviours more reliable for consideration purposes.

Keywords: Al-Najaf City, groundwater aquifer behaviour, groundwater modelling, interface soil layer, Visual MODFLOW

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Authors: Irene Yi

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Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: gendered grammar, misogynistic language, natural language processing, neural networks

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Authors: Lilla Dorina Habsz, Marta Rado

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Popularity is affected by a variety of factors in the primary school such as academic achievement and ethnicity. The main goal of our study was to analyse whether acting white exists in Hungarian elementary schools. In other words, we observed whether Roma students penalize those in-group members who obtain the high academic achievement. Furthermore, to show how popularity is influenced by changes in academic achievement in inter-ethnic relations. The empirical basis of our research was the 'competition and negative networks' longitudinal dataset, which was collected by the MTA TK 'Lendület' RECENS research group. This research followed 11 and 12-year old students for a two-year period. The survey was analysed using fixed and random effect models. Overall, we found a positive correlation between grades and popularity, but no evidence for the acting white effect. However, better grades were more positively evaluated within the majority group than within the minority group, which may further increase inequalities.

Keywords: academic achievement, elementary school, ethnicity, popularity

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Authors: Zi-Yan Liao, Shan-Yu Zhang, Ya-Xian Lin, Ya-Lun Lee, Shih-Chuan Huang, Hong-Ru Lin

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Traditional wound dressings, such as gauze, bandages, etc., are easy to adhere to the tissue fluid exuded from the wound, causing secondary damage to the wound during removal. This study takes this as the idea to develop a hydrogel dressing, to explore that the dressing will not cause secondary damage to the wound when it is torn off, and at the same time, create an environment conducive to wound healing. First, the temperature-sensitive material N-isopropylacrylamide (NIPAAm) was used as the substrate. Due to its low mechanical properties, the hydrogel would break due to pulling during human activities. Polyvinyl alcohol (PVA) interpenetrates into it to enhance the mechanical properties, and a semi-interpenetration (semi-IPN) composed of poly(N-isopropylacrylamide) (PNIPAAm) and polyvinyl alcohol (PVA) was prepared by free radical polymerization. PNIPAAm was cross-linked with N,N'-methylenebisacrylamide (NMBA) in an ice bath in the presence of linear PVA, and tetramethylhexamethylenediamine (TEMED) was added as a promoter to speed up the gel formation. The polymerization stage was carried out at 16°C for 17 hours and washed with distilled water for three days after gel formation, and the water was changed several times in the middle to complete the preparation of semi-IPN hydrogel. Finally, various tests were used to analyze the effects of different ratios of PNIPAAm and PVA on semi-IPN hydrogels. In the swelling test, it was found that the maximum swelling ratio can reach about 50% under the environment of 21°C, and the higher the ratio of PVA, the more water can be absorbed. The saturated moisture content test results show that when more PVA is added, the higher saturated water content. The water vapor transmission rate test results show that the value of the semi-IPN hydrogel is about 57 g/m²/24hr, which is not much related to the proportion of PVA. It is found in the LCST test compared with the PNIPAAm hydrogel; the semi-IPN hydrogel possesses the same critical solution temperature (30-35°C). The semi-IPN hydrogel prepared in this study has a good effect on temperature response and has the characteristics of thermal sensitivity. It is expected that after improvement, it can be used in the treatment of surface wounds, replacing the traditional dressing shortcoming.

Keywords: hydrogel, N-isopropylacrylamide, polyvinyl alcohol, hydrogel wound dressing, semi-interpenetrating polymer network

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Authors: Masao Fujisawa, Hirohito Ikeda, Tomonori Ohata, Miho Yukawa, Hatsumi Aki, Takayoshi Kimura

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We attempted to predict adsorption coefficients by utilizing dispersion energies. We performed liquid-phase free energy calculations based on gas-phase geometries of organic compounds using the DFT and studied the relationship between the adsorption of organic compounds by activated carbon and dispersion energies of the organic compounds. A linear correlation between absorption coefficients and dispersion energies was observed.

Keywords: activated carbon, adsorption, prediction, dispersion energy

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Authors: Shreya Srivastava, Sushovan Ghosh, Sagnik Dey

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Aerosols directly affect Earth’s radiation budget by scattering and absorbing incoming solar radiation and outgoing terrestrial radiation. While the uncertainty in aerosol radiative forcing (ARF) has decreased over the years, it is still higher than that of greenhouse gas forcing, particularly in the South Asian region, due to high heterogeneity in their chemical properties. Understanding the Spatio-temporal heterogeneity of aerosol composition is critical in improving climate prediction. Studies using satellite data, in-situ and aircraft measurements, and models have investigated the Spatio-temporal variability of aerosol characteristics. In this study, we have taken aerosol data from Multi-angle Imaging Spectro-Radiometer (MISR) level-2 version 23 aerosol products retrieved at 4.4 km and radiation data from Clouds and the Earth’s Radiant Energy System (CERES, spatial resolution=1ox1o) for 21 years (2000-2021) over the Indian subcontinent. MISR aerosol product includes size and shapes segregated aerosol optical depth (AOD), Angstrom exponent (AE), and single scattering albedo (SSA). Additionally, 74 aerosol mixtures are included in version 23 data that is used for aerosol speciation. We have seasonally mapped aerosol optical and microphysical properties from MISR for India at quarter degrees resolution. Results show strong Spatio-temporal variability, with a constant higher value of AOD for the Indo-Gangetic Plain (IGP). The contribution of small-size particles is higher throughout the year, spatially during winter months. SSA is found to be overestimated where absorbing particles are present. The climatological map of short wave (SW) ARF at the top of the atmosphere (TOA) shows a strong cooling except in only a few places (values ranging from +2.5o to -22.5o). Cooling due to aerosols is higher in the absence of clouds. Higher negative values of ARF are found over the IGP region, given the high aerosol concentration above the region. Surface ARF values are everywhere negative for our study domain, with higher values in clear conditions. The results strongly correlate with AOD from MISR and ARF from CERES.

Keywords: aerosol Radiative forcing (ARF), aerosol composition, single scattering albedo (SSA), CERES

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Authors: Yuhanis Yusof, Zuriani Mustaffa, Siti Sakira Kamaruddin

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Data mining is a fundamental technique in identifying patterns from large data sets. The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical. Prior to that, data are consolidated so that the resulting mining process may be more efficient. This study investigates the effect of different data normalization techniques, which are Min-max, Z-score, and decimal scaling, on Swarm-based forecasting models. Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC). Forecasting models are later developed to predict the daily spot price of crude oil and gasoline. Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max. Nevertheless, the GWO is more superior that ABC as its model generates the highest accuracy for both crude oil and gasoline price. Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.

Keywords: artificial bee colony, data normalization, forecasting, Grey Wolf optimizer

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Authors: Jakub Zembrzuski, Bartosz Sobczyk, Mikołaj MIśkiewicz

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Besides designing new constructions, engineers all over the world must face another problem – maintenance, repairs, and assessment of the technical condition of existing bridges. To solve more complex issues, it is necessary to be familiar with the theory of finite element method and to have access to the software that provides sufficient tools which to enable create of sometimes significantly advanced numerical models. The paper includes a brief assessment of the technical condition, a description of the in situ non-destructive testing carried out and the FEM models created for global and local analysis. In situ testing was performed using strain gauges and displacement sensors. Numerical models were created using various software and numerical modeling techniques. Particularly noteworthy is the method of modeling riveted joints of the crossbeam of the viaduct. It is a simplified method that consists of the use of only basic numerical tools such as beam and shell finite elements, constraints, and simplified boundary conditions (fixed support and symmetry). The results of the numerical analyses were presented and discussed. It is clearly explained why the structure did not fail, despite the fact that the weld of the deck plate completely failed. A further research problem that was solved was to determine the cause of the rapid increase in values on the stress diagram in the cross-section of the transverse section. The problems were solved using the solely mentioned, simplified method of modeling riveted joints, which demonstrates that it is possible to solve such problems without access to sophisticated software that enables to performance of the advanced nonlinear analysis. Moreover, the obtained results are of great importance in the field of assessing the operation of bridge structures with an orthotropic plate.

Keywords: bridge, diagnostics, FEM simulations, failure, NDT, in situ testing

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Authors: Elizaveta S. Prokofyeva, Svetlana V. Maltseva, Roman D. Zaitsev

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Rapidly evolving modern data analysis technologies in healthcare play a large role in understanding the operation of the system and its characteristics. Nowadays, one of the key tasks in urban healthcare is to optimize the resource allocation. Thus, the application of data analysis in medical institutions to solve optimization problems determines the significance of this study. The purpose of this research was to establish the dependence between the indicators of the effectiveness of the medical institution and its resources. Hospital discharges by diagnosis; hospital days of in-patients and in-patient average length of stay were selected as the performance indicators and the demand of the medical facility. The hospital beds by type of care, medical technology (magnetic resonance tomography, gamma cameras, angiographic complexes and lithotripters) and physicians characterized the resource provision of medical institutions for the developed models. The data source for the research was an open database of the statistical service Eurostat. The choice of the source is due to the fact that the databases contain complete and open information necessary for research tasks in the field of public health. In addition, the statistical database has a user-friendly interface that allows you to quickly build analytical reports. The study provides information on 28 European for the period from 2007 to 2016. For all countries included in the study, with the most accurate and complete data for the period under review, predictive models were developed based on historical panel data. An attempt to improve the quality and the interpretation of the models was made by cluster analysis of the investigated set of countries. The main idea was to assess the similarity of the joint behavior of the variables throughout the time period under consideration to identify groups of similar countries and to construct the separate regression models for them. Therefore, the original time series were used as the objects of clustering. The hierarchical agglomerate algorithm k-medoids was used. The sampled objects were used as the centers of the clusters obtained, since determining the centroid when working with time series involves additional difficulties. The number of clusters used the silhouette coefficient. After the cluster analysis it was possible to significantly improve the predictive power of the models: for example, in the one of the clusters, MAPE error was only 0,82%, which makes it possible to conclude that this forecast is highly reliable in the short term. The obtained predicted values of the developed models have a relatively low level of error and can be used to make decisions on the resource provision of the hospital by medical personnel. The research displays the strong dependencies between the demand for the medical services and the modern medical equipment variable, which highlights the importance of the technological component for the successful development of the medical facility. Currently, data analysis has a huge potential, which allows to significantly improving health services. Medical institutions that are the first to introduce these technologies will certainly have a competitive advantage.

Keywords: data analysis, demand modeling, healthcare, medical facilities

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Authors: M. S. Wickramarachchi, L. S. Nawarathna, C. M. B. Dematawewa

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Hatchery performance is critical for the profitability of poultry breeder operations. Some extrinsic parameters of eggs and breeders cause to increase or decrease the hatchability. This study aims to identify the affecting extrinsic parameters on the commercial hatchability of local chicken's eggs and determine the most efficient classification model with a hatchability rate greater than 90%. In this study, seven extrinsic parameters were considered: egg weight, moisture loss, breeders age, number of fertilised eggs, shell width, shell length, and shell thickness. Multiple linear regression was performed to determine the most influencing variable on hatchability. First, the correlation between each parameter and hatchability were checked. Then a multiple regression model was developed, and the accuracy of the fitted model was evaluated. Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), k-Nearest Neighbors (kNN), Support Vector Machines (SVM) with a linear kernel, and Random Forest (RF) algorithms were applied to classify the hatchability. This grouping process was conducted using binary classification techniques. Hatchability was negatively correlated with egg weight, breeders' age, shell width, shell length, and positive correlations were identified with moisture loss, number of fertilised eggs, and shell thickness. Multiple linear regression models were more accurate than single linear models regarding the highest coefficient of determination (R²) with 94% and minimum AIC and BIC values. According to the classification results, RF, CART, and kNN had performed the highest accuracy values 0.99, 0.975, and 0.972, respectively, for the commercial hatchery process. Therefore, the RF is the most appropriate machine learning algorithm for classifying the breeder outcomes, which are economically profitable or not, in a commercial hatchery.

Keywords: classification models, egg weight, fertilised eggs, multiple linear regression

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Authors: Saif Obaid Alkaabi, Nabil Ayad

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Governments constantly seek to offer faster, more secure, efficient and effective services for their citizens. Recent changes and developments to communication services and technologies, mainly due the Internet, have led to immense improvements in the way governments of advanced countries carry out their interior operations Therefore, advances in e-government services have been broadly adopted and used in various developed countries, as well as being adapted to developing countries. The implementation of advances depends on the utilization of the most innovative structures of data techniques, mainly in web dependent applications, to enhance the main functions of governments. These functions, in turn, have spread to mobile and wireless techniques, generating a new advanced direction called m-government. This paper discusses a selection of available m-government applications and several business modules and frameworks in various fields. Practically, the m-government models, techniques and methods have become the improved version of e-government. M-government offers the potential for applications which will work better, providing citizens with services utilizing mobile communication and data models incorporating several government entities. Developing countries can benefit greatly from this innovation due to the fact that a large percentage of their population is young and can adapt to new technology and to the fact that mobile computing devices are more affordable. The use of models of mobile transactions encourages effective participation through the use of mobile portals by businesses, various organizations, and individual citizens. Although the application of m-government has great potential, it does have major limitations. The limitations include: the implementation of wireless networks and relative communications, the encouragement of mobile diffusion, the administration of complicated tasks concerning the protection of security (including the ability to offer privacy for information), and the management of the legal issues concerning mobile applications and the utilization of services.

Keywords: e-government, m-government, system dependability, system security, trust

Procedia PDF Downloads 370

Authors: Ivan Sosa Gomez

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Digitalization has impacted the entire insurance value chain, and the growing movement towards P2P platforms and the collaborative economy is also beginning to have a significant impact. P2P insurance is defined as innovation, enabling policyholders to pool their capital, self-organize, and self-manage their own insurance. In this context, new InsurTech start-ups are emerging as peer-to-peer (P2P) providers, based on a model that differs from traditional insurance. As a result, although P2P platforms do not change the fundamental basis of insurance, they do enable potentially more efficient business models to be established in terms of ensuring the coverage of risk. It is therefore relevant to determine whether p2p innovation can have substantial effects on the future of the insurance sector. For this purpose, it is considered necessary to develop P2P innovation from a business perspective, as well as to build a comparison between a traditional model and a P2P model from an actuarial perspective. Objectives: The objectives are (1) to represent P2P innovation in the business model compared to the traditional insurance model and (2) to establish a comparison between a traditional model and a P2P model from an actuarial perspective. Methodology: The research design is defined as action research in terms of understanding and solving the problems of a collectivity linked to an environment, applying theory and best practices according to the approach. For this purpose, the study is carried out through the participatory variant, which involves the collaboration of the participants, given that in this design, participants are considered experts. For this purpose, prolonged immersion in the field is carried out as the main instrument for data collection. Finally, an actuarial model is developed relating to the calculation of premiums that allows for the establishment of projections of future scenarios and the generation of conclusions between the two models. Main Contributions: From an actuarial and business perspective, we aim to contribute by developing a comparison of the two models in the coverage of risk in order to determine whether P2P innovation can have substantial effects on the future of the insurance sector.

Keywords: Insurtech, innovation, business model, P2P, insurance

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Authors: Parminder Kaur Heer, Alexei Lapkin

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Due to global challenges of increased competition and demand for more sustainable products/processes, there is a rising pressure on the industry to develop innovative processes. Through Process Intensification (PI) the existing and new processes may be able to attain higher efficiency. However, very few PI options are generally considered. This is because processes are typically analysed at a unit operation level, thus limiting the search space for potential process options. PI performed at more detailed levels of a process can increase the size of the search space. The different levels at which PI can be achieved is unit operations, functional and phenomena level. Physical/chemical phenomena form the lowest level of aggregation and thus, are expected to give the highest impact because all the intensification options can be described by their enhancement. The objective of the current work is thus, generation of numerous process alternatives based on phenomena, and development of their corresponding computer aided models. The methodology comprises: a) automated generation of process options, and b) automated generation of process models. The process under investigation is disintegrated into functions viz. reaction, separation etc., and these functions are further broken down into the phenomena required to perform them. E.g., separation may be performed via vapour-liquid or liquid-liquid equilibrium. A list of phenomena for the process is formed and new phenomena, which can overcome the difficulties/drawbacks of the current process or can enhance the effectiveness of the process, are added to the list. For instance, catalyst separation issue can be handled by using solid catalysts; the corresponding phenomena are identified and added. The phenomena are then combined to generate all possible combinations. However, not all combinations make sense and, hence, screening is carried out to discard the combinations that are meaningless. For example, phase change phenomena need the co-presence of the energy transfer phenomena. Feasible combinations of phenomena are then assigned to the functions they execute. A combination may accomplish a single or multiple functions, i.e. it might perform reaction or reaction with separation. The combinations are then allotted to the functions needed for the process. This creates a series of options for carrying out each function. Combination of these options for different functions in the process leads to the generation of superstructure of process options. These process options, which are formed by a list of phenomena for each function, are passed to the model generation algorithm in the form of binaries (1, 0). The algorithm gathers the active phenomena and couples them to generate the model. A series of models is generated for the functions, which are combined to get the process model. The most promising process options are then chosen subjected to a performance criterion, for example purity of product, or via a multi-objective Pareto optimisation. The methodology was applied to a two-step process and the best route was determined based on the higher product yield. The current methodology can identify, produce and evaluate process intensification options from which the optimal process can be determined. It can be applied to any chemical/biochemical process because of its generic nature.

Keywords: Phenomena, Process intensification, Process models , Process options

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Authors: Kevin Brochard, Herve Le Sourne, Guillaume Barras

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The design of a submarine’s hull is crucial for its operability and crew’s safety, but also complex. Indeed, engineers need to balance lightness, acoustic discretion and resistance to both immersion pressure and environmental attacks. Submarine explosions represent a first-rate threat for the integrity of the hull, whose behavior needs to be properly analyzed. The presented work is focused on the development of a simplified analytical method to study the structural response of a deeply immersed cylinder submitted to an underwater explosion. This method aims to provide engineers a quick estimation of the resulting damage, allowing them to simulate a large number of explosion scenarios. The present research relies on the so-called plastic string on plastic foundation model. A two-dimensional boundary value problem for a cylindrical shell is converted to an equivalent one-dimensional problem of a plastic string resting on a non-linear plastic foundation. For this purpose, equivalence parameters are defined and evaluated by making assumptions on the shape of the displacement and velocity field in the cross-sectional plane of the cylinder. Closed-form solutions for the deformation and velocity profile of the shell are obtained for explosive loading, and compare well with numerical and experimental results. However, the plastic-string model has not yet been adapted for a cylinder in immersion subjected to an explosive loading. In fact, the effects of fluid-structure interaction have to be taken into account. Moreover, when an underwater explosion occurs, several pressure waves are emitted by the gas bubble pulsations, called secondary waves. The corresponding loads, which may produce significant damages to the cylinder, must also be accounted for. The analytical developments carried out to solve the above problem of a shock wave impacting a cylinder, considering fluid-structure interaction will be presented for an unstiffened cylinder. The resulting deformations are compared to experimental and numerical results for different shock factors and different standoff distances.

Keywords: immersed cylinder, rigid plastic material, shock loading, underwater explosion

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Authors: Zhaoxin Luo, Michael Zhu

Abstract:

In natural languages, there are always complex semantic hierarchies. Obtaining the feature representation based on these complex semantic hierarchies becomes the key to the success of the model. Several RNN models have recently been proposed to use latent indicators to obtain the hierarchical structure of documents. However, the model that only uses a single-layer latent indicator cannot achieve the true hierarchical structure of the language, especially a complex language like Chinese. In this paper, we propose a deep layered model that stacks arbitrarily many RNN layers equipped with latent indicators. After using EM and training it hierarchically, our model solves the computational problem of stacking RNN layers and makes it possible to stack arbitrarily many RNN layers. Our deep hierarchical model not only achieves comparable results to large pre-trained models on the Chinese short text classification problem but also achieves state of art results on the Chinese long text classification problem.

Keywords: nature language processing, recurrent neural network, hierarchical structure, document classification, Chinese

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Authors: Yinheng Li

Abstract:

The remarkable advancements in large language models (LLMs) have brought about significant improvements in natural language processing tasks. This paper presents a comprehensive review of in-context learning techniques, focusing on different types of prompts, including discrete, continuous, few-shot, and zero-shot, and their impact on LLM performance. We explore various approaches to prompt design, such as manual design, optimization algorithms, and evaluation methods, to optimize LLM performance across diverse tasks. Our review covers key research studies in prompt engineering, discussing their methodologies and contributions to the field. We also delve into the challenges faced in evaluating prompt performance, given the absence of a single ”best” prompt and the importance of considering multiple metrics. In conclusion, the paper highlights the critical role of prompt design in harnessing the full potential of LLMs and provides insights into the combination of manual design, optimization techniques, and rigorous evaluation for more effective and efficient use of LLMs in various Natural Language Processing (NLP) tasks.

Keywords: in-context learning, prompt engineering, zero-shot learning, large language models

Procedia PDF Downloads 63

Authors: Mark Schudeleit, Christian Sieg, Ferit Küçükay

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This paper describes how to dimension the electric components of a 48V hybrid system considering real customer use. Furthermore, it provides information about savings in energy and CO2 emissions by a customer-tailored 48V hybrid. Based on measured customer profiles, the electric units such as the electric motor and the energy storage are dimensioned. Furthermore, the CO2 reduction potential in real customer use is determined compared to conventional vehicles. Finally, investigations are carried out to specify the topology design and preliminary considerations in order to hybridize a conventional vehicle with a 48V hybrid system. The emission model results from an empiric approach also taking into account the effects of engine dynamics on emissions. We analyzed transient engine emissions during representative customer driving profiles and created emission meta models. The investigation showed a significant difference in emissions when simulating realistic customer driving profiles using the created verified meta models compared to static approaches which are commonly used for vehicle simulation.

Keywords: customer use, dimensioning, hybrid electric vehicles, vehicle simulation, 48V hybrid system

Procedia PDF Downloads 491