Search results for: ring deep beam

Authors: Fuad Hamsyah

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Since last two decades, major disasters have happened in the Philippines and Indonesia as two countries that are located in the pacific ring of fire territory. While in Southeast Asian countries, the process of psychosocial support provision is facing various constraints such as limited number of mental health professionals and the limited knowledge about the provision of psychosocial support for disaster survivors. Yet after the tsunami disaster in 2004, many Asian countries begin to develop policies about the provision of psychosocial interventions as an effort for future disasters preparedness. In addition, mental health professionals have to consider the local cultural values and beliefs in order to provide people with effective psychosocial support since cultural values and beliefs play a significant role in the diversity of psychological distress that forms symptoms formation, and people’s way to seek for psychological assistance. This study is a critical literature review on 130 relevant selected documents and literatures. IASC MHPSS guideline is used as the research framework in doing critical analysis. The purpose of this study is to conduct a critical analysis on the mental health and psychosocial support provision in the Philippines and Indonesia with three main objectives: 1) To describe strengths, weaknesses, and challenges in the process of psychosocial supports given by public and private organizations in emergency settings of disaster in the Philippines and Indonesia, 2) To compare psychosocial support practices between the Philippines and Indonesia, and to identify the good practices among these countries, 3) To learn how cultural values influence the implementation of psychosocial supports in emergency settings of disaster. This research indicated that almost every function from IASC MHPSS guidelines has been implemented effectively in the Philippines and Indonesia, yet not in every detail of IASC MHPSS guidelines. Several similarities and differences are indicated in this study also based on the IASC MHPSS guidelines as the analysis framework. Further, both countries have some good practices that can be useful as an example of a comprehensive psychosocial support implementation. Apart from the IASC MHPSS guideline, cultural values and beliefs in the Philippines such as kanya-kanya syndrome, pakikipakapwa, utang na loob, bahala na, pagkaya are indicated as several cultural values that have strong influences towards people’s attitude and behavior in disaster situations. While in Indonesia, several cultural values such as sabar and nrimo become two important attitudes to cope disaster situations.

Keywords: disaster, Indonesia, psychosocial support, Philippines

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Authors: Xu Rumin, Chen Jianyi, Yue Ti, Wang Yaan

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With the rapid development of subsea oil and gas exploitation, the demand for the related underwater process equipment is increasing fast. In order to reduce the energy consuming, people tend to separate the gas and oil phase directly on the seabed. Accordingly, an advanced separator is needed. In this paper, the pressure drop of a new type of separator named Gas Liquid Cylindrical Cyclone (GLCC) which is used in the subsea system is investigated by both experiments and numerical simulation. In the experiments, the single phase flow and gas-liquid two phase flow in GLCC were tested. For the simulation, the performance of GLCC under both laboratory and industrial conditions was calculated. The Eulerian model was implemented to describe the mixture flow field in the GLCC under experimental conditions and industrial oil-natural gas conditions. Furthermore, a relationship among Euler number (Eu), Reynolds number (Re), and Froude number (Fr) is generated according to similarity analysis and simulation data, which can present the GLCC separation performance of pressure drop. These results can give reference to the design and application of GLCC in deep sea.

Keywords: dimensionless analysis, gas-liquid cylindrical cyclone, numerical simulation, pressure drop

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Authors: Wajeeh Daher, Nimer Baya'a

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High Order Thinking Skills (HOTS) are suggested today as essential for the cognitive development of students and as preparing them for real life skills. Teachers are encouraged to use HOTS activities in the classroom to help their students develop higher order skills and deep thinking. So it is essential to prepare pre-service teachers to write and use HOTS activities for their students. This paper describes a model for integrating HOTS activities with GeoGebra in pre-service teachers’ preparation. This model describes four aspects of HOTS activities and working with them: Activity components, preparation procedure, strategies and processes used in writing a HOTS activity and types of the HOTS activities. In addition, the paper describes the pre-service teachers' difficulties in preparing and working with HOTS activities, as well as their perceptions regarding the use of these activities and GeoGebra in the mathematics classroom. The paper also describes the contribution of a HOTS activity to pupils' learning of mathematics, where this HOTS activity was prepared and taught by one pre-service teacher.

Keywords: high order thinking skills, HOTS activities, pre-service teachers, professional development

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Authors: C. Busuioc, S. I. Jinga, E. Pavel

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The continuous need of more non-volatile memories with a higher storage capacity, smaller dimensions and weight, as well as lower costs, has led to the exploration of optical lithography on active media, as well as patterned magnetic composites. In this context, optical lithography is a technique that can provide a significant decrease of the information bit size to the nanometric scale. However, there are some restrictions that arise from the need of breaking the optical diffraction limit. Major achievements have been obtained by employing a vitoceramic material as active medium and a laser beam operated at low power for the direct writing procedure. Thus, optical discs with ultra-high density were fabricated by a conventional melt-quenching method starting from analytical purity reagents. They were subsequently used for 3D recording based on their photosensitive features. Naturally, the next step consists in the elucidation of the composition and structure of the active centers, in correlation with the use of silver and rare-earth compounds for the synthesis of the optical supports. This has been accomplished by modern characterization methods, namely transmission electron microscopy coupled with selected area electron diffraction, scanning transmission electron microscopy and electron energy loss spectroscopy. The influence of laser diode parameters, silver concentration and fluorescent compounds formation on the writing process and final material properties was investigated. The results indicate performances in terms of capacity with two order of magnitude higher than other reported information storage systems. Moreover, the fluorescent photosensitive vitroceramics may be integrated in other applications which appeal to nanofabrication as the driving force in electronics and photonics fields.

Keywords: data storage, fluorescent compounds, laser writing, vitroceramics

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Authors: Payel Datta, Abhishek Das, Abhishek Roychoudhury, Dhiman Chattopadhyay, Tanushyam Chattopadhyay

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Machine learning (ML) and deep learning (DL) is most predominantly used in image/video processing, natural language processing (NLP), audio and speech recognition but not that much used in system performance evaluation. In this paper, authors are going to describe the architecture of an abstraction layer constructed using ML/DL to detect the system failure. This proposed system is used to detect the system failure by evaluating the performance metrics of an IoT service deployment under constrained infrastructure environment. This system has been tested on the manually annotated data set containing different metrics of the system, like number of threads, throughput, average response time, CPU usage, memory usage, network input/output captured in different hardware environments like edge (atom based gateway) and cloud (AWS EC2). The main challenge of developing such system is that the accuracy of classification should be 100% as the error in the system has an impact on the degradation of the service performance and thus consequently affect the reliability and high availability which is mandatory for an IoT system. Proposed ML/DL classifiers work with 100% accuracy for the data set of nearly 4,000 samples captured within the organization.

Keywords: machine learning, system performance, performance metrics, IoT, edge

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Authors: Livia Herle

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A large part of the Principality of Liechtenstein is covered by forest. Three-quarters of this forest is defined as protective due to the alpine landscape of the country, which is deteriorating the quality of the wood. Nevertheless, the forest is one of the most important sources of raw material. However, out of the wood harvested annually in Liechtenstein, about two-thirds are used directly as an energy source, drastically shortening up the carbon storage cycle of wood. Furthermore, due to climate change, forest structures are changing. Predictions for the forest in Liechtenstein have stated that the spruce will mostly vanish in low altitudes, only being able to survive in the higher regions. In contrast, hardwood species will experience a rise, resulting in a more mixed forest. Thus, the main research focus will be put upon the potential of hardwood as well as prolonging the lifespan of a timber log before ending up as an energy source. An analysis of the local occurrence of hardwood species and their quality will serve as a tool to implement this knowledge upon constructional solutions. As a system that works with short spam timber and thus qualifies for the regional conditions of hardwood, reciprocal frame systems will be further investigated. These can be defined as load-bearing structures with only two beams connecting at a time, avoiding complex joining situations. Furthermore, every beam is mutually supporting. This allows the usage of short pieces of preferably massive wood. As a result, the system permits for an easy assembly but also disassembly. To promote a more circular application of wood, possible cascading scenarios of the structural solutions will be added. In a workshop at the School of Architecture of the University of Liechtenstein in the Sommer Semester 2024, prototypes in 1:1 of reciprocal frame systems using only local hardwood will help as a tool to further test the theoretical analyses.

Keywords: hardwood, cascading wood, reciprocal frames, crooked wood, forest structures, climate change

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Authors: Sirapop Nuannimnoi, Hojjat Baghban, Ching-Yao Huang

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Over the past decade, with the fast development of blockchain technology since the birth of Bitcoin, there has been a massive increase in the usage of Cryptocurrencies. Cryptocurrencies are not seen as an investment opportunity due to the market’s erratic behavior and high price volatility. With the recent success of deep reinforcement learning (DRL), portfolio management can be modeled and automated. In this paper, we propose a novel DRL-based multi-agent system to automatically make proper trading decisions on multiple cryptocurrencies and gain profits in the highly volatile cryptocurrency market. We also extend this multi-agent system with horizontal federated transfer learning for better adapting to the inclusion of new cryptocurrencies in our portfolio; therefore, we can, through the concept of diversification, maximize our profits and minimize the trading risks. Experimental results through multiple simulation scenarios reveal that this proposed algorithmic trading system can offer three promising key advantages over other systems, including maximized profits, minimized risks, and adaptability.

Keywords: cryptocurrency portfolio management, algorithmic trading, federated learning, multi-agent reinforcement learning

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Authors: Justin Reverdi, Sixin Zhang, Saïd Aoues, Fabrice Gamboa, Serge Gratton, Thomas Pellegrini

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In vapor cycle systems, the mass flow sensor plays a key role for different monitoring and control purposes. However, physical sensors can be inaccurate, heavy, cumbersome, expensive, or highly sensitive to vibrations, which is especially problematic when embedded into an aircraft. The conception of a virtual sensor, based on other standard sensors, is a good alternative. This paper has two main objectives. Firstly, a data-driven model using a convolutional neural network is proposed to estimate the mass flow of the compressor. We show that it significantly outperforms the standard polynomial regression model (thermodynamic maps) in terms of the standard MSE metric and engineer performance metrics. Secondly, a semi-automatic segmentation method is proposed to compute the engineer performance metrics for real datasets, as the standard MSE metric may pose risks in analyzing the dynamic behavior of vapor cycle systems.

Keywords: deep learning, convolutional neural network, vapor cycle system, virtual sensor

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Authors: S. J. Addinell, A. F. Grabsch, P. D. Fawell, B. Evans

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The Deep Exploration Technologies Cooperative Research Centre (DET CRC) is researching and developing a new coiled tubing based greenfields mineral exploration drilling system utilising down-hole water-powered percussive drill tooling. This new drilling system is aimed at significantly reducing the costs associated with identifying mineral resource deposits beneath deep, barren cover. This system has shown superior rates of penetration in water-rich, hard rock formations at depths exceeding 500 metres. With fluid flow rates of up to 120 litres per minute at 200 bar operating pressure to energise the bottom hole tooling, excessive quantities of high quality drilling fluid (water) would be required for a prolonged drilling campaign. As a result, drilling fluid recovery and recycling has been identified as a necessary option to minimise costs and logistical effort. While the majority of the cuttings report as coarse particles, a significant fines fraction will typically also be present. To maximise tool life longevity, the percussive bottom hole assembly requires high quality fluid with minimal solids loading and any recycled fluid needs to have a solids cut point below 40 microns and a concentration less than 400 ppm before it can be used to reenergise the system. This paper presents experimental results obtained from the research program during laboratory and field testing of the prototype drilling system. A study of the morphological aspects of the cuttings generated during the percussive drilling process shows a strong power law relationship for particle size distributions. This data is critical in optimising solids control strategies and cuttings dewatering techniques. Optimisation of deployable solids control equipment is discussed and how the required centrate clarity was achieved in the presence of pyrite-rich metasediment cuttings. Key results were the successful pre-aggregation of fines through the selection and use of high molecular weight anionic polyacrylamide flocculants and the techniques developed for optimal dosing prior to scroll decanter centrifugation, thus keeping sub 40 micron solids loading within prescribed limits. Experiments on maximising fines capture in the presence of thixotropic drilling fluid additives (e.g. Xanthan gum and other biopolymers) are also discussed. As no core is produced during the drilling process, it is intended that the particle laden returned drilling fluid is used for top-of-hole geochemical and mineralogical assessment. A discussion is therefore presented on the biasing and latency of cuttings representivity by dewatering techniques, as well as the resulting detrimental effects on depth fidelity and accuracy. Data pertaining to the sample biasing with respect to geochemical signatures due to particle size distributions is presented and shows that, depending on the solids control and dewatering techniques used, it can have unwanted influence on top-of-hole analysis. Strategies are proposed to overcome these effects, improving sample quality. Successful solids control and cuttings dewatering for water-powered percussive drilling is presented, contributing towards the successful advancement of coiled tubing based greenfields mineral exploration.

Keywords: cuttings, dewatering, flocculation, percussive drilling, solids control

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Authors: H. Nikzad, S. Yoshitomi

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In this paper, the optimal seismic design of reinforced concrete shear wall-frame building structures was done using structural optimization. The optimal section sizes were generated through structural optimization based on linear static analysis conforming to American Concrete Institute building design code (ACI 318-14). An analytical procedure was followed to validate the accuracy of the proposed method by comparing stresses on structural members through output files of MATLAB and ETABS. In order to consider the difference of stresses in structural elements by ETABS and MATLAB, and to avoid over-stress members by ETABS, a stress constraint ratio of MATLAB to ETABS was modified and introduced for the most critical load combinations and structural members. Moreover, seismic design of the structure was done following the International Building Code (IBC 2012), American Concrete Institute Building Code (ACI 318-14) and American Society of Civil Engineering (ASCE 7-10) standards. Typical reinforcement requirements for the structural wall, beam and column were discussed and presented using ETABS structural analysis software. The placement and detailing of reinforcement of structural members were also explained and discussed. The outcomes of this study show that the modification of section sizes play a vital role in finding an optimal combination of practical section sizes. In contrast, the optimization problem with size constraints has a higher cost than that of without size constraints. Moreover, the comparison of optimization problem with that of ETABS program shown to be satisfactory and governed ACI 318-14 building design code criteria.

Keywords: structural optimization, seismic design, linear static analysis, etabs, matlab, rc shear wall-frame structures

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Authors: William Whiteley, Jens Gregor

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In recent developments of direct neural network based positron emission tomography (PET) reconstruction, two prominent architectures have emerged for converting measurement data into images: 1) networks that contain fully-connected layers; and 2) networks that primarily use a convolutional encoder-decoder architecture. In this paper, we present a multi-modal direct PET reconstruction method called MDPET, which is a hybrid approach that combines the advantages of both types of networks. MDPET processes raw data in the form of sinograms and histo-images in concert with attenuation maps to produce high quality multi-slice PET images (e.g., 8x440x440). MDPET is trained on a large whole-body patient data set and evaluated both quantitatively and qualitatively against target images reconstructed with the standard PET reconstruction benchmark of iterative ordered subsets expectation maximization. The results show that MDPET outperforms the best previously published direct neural network methods in measures of bias, signal-to-noise ratio, mean absolute error, and structural similarity.

Keywords: deep learning, image reconstruction, machine learning, neural network, positron emission tomography

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Authors: Naman Solanki, Souraj Mondal

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With the world moving towards online communication, the video datastore has exploded in the past few years. Consequently, it has become crucial to analyse participant’s engagement levels in online communication videos. Engagement prediction of people in videos can be useful in many domains, like education, client meetings, dating, etc. Video-level or frame-level prediction of engagement for a user involves the development of robust models that can capture facial micro-emotions efficiently. For the development of an engagement prediction model, it is necessary to have a widely-accepted standard dataset for engagement analysis. DAiSEE is one of the datasets which consist of in-the-wild data and has a gold standard annotation for engagement prediction. Earlier research done using the DAiSEE dataset involved training and testing standard models like CNN-based models, but the results were not satisfactory according to industry standards. In this paper, a multi-level classification approach has been introduced to create a more robust model for engagement analysis using the DAiSEE dataset. This approach has recorded testing accuracies of 0.638, 0.7728, 0.8195, and 0.866 for predicting boredom level, engagement level, confusion level, and frustration level, respectively.

Keywords: computer vision, engagement prediction, deep learning, multi-level classification

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Authors: ELdessouky Sheta, Sayed Elzomor, Haithem Farghali, Kawkab A. Ahmed, Naglaa A. Abd Elkader

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The present investigation has been dealt with Spirocerca (S.) lupi infested mongrel dogs. This parasitic disease is highly infective to human beings and carnivores. The diagnosis march has been comprised the lateral contrast thoracic radiographs, fecal examination, blood profile, endoscopic examination and histopathological sections of deep seated pinch biopsies. These infested dogs have been put under an adopted treatment with Ivermectin injection combined with oral prednisolone. The obtained results reveal an absence of the pessimistic recognitions particularly after 3 weeks from the onset of treatment. Endoscopically the presented esophageal nodules are marked out in the distal third of infested dogs' esophagus as masses assigned into the esophageal lumen and fundus of stomach. The endoscopic outlook of Spirocerca lupi lesions has been considered an integral procedure of the diagnostic march and for evaluation of treatment follow up. The diagnostic procedures and the recommended treatment are the vet's guidance to care for Spirocerca lupi in dogs, hoping in future to prevent this disease from being spread among human beings and other carnivores.

Keywords: endoscopy, esophagus, stomach spirocercosis, dogs

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Authors: Siyao Zhu, Yifang Xu

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After natural and man-made disasters, robots can bypass the danger, expedite the search, and acquire unprecedented situational awareness to design rescue plans. The hands-free requirement from the first responders excludes the use of tedious manual control and operation. In unknown, unstructured, and obstructed environments, natural-language-based supervision is not amenable for first responders to formulate, and is difficult for robots to understand. Brain-computer interface is a promising option to overcome the limitations. This study aims to test the feasibility of using electroencephalography (EEG) signals to decode human intentions and detect the level of consensus on robot-provided information. EEG signals were classified using machine-learning and deep-learning methods to discriminate search intentions and agreement perceptions. The results show that the average classification accuracy for intention recognition and consensus assessment is 67% and 72%, respectively, proving the potential of incorporating recognizable users’ bioelectrical responses into advanced robot-assisted systems for emergency response.

Keywords: consensus assessment, electroencephalogram, emergency response, human-robot collaboration, intention recognition, search and rescue

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Authors: Ebasa Girma, Nathnael Minyelshowa, Lebsework Negash

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The use of unmanned aerial vehicles (UAVs) in precision agriculture has seen a huge increase recently. As such, systems that aim to apply various algorithms on the field need a structured framework of abstractions. This paper defines the various tasks of the UAVs in precision agriculture and models them into an architectural framework. The presented architecture is built on the context that there will be minimal physical intervention to do the tasks defined with multiple coordinated and cooperative UAVs. Various tasks such as image processing, path planning, communication, data acquisition, and field mapping are employed in the architecture to provide an efficient system. Besides, different limitation for applying Multi-UAVs in precision agriculture has been considered in designing the architecture. The architecture provides an autonomous end-to-end solution, starting from mission planning, data acquisition, and image processing framework that is highly efficient and can enable farmers to comprehensively deploy UAVs onto their lands. Simulation and field tests show that the architecture offers a number of advantages that include fault-tolerance, robustness, developer, and user-friendliness.

Keywords: deep learning, multi-UAVs, precision agriculture, UAVs architecture

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Authors: Prashant Dhiman, Viranshu Kumar, Pradeep Joshi

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Lot of research is going on to study the effect of cryogenic treatment on materials. Cryogenic treatment is a heat treatment process which is used widely to enhance the mechanical and metallurgical properties of various materials whether the material is ferrous or non ferrous. In almost all ferrous metals, it is found that retained austenite is converted into martensite. Generally deep cryogenic treatment is done using liquid nitrogen having temperature of -195 ℃. The austenite is unstable at this stage and converts into martensite. In non ferrous materials there presents a microcavity and under the action of stress it becomes crack. When this crack propagates, fracture takes place. As the metal contract under low temperature, by doing cryogenic treatment these microcavities will be filled hence increases the soundness of the material. Properties which are enhanced by cryogenic treatment of both ferrous and non ferrous materials are hardness, tensile strength, wear rate, electrical and thermal conductivity, and others. Also there is decrease in residual stress. A large number of manufacturing process (EDM, CNC etc.) are using cryogenic treatment on different tools or workpiece to reduce their wear. In this Review paper the use of cryogenic heat treatment in different manufacturing has been shown along with their advantages.

Keywords: cyrogenic treatment, EDM (Electrical Discharge Machining), CNC (Computer Numeric Control), Mechanical and Metallurgical Properties

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Authors: Emi Yuda, Yutaka Yoshida, Junichiro Hayano

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Distinct circadian rhythm of activity, i.e., high activity during the day and deep rest at night are a typical feature of a healthy lifestyle. Exposure to the skylight is thought to be an important factor to increase arousal level and maintain normal circadian rhythm. To examine whether sunshine hours influence the day-night contract of activity, we analyzed the relationship between 24-hour heart rate (HR) and weather data of the recording day. We analyzed data in 36,500 males and 49,854 females of Allostatic State Mapping by Ambulatory ECG Repository (ALLSTAR) database in Japan. Median (IQR) sunshine duration was 5.3 (2.8-7.9) hr. While sunshine hours had only modest effects of increasing 24-hour average HR in either gender (P=0.0282 and 0.0248 for male and female) and no significant effects on nighttime HR in either gender, it increased daytime HR (P = 0.0007 and 0.0015) and day-night HF difference in both genders (P < 0.0001 for both) even after adjusting for the effects of average temperature, atmospheric pressure, and humidity. Our observations support for the hypothesis that longer sunshine hours enhance circadian rhythm of activity.

Keywords: big data, circadian rhythm, heart rate, sunshine

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Authors: Saida Haider, Syeda Madiha

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Parkinson’s disease (PD) is a common neurological disorder characterized by motor deficits and loss of dopaminergic neurons. Oxidative stress is said to play a pivotal role in the pathophysiology of the disease. In the present study, PD was induced by injection of rotenone (1.5 mg/kg/day, s.c.) for eight days. Pistachio (800 mg/kg/day, p.o.) was given for two weeks. At the end of treatment brains were dissected out and striatum was isolated for biochemical and neurochemical analysis. Morris water maze (MWM) test and novel object recognition (NOR) task was used to test the memory function while motor behavior was determined by open field test (OFT), Kondziela inverted screen test (KIST), pole test (PT), beam walking test (BWT), inclined plane test (IPT) and footprint (FP) test. Several dietary components have been evaluated as potential therapeutic compounds in many neurodegenerative diseases. Increasing evidence shows that nuts have protective effects against various diseases by improving the oxidative status and reducing lipid peroxidation. Pistachio is the only nut that contains anthocyanin, a potent antioxidant having neuroprotective properties. Results showed that pistachio supplementation significantly restored the rotenone-induced motor deficits and improved the memory performance. Moreover, rats treated with pistachio also exhibited enhanced oxidative status and increased dopamine (DA) and 5-hydroxytryptamine (5-HT) concentration in striatum. In conclusion, to our best knowledge, we have for the first time shown that pistachio nut possesses neuroprotective effects against rotenone-induced motor and cognitive deficits. These beneficial effects of pistachio may be attributed to its high content of natural antioxidant and phenolic compounds. Hence, consumption of pistachio regularly as part of a daily diet can be beneficial in the prevention and treatment of PD.

Keywords: rotenone, pistachio, oxidative stress, Parkinson’s disease

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Authors: Randy Price, Harbir Antil, Rainald Löhner, Fumiya Togashi

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Chemically reacting flows are common in engineering applications such as hypersonic flow, combustion, explosions, manufacturing process, and environmental assessments. The number of reactions in combustion simulations can exceed 100, making a large number of flow and combustion problems beyond the capabilities of current supercomputers. Motivated by this, deep neural networks (DNNs) will be introduced with the goal of eventually replacing the existing chemistry software packages with DNNs. The DNNs used in this paper are motivated by the Residual Neural Network (ResNet) architecture. In the continuum limit, ResNets become an optimization problem constrained by an ODE. Such a feature allows the use of ODE control techniques to enhance the DNNs. In this work, DNNs are constructed, which update the species un at the nᵗʰ timestep to uⁿ⁺¹ at the n+1ᵗʰ timestep. Parallel DNNs are trained for each species, taking in uⁿ as input and outputting one component of uⁿ⁺¹. These DNNs are applied to multiple species and reactions common in chemically reacting flows such as H₂-O₂ reactions. Experimental results show that the DNNs are able to accurately replicate the dynamics in various situations and in the presence of errors.

Keywords: chemical reacting flows, computational fluid dynamics, ODEs, residual neural networks, ResNets

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Authors: Farhad Ghorbandordinejad, Shamsoddin Aref

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Wikis and blogs, defined as educational tools in line with the objectives of collaborative writing, are regarded as innovative ways of writing addressing the problems of conventional types of writing. Although writing in wikis and blogs step in different contexts, they are both aiming at betterment of collaborative writing procedures. It is believed that due to certain reasons bringing in wikis and blogs to learners' life can lead to better performance of writing. This study aimed at dipping into pedagogical aspects of wikis and blogs in the hope of eliminating prior traditional mistakes and bringing students together in a more constructive L2 context. To this end, three groups of intermediate students were experimented in three settings of wiki-group, blog-group and conventional (control) group. Despite conventional group learners, participants in both experimental groups experienced L2 writing in a new telecollaborative context. An achievement test was administered after the treatment to check learners’ degree of improvement in EFL writing. The results of this study provide a deep insight towards the effectiveness of writing in the contexts of wikis and blogs compared with conventional writing procedures. The overall conclusion drawn from the distinction of conventional writing, on one hand, and wikis and blogs, on the other hand, indicates that the latter channels of writing are more constructive for learners’ writing improvements.

Keywords: collaborative writing, wikis, blogs, writing achievement

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Authors: Ali Al-Ghalib, Fouad Mohammad

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The identification of damage in reinforced concrete structures subjected to incremental cracking performance exploiting vibration data is recognized as a challenging topic in the published and heavily cited literature. Therefore, this paper attempts to shine light on the extent of dynamic methods when applied to reinforced concrete beams simulated with various scenarios of defects. For this purpose, three different reinforced concrete beams are tested through the course of the study. The three beams are loaded statically to failure in incremental successive load cycles and later rehabilitated. After each static load stage, the beams are tested under free-free support condition using experimental modal analysis. The beams were all of the same length and cross-sectional area (2.0x0.14x0.09)m, but they were different in concrete compressive strength and the type of damage presented. The experimental modal parameters as damage identification parameters were showed computationally expensive, time consuming and require substantial inputs and considerable expertise. Nonetheless, they were proved plausible for the condition monitoring of the current case study as well as structural changes in the course of progressive loads. It was accentuated that a satisfactory localization and quantification for structural changes (Level 2 and Level 3 of damage identification problem) can only be achieved reasonably through considering frequencies and mode shapes of a system in a proper analytical model. A convenient post analysis process for various datasets of vibration measurements for the three beams is conducted in order to extract, check and correlate the basic modal parameters; namely, natural frequency, modal damping and mode shapes. The results of the extracted modal parameters and their combination are utilized and discussed in this research as quantification parameters.

Keywords: experimental modal analysis, damage identification, structural health monitoring, reinforced concrete beam

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Authors: Gabrielle Robertson, Matthew Downs, Joseph Dagher

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Iron deposition in the brain has been linked with a host of neurological disorders such as Alzheimer’s, Parkinson’s, and Multiple Sclerosis. While some treatment options exist, there are no objective measurement tools that allow for the monitoring of iron levels in the brain in vivo. An emerging Magnetic Resonance Imaging (MRI) method has been recently proposed to deduce iron concentration through quantitative measurement of magnetic susceptibility. This is a multi-step process that involves repeated modeling of physical processes via approximate numerical solutions. For example, the last two steps of this Quantitative Susceptibility Mapping (QSM) method involve I) mapping magnetic field into magnetic susceptibility and II) mapping magnetic susceptibility into iron concentration. Process I involves solving an ill-posed inverse problem by using regularization via injection of prior belief. The end result from Process II highly depends on the model used to describe the molecular content of each voxel (type of iron, water fraction, etc.) Due to these factors, the accuracy and repeatability of QSM have been an active area of research in the MRI and medical imaging community. This work aims to estimate iron concentration in the brain via a single step. A synthetic numerical model of the human head was created by automatically and manually segmenting the human head on a high-resolution grid (640x640x640, 0.4mm³) yielding detailed structures such as microvasculature and subcortical regions as well as bone, soft tissue, Cerebral Spinal Fluid, sinuses, arteries, and eyes. Each segmented region was then assigned tissue properties such as relaxation rates, proton density, electromagnetic tissue properties and iron concentration. These tissue property values were randomly selected from a Probability Distribution Function derived from a thorough literature review. In addition to having unique tissue property values, different synthetic head realizations also possess unique structural geometry created by morphing the boundary regions of different areas within normal physical constraints. This model of the human brain is then used to create synthetic MRI measurements. This is repeated thousands of times, for different head shapes, volume, tissue properties and noise realizations. Collectively, this constitutes a training-set that is similar to in vivo data, but larger than datasets available from clinical measurements. This 3D convolutional U-Net neural network architecture was used to train data-driven Deep Learning models to solve for iron concentrations from raw MRI measurements. The performance was then tested on both synthetic data not used in training as well as real in vivo data. Results showed that the model trained on synthetic MRI measurements is able to directly learn iron concentrations in areas of interest more effectively than other existing QSM reconstruction methods. For comparison, models trained on random geometric shapes (as proposed in the Deep QSM method) are less effective than models trained on realistic synthetic head models. Such an accurate method for the quantitative measurement of iron deposits in the brain would be of important value in clinical studies aiming to understand the role of iron in neurological disease.

Keywords: magnetic resonance imaging, MRI, iron deposition, machine learning, quantitative susceptibility mapping

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Authors: Zi Zhu

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The last decade has witnessed increasing urban environmental uprisings in China, as thousands of citizens swarmed into streets to express their deep concerns about the environmental threat and public health through various collective actions. The prevalent western approaches to collective actions, which usually treat urban riots and social movements as distinct phenomenon, have plagued an adequate analysis of the urban environmental uprisings in China. The increasing urban environmental contention can neither be categorized into riots nor social movements, as they carry the features of both: at first sight, they are spontaneous, disorganized and disruptive with an absence of observable mobilization process; however, unlike riots in the west, these collective actions conveyed explicit demand in a mostly non-destructive way rather than a pure expression of frustration. This article proposes a different approach to urban environmental uprisings in China which concerns the diminishing boundaries between riots and social movements and points to the underlying structural causes to the unique forms of urban environmental contention. Taking the urban anti-PX protests as examples, this article analyzes the societal and political structural environment faced by the Chinese environmental protesters and its influence on the origin and development of their contention.

Keywords: urban environmental uprisings, China, anti-PX protests, opportunity structure

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Authors: Seyed Ali Mahdipour, Maryam Shafeei Sarvestani

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Radiation pressure acceleration (RPA) and target normal sheath acceleration (TNSA) are the most important methods of Laser-accelerated proton beams (LAP) planning systems.LAP has inspired novel applications that can benefit from proton bunch properties different from conventionally accelerated proton beams. The secondary neutron and photon produced in the collision of protons with beamline components are of the important concern in proton therapy. Various published Monte Carlo researches evaluated the beamline and shielding considerations for TNSA method, but there is no studies directly address secondary neutron and photon production from RPA method in LAP. The purpose of this study is to calculate the flux distribution of neutron and photon secondary radiations on the first area ofLAP and to determine the optimize thickness and radius of the energyselector in a LAP planning system based on RPA method. Also, we present the Monte Carlo calculations to determine the appropriate beam pipe for shielding a LAP planning system. The GEANT4 Monte Carlo toolkit has been used to simulate a secondary radiation production in LAP. A section of new multifunctional LAP beamlinehas been proposed, based on the pulsed power solenoid scheme as a GEANT4 toolkit. The results show that the energy selector is the most important source of neutron and photon secondary particles in LAP beamline. According to the calculations, the pure Tungsten energy selector not be the proper case, and using of Tungsten+Polyethylene or Tungsten+Graphitecomposite selectors will reduce the production of neutron and photon intensities by approximately ~10% and ~25%, respectively. Also the optimal radiuses of energy selectors were found to be ~4 cm and ~6 cm for a 3 degree and 5 degree proton deviation angles, respectively.

Keywords: neutron, photon, flux distribution, energy selector, GEANT4 toolkit

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Authors: Osamede Asowata

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The current resurgence of interest in the use of renewable energy is driven by the need to reduce the high environmental impact of fossil-based energy. The aim of this paper is to evaluate the effect of a stationary PV panel on the charging rate of deep-cycle valve regulated lead-acid (DCVRLA) batteries. Stationary PV panels are set to a fixed tilt and orientation angle, which plays a major role in dictating the output power of a PV panel and subsequently on the charging time of a DCVRLA battery. In a basic PV system, an energy storage device that stores the power from the PV panel is necessary due to the fluctuating nature of the PV voltage caused by climatic conditions. The charging and discharging times of a DCVRLA battery were determined for a twelve month period from January through December 2012. Preliminary results, which include regression analysis (R2), conversion-time per week and work-time per day, indicate that a 36 degrees tilt angle produces a good charging rate for a latitude of 26 degrees south throughout the year.

Keywords: tilt and orientation angles, solar chargers, PV panels, storage devices, direct solar radiation.

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Authors: Aleksandras Velykis, Antanas Satkus

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Clay loam and clay soils are typical for northern Lithuania. These soils are susceptible to physical degradation in the case of intensive use of heavy machinery for field operations. However, clayey soils having poor physical properties by origin require more intensive tillage to maintain proper physical condition for grown crops. Therefore not only choice of suitable tillage system is very important for these soils in the region, but also additional search of other measures is essential for good soil physical state maintenance. Research objective: To evaluate the long-term effects of different intensity tillage as well as its combinations with supplementary agronomic practices on improvement of soil physical conditions and environmental sustainability. The experiment examined the influence of deep and shallow ploughing, ploughless tillage, combinations of ploughless tillage with incorporation of lime sludge and cover crop for green manure and application of the same cover crop for mulch without autumn tillage under spring and winter crop growing conditions on clay loam (27% clay, 50% silt, 23% sand) Endocalcaric Endogleyic Cambisol. Methods: The indicators characterizing the impact of investigated measures were determined using the following methods and devices: Soil dry bulk density – by Eijkelkamp cylinder (100 cm3), soil water content – by weighing, soil structure – by Retsch sieve shaker, aggregate stability – by Eijkelkamp wet sieving apparatus, soil mineral nitrogen – in 1 N KCL extract using colorimetric method. Results: Clay loam soil physical state (dry bulk density, structure, aggregate stability, water content) depends on tillage system and its combination with additional practices used. Application of cover crop winter mulch without tillage in autumn, ploughless tillage and shallow ploughing causes the compaction of bottom (15-25 cm) topsoil layer. However, due to ploughless tillage the soil dry bulk density in subsoil (25-35 cm) layer is less compared to deep ploughing. Soil structure in the upper (0-15 cm) topsoil layer and in the seedbed (0-5 cm), prepared for spring crops is usually worse when applying the ploughless tillage or cover crop mulch without autumn tillage. Application of lime sludge under ploughless tillage conditions helped to avoid the compaction and structure worsening in upper topsoil layer, as well as increase aggregate stability. Application of reduced tillage increased soil water content at upper topsoil layer directly after spring crop sowing. However, due to reduced tillage the water content in all topsoil markedly decreased when droughty periods lasted for a long time. Combination of reduced tillage with cover crop for green manure and winter mulch is significant for preserving the environment. Such application of cover crops reduces the leaching of mineral nitrogen into the deeper soil layers and environmental pollution. This work was supported by the National Science Program ‘The effect of long-term, different-intensity management of resources on the soils of different genesis and on other components of the agro-ecosystems’ [grant number SIT-9/2015] funded by the Research Council of Lithuania.

Keywords: clay loam, endocalcaric endogleyic cambisol, mineral nitrogen, physical state

Procedia PDF Downloads 226

Authors: Reihaneh Mahdavishahri

Abstract:

This paper provides a brief introduction to emotionally focused therapy (EFT) and its culturally sensitive and informed application when working with immigrant couples. EFT's grounding in humanistic psychology prioritizes a non-pathologizing and empathic understanding of individuals' experiences, creating a safe space for couples to explore and create new experiences without imposing judgment or prescribing the couple "the right way of interacting" with one another. EFT's emphasis on attachment, bonding, emotions, and corrective emotional experiences makes it a fitting approach to work with multicultural couples, allowing for the corrective emotional experience to be shaped and informed by the couples' unique cultural background. This paper highlights the challenges faced by immigrant couples and explores how immigration adds a complex layer to each partner’s sense of self, their attachment bond, and their sense of safety and security within their relationships. Navigating a new culture, creating a shared sense of purpose, and re-establishing emotional bonds can be daunting for immigrant couples, often leading to a deep sense of disconnection and vulnerability. Reestablishing and fostering secure attachment between the partners in the safety of the therapeutic space can be a protective factor for these couples.

Keywords: attachment, culturally informed care, emotionally focused therapy, immigration

Procedia PDF Downloads 74

Authors: Yakin Hajlaoui, Richard Labib, Jean-François Plante, Michel Gamache

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This study introduces the Multi-Layer Inverse Distance Weighting Model (ML-IDW), inspired by the mathematical formulation of both multi-layer neural networks (ML-NNs) and Inverse Distance Weighting model (IDW). ML-IDW leverages ML-NNs' processing capabilities, characterized by compositions of learnable non-linear functions applied to input features, and incorporates IDW's ability to learn anisotropic spatial dependencies, presenting a promising solution for nonlinear spatial interpolation and learning from complex spatial data. it employ gradient descent and backpropagation to train ML-IDW, comparing its performance against conventional spatial interpolation models such as Kriging and standard IDW on regression and classification tasks using simulated spatial datasets of varying complexity. the results highlight the efficacy of ML-IDW, particularly in handling complex spatial datasets, exhibiting lower mean square error in regression and higher F1 score in classification.

Keywords: deep learning, multi-layer neural networks, gradient descent, spatial interpolation, inverse distance weighting

Procedia PDF Downloads 52

Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski

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In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.

Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection

Procedia PDF Downloads 144

Authors: Navajyoti Panda, R. S. Pawar

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In this study, process parameters like punch angle, die opening, grain direction, and pre-bend condition of the strip for deep draw of high strength low alloy steel HSLA 420 are investigated. The finite element method (FEM) in association with the Taguchi and the analysis of variance (ANOVA) techniques are carried out to investigate the degree of importance of process parameters in V-bending process for HSLA 420&ST12 grade material. From results, it is observed that punch angle had a major influence on the spring-back. Die opening also showed very significant role on spring back. On the other hand, it is revealed that grain direction had the least impact on spring back; however, if strip from flat sheet is taken, then it is less prone to spring back as compared to the strip from sheet metal coil. HyperForm software is used for FEM simulation and experiments are designed using Taguchi method. Percentage contribution of the parameters is obtained through the ANOVA techniques.

Keywords: bending, spring-back, v-bending, FEM, Taguchi, HSLA 420 and St12 materials, HyperForm, profile projector

Procedia PDF Downloads 189