Search results for: analyst forecast accuracy

Authors: Shiyi He, Yan Xia, Xiaoping Ouyang, Liang Chen, Zhongbing Zhang, Jinlu Ruan

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As the key system of the spacecraft, various propelling system have been developing rapidly, including ion thrust, laser thrust, solar sail and other micro-thrusters. However, there still are some shortages in these systems. The ion thruster requires the high-voltage or magnetic field to accelerate, resulting in extra system, heavy quantity and large volume. The laser thrust now is mostly ground-based and providing pulse thrust, restraint by the station distribution and the capacity of laser. The thrust direction of solar sail is limited to its relative position with the Sun, so it is hard to propel toward the Sun or adjust in the shadow.In this paper, a novel nuclear thruster based on alpha decay and spontaneous fission is proposed and the principle of this radiation-thrust with alpha particle has been expounded. Radioactive materials with different released energy, such as 210Po with 5.4MeV and 238Pu with 5.29MeV, attached to a metal film will provides various thrust among 0.02-5uN/cm2. With this repulsive force, radiation is able to be a power source. With the advantages of low system quantity, high accuracy and long active time, the radiation thrust is promising in the field of space debris removal, orbit control of nano-satellite array and deep space exploration. To do further study, a formula lead to the amplitude and direction of thrust by the released energy and decay coefficient is set up. With the initial formula, the alpha radiation elements with the half life period longer than a hundred days are calculated and listed. As the alpha particles emit continuously, the residual charge in metal film grows and affects the emitting energy distribution of alpha particles. With the residual charge or extra electromagnetic field, the emitting of alpha particles performs differently and is analyzed in this paper. Furthermore, three more complex situations are discussed. Radiation element generating alpha particles with several energies in different intensity, mixture of various radiation elements, and cascaded alpha decay are studied respectively. In combined way, it is more efficient and flexible to adjust the thrust amplitude. The propelling model of the spontaneous fission is similar with the one of alpha decay, which has a more complex angular distribution. A new quasi-sphere space propelling system based on the radiation-thrust has been introduced, as well as the collecting and processing system of excess charge and reaction heat. The energy and spatial angular distribution of emitting alpha particles on unit area and certain propelling system have been studied. As the alpha particles are easily losing energy and self-absorb, the distribution is not the simple stacking of each nuclide. With the change of the amplitude and angel of radiation-thrust, orbital variation strategy on space debris removal is shown and optimized.

Keywords: alpha decay, angular distribution, emitting energy, orbital variation, radiation-thruster

Procedia PDF Downloads 171

Authors: Takahiro Nishimura, Kouki Doi, Hiroshi Fujimoto

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Touch-sensitive screens that serve as displays and input devices have been adopted in many portable terminals such as smartphones and personal media players, and the market of touch-sensitive screens has expanded greatly. One of the advantages of touch-sensitive screen is the flexibility in the graphical user interface (GUI) design, and it is imperative to design an appropriate GUI to realize an easy-to-use interface. Moreover, it is important to evaluate the relationship between pointing performance and GUI design. There is much knowledge regarding easy-to-use GUI designs for portable terminals with touch-sensitive screens, and most have focused on GUI design approaches for women or children with small hands. In contrast, GUI design approaches for users with large hands have not received sufficient attention. In this study, to obtain knowledge that contributes to the establishment of individualized easy-to-use GUI design guidelines, we conducted experiments to investigate the relationship between thumb length and pointing performance on portable terminals with touch-sensitive screens. In this study, fourteen college students who participated in the experiment were divided into two groups based on the length of their thumbs. Specifically, we categorized the participants into two groups, thumbs longer than 64.2 mm into L (Long) group, and thumbs longer than 57.4 mm but shorter than 64.2 mm into A (Average) group, based on Japanese anthropometric database. They took part in this study under the authorization of Waseda University’s ‘Ethics Review Committee on Research with Human Subjects’. We created an application for the experimental task and implemented it on the projected capacitive touch-sensitive screen portable terminal (iPod touch (4th generation)). The display size was 3.5 inch and 960 × 640 - pixel resolution at 326 ppi (pixels per inch). This terminal was selected as the experimental device, because of its wide use and market share. The operational procedure of the application is as follows. First, the participants placed their thumb on the start position. Then, one cross-shaped target in a 10 × 7 array of 70 positions appeared at random. The participants pointed the target with their thumb as accurately and as fast as possible. Then, they returned their thumb to the start position and waited. The operation ended when this procedure had been repeated until all 70 targets had each been pointed at once by the participants. We adopted the evaluation indices for absolute error, variable error, and pointing time to investigate pointing performance when using the portable terminal. The results showed that pointing performance varied with thumb length. In particular, on the lower right side of the screen, the performance of L group with long thumb was low. Further, we presented an approach for designing easy-to- use button GUI for users with long thumbs. The contributions of this study include revelation of the relationship between pointing performance and user’s thumb length when using a portable terminal in terms of accuracy, precision, and speed of pointing. We hope that these findings contribute to an easy-to-use GUI design for users with large hands.

Keywords: pointing performance, portable terminal, thumb length, touch-sensitive screen

Procedia PDF Downloads 140

Authors: Jose Daniel Hoyos Giraldo, Jesus Hernan Jimenez Giraldo, Juan Pablo Alvarado Perilla

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Range and endurance are the main limitations of electric aircraft due to the nature of its source of power. The improvement of efficiency on this kind of systems is extremely meaningful to encourage the aircraft operation with less environmental impact. The propeller efficiency highly affects the overall efficiency of the propulsion system; hence its optimization can have an outstanding effect on the aircraft performance. An optimization method is applied to an aircraft propeller in order to maximize its range and endurance by estimating the best combination of geometrical parameters such as diameter and airfoil, chord and pitch distribution for a specific aircraft design at a certain cruise speed, then the rotational speed at which the propeller operates at minimum current consumption is estimated. The optimization is based on the Blade Element Momentum (BEM) method, additionally corrected to account for tip and hub losses, Mach number and rotational effects; furthermore an airfoil lift and drag coefficients approximation is implemented from Computational Fluid Dynamics (CFD) simulations supported by preliminary studies of grid independence and suitability of different turbulence models, to feed the BEM method, with the aim of achieve more reliable results. Additionally, Vortex Theory is employed to find the optimum pitch and chord distribution to achieve a minimum induced loss propeller design. Moreover, the optimization takes into account the well-known brushless motor model, thrust constraints for take-off runway limitations, maximum allowable propeller diameter due to aircraft height and maximum motor power. The BEM-CFD method is validated by comparing its predictions for a known APC propeller with both available experimental tests and APC reported performance curves which are based on Vortex Theory fed with the NASA Transonic Airfoil code, showing a adequate fitting with experimental data even more than reported APC data. Optimal propeller predictions are validated by wind tunnel tests, CFD propeller simulations and a study of how the propeller will perform if it replaces the one of on known aircraft. Some tendency charts relating a wide range of parameters such as diameter, voltage, pitch, rotational speed, current, propeller and electric efficiencies are obtained and discussed. The implementation of CFD tools shows an improvement in the accuracy of BEM predictions. Results also showed how a propeller has higher efficiency peaks when it operates at high rotational speed due to the higher Reynolds at which airfoils present lower drag. On the other hand, the behavior of the current consumption related to the propulsive efficiency shows counterintuitive results, the best range and endurance is not necessary achieved in an efficiency peak.

Keywords: BEM, blade design, CFD, electric aircraft, endurance, optimization, range

Procedia PDF Downloads 86

Authors: Stephen Dankwa, Zheng Wenfeng, Xiaolu Li

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Climate change is recently gaining the attention of many countries across the world. Climate change, which is also known as global warming, referring to the increasing in average surface temperature has been a concern to the Environmental Protection Agency of Ghana. Recently, Ghana has become vulnerable to the effect of the climate change as a result of the dependence of the majority of the population on agriculture. The clearing down of trees to grow crops and burning of charcoal in the country has been a contributing factor to the rise in temperature nowadays in the country as a result of releasing of carbon dioxide and greenhouse gases into the air. Recently, petroleum stations across the cities have been on fire due to this climate changes and which have position Ghana in a way not able to withstand this climate event. As a result, the significant of this research paper is to project how the rise in the average surface temperature will be like at the end of the mid-21st century when agriculture and deforestation are allowed to continue for some time in the country. This study uses the Coupled Modeled Intercomparison Project phase 5 (CMIP5) experiment RCP 8.5 model output data to monitor the future climate changes from 2041-2050, at the end of the mid-21st century over the ten (10) major cities (Accra, Bolgatanga, Cape Coast, Koforidua, Kumasi, Sekondi-Takoradi, Sunyani, Ho, Tamale, Wa) in Ghana. In the models, Support Vector Machine and Random forest, where the cities as a function of heat wave metrics (minimum temperature, maximum temperature, mean temperature, heat wave duration and number of heat waves) assisted to provide more than 50% accuracy to predict and monitor the pattern of the surface air temperature. The findings identified were that the near-surface air temperature will rise between 1°C-2°C (degrees Celsius) over the coastal cities (Accra, Cape Coast, Sekondi-Takoradi). The temperature over Kumasi, Ho and Sunyani by the end of 2050 will rise by 1°C. In Koforidua, it will rise between 1°C-2°C. The temperature will rise in Bolgatanga, Tamale and Wa by 0.5°C by 2050. This indicates how the coastal and the southern part of the country are becoming hotter compared with the north, even though the northern part is the hottest. During heat waves from 2041-2050, Bolgatanga, Tamale, and Wa will experience the highest mean daily air temperature between 34°C-36°C. Kumasi, Koforidua, and Sunyani will experience about 34°C. The coastal cities (Accra, Cape Coast, Sekondi-Takoradi) will experience below 32°C. Even though, the coastal cities will experience the lowest mean temperature, they will have the highest number of heat waves about 62. Majority of the heat waves will last between 2 to 10 days with the maximum 30 days. The surface temperature will continue to rise by the end of the mid-21st century (2041-2050) over the major cities in Ghana and so needs to be addressed to the Environmental Protection Agency in Ghana in order to mitigate this problem.

Keywords: climate changes, CMIP5, Ghana, heat waves, random forest, SVM

Procedia PDF Downloads 176

Authors: Shanza Akram, Sameen Toor, Heba Harb Abu Alkass, Zainab Abdulsalam Altaha, Sara Taha Abdulla, Saleem Imran

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Acute pulmonary embolism (PE) is a common disease and can be fatal. The clinical presentation is variable and nonspecific, making accurate diagnosis difficult. Testing patients with suspected acute PE has increased dramatically. However, the overuse of some tests, particularly CT and D-dimer measurement, may not improve care while potentially leading to patient harm and unnecessary expense. CTPA is the investigation of choice for PE. Its easy availability, accuracy and ability to provide alternative diagnosis has lowered the threshold for performing it, resulting in its overuse. Guidelines have recommended the use of clinical pretest probability tools such as ‘Wells score’ to assess risk of suspected PE. Unfortunately, implementation of guidelines in clinical practice is inconsistent. This has led to low risk patients being subjected to unnecessary imaging, exposure to radiation and possible contrast related complications. Aim: To study the diagnostic yield of CT PA, clinical pretest probability of patients according to wells score and to determine whether or not there was an overuse of CTPA in our service. Methods: CT scans done on patients with suspected P.E in our hospital from 1st January 2014 to 31st December 2014 were retrospectively reviewed. Medical records were reviewed to study demographics, clinical presentation, final diagnosis, and to establish if Wells score and D-Dimer were used correctly in predicting the probability of PE and the need for subsequent CTPA. Results: 100 patients (51male) underwent CT PA in the time period. Mean age was 57 years (24-91 years). Majority of patients presented with shortness of breath (52%). Other presenting symptoms included chest pain 34%, palpitations 6%, collapse 5% and haemoptysis 5%. D Dimer test was done in 69%. Overall Wells score was low (<2) in 28 %, moderate (>2 - < 6) in 47% and high (> 6) in 15% of patients. Wells score was documented in medical notes of only 20% patients. PE was confirmed in 12% (8 male) patients. 4 had bilateral PE’s. In high-risk group (Wells > 6) (n=15), there were 5 diagnosed PEs. In moderate risk group (Wells >2 - < 6) (n=47), there were 6 and in low risk group (Wells <2) (n=28), one case of PE was confirmed. CT scans negative for PE showed pleural effusion in 30, Consolidation in 20, atelactasis in 15 and pulmonary nodule in 4 patients. 31 scans were completely normal. Conclusion: Yield of CT for pulmonary embolism was low in our cohort at 12%. A significant number of our patients who underwent CT PA had low Wells score. This suggests that CT PA is over utilized in our institution. Wells score was poorly documented in medical notes. CT-PA was able to detect alternative pulmonary abnormalities explaining the patient's clinical presentation. CT-PA requires concomitant pretest clinical probability assessment to be an effective diagnostic tool for confirming or excluding PE. . Clinicians should use validated clinical prediction rules to estimate pretest probability in patients in whom acute PE is being considered. Combining Wells scores with clinical and laboratory assessment may reduce the need for CTPA.

Keywords: CT PA, D dimer, pulmonary embolism, wells score

Procedia PDF Downloads 195

Authors: Elham Koohi, Silvio Jose Gumiere, Hossein Bonakdari, Saeid Homayouni

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Water used in agricultural crops can be managed by irrigation scheduling based on soil moisture levels and plant water stress thresholds. Automated irrigation scheduling limits crop physiological damage and yield reduction. Knowledge of crop water stress monitoring approaches can be effective in optimizing the use of agricultural water. Understanding the physiological mechanisms of crop responding and adapting to water deficit ensures sustainable agricultural management and food supply. This aim could be achieved by analyzing and diagnosing crop characteristics and their interlinkage with the surrounding environment. Assessments of plant functional types (e.g., leaf area and structure, tree height, rate of evapotranspiration, rate of photosynthesis), controlling changes, and irrigated areas mapping. Calculating thresholds of soil water content parameters, crop water use efficiency, and Nitrogen status make irrigation scheduling decisions more accurate by preventing water limitations between irrigations. Combining Remote Sensing (RS), the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning Algorithms (MLAs) can improve measurement accuracies and automate irrigation scheduling. This paper is a review structured by surveying about 100 recent research studies to analyze varied approaches in terms of providing high spatial and temporal resolution mapping, sensor-based Variable Rate Application (VRA) mapping, the relation between spectral and thermal reflectance and different features of crop and soil. The other objective is to assess RS indices formed by choosing specific reflectance bands and identifying the correct spectral band to optimize classification techniques and analyze Proximal Optical Sensors (POSs) to control changes. The innovation of this paper can be defined as categorizing evaluation methodologies of precision irrigation (applying the right practice, at the right place, at the right time, with the right quantity) controlled by soil moisture levels and sensitiveness of crops to water stress, into pre-processing, processing (retrieval algorithms), and post-processing parts. Then, the main idea of this research is to analyze the error reasons and/or values in employing different approaches in three proposed parts reported by recent studies. Additionally, as an overview conclusion tried to decompose different approaches to optimizing indices, calibration methods for the sensors, thresholding and prediction models prone to errors, and improvements in classification accuracy for mapping changes.

Keywords: agricultural crops, crop water stress detection, irrigation scheduling, precision agriculture, remote sensing

Procedia PDF Downloads 43

Authors: Alessandra Aparecida Vieira França, Adriana de Paula Lacerda Santos, Mauro Lacerda Santos Filho

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The purpose of this research i to study the behavior of precast prestressed hollow core slabs subjected to shear. In order to achieve this goal, shear tests were performed using hollow core slabs 26,5cm thick, with and without a concrete cover of 5 cm, without cores filled, with two cores filled and three cores filled with concrete. The tests were performed according to the procedures recommended by FIP (1992), the EN 1168:2005 and following the method presented in Costa (2009). The ultimate shear strength obtained within the tests was compared with the values of theoretical resistant shear calculated in accordance with the codes, which are being used in Brazil, noted: NBR 6118:2003 and NBR 14861:2011. When calculating the shear resistance through the equations presented in NBR 14861:2011, it was found that provision is much more accurate for the calculation of the shear strength of hollow core slabs than the NBR 6118 code. Due to the large difference between the calculated results, even for slabs without cores filled, the authors consulted the committee that drafted the NBR 14861:2011 and found that there is an error in the text of the standard, because the coefficient that is suggested, actually presents the double value than the needed one! The ABNT, later on, soon issued an amendment of NBR 14861:2011 with the necessary corrections. During the tests for the present study, it was confirmed that the concrete filling the cores contributes to increase the shear strength of hollow core slabs. But in case of slabs 26,5 cm thick, the quantity should be limited to a maximum of two cores filled, because most of the results for slabs with three cores filled were smaller. This confirmed the recommendation of NBR 14861:2011which is consistent with standard practice. After analyzing the configuration of cracking and failure mechanisms of hollow core slabs during the shear tests, strut and tie models were developed representing the forces acting on the slab at the moment of rupture. Through these models the authors were able to calculate the tensile stress acting on the concrete ties (ribs) and scaled the geometry of these ties. The conclusions of the research performed are the experiments results have shown that the mechanism of failure of the hollow-core slabs can be predicted using the strut-and-tie procedure, within a good range of accuracy. In addition, the needed of the correction of the Brazilian standard to review the correction factor σcp duplicated (in NBR14861/2011), and the limitation of the number of cores (Holes) to be filled with concrete, to increase the strength of the slab for the shear resistance. It is also suggested the increasing the amount of test results with 26.5 cm thick, and a larger range of thickness slabs, in order to obtain results of shear tests with cores concreted after the release of prestressing force. Another set of shear tests on slabs must be performed in slabs with cores filled and cover concrete reinforced with welded steel mesh for comparison with results of theoretical values calculated by the new revision of the standard NBR 14861:2011.

Keywords: prestressed hollow core slabs, shear, strut, tie models

Procedia PDF Downloads 305

Authors: Rajkumar Ghosh

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Earthquakes are natural disasters that pose a significant risk to human life and infrastructure. Effective earthquake mitigation measures require a thorough understanding of the dynamics of seismic occurrences, including thrust movement. Traditionally, estimating thrust movement has relied on typical techniques that may not capture the full complexity of these events. Therefore, investigating alternative approaches, such as incorporating out-of-sequence thrust movement data, could enhance earthquake mitigation strategies. This review aims to provide an overview of the applications of out-of-sequence thrust movement in earthquake mitigation. By examining existing research and studies, the objective is to understand how precise estimation of thrust movement can contribute to improving structural design, analyzing infrastructure risk, and developing early warning systems. The study demonstrates how to estimate out-of-sequence thrust movement using multiple data sources, including GPS measurements, satellite imagery, and seismic recordings. By analyzing and synthesizing these diverse datasets, researchers can gain a more comprehensive understanding of thrust movement dynamics during seismic occurrences. The review identifies potential advantages of incorporating out-of-sequence data in earthquake mitigation techniques. These include improving the efficiency of structural design, enhancing infrastructure risk analysis, and developing more accurate early warning systems. By considering out-of-sequence thrust movement estimates, researchers and policymakers can make informed decisions to mitigate the impact of earthquakes. This study contributes to the field of seismic monitoring and earthquake risk assessment by highlighting the benefits of incorporating out-of-sequence thrust movement data. By broadening the scope of analysis beyond traditional techniques, researchers can enhance their knowledge of earthquake dynamics and improve the effectiveness of mitigation measures. The study collects data from various sources, including GPS measurements, satellite imagery, and seismic recordings. These datasets are then analyzed using appropriate statistical and computational techniques to estimate out-of-sequence thrust movement. The review integrates findings from multiple studies to provide a comprehensive assessment of the topic. The study concludes that incorporating out-of-sequence thrust movement data can significantly enhance earthquake mitigation measures. By utilizing diverse data sources, researchers and policymakers can gain a more comprehensive understanding of seismic dynamics and make informed decisions. However, challenges exist, such as data quality difficulties, modelling uncertainties, and computational complications. To address these obstacles and improve the accuracy of estimates, further research and advancements in methodology are recommended. Overall, this review serves as a valuable resource for researchers, engineers, and policymakers involved in earthquake mitigation, as it encourages the development of innovative strategies based on a better understanding of thrust movement dynamics.

Keywords: earthquake, out-of-sequence thrust, disaster, human life

Procedia PDF Downloads 46

Authors: Angelis P. Barlampas

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Learning Objectives: The purpose of this study is to inform briefly the reader about the applications of AI in chest radiology. Background: Currently, there are 190 FDA-approved radiology AI applications, with 42 (22%) pertaining specifically to thoracic radiology. Imaging findings OR Procedure details Aids of AI in chest radiology1: Detects and segments pulmonary nodules. Subtracts bone to provide an unobstructed view of the underlying lung parenchyma and provides further information on nodule characteristics, such as nodule location, nodule two-dimensional size or three dimensional (3D) volume, change in nodule size over time, attenuation data (i.e., mean, minimum, and/or maximum Hounsfield units [HU]), morphological assessments, or combinations of the above. Reclassifies indeterminate pulmonary nodules into low or high risk with higher accuracy than conventional risk models. Detects pleural effusion . Differentiates tension pneumothorax from nontension pneumothorax. Detects cardiomegaly, calcification, consolidation, mediastinal widening, atelectasis, fibrosis and pneumoperitoneum. Localises automatically vertebrae segments, labels ribs and detects rib fractures. Measures the distance from the tube tip to the carina and localizes both endotracheal tubes and central vascular lines. Detects consolidation and progression of parenchymal diseases such as pulmonary fibrosis or chronic obstructive pulmonary disease (COPD).Can evaluate lobar volumes. Identifies and labels pulmonary bronchi and vasculature and quantifies air-trapping. Offers emphysema evaluation. Provides functional respiratory imaging, whereby high-resolution CT images are post-processed to quantify airflow by lung region and may be used to quantify key biomarkers such as airway resistance, air-trapping, ventilation mapping, lung and lobar volume, and blood vessel and airway volume. Assesses the lung parenchyma by way of density evaluation. Provides percentages of tissues within defined attenuation (HU) ranges besides furnishing automated lung segmentation and lung volume information. Improves image quality for noisy images with built-in denoising function. Detects emphysema, a common condition seen in patients with history of smoking and hyperdense or opacified regions, thereby aiding in the diagnosis of certain pathologies, such as COVID-19 pneumonia. It aids in cardiac segmentation and calcium detection, aorta segmentation and diameter measurements, and vertebral body segmentation and density measurements. Conclusion: The future is yet to come, but AI already is a helpful tool for the daily practice in radiology. It is assumed, that the continuing progression of the computerized systems and the improvements in software algorithms , will redder AI into the second hand of the radiologist.

Keywords: artificial intelligence, chest imaging, nodule detection, automated diagnoses

Procedia PDF Downloads 43

Authors: Helga Heylen

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With dogs saving wildlife in New Zealand since 1890 and governments in Africa, Australia and Canada trusting them to give the best results, Conservation Dogs Ireland want to introduce more detection dogs to protect Europe's native wildlife. Conservation detection dogs are fast, portable and endlessly trainable. They are a cost-effective, highly sensitive and non-invasive way to detect protected and invasive species and wildlife disease. Conservation dogs find targets up to 40 times faster than any other method. They give results instantly, with near-perfect accuracy. They can search for multiple targets simultaneously, with no reduction in efficacy The European Red List indicates the decline in biodiversity has been most rapid in the past 50 years, and the risk of extinction never higher. Just two examples of major threats dogs are trained to tackle are: (I)Japanese Knotweed (Fallopia Japonica), not only a serious threat to ecosystems, crops, structures like bridges and roads - it can wipe out the entire value of a house. The property industry and homeowners are only just waking up to the full extent of the nightmare. When those working in construction on the roads move topsoil with a trace of Japanese Knotweed, it suffices to start a new colony. Japanese Knotweed grows up to 7cm a day. It can stay dormant and resprout after 20 years. In the UK, the cost of removing Japanese Knotweed from the London Olympic site in 2012 was around £70m (€83m). UK banks already no longer lend on a house that has Japanese Knotweed on-site. Legally, landowners are now obliged to excavate Japanese Knotweed and have it removed to a landfill. More and more, we see Japanese Knotweed grow where a new house has been constructed, and topsoil has been brought in. Conservation dogs are trained to detect small fragments of any part of the plant on sites and in topsoil. (II)Zebra mussels (Dreissena Polymorpha) are a threat to many waterways in the world. They colonize rivers, canals, docks, lakes, reservoirs, water pipes and cooling systems. They live up to 3 years and will release up to one million eggs each year. Zebra mussels attach to surfaces like rocks, anchors, boat hulls, intake pipes and boat engines. They cause changes in nutrient cycles, reduction of plankton and increased plant growth around lake edges, leading to the decline of Europe's native mussel and fish populations. There is no solution, only costly measures to keep it at bay. With many interconnected networks of waterways, they have spread uncontrollably. Conservation detection dogs detect the Zebra mussel from its early larvae stage, which is still invisible to the human eye. Detection dogs are more thorough and cost-effective than any other conservation method, and will greatly complement and speed up the work of biologists, surveyors, developers, ecologists and researchers.

Keywords: native biodiversity, conservation detection dogs, invasive species, Japanese Knotweed, zebra mussel

Procedia PDF Downloads 166

Authors: Ali Kadir, O. Anwar Beg

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Thermal barrier coatings are among the most popular methods for providing corrosion protection in high temperature applications including aircraft engine systems, external spacecraft structures, rocket chambers etc. Many different materials are available for such coatings, of which ceramics generally perform the best. Motivated by these applications, the current investigation presents detailed finite element simulations of coating stress analysis for a 3- dimensional, 3-layered model of a test sample representing a typical gas turbine component scenario. Structural steel is selected for the main inner layer, Titanium (Ti) alloy for the middle layer and Silicon Carbide (SiC) for the outermost layer. The model dimensions are 20 mm (width), 10 mm (height) and three 1mm deep layers. ANSYS software is employed to conduct three types of analysis- static structural, thermal stress analysis and also computational fluid dynamic erosion/corrosion analysis (via ANSYS FLUENT). The specified geometry which corresponds to corrosion test samples exactly is discretized using a body-sizing meshing approach, comprising mainly of tetrahedron cells. Refinements were concentrated at the connection points between the layers to shift the focus towards the static effects dissipated between them. A detailed grid independence study is conducted to confirm the accuracy of the selected mesh densities. To recreate gas turbine scenarios; in the stress analysis simulations, static loading and thermal environment conditions of up to 1000 N and 1000 degrees Kelvin are imposed. The default solver was used to set the controls for the simulation with the fixed support being set as one side of the model while subjecting the opposite side to a tabular force of 500 and 1000 Newtons. Equivalent elastic strain, total deformation, equivalent stress and strain energy were computed for all cases. Each analysis was duplicated twice to remove one of the layers each time, to allow testing of the static and thermal effects with each of the coatings. ANSYS FLUENT simulation was conducted to study the effect of corrosion on the model under similar thermal conditions. The momentum and energy equations were solved and the viscous heating option was applied to represent improved thermal physics of heat transfer between the layers of the structures. A Discrete Phase Model (DPM) in ANSYS FLUENT was employed which allows for the injection of continuous uniform air particles onto the model, thereby enabling an option for calculating the corrosion factor caused by hot air injection (particles prescribed 5 m/s velocity and 1273.15 K). Extensive visualization of results is provided. The simulations reveal interesting features associated with coating response to realistic gas turbine loading conditions including significantly different stress concentrations with different coatings.

Keywords: thermal coating, corrosion, ANSYS FEA, CFD

Procedia PDF Downloads 119

Authors: Getaneh Berie Tarekegn, Li-Chia Tai

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With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarms, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Hamadi Cherif, Christophe Coquelet, Paolo Stringari, Denis Clodic, Laura Pellegrini, Stefania Moioli, Stefano Langè

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Biogas is a promising technology which can be used as a vehicle fuel, for heat and electricity production, or injected in the national gas grid. It is storable, transportable, not intermittent and substitutable for fossil fuels. This gas produced from the wastewater treatment by degradation of organic matter under anaerobic conditions is mainly composed of methane and carbon dioxide. To be used as a renewable fuel, biogas, whose energy comes only from methane, must be purified from carbon dioxide and other impurities such as water vapor, siloxanes and hydrogen sulfide. Purification of biogas for this application particularly requires the removal of hydrogen sulfide, which negatively affects the operation and viability of equipment especially pumps, heat exchangers and pipes, causing their corrosion. Several methods are available to eliminate hydrogen sulfide from biogas. Herein, reactive absorption in structured packed column by means of chemical absorption in aqueous sodium hydroxide solutions is considered. This study is based on simulations using Aspen Plus™ V8.0, and comparisons are done with data from an industrial pilot plant treating 85 Nm3/h of biogas which contains about 30 ppm of hydrogen sulfide. The rate-based model approach has been used for simulations in order to determine the efficiencies of separation for different operating conditions. To describe vapor-liquid equilibrium, a γ/ϕ approach has been considered: the Electrolyte NRTL model has been adopted to represent non-idealities in the liquid phase, while the Redlich-Kwong equation of state has been used for the vapor phase. In order to validate the thermodynamic model, Henry’s law constants of each compound in water have been verified against experimental data. Default values available in Aspen Plus™ V8.0 for the properties of pure components properties as heat capacity, density, viscosity and surface tension have also been verified. The obtained results for physical and chemical properties are in a good agreement with experimental data. Reactions involved in the process have been studied rigorously. Equilibrium constants for equilibrium reactions and the reaction rate constant for the kinetically controlled reaction between carbon dioxide and the hydroxide ion have been checked. Results of simulations of the pilot plant purification section show the influence of low temperatures, concentration of sodium hydroxide and hydrodynamic parameters on the selective absorption of hydrogen sulfide. These results show an acceptable degree of accuracy when compared with the experimental data obtained from the pilot plant. Results show also the great efficiency of sodium hydroxide for the removal of hydrogen sulfide. The content of this compound in the gas leaving the column is under 1 ppm.

Keywords: biogas, hydrogen sulfide, reactive absorption, sodium hydroxide, structured packed column

Procedia PDF Downloads 318

Authors: Hisham Arab Alkabeya, A. M. Hughes, J. Adams

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Background: People with Rheumatoid Arthritis (RA) continue to experience problems with hand function despite new drug advances and targeted medical treatment. Consequently, it is important to identify the factors that influence the impact of RA disease on hand function. This systematic review identified observational studies that reported factors that influenced the impact of RA on hand function. Methods: MEDLINE, EMBASE, CINAL, AMED, PsychINFO, and Web of Science database were searched from January 1990 up to March 2017. Full-text articles published in English that described factors related to hand functional disability in people with RA were selected following predetermined inclusion and exclusion criteria. Pertinent data were thoroughly extracted and documented using a pre-designed data extraction form by the lead author, and cross-checked by the review team for completion and accuracy. Factors related to hand function were classified under the domains of the International Classification of Functioning, Disability, and Health (ICF) framework and health-related factors. Three reviewers independently assessed the methodological quality of the included articles using the quality of cross-sectional studies (AXIS) tool. Factors related to hand function that was investigated in two or more studies were explored using a best-evidence synthesis. Results: Twenty articles form 19 studies met the inclusion criteria from 1,271 citations; all presented cross-sectional data (five high quality and 15 low quality studies), resulting in at best limited evidence in the best-evidence synthesis. For the factors classified under the ICF domains, the best-evidence synthesis indicates that there was a range of body structure and function factors that were related with hand functional disability. However, key factors were hand strength, disease activity, and pain intensity. Low functional status (physical, emotional and social) level was found to be related with limited hand function. For personal factors, there is limited evidence that gender is not related with hand function; whereas, conflicting evidence was found regarding the relationship between age and hand function. In the domain of environmental factors, there was limited evidence that work activity was not related with hand function. Regarding health-related factors, there was limited evidence that the level of the rheumatoid factor (RF) was not related to hand function. Finally, conflicting evidence was found regarding the relationship between hand function and disease duration and general health status. Conclusion: Studies focused on body structure and function factors, highlighting a lack of investigation into personal and environmental factors when considering the impact of RA on hand function. The level of evidence which exists was limited, but identified that modifiable factors such as grip or pinch strength, disease activity and pain are the most influential factors on hand function in people with RA. The review findings suggest that important personal and environmental factors that impact on hand function in people with RA are not yet considered or reported in clinical research. Well-designed longitudinal, preferably cohort, studies are now needed to better understand the causality between personal and environmental factors and hand functional disability in people with RA.

Keywords: factors, hand function, rheumatoid arthritis, systematic review

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Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

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Authors: Getaneh Berie Tarekegn, Li-Chia Tai

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With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Georges Ghazi, Magali Gelhaye, Ruxandra Botez

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Over the years, the Flight Management System (FMS) has experienced a continuous improvement of its many features, to the point of becoming the pilot’s primary interface for flight planning operation on the airplane. With the assistance of the FMS, the concept of distance and time has been completely revolutionized, providing the crew members with the determination of the optimized route (or flight plan) from the departure airport to the arrival airport. To accomplish this function, the FMS needs an accurate Aircraft Performance Model (APM) of the aircraft. In general, APMs that equipped most modern FMSs are established before the entry into service of an individual aircraft, and results from the combination of a set of ordinary differential equations and a set of performance databases. Unfortunately, an aircraft in service is constantly exposed to dynamic loads that degrade its flight characteristics. These degradations endow two main origins: airframe deterioration (control surfaces rigging, seals missing or damaged, etc.) and engine performance degradation (fuel consumption increase for a given thrust). Thus, after several years of service, the performance databases and the APM associated to a specific aircraft are no longer representative enough of the actual aircraft performance. It is important to monitor the trend of the performance deterioration and correct the uncertainties of the aircraft model in order to improve the accuracy the flight management system predictions. The basis of this research lies in the new ability to continuously update an Aircraft Performance Model (APM) during flight using an adaptive lookup table technique. This methodology was developed and applied to the well-known Cessna Citation X business aircraft. For the purpose of this study, a level D Research Aircraft Flight Simulator (RAFS) was used as a test aircraft. According to Federal Aviation Administration the level D is the highest certification level for the flight dynamics modeling. Basically, using data available in the Flight Crew Operating Manual (FCOM), a first APM describing the variation of the engine fan speed and aircraft fuel flow w.r.t flight conditions was derived. This model was next improved using the proposed methodology. To do that, several cruise flights were performed using the RAFS. An algorithm was developed to frequently sample the aircraft sensors measurements during the flight and compare the model prediction with the actual measurements. Based on these comparisons, a correction was performed on the actual APM in order to minimize the error between the predicted data and the measured data. In this way, as the aircraft flies, the APM will be continuously enhanced, making the FMS more and more precise and the prediction of trajectories more realistic and more reliable. The results obtained are very encouraging. Indeed, using the tables initialized with the FCOM data, only a few iterations were needed to reduce the fuel flow prediction error from an average relative error of 12% to 0.3%. Similarly, the FCOM prediction regarding the engine fan speed was reduced from a maximum error deviation of 5.0% to 0.2% after only ten flights.

Keywords: aircraft performance, cruise, trajectory optimization, adaptive lookup tables, Cessna Citation X

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Authors: Neoclis Hadjigeorgiou, Spyridon Angelopoulos, Evangelos V. Hristoforou, Paul P. Sotiriadis

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The increasing demand for accurate and reliable magnetic measurements over the past decades has paved the way for the development of different types of magnetic sensing systems as well as of more advanced measurement techniques. Anisotropic Magneto Resistance (AMR) sensors have emerged as a promising solution for applications requiring high resolution, providing an ideal balance between performance and cost. However, certain issues of AMR sensors such as non-linear response and measurement noise are rarely discussed in the relevant literature. In this work, an analog closed loop compensation system is proposed, developed and tested as a means to eliminate the non-linearity of AMR response, reduce the 1/f noise and enhance the sensitivity of magnetic sensor. Additional performance aspects, such as cross-axis and hysteresis effects are also examined. This system was analyzed using an analytical model and a P-Spice model, considering both the sensor itself as well as the accompanying electronic circuitry. In addition, a commercial closed loop architecture Flux-Gate sensor (calibrated and certified), has been used for comparison purposes. Three different experimental setups have been constructed for the purposes of this work, each one utilized for DC magnetic field measurements, AC magnetic field measurements and Noise density measurements respectively. The DC magnetic field measurements have been conducted in laboratory environment employing a cubic Helmholtz coil setup in order to calibrate and characterize the system under consideration. A high-accuracy DC power supply has been used for providing the operating current to the Helmholtz coils. The results were recorded by a multichannel voltmeter The AC magnetic field measurements have been conducted in laboratory environment employing a cubic Helmholtz coil setup in order to examine the effective bandwidth not only of the proposed system but also for the Flux-Gate sensor. A voltage controlled current source driven by a function generator has been utilized for the Helmholtz coil excitation. The result was observed by the oscilloscope. The third experimental apparatus incorporated an AC magnetic shielding construction composed of several layers of electric steel that had been demagnetized prior to the experimental process. Each sensor was placed alone and the response was captured by the oscilloscope. The preliminary experimental results indicate that closed loop AMR response presented a maximum deviation of 0.36% with respect to the ideal linear response, while the corresponding values for the open loop AMR system and the Fluxgate sensor reached 2% and 0.01% respectively. Moreover, the noise density of the proposed close loop AMR sensor system remained almost as low as the noise density of the AMR sensor itself, yet considerably higher than that of the Flux-Gate sensor. All relevant numerical data are presented in the paper.

Keywords: AMR sensor, chopper, closed loop, electronic noise, magnetic noise, memory effects, flux-gate sensor, linearity improvement, sensitivity improvement

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Authors: A. Chatel, I. S. Torreguitart, T. Verstraete

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The paper deals with a single point optimization of the LS89 turbine using an adjoint optimization and defining the design variables within a CAD system. The advantage of including the CAD model in the design system is that higher level constraints can be imposed on the shape, allowing the optimized model or component to be manufactured. However, CAD-based approaches restrict the design space compared to node-based approaches where every node is free to move. In order to preserve a rich design space, we develop a methodology to refine the CAD model during the optimization and to create the best parameterization to use at each time. This study presents a methodology to progressively refine the design space, which combines parametric effectiveness with a differential evolutionary algorithm in order to create an optimal parameterization. In this manuscript, we show that by doing the parameterization at the CAD level, we can impose higher level constraints on the shape, such as the axial chord length, the trailing edge radius and G2 geometric continuity between the suction side and pressure side at the leading edge. Additionally, the adjoint sensitivities are filtered out and only smooth shapes are produced during the optimization process. The use of algorithmic differentiation for the CAD kernel and grid generator allows computing the grid sensitivities to machine accuracy and avoid the limited arithmetic precision and the truncation error of finite differences. Then, the parametric effectiveness is computed to rate the ability of a set of CAD design parameters to produce the design shape change dictated by the adjoint sensitivities. During the optimization process, the design space is progressively enlarged using the knot insertion algorithm which allows introducing new control points whilst preserving the initial shape. The position of the inserted knots is generally assumed. However, this assumption can hinder the creation of better parameterizations that would allow producing more localized shape changes where the adjoint sensitivities dictate. To address this, we propose using a differential evolutionary algorithm to maximize the parametric effectiveness by optimizing the location of the inserted knots. This allows the optimizer to gradually explore larger design spaces and to use an optimal CAD-based parameterization during the course of the optimization. The method is tested on the LS89 turbine cascade and large aerodynamic improvements in the entropy generation are achieved whilst keeping the exit flow angle fixed. The trailing edge and axial chord length, which are kept fixed as manufacturing constraints. The optimization results show that the multilevel optimizations were more efficient than the single level optimization, even though they used the same number of design variables at the end of the multilevel optimizations. Furthermore, the multilevel optimization where the parameterization is created using the optimal knot positions results in a more efficient strategy to reach a better optimum than the multilevel optimization where the position of the knots is arbitrarily assumed.

Keywords: adjoint, CAD, knots, multilevel, optimization, parametric effectiveness

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Authors: Gizem Kayar, Ramazan Bakir, M. Ilkay Koşar, Ceren U. Gencer, Alperen Ayyildiz

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Anatomy classes are crucial for general education of medical students, whereas learning anatomy is quite challenging and requires memorization of thousands of structures. In traditional teaching methods, learning materials are still based on books, anatomy mannequins, or videos. This results in forgetting many important structures after several years. However, more interactive teaching methods like virtual reality, augmented reality, gamification, and motion sensors are becoming more popular since such methods ease the way we learn and keep the data in mind for longer terms. During our study, we designed a virtual reality based digital head anatomy platform to investigate whether a fully interactive anatomy platform is effective to learn anatomy and to understand the level of teaching and learning optimization. The Head is one of the most complicated human anatomy structures, with thousands of tiny, unique structures. This makes the head anatomy one of the most difficult parts to understand during class sessions. Therefore, we developed a fully interactive digital tool with 3D model marking, quiz structures, 2D/3D puzzle structures, and VR support so as to integrate the power of VR and gamification. The project has been developed in Unity game engine with HTC Vive Cosmos VR headset. The head anatomy 3D model has been selected with full skeletal, muscular, integumentary, head, teeth, lymph, and vein system. The biggest issue during the development was the complexity of our model and the marking of it in the 3D world system. 3D model marking requires to access to each unique structure in the counted subsystems which means hundreds of marking needs to be done. Some parts of our 3D head model were monolithic. This is why we worked on dividing such parts to subparts which is very time-consuming. In order to subdivide monolithic parts, one must use an external modeling tool. However, such tools generally come with high learning curves, and seamless division is not ensured. Second option was to integrate tiny colliders to all unique items for mouse interaction. However, outside colliders which cover inner trigger colliders cause overlapping, and these colliders repel each other. Third option is using raycasting. However, due to its own view-based nature, raycasting has some inherent problems. As the model rotate, view direction changes very frequently, and directional computations become even harder. This is why, finally, we studied on the local coordinate system. By taking the pivot point of the model into consideration (back of the nose), each sub-structure is marked with its own local coordinate with respect to the pivot. After converting the mouse position to the world position and checking its relation with the corresponding structure’s local coordinate, we were able to mark all points correctly. The advantage of this method is its applicability and accuracy for all types of monolithic anatomical structures.

Keywords: anatomy, e-learning, virtual reality, 3D model marking

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Authors: Britt Erni, Aldara Vazquez Fernandez, Roland Maurer

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Visuospatial perspective taking (VSPT) is a process that allows to integrate spatial information from different points of view, and to transform the mental images we have of the environment to properly orient our movements and anticipate the location of landmarks during navigation. VSPT is also related to egocentric perspective transformations (imagined rotations or translations of one's point of view) and to infer the visuospatial experiences of another person (e.g. if and how another person sees objects). This process is deeply related to a wide-ranging capacity called the theory of mind (ToM), an essential cognitive function that allows us to regulate our social behaviour by attributing mental representations to individuals in order to make behavioural predictions. VSPT is often considered in the literature as the starting point of the development of the theory of mind. VSPT and ToM include several levels of knowledge that have to be assessed by specific tasks. Unfortunately, the lack of tasks assessing these functions in clinical neuropsychology leads to underestimate, in brain-damaged patients, deficits of these functions which are essential, in everyday life, to regulate our social behaviour (ToM) and to navigate in known and unknown environments (VSPT). Therefore, this study aims to create and standardize a VSPT task in order to explore the cognitive requirements of VSPT and ToM, and to specify their relationship in healthy adults and thereafter in brain-damaged patients. Two versions of a computerized VSPT task were administered to healthy participants (M = 28.18, SD = 4.8 years). In both versions the environment was a 3D representation of 10 different geometric shapes placed on a circular base. Two sets of eight pictures were generated from this: of the environment with an avatar somewhere on its periphery (locations) and of what the avatar sees from that place (views). Two types of questions were asked: a) identify the location from the view, and b) identify the view from the location. Twenty participants completed version 1 of the task and 20 completed the second version, where the views were offset by ±15° (i.e., clockwise or counterclockwise) and participants were asked to choose the closest location or the closest view. The preliminary findings revealed that version 1 is significantly easier than version 2 for accuracy (with ceiling scores for version 1). In version 2, participants responded significantly slower when they had to infer the avatar's view from the latter's location, probably because they spent more time visually exploring the different views (responses). Furthermore, men significantly performed better than women in version 1 but not in version 2. Most importantly, a sensitive task (version 2) has been created for which the participants do not seem to easily and automatically compute what someone is looking at yet which does not involve more heavily other cognitive functions. This study is further completed by including analysis on non-clinical participants with low and high degrees of schizotypy, different socio-educational status, and with a range of older adults to examine age-related and other differences in VSPT processing.

Keywords: mental transformation, spatial cognition, theory of mind, visuospatial perspective taking

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Authors: Shamini Dhana, G. Subrahmanya VRK Rao

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The fashion industry is advancing towards a mindset of zero waste, personalization, creativity, and circularity. The trend of upcycling clothing and materials into personalized fashion is being demanded by the next generation. There is a need for a digital tool to accelerate the process towards mass customization. Dhana’s D/Sphere fashion technology platform uses digital tools to accelerate upcycling. In essence, advanced fashion garments can be designed and developed via reuse, repurposing, recreating activities, and using existing fabric and circulating materials. The D/Sphere platform has the following objectives: to provide (1) An opportunity to develop modern fashion using existing, finished materials and clothing without chemicals or water consumption; (2) The potential for an everyday customer and designer to use the medium of fashion for creative expression; (3) A solution to address the global textile waste generated by pre- and post-consumer fashion; (4) A solution to reduce carbon emissions, water, and energy consumption with the participation of all stakeholders; (5) An opportunity for brands, manufacturers, retailers to work towards zero-waste designs and as an alternative revenue stream. Other benefits of this alternative approach include sustainability metrics, trend prediction, facilitation of disassembly and remanufacture deep learning, and hyperheuristics for high accuracy. A design tool for mass personalization and customization utilizing existing circulating materials and deadstock, targeted to fashion stakeholders will lower environmental costs, increase revenues through up to date upcycled apparel, produce less textile waste during the cut-sew-stitch process, and provide a real design solution for the end customer to be part of circular fashion. The broader impact of this technology will result in a different mindset to circular fashion, increase the value of the product through multiple life cycles, find alternatives towards zero waste, and reduce the textile waste that ends up in landfills. This technology platform will be of interest to brands and companies that have the responsibility to reduce their environmental impact and contribution to climate change as it pertains to the fashion and apparel industry. Today, over 70% of the $3 trillion fashion and apparel industry ends up in landfills. To this extent, the industry needs such alternative techniques to both address global textile waste as well as provide an opportunity to include all stakeholders and drive circular fashion with new personalized products. This type of modern systems thinking is currently being explored around the world by the private sector, organizations, research institutions, and governments. This technological innovation using digital tools has the potential to revolutionize the way we look at communication, capabilities, and collaborative opportunities amongst stakeholders in the development of new personalized and customized products, as well as its positive impacts on society, our environment, and global climate change.

Keywords: circular fashion, deep learning, digital technology platform, personalization

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Authors: Elissandro Voigt Beier, Cristiano Poleto

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The main objective of the research is to assess the loss through the quantification of material culture (archaeological fragments) in rural areas, sites explored economically by machining on seasonal crops, and also permanent, in a hydrographic subsystem Camaquã River in the state of Rio Grande do Sul, Brazil. The study area consists of different micro basins and differs in area, ranging between 1,000 m² and 10,000 m², respectively the largest and the smallest, all with a large number of occurrences and outcrop locations of archaeological material and high density in intense farm environment. In the first stage of the research aimed to identify the dispersion of points of archaeological material through field survey through plot points by the Global Positioning System (GPS), within each river basin, was made use of concise bibliography on the topic in the region, helping theoretically in understanding the old landscaping with preferences of occupation for reasons of ancient historical people through the settlements relating to the practice observed in the field. The mapping was followed by the cartographic development in the region through the development of cartographic products of the land elevation, consequently were created cartographic products were to contribute to the understanding of the distribution of the absolute materials; the definition and scope of the material dispersed; and as a result of human activities the development of revolving letter by mechanization of in situ material, it was also necessary for the preparation of materials found density maps, linking natural environments conducive to ancient historical occupation with the current human occupation. The third stage of the project it is for the systematic collection of archaeological material without alteration or interference in the subsurface of the indigenous settlements, thus, the material was prepared and treated in the laboratory to remove soil excesses, cleaning through previous communication methodology, measurement and quantification. Approximately 15,000 were identified archaeological fragments belonging to different periods of ancient history of the region, all collected outside of its environmental and historical context and it also has quite changed and modified. The material was identified and cataloged considering features such as object weight, size, type of material (lithic, ceramic, bone, Historical porcelain and their true association with the ancient history) and it was disregarded its principles as individual lithology of the object and functionality same. As observed preliminary results, we can point out the change of materials by heavy mechanization and consequent soil disturbance processes, and these processes generate loading of archaeological materials. Therefore, as a next step will be sought, an estimate of potential losses through a mathematical model. It is expected by this process, to reach a reliable model of high accuracy which can be applied to an archeological site of lower density without encountering a significant error.

Keywords: degradation of heritage, quantification in archaeology, watershed, use and occupation of land

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Authors: Ch. Sridevi, S. P. Shailender Kumar, B. Gurudayal, A. Chalapathi Rao, K. Koteswara Rao, P. Srinivasulu

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System reliability and system availability evaluation plays a crucial role in ensuring the seamless operation of complex satellite data reception system with consistent performance for longer periods. This paper presents a novel approach for the same using a case study on one of the antenna systems at satellite data reception ground station in India. The methodology involves analyzing system's components, their failure rates, system's architecture, generation of logical reliability block diagram model and estimating the reliability of the system using the component level mean time between failures considering exponential distribution to derive a baseline estimate of the system's reliability. The model is then validated with collected system level field failure data from the operational satellite data reception systems that includes failure occurred, failure time, criticality of the failure and repair times by using statistical techniques like median rank, regression and Weibull analysis to extract meaningful insights regarding failure patterns and practical reliability of the system and to assess the accuracy of the developed reliability model. The study mainly focused on identification of critical units within the system, which are prone to failures and have a significant impact on overall performance and brought out a reliability model of the identified critical unit. This model takes into account the interdependencies among system components and their impact on overall system reliability and provides valuable insights into the performance of the system to understand the Improvement or degradation of the system over a period of time and will be the vital input to arrive at the optimized design for future development. It also provides a plug and play framework to understand the effect on performance of the system in case of any up gradations or new designs of the unit. It helps in effective planning and formulating contingency plans to address potential system failures, ensuring the continuity of operations. Furthermore, to instill confidence in system users, the duration for which the system can operate continuously with the desired level of 3 sigma reliability was estimated that turned out to be a vital input to maintenance plan. System availability and station availability was also assessed by considering scenarios of clash and non-clash to determine the overall system performance and potential bottlenecks. Overall, this paper establishes a comprehensive methodology for reliability and availability analysis of complex satellite data reception systems. The results derived from this approach facilitate effective planning contingency measures, and provide users with confidence in system performance and enables decision-makers to make informed choices about system maintenance, upgrades and replacements. It also aids in identifying critical units and assessing system availability in various scenarios and helps in minimizing downtime and optimizing resource allocation.

Keywords: exponential distribution, reliability modeling, reliability block diagram, satellite data reception system, system availability, weibull analysis

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Authors: Chuyu Huang, Itsuki Minemi, Kuanlin Chen, Yuki Hirose

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It remains unclear how language speakers are able to perceive phonological contrasts that do not exist on their own. This experiment uses the vowel-length distinction in Japanese, which is phonologically contrastive and co-occurs with tonal change in some cases. For speakers whose first language does not distinguish vowel length, contrastive duration is usually misperceived, e.g., Mandarin speakers. Two alternative hypotheses for how Mandarin speakers would perceive a phonological contrast that does not exist in their language make different predictions. The stress parameter model does not have a clear prediction about the impact of tonal type. Mandarin speakers will likely be not able to perceive vowel length as well as Japanese native speakers do, but the performance might not correlate to tonal type because the prosody of their language is distinctive, which requires users to encode lexical prosody and notice subtle differences in word prosody. By contrast, cue-based phonetic models predict that Mandarin speakers may rely on pitch differences, a secondary cue, to perceive vowel length. Two groups of Mandarin speakers, including naive non-Japanese speakers and beginner learners, were recruited to participate in an AX discrimination task involving two Japanese sound stimuli that contain a phonologically contrastive environment. Participants were asked to indicate whether the two stimuli containing a vowel-length contrast (e.g., maapero vs. mapero) sound the same. The experiment was bifactorial. The first factor contrasted three syllabic positions (syllable position; initial/medial/final), as it would be likely to affect the perceptual difficulty, as seen in previous studies, and the second factor contrasted two pitch types (accent type): one with accentual change that could be distinguished with the lexical tones in Mandarin (the different condition), with the other group having no tonal distinction but only differing in vowel length (the same condition). The overall results showed that a significant main effect of accent type by applying a linear mixed-effects model (β = 1.48, SE = 0.35, p < 0.05), which implies that Mandarin speakers tend to more successfully recognize vowel-length differences when the long vowel counterpart takes on a tone that exists in Mandarin. The interaction between the accent type and the syllabic position is also significant (β = 2.30, SE = 0.91, p < 0.05), showing that vowel lengths in the different conditions are more difficult to recognize in the word-final case relative to the initial condition. The second statistical model, which compares naive speakers to beginners, was conducted with logistic regression to test the effects of the participant group. A significant difference was found between the two groups (β = 1.06, 95% CI = [0.36, 2.03], p < 0.05). This study shows that: (1) Mandarin speakers are likely to use pitch cues to perceive vowel length in a non-native language, which is consistent with the cue-based approaches; (2) an exposure effect was observed: the beginner group achieved a higher accuracy for long vowel perception, which implied the exposure effect despite the short period of language learning experience.

Keywords: cue-based perception, exposure effect, prosodic perception, vowel duration

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Authors: Ionut Vintu, Stefan Laible, Ruth Schulz

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Agricultural robotics has been developing steadily over recent years, with the goal of reducing and even eliminating pesticides used in crops and to increase productivity by taking over human labor. The majority of crops are arranged in rows. The first step towards autonomous robots, capable of driving in fields and performing crop-handling tasks, is for robots to robustly detect the rows of plants. Recent work done towards autonomous driving between plant rows offers big robotic platforms equipped with various expensive sensors as a solution to this problem. These platforms need to be driven over the rows of plants. This approach lacks flexibility and scalability when it comes to the height of plants or distance between rows. This paper proposes instead an algorithm that makes use of cheaper sensors and has a higher variability. The main application is in tree nurseries. Here, plant height can range from a few centimeters to a few meters. Moreover, trees are often removed, leading to gaps within the plant rows. The core idea is to combine row detection algorithms with graph-based localization methods as they are used in SLAM. Nodes in the graph represent the estimated pose of the robot, and the edges embed constraints between these poses or between the robot and certain landmarks. This setup aims to improve individual plant detection and deal with exception handling, like row gaps, which are falsely detected as an end of rows. Four methods were developed for detecting row structures in the fields, all using a point cloud acquired with a 3D LiDAR as an input. Comparing the field coverage and number of damaged plants, the method that uses a local map around the robot proved to perform the best, with 68% covered rows and 25% damaged plants. This method is further used and combined with a graph-based localization algorithm, which uses the local map features to estimate the robot’s position inside the greater field. Testing the upgraded algorithm in a variety of simulated fields shows that the additional information obtained from localization provides a boost in performance over methods that rely purely on perception to navigate. The final algorithm achieved a row coverage of 80% and an accuracy of 27% damaged plants. Future work would focus on achieving a perfect score of 100% covered rows and 0% damaged plants. The main challenges that the algorithm needs to overcome are fields where the height of the plants is too small for the plants to be detected and fields where it is hard to distinguish between individual plants when they are overlapping. The method was also tested on a real robot in a small field with artificial plants. The tests were performed using a small robot platform equipped with wheel encoders, an IMU and an FX10 3D LiDAR. Over ten runs, the system achieved 100% coverage and 0% damaged plants. The framework built within the scope of this work can be further used to integrate data from additional sensors, with the goal of achieving even better results.

Keywords: 3D LiDAR, agricultural robots, graph-based localization, row detection

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Authors: Kai Warsoenke, Maik Mackiewicz

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To achieve a high quality of assembled car body structures, tolerancing is used to ensure a geometric accuracy of the single car body parts. There are two main techniques to determine the required tolerances. The first is tolerance analysis which describes the influence of individually tolerated input values on a required target value. Second is tolerance synthesis to determine the location of individual tolerances to achieve a target value. Both techniques are based on classical statistical methods, which assume certain probability distributions. To ensure competitiveness in both saturated and dynamic markets, production processes in vehicle manufacturing must be flexible and efficient. The dimensional specifications selected for the individual body components and the resulting assemblies have a major influence of the quality of the process. For example, in the manufacturing of forming tools as operating equipment or in the higher level of car body assembly. As part of the metrological process monitoring, manufactured individual parts and assemblies are recorded and the measurement results are stored in databases. They serve as information for the temporary adjustment of the production processes and are interpreted by experts in order to derive suitable adjustments measures. In the production of forming tools, this means that time-consuming and costly changes of the tool surface have to be made, while in the body shop, uncertainties that are difficult to control result in cost-intensive rework. The stored measurement results are not used to intelligently design tolerances in future processes or to support temporary decisions based on real-world geometric data. They offer potential to extend the tolerancing methods through data analysis and machine learning models. The purpose of this paper is to examine real-world measurement data from individual car body components, as well as assemblies, in order to develop an approach for using the data in short-term actions and future projects. For this reason, the measurement data will be analyzed descriptively in the first step in order to characterize their behavior and to determine possible correlations. In the following, a database is created that is suitable for developing machine learning models. The objective is to create an intelligent way to determine the position and number of measurement points as well as the local tolerance range. For this a number of different model types are compared and evaluated. The models with the best result are used to optimize equally distributed measuring points on unknown car body part geometries and to assign tolerance ranges to them. The current results of this investigation are still in progress. However, there are areas of the car body parts which behave more sensitively compared to the overall part and indicate that intelligent tolerancing is useful here in order to design and control preceding and succeeding processes more efficiently.

Keywords: automotive production, machine learning, process optimization, smart tolerancing

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Authors: Stephen Bahooshy

Abstract:

Co-producing services with the people that access them is considered best practice in the United Kingdom, with the Care Act 2014 arguing that people who access services and their carers should be involved in the design, commissioning and delivery of services. Co-production is a way of working with the community, breaking down barriers of access and providing meaningful opportunity for people to engage. Unfortunately, owing to a number of reported factors such as time constraints, practitioner experience and departmental budget restraints, this process is not always followed. In 2019, in a south London borough, d/Deaf people who access services were engaged in the design, commissioning and delivery of an information and advice service that would support their community to access local government services. To do this, sensory impairment social workers and commissioners collaborated to host a series of engagement events with the d/Deaf community. Interpreters were used to enable communication between the commissioners and d/Deaf participants. Initially, the community’s opinions, ideas and requirements were noted. This was then summarized and fed back to the community to ensure accuracy. Subsequently, a service specification was developed which included performance metrics, inclusive of qualitative and quantitative indicators, such as ‘I statements’, whereby participants respond on an adapted Likert scale how much they agree or disagree with a particular statement in relation to their experience of the service. The service specification was reviewed by a smaller group of d/Deaf residents and social workers, to ensure that it met the community’s requirements. The service was then tendered using the local authority’s e-tender process. Bids were evaluated and scored in two parts; part one was by commissioners and social workers and part two was a presentation by prospective providers to an evaluation panel formed of four d/Deaf residents. The internal evaluation panel formed 75% of the overall score, whilst the d/Deaf resident evaluation panel formed 25% of the overall tender score. Co-producing the evaluation panel with social workers and the d/Deaf community meant that commissioners were able to meet the requirements of this community by developing evaluation questions and tools that were easily understood and use by this community. For example, the wording of questions were reviewed and the scoring mechanism consisted of three faces to reflect the d/Deaf residents’ scores instead of traditional numbering. These faces were a happy face, a neutral face and a sad face. By making simple changes to the commissioning and tender evaluation process, d/Deaf people were able to have meaningful involvement in the design and commissioning process for a service that would benefit their community. Co-produced performance metrics means that it is incumbent on the successful provider to continue to engage with people accessing the service and ensure that the feedback is utilized. d/Deaf residents were grateful to have been involved in this process as this was not an opportunity that they had previously been afforded. In recognition of their time, each d/Deaf resident evaluator received a £40 gift voucher, bringing the total cost of this co-production to £160.

Keywords: co-production, community engagement, deaf and hearing impaired, service design

Procedia PDF Downloads 248

Authors: Aliya K. Salahova

Abstract:

Scientific thinking, characterized by purposeful knowledge-seeking and the harmonization of theory and facts, holds a crucial role in preparing young minds for an increasingly complex and technologically advanced world. This abstract presents a research study aimed at fostering scientific thinking in early childhood, focusing on children aged 5 to 9 years, through experiential learning in Kids Science Labs (STEM). The study utilized a longitudinal exploration design, spanning 240 weeks from September 2018 to April 2023, to evaluate the effectiveness of the Kids Science Labs program in developing scientific thinking skills. Participants in the research comprised 72 children drawn from local schools and community organizations. Through a formative psychology-pedagogical experiment, the experimental group engaged in weekly STEM activities carefully designed to stimulate scientific thinking, while the control group participated in daily art classes for comparison. To assess the scientific thinking abilities of the participants, a registration table with evaluation criteria was developed. This table included indicators such as depth of questioning, resource utilization in research, logical reasoning in hypotheses, procedural accuracy in experiments, and reflection on research processes. The data analysis revealed dynamic fluctuations in the number of children at different levels of scientific thinking proficiency. While the development was not uniform across all participants, a main leading factor emerged, indicating that the Kids Science Labs program and formative experiment exerted a positive impact on enhancing scientific thinking skills in children within this age range. The study's findings support the hypothesis that systematic implementation of STEM activities effectively promotes and nurtures scientific thinking in children aged 5-9 years. Enriching education with a specially planned STEM program, tailoring scientific activities to children's psychological development, and implementing well-planned diagnostic and corrective measures emerged as essential pedagogical conditions for enhancing scientific thinking abilities in this age group. The results highlight the significant and positive impact of the systematic-activity approach in developing scientific thinking, leading to notable progress and growth in children's scientific thinking abilities over time. These findings have promising implications for educators and researchers, emphasizing the importance of incorporating STEM activities into educational curricula to foster scientific thinking from an early age. This study contributes valuable insights to the field of science education and underscores the potential of STEM-based interventions in shaping the future scientific minds of young children.

Keywords: Scientific thinking, education, STEM, intervention, Psychology, Pedagogy, collaborative learning, longitudinal study

Procedia PDF Downloads 40

Authors: Tibor Ajtai, Noémi Utry, Máté Pintér, Tomi Smausz, Zoltán Kónya, Béla Hopp, Gábor Szabó, Zoltán Bozóki

Abstract:

Characterization of spectral responses of light absorbing carbonaceous particulate matter (LAC) is of great importance in both modelling its climate effect and interpreting remote sensing measurement data. The residential or domestic combustion of coal is one of the dominant LAC constituent. According to some related assessments the residential coal burning account for roughly half of anthropogenic BC emitted from fossil fuel burning. Despite of its significance in climate the comprehensive investigation of optical properties of residential coal aerosol is really limited in the literature. There are many reason of that starting from the difficulties associated with the controlled burning conditions of the fuel, through the lack of detailed supplementary proximate and ultimate chemical analysis enforced, the interpretation of the measured optical data, ending with many analytical and methodological difficulties regarding the in-situ measurement of coal aerosol spectral responses. Since the gas matrix of ambient can significantly mask the physicochemical characteristics of the generated coal aerosol the accurate and controlled generation of residential coal particulates is one of the most actual issues in this research area. Most of the laboratory imitation of residential coal combustion is simply based on coal burning in stove with ambient air support allowing one to measure only the apparent spectral feature of the particulates. However, the recently introduced methodology based on a laser ablation of solid coal target opens up novel possibilities to model the real combustion procedure under well controlled laboratory conditions and makes the investigation of the inherent optical properties also possible. Most of the methodology for spectral characterization of LAC is based on transmission measurement made of filter accumulated aerosol or deduced indirectly from parallel measurements of scattering and extinction coefficient using free floating sampling. In the former one the accuracy while in the latter one the sensitivity are liming the applicability of this approaches. Although the scientific community are at the common platform that aerosol-phase PhotoAcoustic Spectroscopy (PAS) is the only method for precise and accurate determination of light absorption by LAC, the PAS based instrumentation for spectral characterization of absorption has only been recently introduced. In this study, the investigation of the inherent, spectral features of laser generated and chemically characterized residential coal aerosols are demonstrated. The experimental set-up and its characteristic for residential coal aerosol generation are introduced here. The optical absorption and the scattering coefficients as well as their wavelength dependency are determined by our state-of-the-art multi wavelength PAS instrument (4λ-PAS) and multi wavelength cosinus sensor (Aurora 3000). The quantified wavelength dependency (AAE and SAE) are deduced from the measured data. Finally, some correlation between the proximate and ultimate chemical as well as the measured or deduced optical parameters are also revealed.

Keywords: absorption, scattering, residential coal, aerosol generation by laser ablation

Procedia PDF Downloads 324