Search results for: input output linearization

Authors: Fathi Abid, Bilel Kaffel

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The olive tree, the olive harvest during winter season and the production of olive oil better known by professionals under the name of the crushing operation have interested institutional traders such as olive-oil offices and private companies such as food industry refining and extracting pomace olive oil as well as export-import public and private companies specializing in olive oil. The major problem facing producers of olive oil each winter campaign, contrary to what is expected, it is not whether the harvest will be good or not but whether the sale price will allow them to cover production costs and achieve a reasonable margin of profit or not. These questions are entirely legitimate if we judge by the importance of the issue and the heavy complexity of the uncertainty and competition made tougher by a high level of indebtedness and the experience and expertise of speculators and producers whose objectives are sometimes conflicting. The aim of this paper is to study the formation mechanism of olive oil prices in order to learn about speculators’ behavior and expectations in the market, how they contribute by their industry knowledge and their financial alliances and the size the financial challenge that may be involved for them to build private information hoses globally to take advantage. The methodology used in this paper is based on two stages, in the first stage we study econometrically the formation mechanisms of olive oil price in order to understand the market participant behavior by implementing ARMA, SARMA, GARCH and stochastic diffusion processes models, the second stage is devoted to prediction purposes, we use a combined wavelet- ANN approach. Our main findings indicate that olive oil market participants interact with each other in a way that they promote stylized facts formation. The unstable participant’s behaviors create the volatility clustering, non-linearity dependent and cyclicity phenomena. By imitating each other in some periods of the campaign, different participants contribute to the fat tails observed in the olive oil price distribution. The best prediction model for the olive oil price is based on a back propagation artificial neural network approach with input information based on wavelet decomposition and recent past history.

Keywords: olive oil price, stylized facts, ARMA model, SARMA model, GARCH model, combined wavelet-artificial neural network, continuous-time stochastic volatility mode

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: Jan Schmitt, Sophie Fischer

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After the Corona pandemic, climate change is a further, long-lasting challenge the society must deal with. An ongoing climate change need to be prevented. Nevertheless, the adoption tothe already changed climate conditionshas to be focused in many sectors. Recently, the decisive role of the economic sector with high value added can be seen in the Corona crisis. Hence, manufacturing industry as such a sector, needs to be prepared for climate change and adaption. Several examples from the manufacturing industry show the importance of a strategic effort in this field: The outsourcing of a major parts of the value chain to suppliers in other countries and optimizing procurement logistics in a time-, storage- and cost-efficient manner within a network of global value creation, can lead vulnerable impacts due to climate-related disruptions. E.g. the total damage costs after the 2011 flood disaster in Thailand, including costs for delivery failures, were estimated at 45 billion US dollars worldwide. German car manufacturers were also affected by supply bottlenecks andhave close its plant in Thailand for a short time. Another OEM must reduce the production output. In this contribution, a game-based learning approach is presented, which should enable manufacturing companies to derive their own strategies for climate adaption out of a mix of different actions. Based on data from a regional study of small, medium and large manufacturing companies in Mainfranken, a strongly industrialized region of northern Bavaria (Germany) the game-based learning approach is designed. Out of this, the actual state of efforts due to climate adaption is evaluated. First, the results are used to collect single actions for manufacturing companies and second, further actions can be identified. Then, a variety of climate adaption activities can be clustered according to the scope of activity of the company. The combination of different actions e.g. the renewal of the building envelope with regard to thermal insulation, its benefits and drawbacks leads to a specific strategy for climate adaption for each company. Within the game-based approach, the players take on different roles in a fictionalcompany and discuss the order and the characteristics of each action taken into their climate adaption strategy. Different indicators such as economic, ecologic and stakeholder satisfaction compare the success of the respective measures in a competitive format with other virtual companies deriving their own strategy. A "play through" climate change scenarios with targeted adaptation actions illustrate the impact of different actions and their combination onthefictional company.

Keywords: business strategy, climate change, climate adaption, game-based learning

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Authors: Pappu Kumar, Ajai Singh, Anshuman Singh

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There are different water user sectors such as rural, urban, mining, subsistence and commercial irrigated agriculture, commercial forestry, industry, power generation which are present in the catchment in Subarnarekha River Basin and Ranchi city. There is an inequity issue in the access to water. The development of the rural area, construction of new power generation plants, along with the population growth, the requirement of unmet water demand and the consideration of environmental flows, the revitalization of small-scale irrigation schemes is going to increase the water demands in almost all the water-stressed catchment. The WEAP Model was developed by the Stockholm Environment Institute (SEI) to enable evaluation of planning and management issues associated with water resources development. The WEAP model can be used for both urban and rural areas and can address a wide range of issues including sectoral demand analyses, water conservation, water rights and allocation priorities, river flow simulation, reservoir operation, ecosystem requirements and project cost-benefit analyses. This model is a tool for integrated water resource management and planning like, forecasting water demand, supply, inflows, outflows, water use, reuse, water quality, priority areas and Hydropower generation, In the present study, efforts have been made to access the utility of the WEAP model for water supply and demand analysis for Ranchi city. A detailed works have been carried out and it was tried to ascertain that the WEAP model used for generating different scenario of water requirement, which could help for the future planning of water. The water supplied to Ranchi city was mostly contributed by our study river, Hatiya reservoir and ground water. Data was collected from various agencies like PHE Ranchi, census data of 2011, Doranda reservoir and meteorology department etc. This collected and generated data was given as input to the WEAP model. The model generated the trends for discharge of our study river up to next 2050 and same time also generated scenarios calculating our demand and supplies for feature. The results generated from the model outputs predicting the water require 12 million litter. The results will help in drafting policies for future regarding water supplies and demands under changing climatic scenarios.

Keywords: WEAP model, water demand analysis, Ranchi, scenarios

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Authors: Dina Magnaye

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In the Philippines, the performance of the agricultural sector is gauged through crop productivity and returns from farm production rather than the biodiversity in the agricultural ecosystem. Agricultural development hinges on the overall goal of increasing productivity through intensive agriculture, monoculture system, utilization of high yielding varieties in plants, and genetic upgrading in animals. This merits an analysis of the role of agro-biodiversity in terms of increasing productivity, food security and economic returns from community-based pro-biodiversity enterprises. These enterprises conserve biodiversity while equitably sharing production income in the utilization of biological resources. The study aims to determine how community-based pro-biodiversity enterprises become instrumental in local climate change adaptation and agro-biodiversity conservation as input to local green economic development planning. It also involves an assessment of the role of agrobiodiversity in terms of increasing productivity, food security and economic returns from community-based pro-biodiversity enterprises. The perceptions of the local community members both in urban and upland rural areas on community-based pro-biodiversity enterprises were evaluated. These served as a basis in developing a planning modality that can be mainstreamed in the management of local green economic enterprises to benefit the environment, provide local income opportunities, conserve species diversity, and sustain environment-friendly farming systems and practices. The interviews conducted with organic farmer-owners, entrepreneur-organic farmers, and organic farm workers revealed that pro-biodiversity enterprise such as organic farming involved the cyclic use of natural resources within the carrying capacity of a farm; recognition of the value of tradition and culture especially in the upland rural area; enhancement of socio-economic capacity; conservation of ecosystems in harmony with nature; and climate change mitigation. The suggested planning modality for community-based pro-biodiversity enterprises for a green economy encompasses four (4) phases to include community resource or capital asset profiling; stakeholder vision development; strategy formulation for sustained enterprises; and monitoring and evaluation.

Keywords: agro-biodiversity, agro-biodiversity conservation, local green economy, organic farming, pro-biodiversity enterprise

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Authors: Yusuf U. Datti

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It is becoming increasingly clear that the consumption of resources now enjoyed in the developed nations will be impossible to be sustained worldwide. While developing countries still have the advantage of low consumption and a smaller ecological footprint per person, they cannot simply develop in the same way as other western cities have developed in the past. The severe reality of population and consumption inequalities makes it contentious whether studies done in developed countries can be translated and applied to developing countries. Additional to this disparities, there are few or no metabolism of energy studies in Nigeria. Rather more contentious majority of energy metabolism studies have been done only in developed countries. While researches in Nigeria concentrate on other aspects/principles of sustainability such as water supply, sewage disposal, energy supply, energy efficiency, waste disposal, etc., which will not accurately capture the environmental impact of energy flow in Nigeria, this research will set itself apart by examining the flow of energy in Nigeria and the impact that the flow will have on the environment. The aim of the study is to examine and quantify the metabolic flows of energy in Nigeria and its corresponding environmental impact. The study will quantify the level and pattern of energy inflow and the outflow of greenhouse emissions in Nigeria. This study will describe measures to address the impact of existing energy sources and suggest alternative renewable energy sources in Nigeria that will lower the emission of greenhouse gas emissions. This study will investigate the metabolism of energy in Nigeria through a three-part methodology. The first step involved selecting and defining the study area and some variables that would affect the output of the energy (time of the year, stability of the country, income level, literacy rate and population). The second step involves analyzing, categorizing and quantifying the amount of energy generated by the various energy sources in the country. The third step involves analyzing what effect the variables would have on the environment. To ensure a representative sample of the study area, Africa’s most populous country, with economy that is the second biggest and that is among the top largest oil producing countries in the world is selected. This is due to the understanding that countries with large economy and dense populations are ideal places to examine sustainability strategies; hence, the choice of Nigeria for the study. National data will be utilized unless where such data cannot be found, then local data will be employed which will be aggregated to reflect the national situation. The outcome of the study will help policy-makers better target energy conservation and efficiency programs and enables early identification and mitigation of any negative effects in the environment.

Keywords: built environment, energy metabolism, environmental impact, greenhouse gas emissions and sustainability

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Authors: Nathalie Stephenne, Mathieu Veschkens, Stéphane Palm, Christophe Charlemagne, Jacques Defoux

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According to Europe’s first River basin Management Plans (RBMPs), 56% of European rivers failed to achieve the good status targets of the Water Framework Directive WFD. In Central European countries such as Belgium, even more than 80% of rivers failed to achieve the WFD quality targets. Although the RBMP’s should reduce the stressors and improve water body status, their potential to address multiple stress situations is limited due to insufficient knowledge on combined effects, multi-stress, prioritization of measures, impact on ecology and implementation effects. This paper describes a webGis prototype developed for the Walloon administration to improve the communication and the management of sediment dredging actions carried out in rivers and lakes in the frame of RBMPs. A large number of stakeholders are involved in the management of rivers and lakes in Wallonia. They are in charge of technical aspects (client and dredging operators, organizations involved in the treatment of waste…), management (managers involved in WFD implementation at communal, provincial or regional level) or policy making (people responsible for policy compliance or legislation revision). These different kinds of stakeholders need different information and data to cover their duties but have to interact closely at different levels. Moreover, information has to be shared between them to improve the management quality of dredging operations within the ecological system. In the Walloon legislation, leveling dredged sediments on banks requires an official authorization from the administration. This request refers to spatial information such as the official land use map, the cadastral map, the distance to potential pollution sources. The production of a collective geodatabase can facilitate the management of these authorizations from both sides. The proposed internet system integrates documents, data input, integration of data from disparate sources, map representation, database queries, analysis of monitoring data, presentation of results and cartographic visualization. A prototype of web application using the API geoviewer chosen by the Geomatic department of the SPW has been developed and discussed with some potential users to facilitate the communication, the management and the quality of the data. The structure of the paper states the why, what, who and how of this communication tool.

Keywords: sediments, web application, GIS, rivers management

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Authors: J. D. García Espinel, Paula Pérez Sánchez, Carlos Egea Ruiz, Carlos Lardín Mifsut, Andrés López-Aranguren Oliver

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This paper shortly describes three main actuations over a Waste Water Treatment Plant (WWTP) for reducing its energy consumption: Optimization of the biological reactor in the aeration stage by including new control algorithms and introducing new efficient equipment, the installation of an innovative hybrid system with zero Grid injection (formed by 100kW of PV energy and 5 kW of mini-wind energy generation) and an intelligent management system for load consumption and energy generation control in the most optimum way. This project called RENEWAT, involved in the European Commission call LIFE 2013, has the main objective of reducing the energy consumptions through different actions on the processes which take place in a WWTP and introducing renewable energies on these treatment plants, with the purpose of promoting the usage of treated waste water for irrigation and decreasing the C02 gas emissions. WWTP is always required before waste water can be reused for irrigation or discharged in water bodies. However, the energetic demand of the treatment process is high enough for making the price of treated water to exceed the one for drinkable water. This makes any policy very difficult to encourage the re-use of treated water, with a great impact on the water cycle, particularly in those areas suffering hydric stress or deficiency. The cost of treating waste water involves another climate-change related burden: the energy necessary for the process is obtained mainly from the electric network, which is, in most of the cases in Europe, energy obtained from the burning of fossil fuels. The innovative part of this project is based on the implementation, adaptation and integration of solutions for this problem, together with a new concept of the integration of energy input and operative energy demand. Moreover, there is an important qualitative jump between the technologies used and the alleged technologies to use in the project which give it an innovative character, due to the fact that there are no similar previous experiences of a WWTP including an intelligent discrimination of energy sources, integrating renewable ones (PV and Wind) and the grid.

Keywords: aeration system, biological reactor, CO2 emissions, energy efficiency, hybrid systems, LIFE 2013 call, process optimization, renewable energy sources, wasted water treatment plants

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Authors: Karen Eastman

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More youths and young adults with autism spectrum disorder (ASD) have been entering the workforce in recent years. Historically, students with ASD struggle after leaving high school and experience lower rates of employment, with social skills continuing to be the most problematic area of concern. Special education teachers may find it challenging to identify effective combinations of evidence-based practices (EBPs) and supports to best guide these students. One EBP, Peer Mediated Instruction and Intervention (PMII) has been well documented in the literature as being effective for younger students with autism but not researched as much with older students and adults, particularly in work settings. A need to combine PMII with other EBPs has been identified as a way to achieve a greater positive impact rather than any practice alone. A multiple baseline across skills design was used in this research project with two participants in different settings. PMII was combined with Visual Supports, with typical peers being trained in both practices. PMII is an evidence-based practice used to address social concerns by training peers without disabilities as to how they can provide feedback to and support, the student with ASD with social interactions in structured settings. The peers without disabilities were the instructors, while the adults facilitated the social situations and provided support to both the peers and students with ASD when needed. Because many individuals with ASD learn best with visual input, rather than using only the spoken word (verbal directions and feedback), Visual Supports were used in conjunction with PMII. Visual Supports can include written words, pictures, symbols, videos, or objects. In this project, the Visual Supports used were written social scripts, videos, Stop and Think signs, written reminder cards, a school map, and a pictorial task analysis of work tasks. Variables that may affect intervention outcomes in this project included attendance at school and school-based work settings for both the students with ASD and the peers without disabilities and behaviors and responses from others in the settings. Qualitative data was also collected from observations and surveys with peers about the process and their role. Data indicated that the students with ASD responded more positively to redirection and support from their peers than to teachers and staff and showed an increase in positive interactions with others. Those surveyed indicated a positive attitude toward and response to the use of peer interventions with visual supports.

Keywords: autism, social skills, vocational training, peer interventions

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Authors: Diamant Irene, Dar Tamar

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Burnout, conceptualized within the framework of stress research, is to a large extent a result of a threat on resources of time or a feeling of time shortage. In reaction to numerous tasks, deadlines, high output, management of different duties encompassing work-home conflicts, many individuals experience ‘time pressure’. Time pressure is characterized as the perception of a lack of available time in relation to the amount of workload. It can be a result of local objective constraints, but it can also be a chronic attribute in coping with life. As such, time pressure is associated in the literature with general stress experience and can therefore be a direct, contributory burnout factor. The present study examines the relation of chronic time pressure – feeling of time shortage and of being rushed, with another central aspect in subjective temporal experience - time perspective. Time perspective is a stable personal disposition, capturing the extent to which people subjectively remember the past, live the present and\or anticipate the future. Based on Hobfoll’s Conservation of Resources Theory, it was hypothesized that individuals with chronic time pressure would experience a permanent threat on their time resources resulting in relatively increased burnout. In addition, it was hypothesized that different time perspective profiles, based on Zimbardo’s typology of five dimensions – Past Positive, Past Negative, Present Hedonistic, Present Fatalistic, and Future, would be related to different magnitudes of chronic time pressure and of burnout. We expected that individuals with ‘Past Negative’ or ‘Present Fatalist’ time perspectives would experience more burnout, with chronic time pressure being a moderator variable. Conversely, individuals with a ‘Present Hedonistic’ - with little concern with the future consequences of actions, would experience less chronic time pressure and less burnout. Another temporal experience angle examined in this study is the difference between the actual distribution of time (as in a typical day) versus desired distribution of time (such as would have been distributed optimally during a day). It was hypothesized that there would be a positive correlation between the gap between these time distributions and chronic time pressure and burnout. Data was collected through an online self-reporting survey distributed on social networks, with 240 participants (aged 21-65) recruited through convenience and snowball sampling methods from various organizational sectors. The results of the present study support the hypotheses and constitute a basis for future debate regarding the elements of burnout in the modern work environment, with an emphasis on subjective temporal experience. Our findings point to the importance of chronic and stable temporal experiences, as time pressure and time perspective, in occupational experience. The findings are also discussed with a view to the development of practical methods of burnout prevention.

Keywords: conservation of resources, burnout, time pressure, time perspective

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Authors: Adeola Adenikinju, Olusanya E. Olubusoye, Lateef O. Akinpelu, Dilinna L. Nwobi

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Since the enactment of the Fiscal Responsibility Act (2007), the Federal Government of Nigeria (FGN) has made public its fiscal budget and the subsequent implementation report. A critical review of these documents shows significant variations in the five macroeconomic variables which are inputs in each Presidential budget; oil Production target (mbpd), oil price ($), Foreign exchange rate(N/$), and Gross Domestic Product growth rate (%) and inflation rate (%). This results in underperformance of the Federal budget expected output in terms of non-oil and oil revenue aggregates. This paper evaluates first the existing variance between budgeted and actuals, then the relationship and causality between the determinants of Federal fiscal budget assumptions, and finally the determinants of FGN’s Gross Oil Revenue. The paper employed the use of descriptive statistics, the Autoregressive distributed lag (ARDL) model, and a Profit oil probabilistic model to achieve these objectives. This model permits for both the static and dynamic effect(s) of the independent variable(s) on the dependent variable, unlike a static model that accounts for static or fixed effect(s) only. It offers a technique for checking the existence of a long-run relationship between variables, unlike other tests of cointegration, such as the Engle-Granger and Johansen tests, which consider only non-stationary series that are integrated of the same order. Finally, even with small sample size, the ARDL model is known to generate a valid result, for it is the dependent variable and is the explanatory variable. The results showed that there is a long-run relationship between oil revenue as a proxy for budget performance and its determinants; oil price, produced oil quantity, and foreign exchange rate. There is a short-run relationship between oil revenue and its determinants; oil price, produced oil quantity, and foreign exchange rate. There is a long-run relationship between non-oil revenue and its determinants; inflation rate, GDP growth rate, and foreign exchange rate. The grangers’ causality test results show that there is a mono-directional causality between oil revenue and its determinants. The Federal budget assumptions only explain 68% of oil revenue and 62% of non-oil revenue. There is a mono-directional causality between non-oil revenue and its determinants. The Profit oil Model describes production sharing contracts, joint ventures, and modified carrying arrangements as the greatest contributors to FGN’s gross oil revenue. This provides empirical justification for the selected macroeconomic variables used in the Federal budget design and performance evaluation. The research recommends other variables, debt and money supply, be included in the Federal budget design to explain the Federal budget revenue performance further.

Keywords: ARDL, budget performance, oil price, oil quantity, oil revenue

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Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Federico Garrido, Mostafa El Hayani, Ahmed Shams

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The exploration of the potential of artificial intelligence (AI) in architecture is still embryonic, however, its latent capacity to change design disciplines is significant. 'Synthetic Classism' is a research project that questions the underlying aspects of classically organized architecture not just in aesthetic terms but also from a geometrical and morphological point of view, intending to generate new architectural information using historical examples as source material. The main aim of this paper is to explore the uses of artificial intelligence and machine learning algorithms in architectural design while creating a coherent narrative to be contained within a design process. The purpose is twofold: on one hand, to develop and train machine learning algorithms to produce architectural information of small pavilions and on the other, to synthesize new information from previous architectural drawings. These algorithms intend to 'interpret' graphical information from each pavilion and then generate new information from it. The procedure, once these algorithms are trained, is the following: parting from a line profile, a synthetic 'front view' of a pavilion is generated, then using it as a source material, an isometric view is created from it, and finally, a top view is produced. Thanks to GAN algorithms, it is also possible to generate Front and Isometric views without any graphical input as well. The final intention of the research is to produce isometric views out of historical information, such as the pavilions from Sebastiano Serlio, James Gibbs, or John Soane. The idea is to create and interpret new information not just in terms of historical reconstruction but also to explore AI as a novel tool in the narrative of a creative design process. This research also challenges the idea of the role of algorithmic design associated with efficiency or fitness while embracing the possibility of a creative collaboration between artificial intelligence and a human designer. Hence the double feature of this research, both analytical and creative, first by synthesizing images based on a given dataset and then by generating new architectural information from historical references. We find that the possibility of creatively understand and manipulate historic (and synthetic) information will be a key feature in future innovative design processes. Finally, the main question that we propose is whether an AI could be used not just to create an original and innovative group of simple buildings but also to explore the possibility of fostering a novel architectural sensibility grounded on the specificities on the architectural dataset, either historic, human-made or synthetic.

Keywords: architecture, central pavilions, classicism, machine learning

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Authors: Emmanuel Ophel Gilbert, Williams Speret

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The control for fluid flow behaviours of viscous fluids and heat transfer occurrences within heated mini-channel is considered. Heat transfer and flow characteristics of different viscous liquids, such as engine oil, automatic transmission fluid, one-half ethylene glycol, and deionized water were numerically analyzed. Some mathematical applications such as Fourier series and Laplace Z-Transforms were employed to ascertain the behaviour-wave like structure of these each viscous fluids. The steady, laminar flow and heat transfer equations are reckoned by the aid of numerical simulation technique. Further, this numerical simulation technique is endorsed by using the accessible practical values in comparison with the anticipated local thermal resistances. However, the roughness of this mini-channel that is one of the physical limitations was also predicted in this study. This affects the frictional factor. When an additive such as tetracycline was introduced in the fluid, the heat input was lowered, and this caused pro rata effect on the minor and major frictional losses, mostly at a very minute Reynolds number circa 60-80. At this ascertained lower value of Reynolds numbers, there exists decrease in the viscosity and minute frictional losses as a result of the temperature of these viscous liquids been increased. It is inferred that the three equations and models are identified which supported the numerical simulation via interpolation and integration of the variables extended to the walls of the mini-channel, yields the utmost reliance for engineering and technology calculations for turbulence impacting jets in the near imminent age. Out of reasoning with a true equation that could support this control for the fluid flow, Navier-stokes equations were found to tangential to this finding. Though, other physical factors with respect to these Navier-stokes equations are required to be checkmated to avoid uncertain turbulence of the fluid flow. This paradox is resolved within the framework of continuum mechanics using the classical slip condition and an iteration scheme via numerical simulation method that takes into account certain terms in the full Navier-Stokes equations. However, this resulted in dropping out in the approximation of certain assumptions. Concrete questions raised in the main body of the work are sightseen further in the appendices.

Keywords: frictional losses, heat transfer, laminar flow, mini-channel, number simulation, Reynolds number, turbulence, viscous fluids

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Authors: Harshan Tee Pee

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Climate elements include surface temperatures, rainfall patterns, humidity, type and amount of cloudiness, air pressure and wind speed and direction. Change in one element can have an impact on the regional climate. The scientific predictions indicate that global climate change will increase the number of extreme events, leading to more frequent natural hazards. Global warming is likely to intensify the risk of drought in certain parts and also leading to increased rainfall in some other parts. Drought is a slow advancing disaster and creeping phenomenon– which accumulate slowly over a long period of time. Droughts are naturally linked with aridity. But droughts occur over most parts of the world (both wet and humid regions) and create severe impacts on agriculture, basic household welfare and ecosystems. Drought condition occurs at least every three years in India. India is one among the most vulnerable drought prone countries in the world. The economic impacts resulting from extreme environmental events and disasters are huge as a result of disruption in many economic activities. The focus of this paper is to develop a comprehensive understanding about the distributional impacts of disaster, especially impact of drought on agricultural production and income through a panel study (drought year and one year after the drought) in Raikhel village, Maharashtra, India. The major findings of the study indicate that cultivating area as well as the number of cultivating households reduced after the drought, indicating a shift in the livelihood- households moved from agriculture to non-agriculture. Decline in the gross cropped area and production of various crops depended on the negative income from these crops in the previous agriculture season. All the landholding categories of households except landlords had negative income in the drought year and also the income disparities between the households were higher in that year. In the drought year, the cost of cultivation was higher for all the landholding categories due to the increased cost for irrigation and input cost. In the drought year, agriculture products (50 per cent of the total products) were used for household consumption rather than selling in the market. It is evident from the study that livelihood which was based on natural resources became less attractive to the people to due to the risk involved in it and people were moving to less risk livelihood for their sustenance.

Keywords: climate change, drought, agriculture economics, disaster impact

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Authors: Triveni Gogoi, Rima Chatterjee

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Shear wave velocity (Vs) estimation is an important approach in the seismic exploration and characterization of a hydrocarbon reservoir. There are varying methods for prediction of S-wave velocity, if recorded S-wave log is not available. But all the available methods for Vs prediction are empirical mathematical models. Shear wave velocity can be estimated using P-wave velocity by applying Castagna’s equation, which is the most common approach. The constants used in Castagna’s equation vary for different lithologies and geological set-ups. In this study, multiple regression analysis has been used for estimation of S-wave velocity. The EMERGE module from Hampson-Russel software has been used here for generation of S-wave log. Both single attribute and multi attributes analysis have been carried out for generation of synthetic S-wave log in Upper Assam basin. Upper Assam basin situated in North Eastern India is one of the most important petroleum provinces of India. The present study was carried out using four wells of the study area. Out of these wells, S-wave velocity was available for three wells. The main objective of the present study is a prediction of shear wave velocities for wells where S-wave velocity information is not available. The three wells having S-wave velocity were first used to test the reliability of the method and the generated S-wave log was compared with actual S-wave log. Single attribute analysis has been carried out for these three wells within the depth range 1700-2100m, which corresponds to Barail group of Oligocene age. The Barail Group is the main target zone in this study, which is the primary producing reservoir of the basin. A system generated list of attributes with varying degrees of correlation appeared and the attribute with the highest correlation was concerned for the single attribute analysis. Crossplot between the attributes shows the variation of points from line of best fit. The final result of the analysis was compared with the available S-wave log, which shows a good visual fit with a correlation of 72%. Next multi-attribute analysis has been carried out for the same data using all the wells within the same analysis window. A high correlation of 85% has been observed between the output log from the analysis and the recorded S-wave. The almost perfect fit between the synthetic S-wave and the recorded S-wave log validates the reliability of the method. For further authentication, the generated S-wave data from the wells have been tied to the seismic and correlated them. Synthetic share wave log has been generated for the well M2 where S-wave is not available and it shows a good correlation with the seismic. Neutron porosity, density, AI and P-wave velocity are proved to be the most significant variables in this statistical method for S-wave generation. Multilinear regression method thus can be considered as a reliable technique for generation of shear wave velocity log in this study.

Keywords: Castagna's equation, multi linear regression, multi attribute analysis, shear wave logs

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Authors: Leigh Ann Wilson, Melanie Borrego

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The purpose of institutional learning outcomes (ILOs) is to assess what students across the university know and what they do not. The issue is gathering this information in a systematic and usable way. This presentation will explain how one institution has engineered this process for both student success and maximum faculty curriculum and course design input. At Brandman University, there are three levels of learning outcomes: course, program, and institutional. Institutional Learning Outcomes (ILOs) are mapped to specific courses. Faculty course developers write the signature assignments (SAs) in alignment with the Institutional Learning Outcomes for each course. These SAs use a specific rubric that is applied consistently by every section and every instructor. Each year, the 12-member General Education Team (GET), as a part of their work, conducts the calibration and assessment of the university-wide SAs and the related rubrics for one or two of the five ILOs. GET members, who are senior faculty and administrators who represent each of the university's schools, lead the calibration meetings. Specifically, calibration is a process designed to ensure the accuracy and reliability of evaluating signature assignments by working with peer faculty to interpret rubrics and compare scoring. These calibration meetings include the full time and adjunct faculty members who teach the course to ensure consensus on the application of the rubric. Each calibration session is chaired by a GET representative as well as the course custodian/contact where the ILO signature assignment resides. The overall calibration process GET follows includes multiple steps, such as: contacting and inviting relevant faculty members to participate; organizing and hosting calibration sessions; and reviewing and discussing at least 10 samples of student work from class sections during the previous academic year, for each applicable signature assignment. Conversely, the commitment for calibration teams consist of attending two virtual meetings lasting up to three hours in duration. The first meeting focuses on interpreting the rubric, and the second meeting involves comparing scores for sample work and sharing feedback about the rubric and assignment. Next, participants are expected to follow all directions provided and participate actively, and respond to scheduling requests and other emails within 72 hours. The virtual meetings are recorded for future institutional use. Adjunct faculty are paid a small stipend after participating in both calibration meetings. Full time faculty can use this work on their annual faculty report for "internal service" credit.

Keywords: assessment, assurance of learning, course design, institutional learning outcomes, rubrics, signature assignments

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Authors: P. Mounnarath, U. Schmitz, Ch. Zhang

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Fragility analysis is an effective tool for the seismic vulnerability assessment of civil structures in the last several years. The design of the expansion joints according to various bridge design codes is almost inconsistent, and only a few studies have focused on this problem so far. In this study, the influence of the expansion joint clearances between the girder ends and the abutment backwalls on the seismic fragility assessment of continuous integral bridge frames is investigated. The gaps (ranging from 60 mm, 150 mm, 250 mm and 350 mm) are designed by following two different bridge design code specifications, namely, Caltrans and Eurocode 8-2. Five bridge models are analyzed and compared. The first bridge model serves as a reference. This model uses three-dimensional reinforced concrete fiber beam-column elements with simplified supports at both ends of the girder. The other four models also employ reinforced concrete fiber beam-column elements but include the abutment backfill stiffness and four different gap values. The nonlinear time history analysis is performed. The artificial ground motion sets, which have the peak ground accelerations (PGAs) ranging from 0.1 g to 1.0 g with an increment of 0.05 g, are taken as input. The soil-structure interaction and the P-Δ effects are also included in the analysis. The component fragility curves in terms of the curvature ductility demand to the capacity ratio of the piers and the displacement demand to the capacity ratio of the abutment sliding bearings are established and compared. The system fragility curves are then obtained by combining the component fragility curves. Our results show that in the component fragility analysis, the reference bridge model exhibits a severe vulnerability compared to that of other sophisticated bridge models for all damage states. In the system fragility analysis, the reference curves illustrate a smaller damage probability in the earlier PGA ranges for the first three damage states, they then show a higher fragility compared to other curves in the larger PGA levels. In the fourth damage state, the reference curve has the smallest vulnerability. In both the component and the system fragility analysis, the same trend is found that the bridge models with smaller clearances exhibit a smaller fragility compared to that with larger openings. However, the bridge model with a maximum clearance still induces a minimum pounding force effect.

Keywords: expansion joint clearance, fiber beam-column element, fragility assessment, time history analysis

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Authors: Eduardas Spiriajevas

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Since the end of the 20th century, when Lithuania regained its independence, a process of demographic shrinkage started. Recently, it affects the efficiency of implementation of actions related to regional development policy and geographic scopes of created value added in the regions. The demographic structures of human resources reflect onto the regions and their economic geographic environment. Due to reshaping economies and state reforms on restructuration of economic branches such as agriculture and industry, it affects the economic significance of services’ sector. These processes influence the competitiveness of labor market and its demographic characteristics. Such vivid consequences are appropriate for the structures of human migrations, which affected the processes of demographic ageing of human resources in the regions, especially in peripheral ones. These phenomena of modern times induce the demographic shrinkage of society and its economic geographic characteristics in the actions of regional development and in regional policy. The internal and external migrations of population captured numerous regional economic disparities, and influenced on territorial density and concentration of population of the country and created the economies of spatial unevenness in such small geographically compact country as Lithuania. The processes of territorial reshaping of distribution of population create new regions and their economic environment, which is not corresponding to the main principles of regional policy and its power to create the well-being and to promote the attractiveness for economic development. These are the new challenges of national regional policy and it should be researched in a systematic way of taking into consideration the analytical approaches of regional economy in the context of economic geographic research methods. A comparative territorial analysis according to administrative division of Lithuania in relation to retrospective approach and introduction of method of location quotients, both give the results of economic geographic character with cartographic representations using the tools of spatial analysis provided by technologies of Geographic Information Systems. A set of these research methods provide the new spatially evidenced based results, which must be taken into consideration in reshaping of national regional policy in economic geographic context. Due to demographic shrinkage and increasing differentiation of economic developments within the regions, an input of economic geographic dimension is inevitable. In order to sustain territorial balanced economic development, there is a need to strengthen the roles of regional centers (towns) and to empower them with new economic functionalities for revitalization of peripheral regions, and to increase their economic competitiveness and social capacities on national scale.

Keywords: demographic shrinkage, economic geography, Lithuania, regions

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Authors: Hemant Upadhyay, Tarun Kumar Kundu

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It is utmost important for a blast furnace operator to understand the mechanisms governing the liquid flow, accumulation, drainage and heat transfer between various phases in blast furnace hearth for a stable and efficient blast furnace operation. Abnormal drainage behavior may lead to high liquid build up in the hearth. Operational problems such as pressurization, low wind intake, and lower material descent rates, normally be encountered if the liquid levels in the hearth exceed a critical limit when Hearth coke and Deadman start to float. Similarly, hot metal temperature is an important parameter to be controlled in the BF operation; it should be kept at an optimal level to obtain desired product quality and a stable BF performance. It is not possible to carry out any direct measurement of above due to the hostile conditions in the hearth with chemically aggressive hot liquids. The objective here is to develop a mathematical model to simulate the variation in hot metal / slag accumulation and temperature during the tapping of the blast furnace based on the computed drainage rate, production rate, mass balance, heat transfer between metal and slag, metal and solids, slag and solids as well as among the various zones of metal and slag itself. For modeling purpose, the BF hearth is considered as a pressurized vessel, filled with solid coke particles. Liquids trickle down in hearth from top and accumulate in voids between the coke particles which are assumed thermally saturated. A set of generic mass balance equations gives the amount of metal and slag intake in hearth. A small drainage (tap hole) is situated at the bottom of the hearth and flow rate of liquids from tap hole is computed taking in account the amount of both the phases accumulated their level in hearth, pressure from gases in the furnace and erosion behaviors of tap hole itself. Heat transfer equations provide the exchange of heat between various layers of liquid metal and slag, and heat loss to cooling system through refractories. Based on all that information a dynamic simulation is carried out which provides real time information of liquids accumulation in hearth before and during tapping, drainage rate and its variation, predicts critical event timings during tapping and expected tapping temperature of metal and slag on preset time intervals. The model is in use at JSPL, India BF-II and its output is regularly cross-checked with actual tapping data, which are in good agreement.

Keywords: blast furnace, hearth, deadman, hotmetal

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Authors: Arindam Chaudhuri

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Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

Procedia PDF Downloads 472

Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Victorita Radulescu

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Introduction: The optimum exploitation of agricultural land in the presence of an aquifer polluted by the surface sources requires close monitoring of groundwater level in both periods of intense irrigation and in absence of the irrigations, in times of drought. Currently in Romania, in the south part of the country, the Baragan area, many agricultural lands are confronted with the risk of groundwater pollution in the absence of systematic irrigation, correlated with the climate changes. Basic Methods: The non-steady flow of the groundwater from an aquifer can be described by the Bousinesq’s partial differential equation. The finite element method was used, applied to the porous media needed for the water mass balance equation. By the proper structure of the initial and boundary conditions may be modeled the flow in drainage or injection systems of wells, according to the period of irrigation or prolonged drought. The boundary conditions consist of the groundwater levels required at margins of the analyzed area, in conformity to the reality of the pollutant emissaries, following the method of the double steps. Major Findings/Results: The drainage condition is equivalent to operating regimes on the two or three rows of wells, negative, as to assure the pollutant transport, modeled with the variable flow in groups of two adjacent nodes. In order to obtain the level of the water table, in accordance with the real constraints, are needed, for example, to be restricted its top level below of an imposed value, required in each node. The objective function consists of a sum of the absolute values of differences of the infiltration flow rates, increased by a large penalty factor when there are positive values of pollutant. In these conditions, a balanced structure of the pollutant concentration is maintained in the groundwater. The spatial coordinates represent the modified parameters during the process of optimization and the drainage flows through wells. Conclusions: The presented calculation scheme was applied to an area having a cross-section of 50 km between two emissaries with various levels of altitude and different values of pollution. The input data were correlated with the measurements made in-situ, such as the level of the bedrock, the grain size of the field, the slope, etc. This method of calculation can also be extended to determine the variation of the groundwater in the aquifer following the flood wave propagation in envoys.

Keywords: environmental protection, infiltrations, numerical modeling, pollutant transport through soils

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Authors: RAJ KUMAR G.C.

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There are tremendous opportunities to maximize smallholder farmers’ income from small-scale water resource development through micro irrigation and multiple-use water systems (MUS). MUS are an improved water management approach, developed and tested successfully by iDE that pipes water to a community both for domestic use and for agriculture using efficient micro irrigation. Different MUS models address different landscape constraints, water demand, and users’ preferences. MUS are complemented by micro irrigation kits, which were developed by iDE to enable farmers to grow high-value crops year-round and to use limited water resources efficiently. Over the last 15 years, iDE’s promotion of the MUS approach has encouraged government and other key stakeholders to invest in MUS for better planning of scarce water resources. Currently, about 60% of the cost of MUS construction is covered by the government and community. Based on iDE’s experience, a gravity-fed MUS costs approximately $125 USD per household to construct, and it can increase household income by $300 USD per year. A key element of the MUS approach is keeping farmers well linked to input supply systems and local produce collection centers, which helps to ensure that the farmers can produce a sufficient quantity of high-quality produce that earns a fair price. This process in turn creates an enabling environment for smallholders to invest in MUS and micro irrigation. Therefore, MUS should be seen as an integrated package of interventions –the end users, water sources, technologies, and the marketplace– that together enhance technical, financial, and institutional sustainability. Communities are trained to participate in sustainable water resource management as a part of the MUS planning and construction process. The MUS approach is cost-effective, improves community governance of scarce water resources, helps smallholder farmers to improve rural health and livelihoods, and promotes gender equity. MUS systems are simple to maintain and communities are trained to ensure that they can undertake minor maintenance procedures themselves. All in all, the iDE Nepal MUS offers multiple benefits and represents a practical and sustainable model of the MUS approach. Moreover, there is a growing national consensus that rural water supply systems should be designed for multiple uses, acknowledging that substantial work remains in developing national-level and local capacity and policies for scale-up.

Keywords: multiple-use water systems , small scale water resources, rural livelihoods, practical and sustainable model

Procedia PDF Downloads 273

Authors: Dhruvit S. Berawala, Jann R. Ursin, Obrad Slijepcevic

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Shale gas is one of the most rapidly growing forms of natural gas. Unconventional natural gas deposits are difficult to characterize overall, but in general are often lower in resource concentration and dispersed over large areas. Moreover, gas is densely packed into the matrix through adsorption which accounts for large volume of gas reserves. Gas production from tight shale deposits are made possible by extensive and deep well fracturing which contacts large fractions of the formation. The conventional reservoir modelling and production forecasting methods, which rely on fluid-flow processes dominated by viscous forces, have proved to be very pessimistic and inaccurate. This paper presents a new approach to forecast shale gas production by detailed modeling of gas desorption, diffusion and non-linear flow mechanisms in combination with statistical representation of these processes. The representation of the model involves a cube as a porous media where free gas is present and a sphere (SiC: Sphere in Cube model) inside it where gas is adsorbed on to the kerogen or organic matter. Further, the sphere is considered consisting of many layers of adsorbed gas in an onion-like structure. With pressure decline, the gas desorbs first from the outer most layer of sphere causing decrease in its molecular concentration. The new available surface area and change in concentration triggers the diffusion of gas from kerogen. The process continues until all the gas present internally diffuses out of the kerogen, gets adsorbs onto available surface area and then desorbs into the nanopores and micro-fractures in the cube. Each SiC idealizes a gas pathway and is characterized by sphere diameter and length of the cube. The diameter allows to model gas storage, diffusion and desorption; the cube length takes into account the pathway for flow in nanopores and micro-fractures. Many of these representative but general cells of the reservoir are put together and linked to a well or hydraulic fracture. The paper quantitatively describes these processes as well as clarifies the geological conditions under which a successful shale gas production could be expected. A numerical model has been derived which is then compiled on FORTRAN to develop a simulator for the production of shale gas by considering the spheres as a source term in each of the grid blocks. By applying SiC to field data, we demonstrate that the model provides an effective way to quickly access gas production rates from shale formations. We also examine the effect of model input properties on gas production.

Keywords: adsorption, diffusion, non-linear flow, shale gas production

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Authors: Jeh Patel, Chandrahas Paidi, Ahmed Hambaba

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Traditional methods for analyzing athlete movement often lack the detail and immediacy required for optimal training. This project aims to address this limitation by developing a Real-time human pose estimation system specifically designed to enhance athlete training across various sports. This system leverages the power of convolutional neural networks (CNNs) to provide a comprehensive and immediate analysis of an athlete’s movement patterns during training sessions. The core architecture utilizes dilated convolutions to capture crucial long-range dependencies within video frames. Combining this with the robust encoder-decoder architecture to further refine pose estimation accuracy. This capability is essential for precise joint localization across the diverse range of athletic poses encountered in different sports. Furthermore, by quantifying movement efficiency, power output, and range of motion, the system provides data-driven insights that can be used to optimize training programs. Pose estimation data analysis can also be used to develop personalized training plans that target specific weaknesses identified in an athlete’s movement patterns. To overcome the limitations posed by outdoor environments, the project employs strategies such as multi-camera configurations or depth sensing techniques. These approaches can enhance pose estimation accuracy in challenging lighting and occlusion scenarios, where pose estimation accuracy in challenging lighting and occlusion scenarios. A dataset is collected From the labs of Martin Luther King at San Jose State University. The system is evaluated through a series of tests that measure its efficiency and accuracy in real-world scenarios. Results indicate a high level of precision in recognizing different poses, substantiating the potential of this technology in practical applications. Challenges such as enhancing the system’s ability to operate in varied environmental conditions and further expanding the dataset for training were identified and discussed. Future work will refine the model’s adaptability and incorporate haptic feedback to enhance the interactivity and richness of the user experience. This project demonstrates the feasibility of an advanced pose detection model and lays the groundwork for future innovations in assistive enhancement technologies.

Keywords: computer vision, deep learning, human pose estimation, U-NET, CNN

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Authors: Nicholas McNamara, John Brookes, Michael Williams, Manish Mathew, Elizabeth Brookes, Tristan Yan, Paul Bannon

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Objective: Pericardial effusions are an uncommon but potentially fatal complication after cardiac surgery. The goal of this study was to describe the incidence and risk factors associated with the development of pericardial effusion requiring drainage after coronary artery bypass graft surgery (CABG). Methods: A retrospective analysis was undertaken using prospectively collected data. All adult patients who underwent CABG at our institution between 1st January 2017 and 31st December 2018 were included. Pericardial effusion was diagnosed using transthoracic echocardiography (TTE) performed for clinical suspicion of pre-tamponade or tamponade. Drainage was undertaken if considered clinically necessary and performed via a sub-xiphoid incision, pericardiocentesis, or via re-sternotomy at the discretion of the treating surgeon. Patient demographics, operative characteristics, anticoagulant exposure, and postoperative outcomes were examined to identify those variables associated with the development of pericardial effusion requiring drainage. Tests of association were performed using the Fischer exact test for dichotomous variables and the Student t-test for continuous variables. Logistic regression models were used to determine univariate predictors of pericardial effusion requiring drainage. Results: Between January 1st, 2017, and December 31st, 2018, a total of 408 patients underwent CABG at our institution, and eight (1.9%) required drainage of pericardial effusion. There was no difference in age, gender, or the proportion of patients on preoperative therapeutic heparin between the study and control groups. Univariate analysis identified preoperative atrial arrhythmia (37.5% vs 8.8%, p = 0.03), reduced left ventricular ejection fraction (47% vs 56%, p = 0.04), longer cardiopulmonary bypass (130 vs 84 min, p < 0.01) and cross-clamp (107 vs 62 min, p < 0.01) times, higher drain output in the first four postoperative hours (420 vs 213 mL, p <0.01), postoperative atrial fibrillation (100% vs 32%, p < 0.01), and pleural effusion requiring drainage (87.5% vs 12.5%, p < 0.01) to be associated with development of pericardial effusion requiring drainage. Conclusion: In this study, the incidence of pericardial effusion requiring drainage was 1.9%. Several factors, mainly related to preoperative or postoperative arrhythmia, length of surgery, and pleural effusion requiring drainage, were identified to be associated with developing clinically significant pericardial effusions. High clinical suspicion and low threshold for transthoracic echo are pertinent to ensure this potentially lethal condition is not missed.

Keywords: coronary artery bypass, pericardial effusion, pericardiocentesis, tamponade, sub-xiphoid drainage

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Authors: Jessica Scher Lisa, Eric Shyman

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Autism spectrum disorder (ASD) is a neuro-developmental disorder that is characterized by persistent deficits in social communication and social interaction (i.e. deficits in social-emotional reciprocity, nonverbal communicative behaviors, and establishing/maintaining social relationships), as well as by the presence of repetitive behaviors and perseverative areas of interest (i.e. stereotyped or receptive motor movements, use of objects, or speech, rigidity, restricted interests, and hypo or hyperactivity to sensory input or unusual interest in sensory aspects of the environment). Additionally, diagnoses of ASD require the presentation of symptoms in the early developmental period, marked impairments in adaptive functioning, and a lack of explanation by general intellectual impairment or global developmental delay (although these conditions may be co-occurring). Over the past several decades, many theories have been developed in an effort to explain the root cause of ASD in terms of atypical central cognitive processes. The field of neuroscience is increasingly finding structural and functional differences between autistic and neurotypical individuals using neuro-imaging technology. One main area this research has focused upon is in visuospatial processing, with specific attention to the notion of ‘weak central coherence’ (WCC). This paper offers an analysis of findings from selected studies in order to explore research that challenges the ‘deficit’ characterization of a weak central coherence theory as opposed to a ‘superiority’ characterization of strong local coherence. The weak central coherence theory has long been both supported and refuted in the ASD literature and has most recently been increasingly challenged by advances in neuroscience. The selected studies lend evidence to the notion of amplified localized perception rather than deficient global perception. In other words, WCC may represent superiority in ‘local processing’ rather than a deficit in global processing. Additionally, the right hemisphere and the specific area of the extrastriate appear to be key in both the visual and lexicosemantic process. Overactivity in the striate region seems to suggest inaccuracy in semantic language, which lends itself to support for the link between the striate region and the atypical organization of the lexicosemantic system in ASD.

Keywords: autism spectrum disorder, neurology, visual processing, weak coherence

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Authors: Fredrick Forster, Gareth Ward, Matthew Tubby, Pamela Lithgow, Anne Nortcliffe

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This paper presents the results from a research study of random adult participants accessing one of four different commercially available Virtual Reality (VR) Head Mounted Displays (HMDs) and completing a post user experience reflection questionnaire. The research sort to understand how inclusive commercially available VR HMDs are and identify any associated barriers that could impact the widespread adoption of the devices, specifically in Higher Education (HE). In the UK, education providers are legally required under the Equality Act 2010 to ensure all education facilities are inclusive and reasonable adjustments can be applied appropriately. The research specifically aimed to identify the considerations that academics and learning technologists need to make when adopting the use of commercial VR HMDs in HE classrooms, namely cybersickness, user comfort, Interpupillary Distance, inclusiveness, and user perceptions of VR. The research approach was designed to build upon previously published research on user reflections on presence, usability, and overall HMD comfort, using quantitative and qualitative research methods by way of a questionnaire. The quantitative data included the recording of physical characteristics such as the distance between eye pupils, known as Interpupillary Distance (IPD). VR HMDs require each user’s IPD measurement to enable the focusing of the VR HMDs virtual camera output to the right position in front of the eyes of the user. In addition, the questionnaire captured users’ qualitative reflections and evaluations of the broader accessibility characteristics of the VR HMDs. The initial research activity was accomplished by enabling a random sample of visitors, staff, and students at Canterbury Christ Church University, Kent to use a VR HMD for a set period of time and asking them to complete the post user experience questionnaire. The study identified that there is little correlation between users who experience cyber sickness and car sickness. Also, users with a smaller IPD than average (typically associated with females) were able to use the VR HMDs successfully; however, users with a larger than average IPD reported an impeded experience. This indicates that there is reduced inclusiveness for the tested VR HMDs for users with a higher-than-average IPD which is typically associated with males of certain ethnicities. As action education research, these initial findings will be used to refine the research method and conduct further investigations with the aim to provide verification and validation of the accessibility of current commercial VR HMDs. The conference presentation will report on the research results of the initial study and subsequent follow up studies with a larger variety of adult volunteers.

Keywords: virtual reality, education technology, inclusive technology, higher education

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Authors: Nursultan Turdakyn, Alisher Medeubayev, Didar Meiramov, Zhibek Bekezhankyzy, Desmond Adair, Gulnur Kalimuldina

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In recent years the development of sustainable energy sources is getting extensive research interest due to the ever-growing demand for energy. As an alternative energy source to power small electronic devices, ambient energy harvesting from vibration or human body motion is considered a potential candidate. Despite the enormous progress in the field of battery research in terms of safety, lifecycle and energy density in about three decades, it has not reached the level to conveniently power wearable electronic devices such as smartwatches, bands, hearing aids, etc. For this reason, the development of self-charging power units with excellent flexibility and integrated energy harvesting and storage is crucial. Self-powering is a key idea that makes it possible for the system to operate sustainably, which is now getting more acceptance in many fields in the area of sensor networks, the internet of things (IoT) and implantable in-vivo medical devices. For solving this energy harvesting issue, the self-powering nanogenerators (NGS) were proposed and proved their high effectiveness. Usually, sustainable power is delivered through energy harvesting and storage devices by connecting them to the power management circuit; as for energy storage, the Li-ion battery (LIB) is one of the most effective technologies. Through the movement of Li ions under the driving of an externally applied voltage source, the electrochemical reactions generate the anode and cathode, storing the electrical energy as the chemical energy. In this paper, we present a simultaneous process of converting the mechanical energy into chemical energy in a way that NG and LIB are combined as an all-in-one power system. The electrospinning method was used as an initial step for the development of such a system with a β-PVDF separator. The obtained film showed promising voltage output at different stress frequencies. X-ray diffraction (XRD) and Fourier Transform Infrared Spectroscopy (FT-IR) analysis showed a high percentage of β phase of PVDF polymer material. Moreover, it was found that the addition of 1 wt.% of BTO (Barium Titanate) results in higher quality fibers. When comparing pure PVDF solution with 20 wt.% content and the one with BTO added the latter was more viscous. Hence, the sample was electrospun uniformly without any beads. Lastly, to test the sensor application of such film, a particular testing device has been developed. With this device, the force of a finger tap can be applied at different frequencies so that electrical signal generation is validated.

Keywords: electrospinning, nanogenerators, piezoelectric PVDF, self-charging li-ion batteries

Procedia PDF Downloads 142