Search results for: modeling accuracy

Authors: Z. M. Biyasheva, M. Zh. Tleubergenova, Y. A. Zaripova, A. L. Shakirov, V. V. Dyachkov

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In recent years, interest in ecogenetic and biomedical problems related to the effects on the population of radon and its daughter decay products has increased significantly. Of particular interest is the assessment of the consequence of irradiation at hazardous radon areas, which includes the Almaty region due to the large number of tectonic faults that enhance radon emanation. In connection with the foregoing, the purpose of this work was to study the genetic effects of exposure to supernormal radon doses on the alpha-radiation model. Irradiation does not affect the growth of the cell, but rather its ability to differentiate. In addition, irradiation can lead to somatic mutations, morphoses and modifications. These damages most likely occur from changes in the composition of the substances of the cell. Such changes are epigenetic since they affect the regulatory processes of ontogenesis. Variability in the expression of regulatory genes refers to conditional mutations that modify the formation of signs of intraspecific similarity. Characteristic features of these conditional mutations are the dominant type of their manifestation, phenotypic asymmetry and their instability in the generations. Currently, the terms “morphosis” and “modification” are used to describe epigenetic variability, which are maintained in Drosophila melanogaster cultures using linkaged X- chromosomes, and the mutant X-chromosome is transmitted along the paternal line. In this paper, we investigated the epigenetic effects of alpha particles, whose source in nature is mainly radon and its daughter decay products. In the experiment, an isotope of plutonium-238 (Pu²³⁸), generating radiation with an energy of about 5500 eV, was used as a source of alpha particles. In an experiment in the first generation (F₁), deformities or morphoses were found, which can be called "radiation syndromes" or mutations, the manifestation of which is similar to the pleiotropic action of genes. The proportion of morphoses in the experiment was 1.8%, and in control 0.4%. In this experiment, the morphoses in the flies of the first and second generation looked like black spots, or melanomas on different parts of the imago body; "generalized" melanomas; curled, curved wings; shortened wing; bubble on one wing; absence of one wing, deformation of thorax, interruption and violation of tergite patterns, disruption of distribution of ocular facets and bristles; absence of pigmentation of the second and third legs. Statistical analysis by the Chi-square method showed the reliability of the difference in experiment and control at P ≤ 0.01. On the basis of this, it can be considered that alpha particles, which in the environment are mainly generated by radon and its isotopes, have a mutagenic effect that manifests itself, mainly in the formation of morphoses or deformities.

Keywords: alpha-radiation, genotoxicity, morphoses, radioecology, radon

Procedia PDF Downloads 130

Authors: Sara Honarparast, Omar Chaallal

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Reinforced concrete (RC) coupled shear walls (CSWs) are very effective structural systems in resisting lateral loads due to winds and earthquakes and are particularly used in medium- to high-rise RC buildings. However, most of existing old RC structures were designed for gravity loads or lateral loads well below the loads specified in the current modern seismic international codes. These structures may behave in non-ductile manner due to poorly designed joints, insufficient shear reinforcement and inadequate anchorage length of the reinforcing bars. This has been the main impetus to investigate an appropriate strengthening method to address or attenuate the deficiencies of these structures. The objective of this paper is to twofold: (i) evaluate the seismic performance of existing reinforced concrete coupled shear walls under reversed cyclic loading; and (ii) investigate the seismic performance of RC CSWs strengthened with externally bonded (EB) carbon fiber reinforced polymer (CFRP) sheets. To this end, two CSWs were considered as follows: (a) the first one is representative of old CSWs and therefore was designed according to the 1941 National Building Code of Canada (NBCC, 1941) with conventionally reinforced coupling beams; and (b) the second one, representative of new CSWs, was designed according to modern NBCC 2015 and CSA/A23.3 2014 requirements with diagonally reinforced coupling beam. Both CSWs were simulated using ANSYS software. Nonlinear behavior of concrete is modeled using multilinear isotropic hardening through a multilinear stress strain curve. The elastic-perfectly plastic stress-strain curve is used to simulate the steel material. Bond stress–slip is modeled between concrete and steel reinforcement in conventional coupling beam rather than considering perfect bond to better represent the slip of the steel bars observed in the coupling beams of these CSWs. The old-designed CSW was strengthened using CFRP sheets bonded to the concrete substrate and the interface was modeled using an adhesive layer. The behavior of CFRP material is considered linear elastic up to failure. After simulating the loading and boundary conditions, the specimens are analyzed under reversed cyclic loading. The comparison of results obtained for the two unstrengthened CSWs and the one retrofitted with EB CFRP sheets reveals that the strengthening method improves the seismic performance in terms of strength, ductility, and energy dissipation capacity.

Keywords: carbon fiber reinforced polymer, coupled shear wall, coupling beam, finite element analysis, modern code, old code, strengthening

Procedia PDF Downloads 174

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

Procedia PDF Downloads 127

Authors: Elham Koohikerade, Silvio Jose Gumiere

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In this research, irrigation is highlighted as crucial for improving both the yield and quality of ‎potatoes due to their high sensitivity to soil moisture changes. The study presents a hybrid Long ‎Short-Term Memory (LSTM) model aimed at optimizing irrigation scheduling in potato fields in ‎Quebec City, Canada. This model integrates model-based and satellite-derived datasets to simulate ‎soil moisture content, addressing the limitations of field data. Developed under the guidance of the ‎Food and Agriculture Organization (FAO), the simulation approach compensates for the lack of direct ‎soil sensor data, enhancing the LSTM model's predictions. The model was calibrated using indices ‎like Surface Soil Moisture (SSM), Normalized Vegetation Difference Index (NDVI), Enhanced ‎Vegetation Index (EVI), and Normalized Multi-band Drought Index (NMDI) to effectively forecast ‎soil moisture reductions. Understanding soil moisture and plant development is crucial for assessing ‎drought conditions and determining irrigation needs. This study validated the spectral characteristics ‎of vegetation and soil using ECMWF Reanalysis v5 (ERA5) and Moderate Resolution Imaging ‎Spectrometer (MODIS) data from 2019 to 2023, collected from agricultural areas in Dolbeau and ‎Peribonka, Quebec. Parameters such as surface volumetric soil moisture (0-7 cm), NDVI, EVI, and ‎NMDI were extracted from these images. A regional four-year dataset of soil and vegetation moisture ‎was developed using a machine learning approach combining model-based and satellite-based ‎datasets. The LSTM model predicts soil moisture dynamics hourly across different locations and ‎times, with its accuracy verified through cross-validation and comparison with existing soil moisture ‎datasets. The model effectively captures temporal dynamics, making it valuable for applications ‎requiring soil moisture monitoring over time, such as anomaly detection and memory analysis. By ‎identifying typical peak soil moisture values and observing distribution shapes, irrigation can be ‎scheduled to maintain soil moisture within Volumetric Soil Moisture (VSM) values of 0.25 to 0.30 ‎m²/m², avoiding under and over-watering. The strong correlations between parcels suggest that a ‎uniform irrigation strategy might be effective across multiple parcels, with adjustments based on ‎specific parcel characteristics and historical data trends. The application of the LSTM model to ‎predict soil moisture and vegetation indices yielded mixed results. While the model effectively ‎captures the central tendency and temporal dynamics of soil moisture, it struggles with accurately ‎predicting EVI, NDVI, and NMDI.‎

Keywords: irrigation scheduling, LSTM neural network, remotely sensed indices, soil and vegetation ‎monitoring

Procedia PDF Downloads 17

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

Procedia PDF Downloads 102

Authors: Waleed Omri

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With the growth of the small business sector in emerging markets, developing a better understanding of what drives 'day-to-day' entrepreneurial activities has become an important issue for academicians and practitioners. Innovation, as an entrepreneurial behavior, revolves around individuals who creatively engage in new organizational efforts. In a similar vein, the innovation behaviors and processes at the organizational member level are central to any corporate entrepreneurship strategy. Despite the broadly acknowledged importance of entrepreneurship and innovation at the individual level in the establishment of successful ventures, the literature lacks evidence on how entrepreneurs can effectively harness their skills and knowledge in the workplace. The existing literature illustrates that religion can impact the day-to-day work behavior of entrepreneurs, managers, and employees. Religious beliefs and practices could affect daily entrepreneurial activities by fostering mental abilities and traits such as creativity, intelligence, and self-efficacy. In the present study, we define religious capital as a set of personal and intangible resources, skills, and competencies that emanate from an individual’s religious values, beliefs, practices, and experiences and may be used to increase the quality of economic activities. Religious beliefs and practices give individuals a religious satisfaction, which can lead them to perform better in the workplace. In addition, religious ethics and practices have been linked to various positive employee outcomes in terms of organizational change, job satisfaction, and entrepreneurial intensity. As investigations of their consequences beyond direct task performance are still scarce, we explore if religious capital plays a role in entrepreneurs’ innovative behavior. In sum, this study explores the determinants of individual entrepreneurial behavior by investigating the relationship between religious capital and entrepreneurs’ innovative behavior in the context of small businesses. To further explain and clarify the religious capital-innovative behavior link, the present study proposes a model to examine the mediating role of entrepreneurial creativity. We use both Islamic work ethics (IWE) and Islamic religious practices (IRP) to measure Islamic religious capital. We use structural equation modeling with a robust maximum likelihood estimation to analyze data gathered from 289 Tunisian small businesses and to explore the relationships among the above-described variables. In line with the theory of planned behavior, only religious work ethics are found to increase the innovative behavior of small businesses’ owner-managers. Our findings also clearly demonstrate that the connection between religious capital-related variables and innovative behavior is better understood if the influence of entrepreneurial creativity, as a mediating variable of the aforementioned relationship, is taken into account. By incorporating both religious capital and entrepreneurial creativity into the innovative behavior analysis, this study provides several important practical implications for promoting innovation process in small businesses.

Keywords: entrepreneurial behavior, small business, religion, creativity

Procedia PDF Downloads 224

Authors: Iman Janghorban Esfahani

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Due to the increased use of irrigation in agriculture, the importance and need for highly reliable water pumping systems have significantly increased. The pumping of the groundwater is essential to provide water for both drip and furrow irrigation to increase the agricultural yield, especially in arid regions that suffer from scarcities of surface water. The most common irrigation pumping systems (IPS) consume conventional energies through the use of electric motors and generators or connecting to the electricity grid. Due to the shortage and transportation difficulties of fossil fuels, and unreliable access to the electricity grid, especially in the rural areas, and the adverse environmental impacts of fossil fuel usage, such as greenhouse gas (GHG) emissions, the need for renewable energy sources such as photovoltaic systems (PVS) as an alternative way of powering irrigation pumping systems is urgent. Integration of the photovoltaic systems with irrigation pumping systems as the Photovoltaic Powered-Irrigation Pumping System (PVP-IPS) can avoid fossil fuel dependency and the subsequent greenhouse gas emissions, as well as ultimately lower energy costs and improve efficiency, which made PVP-IPS systems as an environmentally and economically efficient solution for agriculture irrigation in every region. The greatest problem faced by integration of PVP with IPS systems is matching the intermittence of the energy supply with the dynamic water demand. The best solution to overcome the intermittence is to incorporate a storage system into the PVP-IPS to provide water-on-demand as a highly reliable stand-alone irrigation pumping system. The water storage tank (WST) is the most common storage device for PVP-IPS systems. In the integrated PVP-IPS with a water storage tank (PVP-IPS-WST), a water storage tank stores the water pumped by the IPS in excess of the water demand and then delivers it when demands are high. The Freshwater pinch analysis (FWaPA) as an alternative to mathematical modeling was used by other researchers for retrofitting the off-grid battery less photovoltaic-powered reverse osmosis system. However, the Freshwater pinch analysis has not been used to integrate the photovoltaic systems with irrigation pumping system with water storage tanks. In this study, FWaPA graphical and numerical tools were used for retrofitting an existing PVP-IPS system located in Salahadin, Republic of Iraq. The plant includes a 5 kW submersible water pump and 7.5 kW solar PV system. The Freshwater Composite Curve as the graphical tool and Freashwater Storage Cascade Table as the numerical tool were constructed to determine the minimum required outsourced water during operation, optimal amount of delivered electricity to the water pump, and optimal size of the water storage tank for one-year operation data. The results of implementing the FWaPA on the case study show that the PVP-IPS system with a WST as the reliable system can reduce outsourced water by 95.41% compare to the PVP-IPS system without storage tank.

Keywords: irrigation, photovoltaic, pinch analysis, pumping, solar energy

Procedia PDF Downloads 123

Authors: Kostas Kouvaris, Anastasia Eleftheriou, Georgios A. Sarantitis, Apostolos Chondronasios

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To achieve the objective of almost zero carbon energy solutions by 2050, the EU needs to accelerate the development of integrated, highly efficient and environmentally friendly solutions. In this direction, district heating and cooling (DHC) emerges as a viable and more efficient alternative to conventional, decentralized heating and cooling systems, enabling a combination of more efficient renewable and competitive energy supplies. In this paper, we develop a forecasting tool for near real-time local weather and thermal energy demand predictions for an entire DHC network. In this fashion, we are able to extend the functionality and to improve the energy efficiency of the DHC network by predicting and adjusting the heat load that is distributed from the heat generation plant to the connected buildings by the heat pipe network. Two case-studies are considered; one for Vransko, Slovenia and one for Montpellier, France. The data consists of i) local weather data, such as humidity, temperature, and precipitation, ii) weather forecast data, such as the outdoor temperature and iii) DHC operational parameters, such as the mass flow rate, supply and return temperature. The external temperature is found to be the most important energy-related variable for space conditioning, and thus it is used as an external parameter for the energy demand models. For the development of the forecasting tool, we use state-of-the-art deep neural networks and more specifically, recurrent networks with long-short-term memory cells, which are able to capture complex non-linear relations among temporal variables. Firstly, we develop models to forecast outdoor temperatures for the next 24 hours using local weather data for each case-study. Subsequently, we develop models to forecast thermal demand for the same period, taking under consideration past energy demand values as well as the predicted temperature values from the weather forecasting models. The contributions to the scientific and industrial community are three-fold, and the empirical results are highly encouraging. First, we are able to predict future thermal demand levels for the two locations under consideration with minimal errors. Second, we examine the impact of the outdoor temperature on the predictive ability of the models and how the accuracy of the energy demand forecasts decreases with the forecast horizon. Third, we extend the relevant literature with a new dataset of thermal demand and examine the performance and applicability of machine learning techniques to solve real-world problems. Overall, the solution proposed in this paper is in accordance with EU targets, providing an automated smart energy management system, decreasing human errors and reducing excessive energy production.

Keywords: machine learning, LSTMs, district heating and cooling system, thermal demand

Procedia PDF Downloads 117

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: M. V. Rane, Tareke Tekia

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Liquid desiccant based fresh air dehumidifiers can be gainfully deployed for air-conditioning, agro-produce drying and in many industrial processes. Regeneration of liquid desiccant can be done using direct firing, high temperature waste heat or solar energy. Solar energy is clean and available in abundance; however, it is costly to collect. A two stage liquid desiccant fresh air dehumidification system can offer Coefficient of Performance (COP), in the range of 1.6 to 2 for comfort air conditioning applications. High COP helps reduce the size and cost of collectors required. Performance tests on high temperature regenerator of a two stage liquid desiccant fresh air dehumidifier coupled with seasonally tracked flat plate like solar collector will be presented in this paper. The two stage fresh air dehumidifier has four major components: High Temperature Regenerator (HTR), Low Temperature Regenerator (LTR), High and Low Temperature Solution Heat Exchangers and Fresh Air Dehumidifier (FAD). This open system can operate at near atmospheric pressure in all the components. These systems can be simple, maintenance-free and scalable. Environmentally benign, non-corrosive, moderately priced Potassium Formate, KCOOH, is used as a liquid desiccant. Typical KCOOH concentration in the system is expected to vary between 65 and 75%. Dilute liquid desiccant at 65% concentration exiting the fresh air dehumidifier will be pumped and preheated in solution heat exchangers before entering the high temperature solar regenerator. In the solar collector, solution will be regenerated to intermediate concentration of 70%. Steam and saturated solution exiting the solar collector array will be separated. Steam at near atmospheric pressure will then be used to regenerate the intermediate concentration solution up to a concentration of 75% in a low temperature regenerator where moisture vaporized be released in to atmosphere. Condensed steam can be used as potable water after adding a pinch of salt and some nutrient. Warm concentrated liquid desiccant will be routed to solution heat exchanger to recycle its heat to preheat the weak liquid desiccant solution. Evacuated glass tube based seasonally tracked solar collector is used for regeneration of liquid desiccant at high temperature. Temperature of regeneration for KCOOH is 133°C at 70% concentration. The medium temperature collector was designed for temperature range of 100 to 150°C. Double wall polycarbonate top cover helps reduce top losses. Absorber integrated heat storage helps stabilize the temperature of liquid desiccant exiting the collectors during intermittent cloudy conditions, and extends the operation of the system by couple of hours beyond the sunshine hours. This solar collector is light in weight, 12 kg/m2 without absorber integrated heat storage material, and 27 kg/m2 with heat storage material. Cost of the collector is estimated to be 10,000 INR/m2. Theoretical modeling of the collector has shown that the optical efficiency is 62%. Performance test of regeneration of KCOOH will be reported.

Keywords: solar, liquid desiccant, dehumidification, air conditioning, regeneration

Procedia PDF Downloads 332

Authors: Tianyu Wang, Nikita Karandikar

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The uncertainty around cost, safety, and feasibility of the decarbonized shipping fuels has made it increasingly complex for the shipping companies to set pricing strategies and forecast their freight revenues going forward. The increase in the green fuel surcharges will ultimately influence the automobile’s consumer prices. The auto shipping demand (ton-miles) has been gradually shifting from long-haul to short-sea trade over the past years following the relocation of the original equipment manufacturer (OEM) manufacturing to regions such as South America and Southeast Asia. The objective of this paper is twofold: 1) to investigate the car-carriers freight revenue development over the years when the trade pattern is gradually shifting towards short-sea exports 2) to empirically identify the quantitative impact of such trade pattern shifting to mainly freight rate, but also vessel size, fleet size as well as Green House Gas (GHG) emission in Roll on-Roll Off (Ro-Ro) shipping. In this paper, a model of analyzing and forecasting ton-miles and freight revenues for the trade routes of AS-NA (Asia to North America), EU-NA (Europe to North America), and SA-NA (South America to North America) is established by deploying Automatic Identification System (AIS) data and the financial results of a selected car carrier company. More specifically, Wallenius Wilhelmsen Logistics (WALWIL), the Norwegian Ro-Ro carrier listed on Oslo Stock Exchange, is selected as the case study company in this paper. AIS-based ton-mile datasets of WALWIL vessels that are sailing into North America region from three different origins (Asia, Europe, and South America), together with WALWIL’s quarterly freight revenues as reported in trade segments, will be investigated and compared for the past five years (2018-2022). Furthermore, ordinary‐least‐square (OLS) regression is utilized to construct the ton-mile demand and freight revenue forecasting. The determinants of trade pattern shifting, such as import tariffs following the China-US trade war and fuel prices following the 0.1% Emission Control Areas (ECA) zone requirement after IMO2020 will be set as key variable inputs to the machine learning model. The model will be tested on another newly listed Norwegian Car Carrier, Hoegh Autoliner, to forecast its 2022 financial results and to validate the accuracy based on its actual results. GHG emissions on the three routes will be compared and discussed based on a constant emission per mile assumption and voyage distances. Our findings will provide important insights about 1) the trade-off evaluation between revenue reduction and energy saving with the new ton-mile pattern and 2) how the trade flow shifting would influence the future need for the vessel and fleet size.

Keywords: AIS, automobile exports, maritime big data, trade flows

Procedia PDF Downloads 96

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: Young Ik Suh, Tywan G. Martin

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Sports apps in a smartphone provide up-to-date information and fast and convenient access to live games. The market of sports apps has emerged as the second fastest growing app category worldwide. Further, many sports fans use their smartphones to know the schedule of sporting events, players’ position and bios, videos and highlights. In recent years, a growing number of scholars and practitioners alike have emphasized the importance of interactivity with sports apps, hypothesizing that interactivity plays a significant role in enticing sports apps users and that it is a key component in measuring the success of sports apps. Interactivity in sports apps focuses primarily on two functions: (1) two-way communication and (2) active user control, neither of which have been applicable through traditional mass media and communication technologies. Therefore, the purpose of this study is to examine whether the interactivity function on sports apps leads to positive outcomes such as intent to revisit. More specifically, this study investigates how three major functions of interactivity (i.e., two-way communication, active user control, and real-time information) influence the attitude of sports apps users and their intent to revisit the sports apps. The following hypothesis is proposed; interactivity functions will be positively associated with both attitudes toward sports apps and intent to revisit sports apps. The survey questionnaire includes four parts: (1) an interactivity scale, (2) an attitude scale, (3) a behavioral intention scale, and (4) demographic questions. Data are to be collected from ESPN apps users. To examine the relationships among the observed and latent variables and determine the reliability and validity of constructs, confirmatory factor analysis (CFA) is conducted. Structural equation modeling (SEM) is utilized to test hypothesized relationships among constructs. Additionally, this study compares the proposed interactivity model with a rival model to identify the role of attitude as a mediating factor. The findings of the current sports apps study provide several theoretical and practical contributions and implications by extending the research and literature associated with the important role of interactivity functions in sports apps and sports media consumption behavior. Specifically, this study may improve the theoretical understandings of whether the interactivity functions influence user attitudes and intent to revisit sports apps. Additionally, this study identifies which dimensions of interactivity are most important to sports apps users. From practitioners’ perspectives, this findings of this study provide significant implications. More entrepreneurs and investors in the sport industry need to recognize that high-resolution photos, live streams, and up-to-date stats are in the sports app, right at sports fans fingertips. The result will imply that sport practitioners may need to develop sports mobile apps that offer greater interactivity functions to attract sport fans.

Keywords: interactivity, two-way communication, active user control, real time information, sports apps, attitude, intent to revisit

Procedia PDF Downloads 131

Authors: C. Deangeli, P. Obentaku Obenebot, O. Omwanghe

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The study focuses on the analysis of wellbore instability in rock masses affected by weakness planes. The occurrence of failure in such a type of rocks can occur in the rock matrix and/ or along the weakness planes, in relation to the mud weight gradient. In this case the simple Kirsch solution coupled with a failure criterion cannot supply a suitable scenario for borehole instabilities. Two different numerical approaches have been used in order to investigate the onset of local failure at the wall of a borehole. For each type of approach the influence of the inclination of weakness planes has been investigates, by considering joint sets at 0°, 35° and 90° to the horizontal. The first set of models have been carried out with FLAC 2D (Fast Lagrangian Analysis of Continua) by considering the rock material as a continuous medium, with a Mohr Coulomb criterion for the rock matrix and using the ubiquitous joint model for accounting for the presence of the weakness planes. In this model yield may occur in either the solid or along the weak plane, or both, depending on the stress state, the orientation of the weak plane and the material properties of the solid and weak plane. The second set of models have been performed with PFC2D (Particle Flow code). This code is based on the Discrete Element Method and considers the rock material as an assembly of grains bonded by cement-like materials, and pore spaces. The presence of weakness planes is simulated by the degradation of the bonds between grains along given directions. In general the results of the two approaches are in agreement. However the discrete approach seems to capture more complex phenomena related to local failure in the form of grain detachment at wall of the borehole. In fact the presence of weakness planes in the discontinuous medium leads to local instability along the weak planes also in conditions not predicted from the continuous solution. In general slip failure locations and directions do not follow the conventional wellbore breakout direction but depend upon the internal friction angle and the orientation of the bedding planes. When weakness plane is at 0° and 90° the behaviour are similar to that of a continuous rock material, but borehole instability is more severe when weakness planes are inclined at an angle between 0° and 90° to the horizontal. In conclusion, the results of the numerical simulations show that the prediction of local failure at the wall of the wellbore cannot disregard the presence of weakness planes and consequently the higher mud weight required for stability for any specific inclination of the joints. Despite the discrete approach can simulate smaller areas because of the large number of particles required for the generation of the rock material, however it seems to investigate more correctly the occurrence of failure at the miscroscale and eventually the propagation of the failed zone to a large portion of rock around the wellbore.

Keywords: continuous- discontinuous, numerical modelling, weakness planes wellbore, FLAC 2D

Procedia PDF Downloads 484

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

Procedia PDF Downloads 92

Authors: Vladimir Messerle, Alfred Mosse, Alexandr Ustimenko, Oleg Lavrichshev

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Hygiene and sanitary study of typical medical-biological waste made in Kazakhstan, Russia, Belarus and other countries show that their risk to the environment is much higher than that of most chemical wastes. For example, toxicity of solid waste (SW) containing cytotoxic drugs and antibiotics is comparable to toxicity of radioactive waste of high and medium level activity. This report presents the results of the thermodynamic analysis of thermal processing of SW and experiments at the developed plasma unit for SW processing. Thermodynamic calculations showed that the maximum yield of the synthesis gas at plasma gasification of SW in air and steam mediums is achieved at a temperature of 1600K. At the air plasma gasification of SW high-calorific synthesis gas with a concentration of 82.4% (СO – 31.7%, H2 – 50.7%) can be obtained, and at the steam plasma gasification – with a concentration of 94.5% (СO – 33.6%, H2 – 60.9%). Specific heat of combustion of the synthesis gas produced by air gasification amounts to 14267 kJ/kg, while by steam gasification - 19414 kJ/kg. At the optimal temperature (1600 K), the specific power consumption for air gasification of SW constitutes 1.92 kWh/kg, while for steam gasification - 2.44 kWh/kg. Experimental study was carried out in a plasma reactor. This is device of periodic action. The arc plasma torch of 70 kW electric power is used for SW processing. Consumption of SW was 30 kg/h. Flow of plasma-forming air was 12 kg/h. Under the influence of air plasma flame weight average temperature in the chamber reaches 1800 K. Gaseous products are taken out of the reactor into the flue gas cooling unit, and the condensed products accumulate in the slag formation zone. The cooled gaseous products enter the gas purification unit, after which via gas sampling system is supplied to the analyzer. Ventilation system provides a negative pressure in the reactor up to 10 mm of water column. Condensed products of SW processing are removed from the reactor after its stopping. By the results of experiments on SW plasma gasification the reactor operating conditions were determined, the exhaust gas analysis was performed and the residual carbon content in the slag was determined. Gas analysis showed the following composition of the gas at the exit of gas purification unit, (vol.%): СO – 26.5, H2 – 44.6, N2–28.9. The total concentration of the syngas was 71.1%, which agreed well with the thermodynamic calculations. The discrepancy between experiment and calculation by the yield of the target syngas did not exceed 16%. Specific power consumption for SW gasification in the plasma reactor according to the results of experiments amounted to 2.25 kWh/kg of working substance. No harmful impurities were found in both gas and condensed products of SW plasma gasification. Comparison of experimental results and calculations showed good agreement. Acknowledgement—This work was supported by Ministry of Education and Science of the Republic of Kazakhstan and Ministry of Education and Science of the Russian Federation (Agreement on grant No. 14.607.21.0118, project RFMEF160715X0118).

Keywords: coal, efficiency, ignition, numerical modeling, plasma-fuel system, plasma generator

Procedia PDF Downloads 234

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

Procedia PDF Downloads 179

Authors: Nirjhar Dhang, S. Vinay Kumar

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Concrete is a non-homogeneous mix of coarse aggregates, sand, cement, air-voids and interfacial transition zone (ITZ) around aggregates. Adoption of these complex structures and material properties in numerical simulation would lead us to better understanding and design of concrete. In this work, the mesoscale model of concrete has been prepared from X-ray computerized tomography (CT) image. These images are converted into computer model and numerically simulated using commercially available finite element software. The mesoscale models are simulated under the influence of compressive displacement. The effect of shape and distribution of aggregates, continuous and discrete ITZ thickness, voids, and variation of mortar strength has been investigated. The CT scan of concrete cube consists of series of two dimensional slices. Total 49 slices are obtained from a cube of 150mm and the interval of slices comes approximately 3mm. In CT scan images, the same cube can be CT scanned in a non-destructive manner and later the compression test can be carried out in a universal testing machine (UTM) for finding its strength. The image processing and extraction of mortar and aggregates from CT scan slices are performed by programming in Python. The digital colour image consists of red, green and blue (RGB) pixels. The conversion of RGB image to black and white image (BW) is carried out, and identification of mesoscale constituents is made by putting value between 0-255. The pixel matrix is created for modeling of mortar, aggregates, and ITZ. Pixels are normalized to 0-9 scale considering the relative strength. Here, zero is assigned to voids, 4-6 for mortar and 7-9 for aggregates. The value between 1-3 identifies boundary between aggregates and mortar. In the next step, triangular and quadrilateral elements for plane stress and plane strain models are generated depending on option given. Properties of materials, boundary conditions, and analysis scheme are specified in this module. The responses like displacement, stresses, and damages are evaluated by ABAQUS importing the input file. This simulation evaluates compressive strengths of 49 slices of the cube. The model is meshed with more than sixty thousand elements. The effect of shape and distribution of aggregates, inclusion of voids and variation of thickness of ITZ layer with relation to load carrying capacity, stress-strain response and strain localizations of concrete have been studied. The plane strain condition carried more load than plane stress condition due to confinement. The CT scan technique can be used to get slices from concrete cores taken from the actual structure, and the digital image processing can be used for finding the shape and contents of aggregates in concrete. This may be further compared with test results of concrete cores and can be used as an important tool for strength evaluation of concrete.

Keywords: concrete, image processing, plane strain, interfacial transition zone

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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

Procedia PDF Downloads 39

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

Procedia PDF Downloads 252

Authors: Zongyan Yang, Gagan K. Jena, Sankar B. Karanam, Noora M. A. Hokal

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Jumeirah First beach, a segment of coastline of length 1.5 km, is one of the popular public beaches in Dubai, UAE. The stability of the beach has been affected by several coastal developmental projects, including The World, Island 2 and La Mer. A comprehensive stabilization scheme comprising of two composite groynes (of lengths 90 m and 125m), modification to the northern breakwater of Jumeirah Fishing Harbour and beach re-nourishment was implemented by Dubai Municipality in 2012. However, the performance of the implemented stabilization scheme has been compromised by La Mer project (built in 2016), which modified the wave climate at the Jumeirah First beach. The objective of the coastal modelling studies is to establish design basis for further beach stabilization scheme(s). Comprehensive coastal modelling studies had been conducted to establish the nearshore wave climate, equilibrium beach orientations and stable beach plan forms. Based on the outcomes of the modeling studies, recommendation had been made to extend the composite groynes to stabilize the Jumeirah First beach. Wave transformation was performed following an interpolation approach with wave transformation matrixes derived from simulations of a possible range of wave conditions in the region. The Dubai coastal wave model is developed with MIKE21 SW. The offshore wave conditions were determined from PERGOS wave data at 4 offshore locations with consideration of the spatial variation. The lateral boundary conditions corresponding to the offshore conditions, at Dubai/Abu Dhabi and Dubai Sharjah borders, were derived with application of LitDrift 1D wave transformation module. The Dubai coastal wave model was calibrated with wave records at monitoring stations operated by Dubai Municipality. The wave transformation matrix approach was validated with nearshore wave measurement at a Dubai Municipality monitoring station in the vicinity of the Jumeirah First beach. One typical year wave time series was transformed to 7 locations in front of the beach to count for the variation of wave conditions which are affected by adjacent and offshore developments. Equilibrium beach orientations were estimated with application of LitDrift by finding the beach orientations with null annual littoral transport at the 7 selected locations. The littoral transport calculation results were compared with beach erosion/accretion quantities estimated from the beach monitoring program (twice a year including bathymetric and topographical surveys). An innovative integral method was developed to outline the stable beach plan forms from the estimated equilibrium beach orientations, with predetermined minimum beach width. The optimal lengths for the composite groyne extensions were recommended based on the stable beach plan forms.

Keywords: composite groyne, equilibrium beach orientation, stable beach plan form, wave transformation matrix

Procedia PDF Downloads 236

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: Diana Mitova, Krassimir Mitrev

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To prepare the current generation for the future, education systems need to change. It implies a way of learning that meets the demands of the times and the environment in which we live. Productive interaction in the educational process implies an interactive learning environment and the possibility of personal development of learners based on communication and mutual dialogue, cooperation and good partnership in decision-making. Students need not only theoretical knowledge, but transferable skills that will help them to become inventors and entrepreneurs, to implement ideas. STEM education , is now a real necessity for the modern school. Through learning in a "learning company", students master examples from classroom practice, simulate real life situations, group activities and apply basic interactive learning strategies and techniques. The learning company is the subject of this study, reduced to entrepreneurship training in STEM - technologies that encourage students to think outside the traditional box. STEM learning focuses the teacher's efforts on modeling entrepreneurial thinking and behavior in students and helping them solve problems in the world of business and entrepreneurship. Learning based on the implementation of various STEM projects in extracurricular activities, experiential learning, and an interdisciplinary approach are means by which educators better connect the local community and private businesses. Learners learn to be creative, experiment and take risks and work in teams - the leading characteristics of any innovator and future entrepreneur. This article presents some European policies on STEM and entrepreneurship education. It also shares best practices for training company training , with the integration of STEM in the learning company training environment. The main results boil down to identifying some advantages and problems in STEM entrepreneurship education. The benefits of using integrative approaches to teach STEM within a training company are identified, as well as the positive effects of project-based learning in a training company using STEM. Best practices for teaching entrepreneurship through extracurricular activities using STEM within a training company are shared. The following research methods are applied in this research paper: Theoretical and comparative analysis of principles and policies of European Union countries and Bulgaria in the field of entrepreneurship education through a training company. Experiences in entrepreneurship education through extracurricular activities with STEM application within a training company are shared. A questionnaire survey to investigate the motivation of secondary vocational school students to learn entrepreneurship through a training company and their readiness to start their own business after completing their education. Within the framework of learning through a "learning company" with the integration of STEM, the activity of the teacher-facilitator includes the methods: counseling, supervising and advising students during work. The expectation is that students acquire the key competence "initiative and entrepreneurship" and that the cooperation between the vocational education system and the business in Bulgaria is more effective.

Keywords: STEM, entrepreneurship, training company, extracurricular activities

Procedia PDF Downloads 78

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: Pirta Palola, Richard Bailey, Lisa Wedding

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Societies and economies worldwide fundamentally depend on natural capital. Alarmingly, natural capital assets are quickly depreciating, posing an existential challenge for humanity. The development of robust natural capital accounting systems is essential for transitioning towards sustainable economic systems and ensuring sound management of capital assets. However, the accurate, equitable and comprehensive estimation of natural capital asset stocks and their accounting values still faces multiple challenges. In particular, the representation of socio-cultural values held by groups or communities has arguably been limited, as to date, the valuation of natural capital assets has primarily been based on monetary valuation methods and assumptions of individual rationality. People relate to and value the natural environment in multiple ways, and no single valuation method can provide a sufficiently comprehensive image of the range of values associated with the environment. Indeed, calls have been made to improve the representation of multiple types of value (instrumental, intrinsic, and relational) and diverse ontological and epistemological perspectives in environmental valuation. This study addresses this need by establishing a novel valuation framework, Environmental Value Functions (EVF), that allows for the integration of multiple types of value in natural capital accounting systems. The EVF framework is based on the estimation and application of value functions, each of which describes the relationship between the value and quantity (or quality) of an ecosystem component of interest. In this framework, values are estimated in terms of change relative to the current level instead of calculating absolute values. Furthermore, EVF was developed to also support non-marginalist conceptualizations of value: it is likely that some environmental values cannot be conceptualized in terms of marginal changes. For example, ecological resilience value may, in some cases, be best understood as a binary: it either exists (1) or is lost (0). In such cases, a logistic value function may be used as the discriminator. Uncertainty in the value function parameterization can be considered through, for example, Monte Carlo sampling analysis. The use of EVF is illustrated with two conceptual examples. For the first time, EVF offers a clear framework and concrete methodology for the representation of multiple types of value in natural capital accounting systems, simultaneously enabling 1) the complementary use and integration of multiple valuation methods (monetary and non-monetary); 2) the synthesis of information from diverse knowledge systems; 3) the recognition of value incommensurability; 4) marginalist and non-marginalist value analysis. Furthermore, with this advancement, the coupling of EVF and ecosystem modeling can offer novel insights to the study of spatial-temporal dynamics in natural capital asset values. For example, value time series can be produced, allowing for the prediction and analysis of volatility, long-term trends, and temporal trade-offs. This approach can provide essential information to help guide the transition to a sustainable economy.

Keywords: economics of biodiversity, environmental valuation, natural capital, value function

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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

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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 250

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

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Authors: Felipe M. de Freitas, Ícaro V. Soares, Lucas L. L. Fortes, Sandro T. M. Gonçalves, Úrsula D. C. Resende

Abstract:

There is a dispersed energy in Radio Frequencies (RF) that can be reused to power electronics circuits such as: sensors, actuators, identification devices, among other systems, without wire connections or a battery supply requirement. In this context, there are different types of energy harvesting systems, including rectennas, coil systems, graphene and new materials. A secondary step of an energy harvesting system is the rectification of the collected signal which may be carried out, for example, by the combination of one or more Schottky diodes connected in series or shunt. In the case of a rectenna-based system, for instance, the diode used must be able to receive low power signals at ultra-high frequencies. Therefore, it is required low values of series resistance, junction capacitance and potential barrier voltage. Due to this low-power condition, voltage multiplier configurations are used such as voltage doublers or modified bridge converters. Lowpass filter (LPF) at the input, DC output filter, and a resistive load are also commonly used in the rectifier design. The electronic circuits projects are commonly analyzed through simulation in SPICE (Simulation Program with Integrated Circuit Emphasis) environment. Despite the remarkable potential of SPICE-based simulators for complex circuit modeling and analysis of quasi-static electromagnetic fields interaction, i.e., at low frequency, these simulators are limited and they cannot model properly applications of microwave hybrid circuits in which there are both, lumped elements as well as distributed elements. This work proposes, therefore, the electromagnetic modelling of electronic components in order to create models that satisfy the needs for simulations of circuits in ultra-high frequencies, with application in rectifiers coupled to antennas, as in energy harvesting systems, that is, in rectennas. For this purpose, the numerical method FDTD (Finite-Difference Time-Domain) is applied and SPICE computational tools are used for comparison. In the present work, initially the Ampere-Maxwell equation is applied to the equations of current density and electric field within the FDTD method and its circuital relation with the voltage drop in the modeled component for the case of lumped parameter using the FDTD (Lumped-Element Finite-Difference Time-Domain) proposed in for the passive components and the one proposed in for the diode. Next, a rectifier is built with the essential requirements for operating rectenna energy harvesting systems and the FDTD results are compared with experimental measurements.

Keywords: energy harvesting system, LE-FDTD, rectenna, rectifier, wireless power systems

Procedia PDF Downloads 109

Authors: Marco Ranzato, Maguelone Vignes

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The co-design process recently run by the trans-disciplinary urban laboratory Metrolab Brussels for outlining the architecture of a future integrated health centre in Brussels (Belgium) has highlighted that a buffer place open to the local community is the appropriate cornerstone around which organizing a space where diverse professionals and patients are together. In the context of the migrants 'crisis' in Europe, the growing number of vulnerable people in Brussels and the increasing complexity of the health and welfare systems, the NGO Doctors of the World (DoW) has launched a project funded by The European Regional Development Fund, and aiming to create a new community centre combining social and health services in a poor but changing neighborhood of Brussels. Willing not to make a 'ghetto' of this new integrated service, the NGO looks at hosting different publics in order to make the poorest, marginal and most vulnerable people access to a regular kind of service. As a trans-disciplinary urban research group, Metrolab has been involved in the process of co-designing the architecture of the future centre with a set of various health professionals, social workers, and patients’ representatives. Metrolab drawn on the participants’ practice experiences and knowledge of hosting different kinds of publics and professions in a same structure in order to imagine what rooms should fit into the centre, what atmosphere they should convey, how should they be interrelated and organized, and, concurrently, how the building should fit into the urban frame of its neighborhood. The result is that, in order for an integrated health centre framed in the landscape of a disadvantaged neighborhood to function, it has to work as social community centre offering accessibility and conviviality to diverse social groups. This paper outlines the methodology that Metrolab used to design and conduct, in close collaboration with DoW, a series of 3 workshops. Through sketching and paper modeling, the methodology made participants talk about their experience by projecting them into a situation. It included a combination of individual and collective work in order to sharp participants’ eyes on architectural forms, explicit their thoughts and experience through inter-subjectivity and imagine solutions to the challenges they raised. Such a collaborative method encompasses several challenges about patients’ participation and representation, replicability of the conditions of success and the plurality of the research findings communication formats. This paper underlines how this participatory process has contributed to build knowledge on the few-documented topic of the architecture of community health centres. More importantly, the contribution builds on this participatory process to discuss the importance of adapting the architecture of the new integrated health centre to the changing population of Brussels and to the issues of its specific neighborhood.

Keywords: co-design, health, social innovation, urban lab

Procedia PDF Downloads 156