Search results for: predict

Authors: Scott Snyder

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Grading approaches in higher education have not changed meaningfully in over 100 years. While there is variation in the types of grades assigned across countries, most use approaches based on simple ordinal scales (e.g, letter grades). While grades are generally viewed as an indication of a student's performance, challenges arise regarding the clarity, validity, and reliability of letter grades. Research about grading in higher education has primarily focused on grade inflation, student attitudes toward grading, impacts of grades, and benefits of plus-minus letter grade systems. Little research is available about alternative approaches to grading, varying approaches used by faculty within and across colleges, and faculty attitudes toward grades and alternative approaches to grading. To begin to address these gaps, a survey was conducted of faculty in a sample of departments at three diverse colleges in a southeastern state in the US. The survey focused on faculty experiences with and attitudes toward grading, the degree to which faculty innovate in teaching and grading practices, and faculty interest in alternatives to the point system approach to grading. Responses were received from 104 instructors (21% response rate). The majority reported that teaching accounted for 50% or more of their academic duties. Almost all (92%) of respondents reported using point and percentage systems for their grading. While all respondents agreed that grades should reflect the degree to which objectives were mastered, half indicated that grades should also reflect effort or improvement. Over 60% felt that grades should be predictive of success in subsequent courses or real life applications. Most respondents disagreed that grades should compare students to other students. About 42% worried about their own grade inflation and grade inflation in their college. Only 17% disagreed that grades mean different things based on the instructor while 75% thought it would be good if there was agreement. Less than 50% of respondents felt that grades were directly useful for identifying students who should/should not continue, identify strengths/weaknesses, predict which students will be most successful, or contribute to program monitoring of student progress. Instructors were less willing to modify assessment than they were to modify instruction and curriculum. Most respondents (76%) were interested in learning about alternative approaches to grading (e.g., specifications grading). The factors that were most associated with willingness to adopt a new grading approach were clarity to students and simplicity of adoption of the approach. Follow-up studies are underway to investigate implementations of alternative grading approaches, expand the study to universities and departments not involved in the initial study, examine student attitudes about alternative approaches, and refine the measure of attitude toward adoption of alternative grading practices within the survey. Workshops about challenges of using percentage and point systems for determining grades and workshops regarding alternative approaches to grading are being offered.

Keywords: alternative approaches to grading, grades, higher education, letter grades

Procedia PDF Downloads 86

Authors: Denisa Mackova, Andrea Pokorna

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The management of postoperative pain is a meaningful part of quality care. The experience and knowledge of registered nurses in postoperative pain management can be influenced by local know-how. Therefore, the research helps to understand the cultural differences between two countries with the aim of evaluating the management of postoperative pain management among the nurses from the Czech Republic and the Kingdom of Saudi Arabia. Both countries have different procedures on managing postoperative pain and the research will provide an understanding of both the advantages and disadvantages of the procedures and also highlight the knowledge and experience of registered nurses in both countries. Between the Czech Republic and the Kingdom of Saudi Arabia, the expectation is for differing results in the usage of opioid analgesia for the patients postoperatively and in the experience of registered nurses with Patient Controlled Analgesia. The aim is to evaluate the knowledge and awareness of registered nurses and to merge the data with the postoperative pain management in the early postoperative period in the Czech Republic and the Kingdom of Saudi Arabia. Also, the aim is to assess the knowledge and experience of registered nurses by using Patient Controlled Analgesia and epidural analgesia treatment in the early postoperative period. The criteria for those providing input into the study, are registered nurses, working in surgical settings (standard departments, post-anesthesia care unit, day care surgery or ICU’s) caring for patients in the postoperative period. Method: Research is being conducted by questionnaires. It is a quantitative research, a comparative study of registered nurses in the Czech Republic and the Kingdom of Saudi Arabia. Questionnaire surveys were distributed through an electronic Bristol online survey. Results: The collection of the data in the Kingdom of Saudi Arabia has been completed successfully, with 550 respondents, 77 were excluded and 473 respondents were included for statistical data analysis. The outcome of the research is expected to highlight the differences in treatment through Patient Controlled Analgesia, with more frequent use in the Kingdom of Saudi Arabia. A similar assumption is expected for treatment conducted by analgesia. We predict that opioids will be used more regularly in the Kingdom of Saudi Arabia, whilst therapy through NSAID’s being the most common approach in the Czech Republic. Discussion/Conclusion: The majority of respondents from the Kingdom of Saudi Arabia were female registered nurses from a multitude of nations. We are expecting a similar split in gender between the Czech Republic respondents; however, there will be a smaller number of nationalities. Relevance for research and practice: Output from the research will assess the knowledge, experience and practice of patient controlled analgesia and epidural analgesia treatment. Acknowledgement: This research was accepted and affiliated to the project: Postoperative pain management, knowledge and experience registered nurses (Czech Republic and Kingdom of Saudi Arabia) – SGS05/2019-2020.

Keywords: acute postoperative pain, epidural analgesia, nursing care, patient controlled analgesia

Procedia PDF Downloads 169

Authors: Amir Nazemi, Milad Ramezankhani, Marian Kӧrber, Abbas S. Milani

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The excellent strength to weight ratio of woven fabric composites, along with their high formability, is one of the primary design parameters defining their increased use in modern manufacturing processes, including those in aerospace and automotive. However, for emerging automated preform processes under the smart manufacturing paradigm, complex geometries of finished components continue to bring several challenges to the designers to cope with manufacturing defects on site. Wrinklinge. g. is a common defectoccurring during the forming process and handling of semi-finished textile composites. One of the main reasons for this defect is the weak bending stiffness of fibers in unconsolidated state, causing excessive relative motion between them. Further challenges are represented by the automated handling of large-area fiber blanks with specialized gripper systems. For fabric composites forming simulations, the finite element (FE)method is a longstanding tool usedfor prediction and mitigation of manufacturing defects. Such simulations are predominately meant, not only to predict the onset, growth, and shape of wrinkles but also to determine the best processing condition that can yield optimized positioning of the fibers upon forming (or robot handling in the automated processes case). However, the need for use of small-time steps via explicit FE codes, facing numerical instabilities, as well as large computational time, are among notable drawbacks of the current FEtools, hindering their extensive use as fast and yet efficient digital twins in industry. This paper presents a novel woven fabric simulation technique through the application of the material point method (MPM), which enables the use of much larger time steps, facing less numerical instabilities, hence the ability to run significantly faster and efficient simulationsfor fabric materials handling and forming processes. Therefore, this method has the ability to enhance the development of automated fiber handling and preform processes by calculating the physical interactions with the MPM fiber models and rigid tool components. This enables the designers to virtually develop, test, and optimize their processes based on either algorithmicor Machine Learning applications. As a preliminary case study, forming of a hemispherical plain weave is shown, and the results are compared to theFE simulations, as well as experiments.

Keywords: material point method, woven fabric composites, forming, material handling

Procedia PDF Downloads 167

Authors: Saahith M. S., Sivakami R.

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In the realm of football analytics, particularly focusing on predicting football player performance, the ability to forecast player success accurately is of paramount importance for teams, managers, and fans. This study introduces an elaborate examination of predicting football player performance through the integration of data visualization methods and machine learning algorithms. The research entails the compilation of an extensive dataset comprising player attributes, conducting data preprocessing, feature selection, model selection, and model training to construct predictive models. The analysis within this study will involve delving into feature significance using methodologies like Select Best and Recursive Feature Elimination (RFE) to pinpoint pertinent attributes for predicting player performance. Various machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN), will be explored to develop predictive models. The evaluation of each model's performance utilizing metrics such as Mean Squared Error (MSE) and R-squared will be executed to gauge their efficacy in predicting player performance. Furthermore, this investigation will encompass a top player analysis to recognize the top-performing players based on the anticipated overall performance scores. Nationality analysis will entail scrutinizing the player distribution based on nationality and investigating potential correlations between nationality and player performance. Positional analysis will concentrate on examining the player distribution across various positions and assessing the average performance of players in each position. Age analysis will evaluate the influence of age on player performance and identify any discernible trends or patterns associated with player age groups. The primary objective is to predict a football player's overall performance accurately based on their individual attributes, leveraging data-driven insights to enrich the comprehension of player success on the field. By amalgamating data visualization and machine learning methodologies, the aim is to furnish valuable tools for teams, managers, and fans to effectively analyze and forecast player performance. This research contributes to the progression of sports analytics by showcasing the potential of machine learning in predicting football player performance and offering actionable insights for diverse stakeholders in the football industry.

Keywords: football analytics, player performance prediction, data visualization, machine learning algorithms, random forest, decision tree, linear regression, support vector regression, artificial neural networks, model evaluation, top player analysis, nationality analysis, positional analysis

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Authors: Netasha Shaikh, Bryony Wood, Demitra Tsivos, Michael Knight, Risto Kauppinen, Elizabeth Coulthard

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Background: Effective treatment of dementia will require early diagnosis, before significant brain damage has accumulated. Memory loss is an early symptom of Alzheimer’s disease (AD). The hippocampus, a brain area critical for memory, degenerates early in the course of AD. The hippocampus comprises several subfields. In contrast to healthy aging where CA3 and dentate gyrus are the hippocampal subfields with most prominent atrophy, in AD the CA1 and subiculum are thought to be affected early. Conventional clinical structural neuroimaging is not sufficiently sensitive to identify preferential atrophy in individual subfields. Here, we will explore the sensitivity of new magnetic resonance imaging (MRI) sequences designed to interrogate medial temporal regions as an early marker of Alzheimer’s. As it is likely a combination of tests may predict early Alzheimer’s disease (AD) better than any single test, we look at the potential efficacy of such imaging alone and in combination with standard and novel cognitive tasks of hippocampal dependent memory. Methods: 20 patients with mild cognitive impairment (MCI), 20 with mild-moderate AD and 20 age-matched healthy elderly controls (HC) are being recruited to undergo 3T MRI (with sequences designed to allow volumetric analysis of hippocampal subfields) and a battery of cognitive tasks (including Paired Associative Learning from CANTAB, Hopkins Verbal Learning Test and a novel hippocampal-dependent abstract word memory task). AD participants and healthy controls are being tested just once whereas patients with MCI will be tested twice a year apart. We will compare subfield size between groups and correlate subfield size with cognitive performance on our tasks. In the MCI group, we will explore the relationship between subfield volume, cognitive test performance and deterioration in clinical condition over a year. Results: Preliminary data (currently on 16 participants: 2 AD; 4 MCI; 9 HC) have revealed subfield size differences between subject groups. Patients with AD perform with less accuracy on tasks of hippocampal-dependent memory, and MCI patient performance and reaction times also differ from healthy controls. With further testing, we hope to delineate how subfield-specific atrophy corresponds with changes in cognitive function, and characterise how this progresses over the time course of the disease. Conclusion: Novel sequences on a MRI scanner such as those in route in clinical use can be used to delineate hippocampal subfields in patients with and without dementia. Preliminary data suggest that such subfield analysis, perhaps in combination with cognitive tasks, may be an early marker of AD.

Keywords: Alzheimer's disease, dementia, memory, cognition, hippocampus

Procedia PDF Downloads 561

Authors: J. Fox, J. Randhawa, M. Chan, L. Campbell, A. Weakely, D. J. Harvey, S. Tomaszewski Farias

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Early identification of individuals at risk for conversion to dementia provides an opportunity for preventative treatment. Many older adults (30-60%) report specific subjective cognitive decline (SCD); however, previous research is inconsistent in terms of what types of complaints predict future cognitive decline. The purpose of this study is to identify which specific complaints from the Everyday Cognition Scales (ECog) scales, a measure of self-reported concerns for everyday abilities across six cognitive domains, are associated with: 1) conversion from a clinical diagnosis of normal to either MCI or dementia (categorical variable) and 2) progressive cognitive decline in memory and executive function (continuous variables). 415 cognitively normal older adults were monitored annually for an average of 5 years. Cox proportional hazards models were used to assess associations between self-reported ECog items and progression to impairment (MCI or dementia). A total of 114 individuals progressed to impairment; the mean time to progression was 4.9 years (SD=3.4 years, range=0.8-13.8). Follow-up models were run controlling for depression. A subset of individuals (n=352) underwent repeat cognitive assessments for an average of 5.3 years. For those individuals, mixed effects models with random intercepts and slopes were used to assess associations between ECog items and change in neuropsychological measures of episodic memory or executive function. Prior to controlling for depression, subjective concerns on five of the eight Everyday Memory items, three of the nine Everyday Language items, one of the seven Everyday Visuospatial items, two of the five Everyday Planning items, and one of the six Everyday Organization items were associated with subsequent diagnostic conversion (HR=1.25 to 1.59, p=0.003 to 0.03). However, after controlling for depression, only two specific complaints of remembering appointments, meetings, and engagements and understanding spoken directions and instructions were associated with subsequent diagnostic conversion. Episodic memory in individuals reporting no concern on ECog items did not significantly change over time (p>0.4). More complaints on seven of the eight Everyday Memory items, three of the nine Everyday Language items, and three of the seven Everyday Visuospatial items were associated with a decline in episodic memory (Interaction estimate=-0.055 to 0.001, p=0.003 to 0.04). Executive function in those reporting no concern on ECog items declined slightly (p <0.001 to 0.06). More complaints on three of the eight Everyday Memory items and three of the nine Everyday Language items were associated with a decline in executive function (Interaction estimate=-0.021 to -0.012, p=0.002 to 0.04). These findings suggest that specific complaints across several cognitive domains are associated with diagnostic conversion. Specific complaints in the domains of Everyday Memory and Language are associated with a decline in both episodic memory and executive function. Increased monitoring and treatment of individuals with these specific SCD may be warranted.

Keywords: alzheimer’s disease, dementia, memory complaints, mild cognitive impairment, risk factors, subjective cognitive decline

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Authors: Mitakshara Sharma, S. K. Nema, Sanjeev Narang

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Diabetes mellitus (DM) is a group of metabolic disorders characterized by metabolic abnormalities, chronic hyperglycaemia and long term macrovascular & microvascular complications. Vascular complications are due to platelet hyperactivity and dysfunction, increased inflammation, altered coagulation and endothelial dysfunction. Large proportion of patients with Type II DM suffers from preventable vascular angiopathies, and there is need to develop risk factor modifications and interventions to reduce impact of complications. These complications are attributed to platelet activation, recognised by increase in Platelet Volume Indices (PVI) including Mean Platelet Volume (MPV) and Platelet Distribution Width (PDW). The current study is prospective analytical study conducted over 2 years. Out of 1100 individuals, 930 individuals fulfilled inclusion criteria and were segregated into three groups on basis of glycosylated haemoglobin (HbA1C): - (a) Diabetic, (b) Non-Diabetic and (c) Subjects with Impaired fasting glucose (IFG) with 300 individuals in IFG and non-diabetic groups & 330 individuals in diabetic group. Further, diabetic group was divided into two groups on the basis of presence or absence of known diabetes related vascular complications. Samples for HbA1c and PVI were collected using Ethylene diamine tetraacetic acid (EDTA) as anticoagulant and processed on SYSMEX-X-800i autoanalyser. The study revealed gradual increase in PVI from non-diabetics to IFG to diabetics. PVI were markedly increased in diabetic patients. MPV and PDW of diabetics, IFG and non diabetics were (17.60 ± 2.04)fl, (11.76 ± 0.73)fl, (9.93 ± 0.64)fl and (19.17 ± 1.48)fl, (15.49 ± 0.67)fl, (10.59 ± 0.67)fl respectively with a significant p value 0.00 and a significant positive correlation (MPV-HbA1c r = 0.951; PDW-HbA1c r = 0.875). MPV & PDW of subjects with diabetes related complications were higher as compared to those without them and were (17.51±0.39)fl & (15.14 ± 1.04)fl and (20.09 ± 0.98) fl & (18.96 ± 0.83)fl respectively with a significant p value 0.00. There was a significant positive correlation between PVI and duration of diabetes across the groups (MPV-HbA1c r = 0.951; PDW-HbA1c r = 0.875). However, a significant negative correlation was found between glycaemic levels and total platelet count (PC- HbA1c r =-0.164). This is multi-parameter and comprehensive study with an adequately powered study design. It can be concluded from our study that PVI are extremely useful and important indicators of impending vascular complications in all patients with deranged glycaemic control. Introduction of automated cell counters has facilitated the availability of PVI as routine parameters. PVI is a useful means for identifying larger & active platelets which play important role in development of micro and macro angiopathic complications of diabetes leading to mortality and morbidity. PVI can be used as cost effective markers to predict and prevent impending vascular events in patients with Diabetes mellitus especially in developing countries like India. PVI, if incorporated into protocols for management of diabetes, could revolutionize care and curtail the ever increasing cost of patient management.

Keywords: diabetes, IFG, HbA1C, MPV, PDW, PVI

Procedia PDF Downloads 249

Authors: Mehrnaz Mostafavi

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Recent advances in single-molecule protein identification (ID) and quantification techniques are poised to revolutionize proteomics, enabling researchers to delve into single-cell proteomics and identify low-abundance proteins crucial for biomedical and clinical research. This paper introduces a different approach to single-molecule protein ID and quantification using tri-color amino acid tags and a plasmonic nanopore device. A comprehensive simulator incorporating various physical phenomena was designed to predict and model the device's behavior under diverse experimental conditions, providing insights into its feasibility and limitations. The study employs a whole-proteome single-molecule identification algorithm based on convolutional neural networks, achieving high accuracies (>90%), particularly in challenging conditions (95–97%). To address potential challenges in clinical samples, where post-translational modifications affecting labeling efficiency, the paper evaluates protein identification accuracy under partial labeling conditions. Solid-state nanopores, capable of processing tens of individual proteins per second, are explored as a platform for this method. Unlike techniques relying solely on ion-current measurements, this approach enables parallel readout using high-density nanopore arrays and multi-pixel single-photon sensors. Convolutional neural networks contribute to the method's versatility and robustness, simplifying calibration procedures and potentially allowing protein ID based on partial reads. The study also discusses the efficacy of the approach in real experimental conditions, resolving functionally similar proteins. The theoretical analysis, protein labeler program, finite difference time domain calculation of plasmonic fields, and simulation of nanopore-based optical sensing are detailed in the methods section. The study anticipates further exploration of temporal distributions of protein translocation dwell-times and the impact on convolutional neural network identification accuracy. Overall, the research presents a promising avenue for advancing single-molecule protein identification and quantification with broad applications in proteomics research. The contributions made in methodology, accuracy, robustness, and technological exploration collectively position this work at the forefront of transformative developments in the field.

Keywords: nano proteomics, nanopore-based optical sensing, deep learning, artificial intelligence

Procedia PDF Downloads 59

Authors: Silvia Portarena, Chiara Anselmi, Chiara Baldacchini, Enrico Brugnoli

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Extra virgin olive oil (EVOO) is a complex matrix mainly composed by fatty acid and other minor compounds, among which carotenoids are well known for their antioxidative function that is a key mechanism of protection against cancer, cardiovascular diseases, and macular degeneration in humans. EVOO composition in terms of such constituents is generally the result of a complex combination of genetic, agronomical and environmental factors. To selectively improve the quality of EVOOs, the role of each factor on its biochemical composition need to be investigated. By selecting fruits from four different cultivars similarly grown and harvested, it was demonstrated that Raman spectroscopy, combined with chemometric analysis, is able to discriminate the different cultivars, also as a function of the harvest date, based on the relative content and composition of fatty acid and carotenoids. In particular, a correct classification up to 94.4% of samples, according to the cultivar and the maturation stage, was obtained. Moreover, by using gas chromatography and high-performance liquid chromatography as reference techniques, the Raman spectral features further allowed to build models, based on partial least squares regression, that were able to predict the relative amount of the main fatty acids and the main carotenoids in EVOO, with high coefficients of determination. Besides genetic factors, climatic parameters, such as light exposition, distance from the sea, temperature, and amount of precipitations could have a strong influence on EVOO composition of both major and minor compounds. This suggests that the Raman spectra could act as a specific fingerprint for the geographical discrimination and authentication of EVOO. To understand the influence of environment on EVOO Raman spectra, samples from seven regions along the Italian coasts were selected and analyzed. In particular, it was used a dual approach combining Raman spectroscopy and isotope ratio mass spectrometry (IRMS) with principal component and linear discriminant analysis. A correct classification of 82% EVOO based on their regional geographical origin was obtained. Raman spectra were obtained by Super Labram spectrometer equipped with an Argon laser (514.5 nm wavelenght). Analyses of stable isotope content ratio were performed using an isotope ratio mass spectrometer connected to an elemental analyzer and to a pyrolysis system. These studies demonstrate that RR spectroscopy is a valuable and useful technique for the analysis of EVOO. In combination with statistical analysis, it makes possible the assessment of specific samples’ content and allows for classifying oils according to their geographical and varietal origin.

Keywords: authentication, chemometrics, olive oil, raman spectroscopy

Procedia PDF Downloads 317

Authors: Hind Alshoubaki

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The world is now confronting a widespread urban phenomenon: refugee camps, which have mostly been established in ‘rushing mode,’ pointing toward affording temporary settlements for refugees that provide them with minimum levels of safety, security and protection from harsh weather conditions within a very short time period. In fact, those emergency settlements are transforming into permanent ones since time is a decisive factor in terms of construction and camps’ age. These play an essential role in transforming their temporary character into a permanent one that generates deep modifications to the city’s territorial structure, shaping a new identity and creating a contentious change in the city’s form and history. To achieve a better understanding for the transformation of refugee camps, this study is based on a mixed-methods approach: the qualitative approach explores different refugee camps and analyzes their transformation process in terms of population density and the changes to the city’s territorial structure and urban features. The quantitative approach employs a statistical regression analysis as a reliable prediction of refugees’ satisfaction within the Zaatari camp in order to predict its future transformation. Obviously, refugees’ perceptions of their current conditions will affect their satisfaction, which plays an essential role in transforming emergency settlements into permanent cities over time. The test basically discusses five main themes: the access and readiness of schools, the dispersion of clinics and shopping centers; the camp infrastructure, the construction materials, and the street networks. The statistical analysis showed that Syrian refugees were not satisfied with their current conditions inside the Zaatari refugee camp and that they had started implementing changes according to their needs, desires, and aspirations because they are conscious about the fact of their prolonged stay in this settlement. Also, the case study analyses showed that neglecting the fact that construction takes time leads settlements being created with below-minimum standards that are deteriorating and creating ‘slums,’ which lead to increased crime rates, suicide, drug use and diseases and deeply affect cities’ urban tissues. For this reason, recognizing the ‘temporary-eternal’ character of those settlements is the fundamental concept to consider refugee camps from the beginning as definite permanent cities. This is the key factor to minimize the trauma of displacement on both refugees and the hosting countries. Since providing emergency settlements within a short time period does not mean using temporary materials, having a provisional character or creating ‘makeshift cities.’

Keywords: refugee, refugee camp, temporary, Zaatari

Procedia PDF Downloads 115

Authors: Gideon C. Joseph

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The ability to localize mobile devices is quite important, as some applications may require location information of these devices to operate or deliver better services to the users. Although there are several ways of acquiring location data of mobile devices, the WLAN fingerprinting approach has been considered in this work. This approach uses the Received Signal Strength Indicator (RSSI) measurement as a function of the position of the mobile device. RSSI is a quantitative technique of describing the radio frequency power carried by a signal. RSSI may be used to determine RF link quality and is very useful in dense traffic scenarios where interference is of major concern, for example, indoor environments. This research aims to design a system that can predict the location of a mobile device, when supplied with the mobile’s RSSIs. The developed system takes as input the RSSIs relating to the mobile device, and outputs parameters that describe the location of the device such as the longitude, latitude, floor, and building. The relationship between the Received Signal Strengths (RSSs) of mobile devices and their corresponding locations is meant to be modelled; hence, subsequent locations of mobile devices can be predicted using the developed model. It is obvious that describing mathematical relationships between the RSSIs measurements and localization parameters is one option to modelling the problem, but the complexity of such an approach is a serious turn-off. In contrast, we propose an intelligent system that can learn the mapping of such RSSIs measurements to the localization parameters to be predicted. The system is capable of upgrading its performance as more experiential knowledge is acquired. The most appealing consideration to using such a system for this task is that complicated mathematical analysis and theoretical frameworks are excluded or not needed; the intelligent system on its own learns the underlying relationship in the supplied data (RSSI levels) that corresponds to the localization parameters. These localization parameters to be predicted are of two different tasks: Longitude and latitude of mobile devices are real values (regression problem), while the floor and building of the mobile devices are of integer values or categorical (classification problem). This research work presents artificial neural network based intelligent systems to model the relationship between the RSSIs predictors and the mobile device localization parameters. The designed systems were trained and validated on the collected WLAN fingerprint database. The trained networks were then tested with another supplied database to obtain the performance of trained systems on achieved Mean Absolute Error (MAE) and error rates for the regression and classification tasks involved therein.

Keywords: indoor localization, WLAN fingerprinting, neural networks, classification, regression

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Authors: William C. Alexander

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The oscillating water column (OSC) wave energy converter (WEC) with deep water reactance (DWR) consists of a large hollow sphere filled with seawater at the base, referred to as the ‘stabilizer’, a hollow cylinder at the top of the device, with a said cylinder having a bottom open to the sea and a sealed top save for an orifice which leads to an air turbine, and a long, narrow rod connecting said stabilizer with said cylinder. A small amount of ballast at the bottom of the stabilizer and a small amount of floatation in the cylinder keeps the device upright in the sea. The floatation is set such that the mean water level is nominally halfway up the cylinder. The entire device is loosely moored to the seabed to keep it from drifting away. In the presence of ocean waves, seawater will move up and down within the cylinder, producing the ‘oscillating water column’. This gives rise to air pressure within the cylinder alternating between positive and negative gauge pressure, which in turn causes air to alternately leave and enter the cylinder through said top-cover situated orifice. An air turbine situated within or immediately adjacent to said orifice converts the oscillating airflow into electric power for transport to shore or elsewhere by electric power cable. Said oscillating air pressure produces large up and down forces on the cylinder. Said large forces are opposed through the rod to the large mass of water retained within the stabilizer, which is located deep enough to be mostly free of any wave influence and which provides the deepwater reactance. The cylinder and stabilizer form a spring-mass system which has a vertical (heave) resonant frequency. The diameter of the cylinder largely determines the power rating of the device, while the size (and water mass within) of the stabilizer determines said resonant frequency. Said frequency is chosen to be on the lower end of the wave frequency spectrum to maximize the average power output of the device over a large span of time (such as a year). The upper portion of the device (the cylinder) moves laterally (surge) with the waves. This motion is accommodated with minimal loading on the said rod by having the stabilizer shaped like a sphere, allowing the entire device to rotate about the center of the stabilizer without rotating the seawater within the stabilizer. A full-scale device of this type may have the following dimensions. The cylinder may be 16 meters in diameter and 30 meters high, the stabilizer 25 meters in diameter, and the rod 55 meters long. Simulations predict that this will produce 1,400 kW in waves of 3.5-meter height and 12 second period, with a relatively flat power curve between 5 and 16 second wave periods, as will be suitable for an open-ocean location. This is nominally 10 times higher power than similar-sized WEC spar buoys as reported in the literature, and the device is projected to have only 5% of the mass per unit power of other OWC converters.

Keywords: oscillating water column, wave energy converter, spar bouy, stabilizer

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Authors: Chunhua Liao, Jinfei Wang, Bo Shan, Yang Song, Yongjun He, Taifeng Dong

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Winter wheat is one of the main crops in Canada. Forecasting of within-field variability of yield in winter wheat at the early stages is essential for precision farming. However, the crop yield modelling based on high spatial resolution satellite data is generally affected by the lack of continuous satellite observations, resulting in reducing the generalization ability of the models and increasing the difficulty of crop yield forecasting at the early stages. In this study, the correlations between Sentinel-2 data (vegetation indices and reflectance) and yield data collected by combine harvester were investigated and a generalized multivariate linear regression (MLR) model was built and tested with data acquired in different years. It was found that the four-band reflectance (blue, green, red, near-infrared) performed better than their vegetation indices (NDVI, EVI, WDRVI and OSAVI) in wheat yield prediction. The optimum phenological stage for wheat yield prediction with highest accuracy was at the growing stages from the end of the flowering to the beginning of the filling stage. The best MLR model was therefore built to predict wheat yield before harvest using Sentinel-2 data acquired at the end of the flowering stage. Further, to improve the ability of the yield prediction at the early stages, three simple unsupervised domain adaptation (DA) methods were adopted to transform the reflectance data at the early stages to the optimum phenological stage. The winter wheat yield prediction using multiple vegetation indices showed higher accuracy than using single vegetation index. The optimum stage for winter wheat yield forecasting varied with different fields when using vegetation indices, while it was consistent when using multispectral reflectance and the optimum stage for winter wheat yield prediction was at the end of flowering stage. The average testing RMSE of the MLR model at the end of the flowering stage was 604.48 kg/ha. Near the booting stage, the average testing RMSE of yield prediction using the best MLR was reduced to 799.18 kg/ha when applying the mean matching domain adaptation approach to transform the data to the target domain (at the end of the flowering) compared to that using the original data based on the models developed at the booting stage directly (“MLR at the early stage”) (RMSE =1140.64 kg/ha). This study demonstrated that the simple mean matching (MM) performed better than other DA methods and it was found that “DA then MLR at the optimum stage” performed better than “MLR directly at the early stages” for winter wheat yield forecasting at the early stages. The results indicated that the DA had a great potential in near real-time crop yield forecasting at the early stages. This study indicated that the simple domain adaptation methods had a great potential in crop yield prediction at the early stages using remote sensing data.

Keywords: wheat yield prediction, domain adaptation, Sentinel-2, within-field scale

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Authors: Tamar Grdzelishvili, Zaza Sinauridze

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Introduction: Preeclampsia is a complication of the second trimester of pregnancy, which is characterized by high morbidity and multiorgan damage. Many complex pathogenic mechanisms are now implicated to be responsible for this disease (1). Preeclampsia is one of the leading causes of maternal mortality worldwide. Statistics are enough to convince you of the seriousness of this pathology: about 100,000 women die of preeclampsia every year. It occurs in 3-14% (varies significantly depending on racial origin or ethnicity and geographical region) of pregnant women, in 75% of cases - in a mild form, and in 25% - in a severe form. During severe pre-eclampsia-eclampsia, perinatal mortality increases by 5 times and stillbirth by 9.6 times. Considering that the only way to treat the disease is to end the pregnancy, the main thing is timely diagnosis and prevention of the disease. Identification of high-risk pregnant women for PE and giving prophylaxis would reduce the incidence of preterm PE. First-trimester screening model developed by the Fetal Medicine Foundation (FMF), which uses the Bayes-theorem to combine maternal characteristics and medical history together with measurements of mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor, has been proven to be effective and have superior screening performance to that of traditional risk factor-based approach for the prediction of PE (2) Methods: Retrospective single center screening study. The study population consisted of women from the Tbilisi maternity hospital “Pineo medical ecosystem” who met the following criteria: they spoke Georgian, English, or Russian and agreed to participate in the study after discussing informed consent and answering questions. Prior to the study, the informed consent forms approved by the Institutional Review Board were obtained from the study subjects. Early assessment of preeclampsia was performed between 11-13 weeks of pregnancy. The following were evaluated: anamnesis, dopplerography of the uterine artery, mean arterial blood pressure, and biochemical parameter: Pregnancy-associated plasma protein A (PAPP-A). Individual risk assessment was performed with performed by Fast Screen 3.0 software ThermoFisher scientific. Results: A total of 513 women were recruited and through the study, 51 women were diagnosed with preeclampsia (34.5% in the pregnant women with high risk, 6.5% in the pregnant women with low risk; P<0.000 1). Conclusions: First-trimester screening combining maternal factors with uterine artery Doppler, blood pressure, and pregnancy-associated plasma protein-A is useful to predict PE in a routine care setting. More patient studies are needed for final conclusions. The research is still ongoing.

Keywords: first-trimester, preeclampsia, screening, pregnancy-associated plasma protein

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Authors: Giorgia Carratta

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Since the beginning of mass production, plastic items have been crucial in our daily lives. Thanks to their physical and chemical properties, plastic materials have proven almost irreplaceable in a number of economic sectors such as packaging, automotive, building and construction, textile, and many others. At the same time, the disruptive consequences of plastic pollution have been progressively brought to light in all environmental compartments. The overaccumulation of plastics in the environment, and its adverse effects on habitats, wildlife, and (most likely) human health, represents a call for action to decision-makers around the globe. From a regulatory perspective, plastic production is an unprecedented challenge at all levels of governance. At the international level, the design of new legal instruments, the amendment of existing ones, and the coordination among the several relevant policy areas requires considerable effort. Under the pressure of both increasing scientific evidence and a concerned public opinion, countries seem to slowly move towards the discussion of a new international ‘plastic treaty.’ However, whether, how, and with which scopes such instrument would be adopted is still to be seen. Additionally, governments are establishing regional-basedstrategies, prone to consider the specificities of the plastic issue in a certain geographical area. Thanks to the new Circular Economy Action Plan, approved in March 2020 by the European Commission, EU countries are slowly but steadily shifting to a carbon neutral, circular economy in the attempt to reduce the pressure on natural resources and, parallelly, facilitate sustainable economic growth. In this context, the EU Plastic Strategy is promising to change the way plastic is designed, produced, used, and treated after consumption. In fact, only in the EU27 Member States, almost 26 million tons of plastic waste are generated herein every year, whose 24,9% is still destined to landfill. Positive effects of the Strategy also include a more effective protection of our environment, especially the marine one, the reduction of greenhouse gas emissions, a reduced need for imported fossil energy sources, more sustainable production and consumption patterns. As promising as it may sound, the road ahead is still long. The need to implement these measures in domestic legislations makes their outcome difficult to predict at the moment. An analysis of the current international and European Union legal framework on plastic pollution, binding, and voluntary instruments included, could serve to detect ‘blind spots’ in the current governance as well as to facilitate the development of policy interventions along the plastic value chain, where it appears more needed.

Keywords: environmental law, European union, governance, plastic pollution, sustainability

Procedia PDF Downloads 94

Authors: Chi Zhang, Hua-Peng Chen

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With the increasingly demanding resources in the word, renewable and clean energy has been considered as an alternative way to replace traditional ones. Thus, one of practical examples for using wind energy is wind turbine, which has gained more attentions in recent research. Like most offshore structures, the blades, which is the most critical components of the wind turbine, will be subjected to millions of loading cycles during service life. To operate safely in marine environments, the blades are typically made from fibre reinforced composite materials to resist fatigue delamination and harsh environment. The fatigue crack development of blades is uncertain because of indeterminate mechanical properties for composite and uncertainties under offshore environment like wave loads, wind loads, and humid environments. There are three main delamination failure modes for composite blades, and the most common failure type in practices is subjected to mixed mode loading, typically a range of opening (mode 1) and shear (mode 2). However, the fatigue crack development for mixed mode cannot be predicted as deterministic values because of various uncertainties in realistic practical situation. Therefore, selecting an effective stochastic model to evaluate the mixed mode behaviour of wind turbine blades is a critical issue. In previous studies, gamma process has been considered as an appropriate stochastic approach, which simulates the stochastic deterioration process to proceed in one direction such as realistic situation for fatigue damage failure of wind turbine blades. On the basis of existing studies, various Paris Law equations are discussed to simulate the propagation of the fatigue crack growth. This paper develops a Paris model with the stochastic deterioration modelling according to gamma process for predicting fatigue crack performance in design service life. A numerical example of wind turbine composite materials is investigated to predict the mixed mode crack depth by Paris law and the probability of fatigue failure by gamma process. The probability of failure curves under different situations are obtained from the stochastic deterioration model for comparisons. Compared with the results from experiments, the gamma process can take the uncertain values into consideration for crack propagation of mixed mode, and the stochastic deterioration process shows a better agree well with realistic crack process for composite blades. Finally, according to the predicted results from gamma stochastic model, assessment strategies for composite blades are developed to reduce total lifecycle costs and increase resistance for fatigue crack growth.

Keywords: Reinforced fibre composite, Wind turbine blades, Fatigue delamination, Mixed failure mode, Stochastic process.

Procedia PDF Downloads 398

Authors: S.K. Bhandari, E. Veneklaas, L. McCaw, R. Mazanec, K. Whitford, M. Renton

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Eucalyptus marginata, E. diversicolor and Corymbia calophylla form widespread forests in south-west Western Australia (SWWA). These forests have economic and ecological importance, and therefore, tree growth and sustainable management are of high priority. This paper aimed to analyse and model the growth of these species at both stand and individual levels, but this presentation will focus on predicting the growth of E. Marginata at the individual tree level. More specifically, the study wanted to investigate how well individual E. marginata tree growth could be predicted by considering the diameter and height of the tree at the start of the growth period, and whether this prediction could be improved by also accounting for the competition from neighbouring trees in different ways. The study also wanted to investigate how many neighbouring trees or what neighbourhood distance needed to be considered when accounting for competition. To achieve this aim, the Pearson correlation coefficient was examined among competition indices (CIs), between CIs and dbh growth, and selected the competition index that can best predict the diameter growth of individual trees of E. marginata forest managed under different thinning regimes at Inglehope in SWWA. Furthermore, individual tree growth models were developed using simple linear regression, multiple linear regression, and linear mixed effect modelling approaches. Individual tree growth models were developed for thinned and unthinned stand separately. The developed models were validated using two approaches. In the first approach, models were validated using a subset of data that was not used in model fitting. In the second approach, the model of the one growth period was validated with the data of another growth period. Tree size (diameter and height) was a significant predictor of growth. This prediction was improved when the competition was included in the model. The fit statistic (coefficient of determination) of the model ranged from 0.31 to 0.68. The model with spatial competition indices validated as being more accurate than with non-spatial indices. The model prediction can be optimized if 10 to 15 competitors (by number) or competitors within ~10 m (by distance) from the base of the subject tree are included in the model, which can reduce the time and cost of collecting the information about the competitors. As competition from neighbours was a significant predictor with a negative effect on growth, it is recommended including neighbourhood competition when predicting growth and considering thinning treatments to minimize the effect of competition on growth. These model approaches are likely to be useful tools for the conservations and sustainable management of forests of E. marginata in SWWA. As a next step in optimizing the number and distance of competitors, further studies in larger size plots and with a larger number of plots than those used in the present study are recommended.

Keywords: competition, growth, model, thinning

Procedia PDF Downloads 111

Authors: Mohd Azman Abdul Latif, Erwan Basiron

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A flaw or drift from expected operational performance in one component (NAND, PMIC, controller, DRAM, etc.) may affect the reliability of the entire Solid State Drive (SSD) system. Therefore, it is important to ensure the required quality of each individual component through qualification testing specified using standards or user requirements. Qualification testing is time-consuming and comes at a substantial cost for product manufacturers. A highly technical team, from all the eminent stakeholders is embarking on reliability prediction from beginning of new product development, identify critical to reliability parameters, perform full-blown characterization to embed margin into product reliability and establish control to ensure the product reliability is sustainable in the mass production. The paper will discuss a comprehensive development framework, comprehending SSD end to end from design to assembly, in-line inspection, in-line testing and will be able to predict and to validate the product reliability at the early stage of new product development. During the design stage, the SSD will go through intense reliability margin investigation with focus on assembly process attributes, process equipment control, in-process metrology and also comprehending forward looking product roadmap. Once these pillars are completed, the next step is to perform process characterization and build up reliability prediction modeling. Next, for the design validation process, the reliability prediction specifically solder joint simulator will be established. The SSD will be stratified into Non-Operating and Operating tests with focus on solder joint reliability and connectivity/component latent failures by prevention through design intervention and containment through Temperature Cycle Test (TCT). Some of the SSDs will be subjected to the physical solder joint analysis called Dye and Pry (DP) and Cross Section analysis. The result will be feedbacked to the simulation team for any corrective actions required to further improve the design. Once the SSD is validated and is proven working, it will be subjected to implementation of the monitor phase whereby Design for Assembly (DFA) rules will be updated. At this stage, the design change, process and equipment parameters are in control. Predictable product reliability at early product development will enable on-time sample qualification delivery to customer and will optimize product development validation, effective development resource and will avoid forced late investment to bandage the end-of-life product failures. Understanding the critical to reliability parameters earlier will allow focus on increasing the product margin that will increase customer confidence to product reliability.

Keywords: e2e reliability prediction, SSD, TCT, solder joint reliability, NUDD, connectivity issues, qualifications, characterization and control

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Authors: Benjamin Castillo, Luis Pastenes, Fernando Cerdova

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The contamination of food by microbial agents is a common problem in the industry, especially regarding the elaboration of animal source products. Incorrect manipulation of the machinery or on the raw materials can cause a decrease in production or an epidemiological outbreak due to intoxication. In order to improve food product quality, different methods have been used to reduce or, at least, to slow down the growth of the pathogens, especially deteriorated, infectious or toxigenic bacteria. These methods are usually carried out under low temperatures and short processing time (abiotic agents), along with the application of antibacterial substances, such as bacteriocins (biotic agents). This, in a controlled and efficient way that fulfills the purpose of bacterial control without damaging the final product. Therefore, the objective of the present study is to design a secondary mathematical model that allows the prediction of both the biotic and abiotic factor impact associated with animal source food processing. In order to accomplish this objective, the authors propose a three-dimensional differential equation model, whose components are: bacterial growth, release, production and artificial incorporation of bacteriocins and changes in pH levels of the medium. These three dimensions are constantly being influenced by the temperature of the medium. Secondly, this model adapts to an idealized situation of cross-contamination animal source food processing, with the study agents being both the animal product and the contact surface. Thirdly, the stochastic simulations and the parametric sensibility analysis are compared with referential data. The main results obtained from the analysis and simulations of the mathematical model were to discover that, although bacterial growth can be stopped in lower temperatures, even lower ones are needed to eradicate it. However, this can be not only expensive, but counterproductive as well in terms of the quality of the raw materials and, on the other hand, higher temperatures accelerate bacterial growth. In other aspects, the use and efficiency of bacteriocins are an effective alternative in the short and medium terms. Moreover, an indicator of bacterial growth is a low-level pH, since lots of deteriorating bacteria are lactic acids. Lastly, the processing times are a secondary agent of concern when the rest of the aforementioned agents are under control. Our main conclusion is that when acclimating a mathematical model within the context of the industrial process, it can generate new tools that predict bacterial contamination, the impact of bacterial inhibition, and processing method times. In addition, the mathematical modeling proposed logistic input of broad application, which can be replicated on non-meat food products, other pathogens or even on contamination by crossed contact of allergen foods.

Keywords: bacteriocins, cross-contamination, mathematical model, temperature

Procedia PDF Downloads 125

Authors: Debabrata Adhikari, Mikhail Matveev, Louise Brown, Andy Long, Jan Kočí

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A good understanding of mesoscale permeability of complex architectures in fibrous porous preforms is of particular interest in order to achieve efficient and cost-effective resin impregnation of liquid composite molding (LCM). Fabrics used in structural reinforcements are typically woven or stitched. However, 3D fabric reinforcement is of particular interest because of the versatility in the weaving pattern with the binder yarn and in-plain yarn arrangements to manufacture thick composite parts, overcome the limitation in delamination, improve toughness etc. To predict the permeability based on the available pore spaces between the inter yarn spaces, unit cell-based computational fluid dynamics models have been using the Stokes Darcy model. Typically, the preform consists of an arrangement of yarns with spacing in the order of mm, wherein each yarn consists of thousands of filaments with spacing in the order of μm. The fluid flow during infusion exchanges the mass between the intra and inter yarn channels, meaning there is no dead-end of flow between the mesopore in the inter yarn space and the micropore in the yarn. Several studies have employed the Brinkman equation to take into account the flow through dual-scale porosity reinforcement to estimate their permeability. Furthermore, to reduce the computational effort of dual scale flow, scale separation criteria based on the ratio between yarn permeability to the yarn spacing was also proposed to quantify the dual scale and negligible micro-scale flow regime for the prediction of mesoscale permeability. In the present work, the key parameter to identify the influence of intra yarn permeability on the mesoscale permeability has been investigated with the systematic study of weft and warp yarn spacing on the plane weave as well as the position of binder yarn and number of in-plane yarn layers on 3D weave fabric. The permeability tensor has been estimated using an OpenFOAM-based model for the various weave pattern with idealized geometry of yarn implemented using open-source software TexGen. Additionally, scale separation criterion has been established based on the various configuration of yarn permeability for the 3D fabric with both the isotropic and anisotropic yarn from Gebart’s model. It was observed that the variation of mesoscale permeability Kxx within 30% when the isotropic porous yarn is considered for a 3D fabric with binder yarn. Furthermore, the permeability model developed in this study will be used for multi-objective optimizations of the preform mesoscale geometry in terms of yarn spacing, binder pattern, and a number of layers with an aim to obtain improved permeability and reduced void content during the LCM process.

Keywords: permeability, 3D fabric, dual-scale flow, liquid composite molding

Procedia PDF Downloads 83

Authors: Martin Pellon Consunji

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Due to the rise in popularity of Bitcoin and other crypto assets as a store of wealth and speculative investment, there is an ever-growing demand for automated trading tools, such as bots, in order to gain an advantage over the market. Traditionally, trading in the stock market was done by professionals with years of training who understood patterns and exploited market opportunities in order to gain a profit. However, nowadays a larger portion of market participants are at minimum aided by market-data processing bots, which can generally generate more stable signals than the average human trader. The rise in trading bot usage can be accredited to the inherent advantages that bots have over humans in terms of processing large amounts of data, lack of emotions of fear or greed, and predicting market prices using past data and artificial intelligence, hence a growing number of approaches have been brought forward to tackle this task. However, the general limitation of these approaches can still be broken down to the fact that limited historical data doesn’t always determine the future, and that a lot of market participants are still human emotion-driven traders. Moreover, developing markets such as those of the cryptocurrency space have even less historical data to interpret than most other well-established markets. Due to this, some human traders have gone back to the tried-and-tested traditional technical analysis tools for exploiting market patterns and simplifying the broader spectrum of data that is involved in making market predictions. This paper proposes a method which uses neuro evolution techniques on both sentimental data and, the more traditionally human-consumed, technical analysis data in order to gain a more accurate forecast of future market behavior and account for the way both automated bots and human traders affect the market prices of Bitcoin and other cryptocurrencies. This study’s approach uses evolutionary algorithms to automatically develop increasingly improved populations of bots which, by using the latest inflows of market analysis and sentimental data, evolve to efficiently predict future market price movements. The effectiveness of the approach is validated by testing the system in a simulated historical trading scenario, a real Bitcoin market live trading scenario, and testing its robustness in other cryptocurrency and stock market scenarios. Experimental results during a 30-day period show that this method outperformed the buy and hold strategy by over 260% in terms of net profits, even when taking into consideration standard trading fees.

Keywords: neuro-evolution, Bitcoin, trading bots, artificial neural networks, technical analysis, evolutionary algorithms

Procedia PDF Downloads 106

Authors: Eileen Y. Wong, Katherine Jin, Anat Talmon

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Background: Help seeking behaviors are highly contingent on socio-cultural factors such as ethnicity. Both Latino and Asian Americans underutilize mental health services compared to their White American counterparts. This difference may be related to the composite of one’s social support system, which includes family cohesion and social networks. Previous studies have found that Latino families are characterized by higher levels of family cohesion and social support, and Asian American families with greater family cohesion exhibit lower levels of help seeking behaviors. While both are broadly considered collectivist communities, within-culture variability is also significant. Therefore, this study aims to investigate the relationship between help seeking behaviors in the two cultures with levels of family cohesion and strength of social network. We also consider such relationships in light of previous traumatic events and diagnoses, particularly post-traumatic stress disorder (PTSD), to understand whether clinically diagnosed individuals differ in their strength of network and help seeking behaviors. Method: An adult sample (N = 2,990) from the National Latino and Asian American Study (NLAAS) provided data on participants’ social network, family cohesion, likelihood of seeking professional help, and DSM-IV diagnoses. T-tests compared Latino American (n = 1,576) and Asian American respondents (n = 1,414) in strength of social network, level of family cohesion, and likelihood of seeking professional help. Linear regression models were used to identify the probability of help-seeking behavior based on ethnicity, PTSD diagnosis, and strength of social network. Results: Help-seeking behavior was significantly associated with family cohesion and strength of social network. It was found that higher frequency of expressing one’s feelings with family significantly predicted lower levels of help-seeking behaviors (β = [-.072], p = .017), while higher frequency of spending free time with family significantly predicted higher levels of help-seeking behaviors (β = [.129], p = .002) in the Asian American sample. Subjective importance of family relations compared to that of one’s peers also significantly predict higher levels of help-seeking behaviors (β = [.095], p = .011) in the Asian American sample. Frequency of sharing one’s problems with relatives significantly predicted higher levels of help-seeking behaviors (β = [.113], p < .01) in the Latino American sample. A PTSD diagnosis did not have any significant moderating effect. Conclusion: Considering the underutilization of mental health services in Latino and Asian American minority groups, it is crucial to understand ways in which help seeking behavior can be encouraged. Our findings suggest that different dimensions within family cohesion and social networks have differential impacts on help-seeking behavior. Given the multifaceted nature of family cohesion and cultural relevance, the implications of our findings for theory and practice will be discussed.

Keywords: family cohesion, social networks, Asian American, Latino American, help-seeking behavior

Procedia PDF Downloads 47

Authors: Swati Tomar, Sunil Kumar Gupta

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Anammox is a novel and promising technology that has changed the traditional concept of biological nitrogen removal. The process facilitates direct oxidation of ammonical nitrogen under anaerobic conditions with nitrite as an electron acceptor without the addition of external carbon sources. The present study investigated the feasibility of anammox hybrid reactor (AHR) combining the dual advantages of suspended and attached growth media for biodegradation of ammonical nitrogen in wastewater. The experimental unit consisted of 4 nos. of 5L capacity AHR inoculated with mixed seed culture containing anoxic and activated sludge (1:1). The process was established by feeding the reactors with synthetic wastewater containing NH4-H and NO2-N in the ratio 1:1 at HRT (hydraulic retention time) of 1 day. The reactors were gradually acclimated to higher ammonium concentration till it attained pseudo steady state removal at a total nitrogen concentration of 1200 mg/l. During this period, the performance of the AHR was monitored at twelve different HRTs varying from 0.25-3.0 d with increasing NLR from 0.4 to 4.8 kg N/m3d. AHR demonstrated significantly higher nitrogen removal (95.1%) at optimal HRT of 1 day. Filter media in AHR contributed an additional 27.2% ammonium removal in addition to 72% reduction in the sludge washout rate. This may be attributed to the functional mechanism of filter media which acts as a mechanical sieve and reduces the sludge washout rate many folds. This enhances the biomass retention capacity of the reactor by 25%, which is the key parameter for successful operation of high rate bioreactors. The effluent nitrate concentration, which is one of the bottlenecks of anammox process was also minimised significantly (42.3-52.3 mg/L). Process kinetics was evaluated using first order and Grau-second order models. The first-order substrate removal rate constant was found as 13.0 d-1. Model validation revealed that Grau second order model was more precise and predicted effluent nitrogen concentration with least error (1.84±10%). A new mathematical model based on mass balance was developed to predict N2 gas in AHR. The mass balance model derived from total nitrogen dictated significantly higher correlation (R2=0.986) and predicted N2 gas with least error of precision (0.12±8.49%). SEM study of biomass indicated the presence of the heterogeneous population of cocci and rod shaped bacteria of average diameter varying from 1.2-1.5 mm. Owing to enhanced NRE coupled with meagre production of effluent nitrate and its ability to retain high biomass, AHR proved to be the most competitive reactor configuration for dealing with nitrogen laden wastewater.

Keywords: anammox, filter media, kinetics, nitrogen removal

Procedia PDF Downloads 370

Authors: Eitan Elaad, Yeela Gal-Gonen

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Two studies examined gender differences in lying when the truth-telling bias prevailed and when inspiring lying and distrust. The first study used 156 participants from the community (78 pairs). First, participants completed the Narcissistic Personality Inventory, the Lie- and Truth Ability Assessment Scale (LTAAS), and the Rational-Experiential Inventory. Then, they participated in a deception game where they performed as senders and receivers of true and false communications. Their goal was to retain as many points as possible according to a payoff matrix that specified the reward they would gain for any possible outcome. Results indicated that males in the sender position lied more and were more successful tellers of lies and truths than females. On the other hand, males, as receivers, trusted less than females but were not better at detecting lies and truths. We explained the results by a. Male's high perceived lie-telling ability. We observed that confidence in telling lies guided participants to increase their use of lies. Male's lie-telling confidence corresponded to earlier accounts that showed a consistent association between high self-assessed lying ability, reports of frequent lying, and predictions of actual lying in experimental settings; b. Male's narcissistic features. Earlier accounts described positive relations between narcissism and reported lying or unethical behavior in everyday life situations. Predictions about the association between narcissism and frequent lying received support in the present study. Furthermore, males scored higher than females on the narcissism scale; and c. Male's experiential thinking style. We observed that males scored higher than females on the experiential thinking style scale. We further hypothesized that the experiential thinking style predicts frequent lying in the deception game. Results confirmed the hypothesis. The second study used one hundred volunteers (40 females) who underwent the same procedure. However, the payoff matrix encouraged lying and distrust. Results showed that male participants lied more than females. We found no gender differences in trust. Males and females did not differ in their success of telling and detecting lies and truths. Participants also completed the LTAAS questionnaire. Males assessed their lie-telling ability higher than females, but the ability assessment did not predict lying frequency. A final note. The present design is limited to low stakes. Participants knew that they were participating in a game, and they would not experience any consequences from their deception in the game. Therefore, we advise caution when applying the present results to lying under high stakes.

Keywords: gender, lying, detection of deception, information processing style, self-assessed lying ability

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Authors: Ali Hashemifarzad, Jens Zum Hingst

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The structure of the world’s energy systems has changed significantly over the past years. One of the most important challenges in the 21st century in Germany (and also worldwide) is the energy transition. This transition aims to comply with the recent international climate agreements from the United Nations Climate Change Conference (COP21) to ensure sustainable energy supply with minimal use of fossil fuels. Germany aims for complete decarbonization of the energy sector by 2050 according to the federal climate protection plan. One of the stipulations of the Renewable Energy Sources Act 2017 for the expansion of energy production from renewable sources in Germany is that they cover at least 80% of the electricity requirement in 2050; The Gross end energy consumption is targeted for at least 60%. This means that by 2050, the energy supply system would have to be almost completely converted to renewable energy. An essential basis for the development of such a sustainable energy supply from 100% renewable energies is to predict the energy requirement by 2050. This study presents two scenarios for the final energy demand in Germany in 2050. In the first scenario, the targets for energy efficiency increase and demand reduction are set very ambitiously. To build a comparison basis, the second scenario provides results with less ambitious assumptions. For this purpose, first, the relevant framework conditions (following CUTEC 2016) were examined, such as the predicted population development and economic growth, which were in the past a significant driver for the increase in energy demand. Also, the potential for energy demand reduction and efficiency increase (on the demand side) was investigated. In particular, current and future technological developments in energy consumption sectors and possible options for energy substitution (namely the electrification rate in the transport sector and the building renovation rate) were included. Here, in addition to the traditional electricity sector, the areas of heat, and fuel-based consumptions in different sectors such as households, commercial, industrial and transport are taken into account, supporting the idea that for a 100% supply from renewable energies, the areas currently based on (fossil) fuels must be almost completely be electricity-based by 2050. The results show that in the very ambitious scenario a final energy demand of 1,362 TWh/a is required, which is composed of 818 TWh/a electricity, 229 TWh/a ambient heat for electric heat pumps and approx. 315 TWh/a non-electric energy (raw materials for non-electrifiable processes). In the less ambitious scenario, in which the targets are not fully achieved by 2050, the final energy demand will need a higher electricity part of almost 1,138 TWh/a (from the total: 1,682 TWh/a). It has also been estimated that 50% of the electricity revenue must be saved to compensate for fluctuations in the daily and annual flows. Due to conversion and storage losses (about 50%), this would mean that the electricity requirement for the very ambitious scenario would increase to 1,227 TWh / a.

Keywords: energy demand, energy transition, German Energiewende, 100% renewable energy production

Procedia PDF Downloads 118

Authors: L. C. Nwosu, C. O. Adedire, M. O. Ashamo, E. O. Ogunwolu

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The maize weevil, Sitophilus zeamais Motschulsky is a notorious pest of stored maize (Zea mays L.). The development of resistant maize varieties to manage weevils is a major breeding objective. The study investigated the parameters and mechanisms that confer resistance on a maize variety to S. zeamais infestation using twenty elite maize varieties. Detailed morphological, physical and chemical studies were conducted on whole-maize grain and the grain pericarp. Resistance was assessed at 33, 56, and 90 days post infestation using weevil mortality rate, weevil survival rate, percent grain damage, percent grain weight loss, weight of grain powder, oviposition rate and index of susceptibility as indices rated on a scale developed by the present study and on Dobie’s modified scale. Linear regression models that can predict maize grain damage in relation to the duration of storage were developed and applied. The resistant varieties identified particularly 2000 SYNEE-WSTR and TZBRELD3C5 with very high degree of resistance should be used singly or best in an integrated pest management system for the control of S. zeamais infestation in stored maize. Though increases in the physical properties of grain hardness, weight, length, and width increased varietal resistance, it was found that the bases of resistance were increased chemical attributes of phenolic acid, trypsin inhibitor and crude fibre while the bases of susceptibility were increased protein, starch, magnesium, calcium, sodium, phosphorus, manganese, iron, cobalt and zinc, the role of potassium requiring further investigation. Characters that conferred resistance on the test varieties were found distributed in the pericarp and the endosperm of the grains. Increases in grain phenolic acid, crude fibre, and trypsin inhibitor adversely and significantly affected the bionomics of the weevil on further assessment. The flat side of a maize grain at the point of penetration was significantly preferred by the weevil. Why the south area of the flattened side of a maize grain was significantly preferred by the weevil is clearly unknown, even though grain-face-type seemed to be a contributor in the study. The preference shown to the south area of the grain flat side has implications for seed viability. The study identified antibiosis, preference, antixenosis, and host evasion as the mechanisms of maize post harvest resistance to Sitophilus zeamais infestation.

Keywords: maize weevil, resistant, parameters, mechanisms, preference

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Authors: Rachel S. E. Lim, Winnie L. C. Tan

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Visual literacy is about making meaning through the interaction of images, words, and sounds. Graphic communication students typically develop visual literacy through critique and production of studio-based projects for their portfolios. However, the abrupt switch to online learning during the COVID-19 pandemic has made it necessary to consider new strategies of visualization and planning to scaffold teaching and learning. This study, therefore, investigated how MIRO, a cloud-based visual collaboration platform, could be used to develop the visual literacy confidence of 30 diploma in graphic communication students attending a graphic design course at a Singapore arts institution. Due to COVID-19, the course was taught fully online throughout a 16-week semester. Guided by Kolb’s Experiential Learning Cycle, the two lecturers developed students’ engagement with visual literacy concepts through different activities that facilitated concrete experiences, reflective observation, abstract conceptualization, and active experimentation. Throughout the semester, students create, collaborate, and centralize communication in MIRO with infinite canvas, smart frameworks, a robust set of widgets (i.e., sticky notes, freeform pen, shapes, arrows, smart drawing, emoticons, etc.), and powerful platform capabilities that enable asynchronous and synchronous feedback and interaction. Students then drew upon these multimodal experiences to brainstorm, research, and develop their motion design project. A survey was used to examine students’ perceptions of engagement (E), confidence (C), learning strategies (LS). Using multiple regression, it¬ was found that the use of MIRO helped students develop confidence (C) with visual literacy, which predicted performance score (PS) that was measured against their application of visual literacy to the creation of their motion design project. While students’ learning strategies (LS) with MIRO did not directly predict confidence (C) or performance score (PS), it fostered positive perceptions of engagement (E) which in turn predicted confidence (C). Content analysis of students’ open-ended survey responses about their learning strategies (LS) showed that MIRO provides organization and structure in documenting learning progress, in tandem with establishing standards and expectations as a preparatory ground for generating feedback. With the clarity and sequence of the mentioned conditions set in place, these prerequisites then lead to the next level of personal action for self-reflection, self-directed learning, and time management. The study results show that the affordances of MIRO can develop visual literacy and make up for the potential pitfalls of student isolation, communication, and engagement during online learning. The context of how MIRO could be used by lecturers to orientate students for learning in visual literacy and studio-based projects for future development are discussed.

Keywords: design education, graphic communication, online learning, visual literacy

Procedia PDF Downloads 103

Authors: Peter G. Hollis, Kim G. Clarke

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Accurate quantification of the overall volumetric oxygen transfer coefficient (KLa) is ubiquitously measured in bioprocesses by analysing the response of dissolved oxygen (DO) to a step change in the oxygen partial pressure in the sparge gas using a DO probe. Typically, the response lag (τ) of the probe has been ignored in the calculation of KLa when τ is less than the reciprocal KLa, failing which a constant τ has invariably been assumed. These conventions have now been reassessed in the context of multiphase bioprocesses, such as a hydrocarbon-based system. Here, significant variation of τ in response to changes in process conditions has been documented. Experiments were conducted in a 5 L baffled stirred tank bioreactor (New Brunswick) in a simulated hydrocarbon-based bioprocess comprising a C14-20 alkane-aqueous dispersion with suspended non-viable Saccharomyces cerevisiae solids. DO was measured with a polarographic DO probe fitted with a Teflon membrane (Mettler Toledo). The DO concentration response to a step change in the sparge gas oxygen partial pressure was recorded, from which KLa was calculated using a first order model (without incorporation of τ) and a second order model (incorporating τ). τ was determined as the time taken to reach 63.2% of the saturation DO after the probe was transferred from a nitrogen saturated vessel to an oxygen saturated bioreactor and is represented as the inverse of the probe constant (KP). The relative effects of the process parameters on KP were quantified using a central composite design with factor levels typical of hydrocarbon bioprocesses, namely 1-10 g/L yeast, 2-20 vol% alkane and 450-1000 rpm. A response surface was fitted to the empirical data, while ANOVA was used to determine the significance of the effects with a 95% confidence interval. KP varied with changes in the system parameters with the impact of solid loading statistically significant at the 95% confidence level. Increased solid loading reduced KP consistently, an effect which was magnified at high alkane concentrations, with a minimum KP of 0.024 s-1 observed at the highest solids loading of 10 g/L. This KP was 2.8 fold lower that the maximum of 0.0661 s-1 recorded at 1 g/L solids, demonstrating a substantial increase in τ from 15.1 s to 41.6 s as a result of differing process conditions. Importantly, exclusion of KP in the calculation of KLa was shown to under-predict KLa for all process conditions, with an error up to 50% at the highest KLa values. Accurate quantification of KLa, and therefore KP, has far-reaching impact on industrial bioprocesses to ensure these systems are not transport limited during scale-up and operation. This study has shown the incorporation of τ to be essential to ensure KLa measurement accuracy in multiphase bioprocesses. Moreover, since τ has been conclusively shown to vary significantly with process conditions, it has also been shown that it is essential for τ to be determined individually for each set of process conditions.

Keywords: effect of process conditions, measuring oxygen transfer coefficients, multiphase bioprocesses, oxygen probe response lag

Procedia PDF Downloads 256

Authors: Tereza Hruskova

Abstract:

200 million people globally experience serious mental health problems, and only one third seek professional help, and help-seeking is described as a last resort. Adolescents and young adults have a high prevalence of mental illness. Mental stigma is a key element in the decision to seek help and is divided into (i) self-stigma (self-stigmatization), including internal beliefs, low self-esteem, and lower quality of life, and (ii) public stigma (social stigma) containing stereotypes, beliefs and society's disapproval of help-seeking having a negative effect on help-seeking and our attitudes. Previous research has mainly focused on examining the construct of help seeking, avoidance, and delaying separately and trying to find out why people do not seek help in time and what obstacles stand in the way. Barriers are not static and may change over time and the stage of help-seeking. Attitudes are closely related to self-stigma and social stigma and predict whether a person will seek help. Barriers (stigmatization, a sense of humiliation, insufficient recognition of the problem, preferences, solving it alone, and distrust of a professional) and facilitators (previous experience with mental problems, social support, and help from others) are factors influencing help-seeking. The current research on the Czech population of young adults responds to the gap between a person with mental health problems and actually seeking professional help. The aim of the study is to describe in detail the individual constructs and factors, to understand the person seeking help, and to define possible obstacles on this path of seeking help. A sample of approximately 250 participants (age 18-35) would take part in the online questionnaire, conducted in May-June 2023, and would be administered a demographic questionnaire and four scales measuring attitudes (Attitudes Toward Seeking Professional Psychological Help – Short form), barriers (Barrier to Help Seeking Scale), self-stigma (Self Stigma of Seeking Help) and stigmatization (Perceptions of Stigmatization by Others for seeking help). Firstly, all four scales would be translated into the Czech language. The aim is (I) to determine the validity and reliability of the Czech translation of the scales, (II) to examine the factors of the scales on the Czech population and compare them retrospectively with the results of reliability and validity from the original language of the scales and (III) to examine the connections between attitudes towards seeking, avoidance or delaying the search for professional psychological help due to the demographic and individual differences of the participants, barriers, self-stigmatization and social stigmatization. We expect to carry out the first study on the given topic in the Czech Republic, to identify and better understand the factors leading to the avoidance of seeking professional help and to reveal the relationships between stigmatization, attitudes and barriers leading to the avoidance or postponement of seeking professional help. The belief is to find out whether the Czech population of young adults differs from the data found on the foreign population in individual constructs, as cultural differences in individual countries were found.

Keywords: mental health, stigma, problems, seeking psychological help

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Authors: Diana Vieira Figueiredo, Rita Ramos Miguel, Maria do Céu Salvador, Luiza Nobre-Lima, Daniel RIjo, Paula Vagos

Abstract:

Social Anxiety Disorder (SAD) is characterized by a marked and persistent fear of social and/or performance situations in which one may be exposed to the scrutiny of others.  SAD has its usual onset and is highly prevalent during adolescence; if left untreated, it often has a chronic and unremitting course. So, it seems important to understand the psychological processes that might predict the development of SAD. One of these processes may be self-compassion, which has been found to be associated with social anxiety in both adults and adolescents. Self-compassion involves three main components, each with a positive (compassionate behavior) and negative (uncompassionate behavior) pole – self-kindness versus self-judgment, common humanity versus isolation, and mindfulness versus over-identification. The negative indicators of self-compassion (self-judgement, isolation, and over-identification) were found to be more strongly linked to mental health problems than the positive indicators (self-kindness, common humanity, and mindfulness). Additionally, negative associations were found between the positive indicators of self-compassion (self-kindness, common humanity, mindfulness) and psychopathology. The current study aimed to investigate the role of self-kindness, self-judgment, common humanity, isolation, mindfulness, and over-identification in the likelihood of an adolescent presenting SAD by comparing groups of normative and socially anxious adolescents. The sample consisted of 32 adolescents (Mage = 15.88, SD = .833) of which 23 were girls. Adolescents were assessed through a clinical structured interview that led 17 to be assigned to the clinical group (presenting a primary diagnosis of SAD) and 15 to be assigned to the non-clinical group (presenting no clinical diagnosis). Variables under study were measured through the Self-Compassion Scale for adolescents (SCS-A), which assesses the six indicators of self-compassion presented above. Six separate models were tested, each with one of the subscales of the SCS-A as the independent variable and with the group (clinical versus non-clinical) as the dependent variable. The models considering isolation, over-identification, self-judgement, and self-kindness fitted the data and accurately predicted group belonging for between 75% to 84.4% of cases. Results indicated that the log of the odds of an adolescent presenting SAD was positively related to isolation, over-identification, and self-judgement and negatively associated with self-kindness. Findings provide support for the idea that decreased self-compassion may place adolescents at increased risk for experiencing clinical levels of social anxiety: on the one hand, adolescents with higher levels of isolation, over-identification, and self-judgement seem to be more prone to the development of psychopathological levels of social anxiety; on the other hand, self-kindness may play a protective role in the development of SAD in this developmental phase. So, if focusing on social feared consequences and perceiving to be different from others may be distinctive features of SAD, developing self-kindness may be the antidote to promote diminished levels of social anxiety and more.

Keywords: adolescents, social anxiety disorder, self-compassion, diagnosis odds-ration

Procedia PDF Downloads 146