Search results for: randomized response model

Authors: Melaku Takele Abate, Abbi Lemma Wodajo, Adula Bekele Hunde

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This study aimed at exploring mathematics teachers' reactions, learning, school leaders’ support, and use of the Lesson Study for Transformative Assessment (LSforTA) program ideas in practice. The LSforTA program was new, and therefore, a local and grounded approach was needed to examine teachers’ knowledge and skills acquired using LSforTA. So, a design-based research approach was selected to evaluate and refine the LSforTA approach. The results showed that LSforTA increased teachers' knowledge and use of different levels of mathematics assessment tasks. The program positively affected teachers' practices of transformative assessment and enhanced their knowledge and skills in assessing students in a transformative way. The paper concludes how the LSforTA procedures were adapted in response to this evaluation and provides suggestions for future development and research.

Keywords: classroom assessment, feedback practices, lesson study, mathematics, design-based research

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Authors: Feng-Sheng Wang, Chao-Ting Cheng

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Developing a drug from conception to launch is costly and time-consuming. Computer-aided methods can reduce research costs and accelerate the development process during the early drug discovery and development stages. This study developed a fuzzy multi-objective hierarchical optimization framework for identifying potential anticancer targets in a metabolic model. First, RNA-seq expression data of colorectal cancer samples and their healthy counterparts were used to reconstruct tissue-specific genome-scale metabolic models. The aim of the optimization framework was to identify anticancer targets that lead to cancer cell death and evaluate metabolic flux perturbations in normal cells that have been caused by cancer treatment. Four objectives were established in the optimization framework to evaluate the mortality of cancer cells for treatment and to minimize side effects causing toxicity-induced tumorigenesis on normal cells and smaller metabolic perturbations. Through fuzzy set theory, a multiobjective optimization problem was converted into a trilevel maximizing decision-making (MDM) problem. The applied nested hybrid differential evolution was applied to solve the trilevel MDM problem using two nutrient media to identify anticancer targets in the genome-scale metabolic model of colorectal cancer, respectively. Using Dulbecco’s Modified Eagle Medium (DMEM), the computational results reveal that the identified anticancer targets were mostly involved in cholesterol biosynthesis, pyrimidine and purine metabolisms, glycerophospholipid biosynthetic pathway and sphingolipid pathway. However, using Ham’s medium, the genes involved in cholesterol biosynthesis were unidentifiable. A comparison of the uptake reactions for the DMEM and Ham’s medium revealed that no cholesterol uptake reaction was included in DMEM. Two additional media, i.e., a cholesterol uptake reaction was included in DMEM and excluded in HAM, were respectively used to investigate the relationship of tumor cell growth with nutrient components and anticancer target genes. The genes involved in the cholesterol biosynthesis were also revealed to be determinable if a cholesterol uptake reaction was not induced when the cells were in the culture medium. However, the genes involved in cholesterol biosynthesis became unidentifiable if such a reaction was induced.

Keywords: Cancer metabolism, genome-scale metabolic model, constraint-based model, multilevel optimization, fuzzy optimization, hybrid differential evolution

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Authors: Supaporn Yamuangmorn, Chanakan Prom-U-Thai

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Wetland and upland purple rice are the two major types classified by its original ecotypes in Northern Thailand. Wetland rice is grown under flooded condition from transplanting until the mutuality, while upland rice is naturally grown under well-drained soil known as aerobic cultivations. Both ecotypes can be grown and adapted to the reverse systems but little is known on its responses of grain yield and qualities between the 2 ecotypes. This study evaluated responses of grain yield as well as anthocyanin and antioxidant capacity between the wetland and upland purple rice genotypes grown in the submerged and aerobic conditions. A factorial arrangement in a randomized complete block design (RCBD) with two factors of rice genotype and water condition were carried out in three replications. The two wetland genotypes (Kum Doi Saket: KDK and Kum Phayao: KPY) and two upland genotypes (Kum Hom CMU: KHCMU and Pieisu1: PES1) were used in this study by growing under submerged and aerobic conditions. Grain yield was affected by the interaction between water condition and rice genotype. The wetland genotypes, KDK and KPY grown in the submerged condition produced about 2.7 and 0.8 times higher yield than in the aerobic condition, respectively. The 0.4 times higher grain yield of upland genotype (PES1) was found in the submerged condition than in the aerobic condition, but no significant differences in KHCMU. In the submerged condition, all genotypes produced higher yield components of tiller number, panicle number and percent filled grain than in the aerobic condition by 24% and 32% and 11%, respectively. The thousand grain weight and spikelet number were affected by water condition differently among genotypes. The wetland genotypes, KDK and KPY, and upland genotype, PES1, grown in the submerged condition produced about 19-22% higher grain weight than in the aerobic condition. The similar effect was found in spikelet number which the submerged condition of wetland genotypes, KDK and KPY, and the upland genotype, KHCMU, had about 28-30% higher than the aerobic condition. In contrast, the anthocyanin concentration and antioxidant capacity were affected by both the water condition and genotype. Rice grain grown in the aerobic condition had about 0.9 and 2.6 times higher anthocyanin concentration than in the submerged condition was found in the wetland rice, KDK and upland rice, KHCMU, respectively. Similarly, the antioxidant capacity of wetland rice, KDK and upland rice, KHCMU were 0.5 and 0.6 times higher in aerobic condition than in the submerged condition. There was a negative correlation between grain yield and anthocyanin concentration in wetland genotype KDK and upland genotype KHCMU, but it was not found in the other genotypes. This study indicating that some rice genotype can be adapted in the reverse ecosystem in both grain yield and quality, especially in the wetland genotype KPY and upland genotype PES1. To maximize grain yield and quality of purple rice, proper water management condition is require with a key consideration on difference responses among genotypes. Increasing number of rice genotypes in both ecotypes is needed to confirm their responses on water management.

Keywords: purple rice, water condition, anthocyanin, grain yield

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Authors: Karishma Kashyap, Subha D. Parida

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Rapid population growth and urbanization is creating pressure throughout the world. This has a dramatic effect on a lot of elements which include water, food, transportation, energy, infrastructure etc. as few of the key services. Built environment sector is growing concurrently to meet the needs of urbanization. Due to such large scale development of buildings, there is a need for them to be monitored and managed efficiently. Along with appropriate management, climate adaptation is highly crucial as well because buildings are one of the major sources of greenhouse gas emission in their operation phase. Buildings to be adaptive need to provide a triple bottom approach to sustainability i.e., being socially, environmentally and economically sustainable. Hence, in order to deliver these sustainability outcomes, there is a growing understanding and thrive towards switching to green buildings or renovating new ones as per green standards wherever possible. Academic institutions in particular have been following this trend globally. This is highly significant as universities usually have high occupancy rates because they manage a large building portfolio. Also, as universities accommodate the future generation of architects, policy makers etc., they have the potential of setting themselves as a best industry practice model for research and innovation for the rest to follow. Hence their climate adaptation, sustainable growth and performance management becomes highly crucial in order to provide the best services to users. With the objective of evaluating appropriate management mechanisms within academic institutions, a feasibility study was carried out in a recent 5-Star Green Star rated university building (housing the School of Construction) in Victoria (south-eastern state of Australia). The key aim was to understand the behavioral and social aspect of the building users, management and the impact of their relationship on overall building sustainability. A survey was used to understand the building occupant’s response and reactions in terms of their work environment and management. A report was generated based on the survey results complemented with utility and performance data which were then used to evaluate the management structure of the university. Followed by the report, interviews were scheduled with the facility and asset managers in order to understand the approach they use to manage the different buildings in their university campuses (old, new, refurbished), respective building and parameters incorporated in maintaining the Green Star performance. The results aimed at closing the communication and feedback loop within the respective institutions and assist the facility managers to deliver appropriate stakeholder engagement. For the wider design community, analysis of the data highlights the applicability and significance of prioritizing key stakeholders, integrating desired engagement policies within an institution’s management structures and frameworks and their effect on building performance

Keywords: building optimization, green building, post occupancy evaluation, stakeholder engagement

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Authors: Aldona Anna Osajda

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Suicide is a phenomenon that worries both the public and scientists in various fields. In society, suicide is a taboo subject, and in addition, there are many myths and stereotypes that are detrimental to the proper understanding and appropriate response of a person at risk of suicide. It is necessary to educate society and the suicide prevention system for various age groups. The research covers the level of knowledge and views of Polish society, including teachers and youth, regarding suicides. The main research problem is to establish the level of awareness of Polish society about the phenomenon of suicides. The study will be based on the diagnostic survey method, using the survey technique. Information about the research will be disseminated electronically on the Internet via social messaging. The collected data will be analyzed using appropriate statistics. On the basis of the obtained results, answers will be given to research questions, which will become the basis for designing an appropriate educational strategy for the society in the field of suicide and developing recommendations and recommendations for teachers to conduct classes in the field of suicide prevention for children and adolescents.

Keywords: phenomenon of suicides, suicide, suicide prevention, suicidology

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Authors: Smitha Haridasan, Zelalem Demissie, Atri Dutta, Ajita Rattani

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Aided by the wind, all it takes is one ember and a few minutes to create a wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision-based techniques have been proposed for the early detection of forest fire using video surveillance. Several computer vision-based methods have been proposed to predict and detect forest fires at various spectrums, namely, RGB, HSV, and YCbCr. The aim of this paper is to propose a multi-spectral deep learning model that combines information from different spectrums at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available datasets is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 4.68 % over those based on a single spectrum for fire detection.

Keywords: deep learning, forest fire detection, multi-spectral learning, natural hazard detection

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Authors: Misheck Musokwa, Paramu Mafongoya, Simon Lorentz

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Seasonal shortages of fodder during the dry season is a major constraint to smallholder livestock farmers in South Africa. To mitigate the shortage of fodder, legume trees can be intercropped with pastures which can diversify the sources of feed and increase the amount of protein for grazing animals. The objective was to evaluate dry matter yield of Panicum maximum and land productivity under different fodder production systems during 2016/17-2017/18 seasons at Empangeni (28.6391° S and 31.9400° E). A randomized complete block design, replicated three times was used, the treatments were sole Panicum maximum, Panicum maximum + Sesbania sesban, Panicum maximum + pigeonpea, sole Sesbania sesban, Sole pigeonpea. Three months S.sesbania seedlings were transplanted whilst pigeonpea was direct seeded at spacing of 1m x 1m. P. maximum seeds were drilled at a respective rate of 7.5 kg/ha having an inter-row spacing of 0.25 m apart. In between rows of trees P. maximum seeds were drilled. The dry matter yield harvesting times were separated by six months’ timeframe. A 0.25 m² quadrant randomly placed on 3 points on the plot was used as sampling area during harvesting P. maximum. There was significant difference P < 0.05 across 3 harvests and total dry matter. P. maximum had higher dry matter yield as compared to both intercrops at first harvest and total. The second and third harvest had no significant difference with pigeonpea intercrop. The results was in this order for all 3 harvest: P. maximum (541.2c, 1209.3b and 1557b) kg ha¹ ≥ P. maximum + pigeonpea (157.2b, 926.7b and 1129b) kg ha¹ > P. maximum + S. sesban (36.3a, 282a and 555a) kg ha¹. Total accumulation of dry matter yield of P. maximum (3307c kg ha¹) > P. maximum + pigeonpea (2212 kg ha¹) ≥ P. maximum + S. sesban (874 kg ha¹). There was a significant difference (P< 0.05) on seed yield for trees. Pigeonpea (1240.3 kg ha¹) ≥ Pigeonpea + P. maximum (862.7 kg ha¹) > S.sesbania (391.9 kg ha¹) ≥ S.sesbania + P. maximum. The Land Equivalent Ratio (LER) was in the following order P. maximum + pigeonpea (1.37) > P. maximum + S. sesban (0.84) > Pigeonpea (0.59) ≥ S. Sesbania (0.57) > P. maximum (0.26). Results indicates that it is beneficial to have P. maximum intercropped with pigeonpea because of higher land productivity. Planting grass with pigeonpea was more beneficial than S. sesban with grass or sole cropping in terms of saving the shortage of arable land. P. maximum + pigeonpea saves a substantial (37%) land which can be subsequently be used for other crop production. Pigeonpea is recommended as an intercrop with P. maximum due to its higher LER and combined production of livestock feed, human food, and firewood. Panicum grass is low in crude protein though high in carbohydrates, there is a need for intercropping it with legume trees. A farmer who buys concentrates can reduce costs by combining P. maximum with pigeonpea this will provide a balanced diet at low cost.

Keywords: fodder, livestock, productivity, smallholder farmers

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Authors: I. Touaїbia, E. Azzag, O. Narjes

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Power supply networks are growing continuously and their reliability is getting more important than ever. The complexity of the whole network comprises numerous components that can fail and interrupt the power supply for the end user. Underground distribution systems are normally exposed to permanent faults, due to specific construction characteristics. In these systems, visual inspection cannot be performed. In order to enhance service restoration, accurate fault location techniques must be applied. This paper describes the different faults that affect the underground distribution system of SONELGAZ (National Society of Electricity and Gas of Algeria), and cable fault location procedure with impulse reflection method (TDR), based in the analyses of the cable response of the electromagnetic impulse, allows cable fault prelocation. The results are obtained from real test in the underground distribution feeder from electrical network of energy distribution company of Souk-Ahras, in order to know the influence of cable characteristics in the types and frequency of faults.

Keywords: distribution networks, fault location, TDR, underground cable

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Authors: Juan Sebastián Hernández

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The Nevado del Ruíz volcano, located between the limits of the Departments of Caldas and Tolima in Colombia, presented an unstable behaviour in the course of the year 2020, this volcanic activity led to secondary effects on the crust, which is why the prediction of deformations becomes the task of geoscientists. In the course of this article, the use of tropospheric variables such as evapotranspiration, UV aerosol index, carbon monoxide, nitrogen dioxide, methane, surface temperature, among others, is used to train a set of neural networks that can predict the behaviour of the resulting phase of an unrolled interferogram with the DInSAR technique, whose main objective is to identify and characterise the behaviour of the crust based on the environmental conditions. For this purpose, variables were collected, a generalised linear model was created, and a set of neural networks was created. After the training of the network, validation was carried out with the test data, giving an MSE of 0.17598 and an associated r-squared of approximately 0.88454. The resulting model provided a dataset with good thematic accuracy, reflecting the behaviour of the volcano in 2020, given a set of environmental characteristics.

Keywords: crustal deformation, Tropomi, neural networks (ANN), volcanic activity, DInSAR

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Authors: Yang Liu

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Reinforced concrete (RC) shear wall structures are one of the most popular and efficient structural forms for medium- and high-rise buildings to resist the action of earthquake loading. Thus, it is of great significance to evaluate the seismic demands of the RC shear walls. In this paper, the application of the extend spectrum-based pushover analysis (ESPA) method on the seismic evaluation of the shear wall structure is presented. The ESPA method includes a nonlinear consecutive pushover analysis procedure and a linear elastic modal response analysis procedure to consider the combination of modes in both elastic and inelastic cases. It is found from the results of case study that the ESPA method can predict the seismic performance of shear wall structures, including internal forces and deformations very well.

Keywords: reinforced concrete shear wall, seismic performance, high mode effect, nonlinear analysis

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Authors: Nicolas Lopez-Villalobos, Dorian J. Garrick, Hugh T. Blair

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Many dairy goat farmers of New Zealand milk herds of mixed breed does. Simultaneous evaluation of sires and does across breed is required to select the best animals for breeding on a common basis. Across-breed estimated breeding values (EBV) and estimated producing values for 208-day lactation yields of milk (MY), fat (FY), protein (PY) and somatic cell score (SCS; LOG2(SCC) of Saanen, Nubian, Alpine, Toggenburg and crossbred dairy goats from 75 herds were estimated using a test day model. Evaluations were based on 248,734 herd-test records representing 125,374 lactations from 65,514 does sired by 930 sires over 9 generations. Averages of MY, FY and PY were 642 kg, 21.6 kg and 19.8 kg, respectively. Average SCC and SCS were 936,518 cells/ml milk and 9.12. Pure-bred Saanen does out-produced other breeds in MY, FY and PY. Average EBV for MY, FY and PY compared to a Saanen base were Nubian -98 kg, 0.1 kg and -1.2 kg; Alpine -64 kg, -1.0 kg and -1.7 kg; and Toggenburg -42 kg, -1.0 kg and -0.5 kg. First-cross heterosis estimates were 29 kg MY, 1.1 kg FY and 1.2 kg PY. Average EBV for SCS compared to a Saanen base were Nubian 0.041, Alpine -0.083 and Toggenburg 0.094. Heterosis for SCS was 0.03. Breeding values are combined with respective economic values to calculate an economic index used for ranking sires and does to reflect farm profit.

Keywords: breed effects, dairy goats, milk traits, test-day model

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Authors: Odete Paiva, Cláudia Seabra, José Luís Abrantes, Fernanda Cravidão

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There is a clear ‘cult of authenticity’, at least in modern Western society. So, there is a need to analyze the tourist perception of authenticity, bearing in mind the destination, its attractions, motivations, cultural distance, and contact with other tourists. Our study seeks to investigate the relationship among cultural values, image, sense of place, perception of authenticity and behavior intentions at World Heritage Historic Centers. From a theoretical perspective, few researches focus on the impact of cultural values, image and sense of place on authenticity and intentions behavior in tourists. The intention of this study is to help close this gap. A survey was applied to collect data from tourists visiting two World Heritage Historic Centers – Guimarães in Portugal and Cordoba in Spain. Data was analyzed in order to establish a structural equation model (SEM). Discussion centers on the implications of model to theory and managerial development of tourism strategies. Recommendations for destinations managers and promoters and tourist organizations administrators are addressed.

Keywords: authenticity perception, behavior intentions, cultural tourism, cultural values, world heritage historic centers

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Authors: S. Nandagopalan, N. Pradeep

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The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, Bayesian, echocardiographic image, feature vector

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Authors: Sarah E. Nelson, Casey Weiner, Alexander Sigmon, Jun Hua, Haris I. Sair, Jose I. Suarez, Robert D. Stevens

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Graph-theoretical information from structural connectomes indicated significant connectivity changes and improved acute prognostication in a Random Forest (RF) model in aneurysmal subarachnoid hemorrhage (aSAH), which can lead to significant morbidity and mortality and has traditionally been fraught by poor methods to predict outcome. This study’s hypothesis was that structural connectivity changes occur in canonical brain networks of acute aSAH patients, and that these changes are associated with functional outcome at six months. In a prospective cohort of patients admitted to a single institution for management of acute aSAH, patients underwent diffusion tensor imaging (DTI) as part of a multimodal MRI scan. A weighted undirected structural connectome was created of each patient’s images using Constant Solid Angle (CSA) tractography, with 176 regions of interest (ROIs) defined by the Johns Hopkins Eve atlas. ROIs were sorted into four networks: Default Mode Network, Executive Control Network, Salience Network, and Whole Brain. The resulting nodes and edges were characterized using graph-theoretic features, including Node Strength (NS), Betweenness Centrality (BC), Network Degree (ND), and Connectedness (C). Clinical (including demographics and World Federation of Neurologic Surgeons scale) and graph features were used separately and in combination to train RF and Logistic Regression classifiers to predict two outcomes: dichotomized modified Rankin Score (mRS) at discharge and at six months after discharge (favorable outcome mRS 0-2, unfavorable outcome mRS 3-6). A total of 56 aSAH patients underwent DTI a median (IQR) of 7 (IQR=8.5) days after admission. The best performing model (RF) combining clinical and DTI graph features had a mean Area Under the Receiver Operator Characteristic Curve (AUROC) of 0.88 ± 0.00 and Area Under the Precision Recall Curve (AUPRC) of 0.95 ± 0.00 over 500 trials. The combined model performed better than the clinical model alone (AUROC 0.81 ± 0.01, AUPRC 0.91 ± 0.00). The highest-ranked graph features for prediction were NS, BC, and ND. These results indicate reorganization of the connectome early after aSAH. The performance of clinical prognostic models was increased significantly by the inclusion of DTI-derived graph connectivity metrics. This methodology could significantly improve prognostication of aSAH.

Keywords: connectomics, diffusion tensor imaging, graph theory, machine learning, subarachnoid hemorrhage

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Authors: Ranjit Kumar Sahu, Ramakar Jha

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Urban Water Management is the practice of managing freshwater, waste water, and storm water as components of a basin-wide management plan. It builds on existing water supply and sanitation considerations within an urban settlement by incorporating urban water management within the scope of the entire river basin. The pervasive problems generated by urban development have prompted, in the present work, to study the spatial extent of urbanization in Golden Triangle of Odisha connecting the cities Bhubaneswar (20.2700° N, 85.8400° E), Puri (19.8106° N, 85.8314° E) and Konark (19.9000° N, 86.1200° E)., and patterns of periodic changes in urban development (systematic/random) in order to develop future plans for (i) urbanization promotion areas, and (ii) urbanization control areas. Remote Sensing, using USGS (U.S. Geological Survey) Landsat8 maps, supervised classification of the Urban Sprawl has been done for during 1980 - 2014, specifically after 2000. This Work presents the following: (i) Time series analysis of Hydrological data (ground water and rainfall), (ii) Application of SWMM (Storm Water Management Model) and other soft computing techniques for Urban Water Management, and (iii) Uncertainty analysis of model parameters (Urban Sprawl and correlation analysis). The outcome of the study shows drastic growth results in urbanization and depletion of ground water levels in the area that has been discussed briefly. Other relative outcomes like declining trend of rainfall and rise of sand mining in local vicinity has been also discussed. Research on this kind of work will (i) improve water supply and consumption efficiency (ii) Upgrade drinking water quality and waste water treatment (iii) Increase economic efficiency of services to sustain operations and investments for water, waste water, and storm water management, and (iv) engage communities to reflect their needs and knowledge for water management.

Keywords: Storm Water Management Model (SWMM), uncertainty analysis, urban sprawl, land use change

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Authors: Negar Sartipzadeh

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Energy-conscious design, which coordinates with the Earth ecological systems during its life cycle, has the least negative impact on the environment with the least waste of resources. Due to the increasing in world population as well as the consumption of fossil fuels that cause the production of greenhouse gasses and environmental pollution, mankind is looking for renewable and also sustainable energies. The Iranian native construction is a clear evidence of energy-aware designing. Our predecessors were forced to rely on the natural resources and sustainable energies as well as environmental issues which have been being considered in the recent world. One of these endless energies is wind energy. Iranian traditional architecture foundations is a appropriate model in solving the environmental crisis and the contemporary energy. What will come in this paper is an effort to recognition and introduction of the unique characteristics of the Iranian architecture in the application of aerodynamic and hydraulic energies derived from the wind, which are the most common and major type of using sustainable energies in the traditional architecture of Iran. Therefore, the recent research attempts to offer a hybrid system suggestions for application in new constructions designing in a region such as Nashtifan, which has potential through reviewing windmills and how they deal with sustainable energy sources, as a model of Iranian native construction.

Keywords: renewable energy, sustainable building, windmill, Iranian architecture

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Authors: Massimo Miccoli, Luca Marangoni, Alberto Aniello Scaringi, Alessandro Marceddu, Alessandro Amicone

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The susceptibility of deep neural networks (DNNs) to adversarial manipulations is a recognized challenge within the computer vision domain. Adversarial examples, crafted by adding subtle yet malicious alterations to benign images, exploit this vulnerability. Various defense strategies have been proposed to safeguard DNNs against such attacks, stemming from diverse research hypotheses. Building upon prior work, our approach involves the utilization of autoencoder models. Autoencoders, a type of neural network, are trained to learn representations of training data and reconstruct inputs from these representations, typically minimizing reconstruction errors like mean squared error (MSE). Our autoencoder was trained on a dataset of benign examples; learning features specific to them. Consequently, when presented with significantly perturbed adversarial examples, the autoencoder exhibited high reconstruction errors. The architecture of the autoencoder was tailored to the dimensions of the images under evaluation. We considered various image sizes, constructing models differently for 256x256 and 512x512 images. Moreover, the choice of the computer vision model is crucial, as most adversarial attacks are designed with specific AI structures in mind. To mitigate this, we proposed a method to replace image-specific dimensions with a structure independent of both dimensions and neural network models, thereby enhancing robustness. Our multi-modal autoencoder reconstructs the spectral representation of images across the red-green-blue (RGB) color channels. To validate our approach, we conducted experiments using diverse datasets and subjected them to adversarial attacks using models such as ResNet50 and ViT_L_16 from the torch vision library. The autoencoder extracted features used in a classification model, resulting in an MSE (RGB) of 0.014, a classification accuracy of 97.33%, and a precision of 99%.

Keywords: adversarial attacks, malicious images detector, binary classifier, multimodal transformer autoencoder

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Authors: Julia Zimmerman, Gaurav Savant

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This study aims to evaluate the efficacy of the U.S. Army Corp of Engineers’ River Analysis System (HEC-RAS) application to modeling the hydraulics of estuaries. HEC-RAS has been broadly used for a variety of riverine applications. However, it has not been widely applied to the study of circulation in estuaries. This report details the model development and validation of a combined 1D/2D unsteady flow hydraulic model using HEC-RAS for estuaries and they are associated with tidally influenced rivers. Two estuaries, Galveston Bay and Delaware Bay, were used as case studies. Galveston Bay, a bar-built, vertically mixed estuary, was modeled for the 2005 calendar year. Delaware Bay, a drowned river valley estuary, was modeled from October 22, 2019, to November 5, 2019. Water surface elevation was used to validate both models by comparing simulation results to NOAA’s Center for Operational Oceanographic Products and Services (CO-OPS) gauge data. Simulations were run using the Diffusion Wave Equations (DW), the Shallow Water Equations, Eulerian-Lagrangian Method (SWE-ELM), and the Shallow Water Equations Eulerian Method (SWE-EM) and compared for both accuracy and computational resources required. In general, the Diffusion Wave Equations results were found to be comparable to the two Shallow Water equations sets while requiring less computational power. The 1D/2D combined approach was valid for study areas within the 2D flow area, with the 1D flow serving mainly as an inflow boundary condition. Within the Delaware Bay estuary, the HEC-RAS DW model ran in 22 minutes and had an average R² value of 0.94 within the 2-D mesh. The Galveston Bay HEC-RAS DW ran in 6 hours and 47 minutes and had an average R² value of 0.83 within the 2-D mesh. The longer run time and lower R² for Galveston Bay can be attributed to the increased length of the time frame modeled and the greater complexity of the estuarine system. The models did not accurately capture tidal effects within the 1D flow area.

Keywords: Delaware bay, estuarine hydraulics, Galveston bay, HEC-RAS, one-dimensional modeling, two-dimensional modeling

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Authors: Rim Chang Hyon, Song Hak Jin, Song Kwang Hyok, Jong Ki Hun

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The calibration of the articulated arm coordinate measuring machine (AACMM) is key to improving calibration accuracy and saving calibration time. To reduce the time consumed for calibration, we should choose the proper calibration gauges and develop a reasonable calibration method. In addition, we should get the exact optimal solution by accurately removing the rough errors within the experimental data. In this paper, we present a calibration method of the portable coordinate measuring arm (PCMA) using the 1.2m long bar guage with cone-holes. First, we determine the locations of the bar gauge and establish an optimal objective function for identifying the structural parameter errors. Next, we make a mathematical model of the calibration algorithm and present a new mathematical method to remove the rough errors within calibration data. Finally, we find the optimal solution to identify the kinematic parameter errors by using Levenberg-Marquardt algorithm. The experimental results show that our calibration method is very effective in saving the calibration time and improving the calibration accuracy.

Keywords: AACMM, kinematic model, parameter identify, measurement accuracy, calibration

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Authors: Verónica Díaz Quezada

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Despite the wide variety of educational resources on the market and the advances produced in the technological field, the practice of teaching continues to be supported mainly by textbooks. This article reports on descriptive research with qualitative methodology carried out on secondary school mathematics teachers in a region of Chile, in order to describe the use and the indicators of appreciation that teachers have on the textbooks distributed by the official body to public educational establishments. Data were collected through an open response opinion questionnaire. According to the results, among the texts available for the annual performance of their teaching work, the expository and technological books predominate, to the detriment of comprehensive books. The exhibition structure favors master expositions and repetitive exercises, while, with the technological structure, a productive exercise is attempted, proposing numerous applications with the intention of giving meaning to the different mathematical rules and procedures. In relation to the indicators of appreciation that teachers have regarding the use of mathematics textbooks, the suitability and quality of the teaching resources are verified as the most satisfying characteristic.

Keywords: mathematics, secondary school, teachers, textbooks

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Authors: Rubaiyea Uddin, Rebecca Taylor, Emily Levesque

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The hippocampus, located in the limbic system, is most commonly known for its role in memory and spatial navigation (as cited in Brain Reward and Pathways). It maintains an especially important role in specifically episodic and declarative memory. The hippocampus has also recently been linked to dopamine, the reward pathway’s primary neurotransmitter. Since research has found that dopamine also contributes to memory consolidation and hippocampal plasticity, this neurotransmitter is potentially responsible for contributing to the hippocampus’s role in memory formation. In this experiment we tested to see the effect of tactile cues on spatial navigation for eight different mice. We used a radial arm that had one designated “reward” arm containing sucrose. The presence or absence of bedding was our tactile cue. We attempted to see if the memory of that cue would enhance the mice’s memory of having received the reward in that arm. The results from our study showed there was no significant response from the use of tactile cues on spatial navigation on our 129 mice. Tactile cues therefore do not influence spatial navigation.

Keywords: mice, radial arm maze, memory, spatial navigation, tactile cues, hippocampus, reward, sensory skills, Alzheimer's, neuro-degenerative diseases

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Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

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Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

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Authors: M. Varin, A. M. Dubois, R. Gadbois-Langevin, B. Chalghaf

Abstract:

Good knowledge of the physical environment is essential for an integrated forest planning. This information enables better forecasting of operating costs, determination of cutting volumes, and preservation of ecologically sensitive areas. The use of satellite images in stereoscopic pairs gives the capacity to generate high precision 3D models, which are scale-adapted for harvesting operations. These models could represent an alternative to 3D LiDAR data, thanks to their advantageous cost of acquisition. The objective of the study was to assess the quality of stereo-derived canopy height models (CHM) in comparison to a traditional LiDAR CHM and ground tree-height samples. Two study sites harboring two different forest stand types (broadleaf and conifer) were analyzed using stereo pairs and tri-stereo images from the WorldView-3 satellite to calculate CHM. Acquisition of multispectral images from an Unmanned Aerial Vehicle (UAV) was also realized on a smaller part of the broadleaf study site. Different algorithms using two softwares (PCI Geomatica and Correlator3D) with various spatial resolutions and band selections were tested to select the 3D modeling technique, which offered the best performance when compared with LiDAR. In the conifer study site, the CHM produced with Corelator3D using only the 50-cm resolution panchromatic band was the one with the smallest Root-mean-square deviation (RMSE: 1.31 m). In the broadleaf study site, the tri-stereo model provided slightly better performance, with an RMSE of 1.2 m. The tri-stereo model was also compared to the UAV, which resulted in an RMSE of 1.3 m. At individual tree level, when ground samples were compared to satellite, lidar, and UAV CHM, RMSE were 2.8, 2.0, and 2.0 m, respectively. Advanced analysis was done for all of these cases, and it has been noted that RMSE is reduced when the canopy cover is higher when shadow and slopes are lower and when clouds are distant from the analyzed site.

Keywords: very high spatial resolution, satellite imagery, WorlView-3, canopy height models, CHM, LiDAR, unmanned aerial vehicle, UAV

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Authors: Amira Abdelrasoul, Huu Doan, Ali Lohi

Abstract:

An efficient and well-planned ultrafiltration process is becoming a necessity for monetary returns in the industrial settings. The aim of the present study was to develop a mathematical model for an accurate prediction of ultrafiltration membrane fouling of latex effluent applied to homogeneous and heterogeneous membranes with uniform and non-uniform pore sizes, respectively. The models were also developed for an accurate prediction of power consumption that can handle the large-scale purposes. The model incorporated the fouling attachments as well as chemical and physical factors in membrane fouling for accurate prediction and scale-up application. Both Polycarbonate and Polysulfone flat membranes, with pore sizes of 0.05 µm and a molecular weight cut-off of 60,000, respectively, were used under a constant feed flow rate and a cross-flow mode in ultrafiltration of the simulated paint effluent. Furthermore, hydrophilic ultrafilic and hydrophobic PVDF membranes with MWCO of 100,000 were used to test the reliability of the models. Monodisperse particles of 50 nm and 100 nm in diameter, and a latex effluent with a wide range of particle size distributions were utilized to validate the models. The aggregation and the sphericity of the particles indicated a significant effect on membrane fouling.

Keywords: membrane fouling, mathematical modeling, power consumption, attachments, ultrafiltration

Procedia PDF Downloads 461

Authors: Masood Abdollahi, Seyed Naser Moghaddas Tafreshi

Abstract:

Expanded polystyrene (EPS) geofoam is a cellular geosynthetic material that can be used to protect lifelines (e.g. pipelines, electricity cables, etc.) below ground. Post and beam system is the most recent configuration of EPS blocks which can be implemented for this purpose. It provides a void space atop lifelines which allows settlement of the loading surface with imposing no pressure on the lifelines system. This paper investigates the efficiency of the configuration of post-beam system subjected to static loading. To evaluate the soil surface settlement, beam deformation and transferred pressure over the beam, laboratory tests using two different densities for EPS blocks are conducted. The effect of geogrid-reinforcing the cover soil on system response is also investigated. The experimental results show favorable performance of EPS post and beam configuration in protecting underground lifelines. 

Keywords: beam deformation, EPS block, laboratory test, post-Beam system, soil surface settlement

Procedia PDF Downloads 229

Authors: Yiyuan Tao

Abstract:

Under the impact of global warming together with the intensification of human activities, the hydrological regimes may be altered, and the traditional stationary assumption was no longer satisfied. However, most of the current design standards of water infrastructures were still based on the hypothesis of stationarity, which may inevitably result in severe biases. Many critical impacts of climate on ecosystems, society, and the economy are controlled by extreme events rather than mean values. Therefore, it is of great significance to identify the non-stationarity of precipitation extremes and model the precipitation extremes in a nonstationary framework. The Wei River Basin (WRB), located in a continental monsoon climate zone in China, is selected as a case study in this study. Six extreme precipitation indices were employed to investigate the changing patterns and stationarity of precipitation extremes in the WRB. To identify if precipitation extremes are stationary, the Mann-Kendall trend test and the Pettitt test, which is used to examine the occurrence of abrupt changes are adopted in this study. Extreme precipitation indices series are fitted with non-stationary distributions that selected from six widely used distribution functions: Gumbel, lognormal, Weibull, gamma, generalized gamma and exponential distributions by means of the time-varying moments model generalized additive models for location, scale and shape (GAMLSS), where the distribution parameters are defined as a function of time. The results indicate that: (1) the trends were not significant for the whole WRB, but significant positive/negative trends were still observed in some stations, abrupt changes for consecutive wet days (CWD) mainly occurred in 1985, and the assumption of stationarity is invalid for some stations; (2) for these nonstationary extreme precipitation indices series with significant positive/negative trends, the GAMLSS models are able to capture well the temporal variations of the indices, and perform better than the stationary model. Finally, the differences between the quantiles of nonstationary and stationary models are analyzed, which highlight the importance of nonstationary modeling of precipitation extremes in the WRB.

Keywords: extreme precipitation, GAMLSSS, non-stationary, Wei River Basin

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Authors: Loredana G. Marcu, David Marcu, Sanda M. Filip

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Advanced head and neck cancers are aggressive tumours, which require aggressive treatment. Treatment efficiency is often hindered by cancer cell repopulation during radiotherapy, which is due to various mechanisms triggered by the loss of tumour cells and involves both stem and differentiated cells. The aim of the current paper is to present in silico simulations of radiotherapy schedules on a virtual head and neck tumour grown with biologically realistic kinetic parameters. Using the linear quadratic formalism of cell survival after radiotherapy, altered fractionation schedules employing various treatment breaks for normal tissue recovery are simulated and repopulation mechanism implemented in order to evaluate the impact of various cancer cell contribution on tumour behaviour during irradiation. The model has shown that the timing of treatment breaks is an important factor influencing tumour control in rapidly proliferating tissues such as squamous cell carcinomas of the head and neck. Furthermore, not only stem cells but also differentiated cells, via the mechanism of abortive division, can contribute to malignant cell repopulation during treatment.

Keywords: radiation, tumour repopulation, squamous cell carcinoma, stem cell

Procedia PDF Downloads 258

Authors: F. Djaafar, B. Hadri, G. Bachir

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This paper presents the design parameters for a thin film 3J InGaP/GaAs/Ge solar cell with a simulated maximum efficiency of 32.11% using Tcad Silvaco. Design parameters include the doping concentration, molar fraction, layers’ thickness and tunnel junction characteristics. An initial dual junction InGaP/GaAs model of a previous published heterojunction cell was simulated in Tcad Silvaco to accurately predict solar cell performance. To improve the solar cell’s performance, we have fixed meshing, material properties, models and numerical methods. However, thickness and layer doping concentration were taken as variables. We, first simulate the InGaP\GaAs dual junction cell by changing the doping concentrations and thicknesses which showed an increase in efficiency. Next, a triple junction InGaP/GaAs/Ge cell was modeled by adding a Ge layer to the previous dual junction InGaP/GaAs model with an InGaP /GaAs tunnel junction.

Keywords: heterojunction, modeling, simulation, thin film, Tcad Silvaco

Procedia PDF Downloads 359

Authors: E. K. Mendez, N. A. Salazar, C. E. Orrego

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There is undoubted proof that increasing the intake of fruit lessens the risk of hypertension, coronary heart disease, stroke, and probable evidence that lowers the risk of cancer. Proper fruit drying is an excellent alternative to make their shelf-life longer, commercialization easier, and ready-to-eat healthy products or ingredients. The conventional way of drying is by hot air forced convection. However, this process step often requires a very long residence time; furthermore, it is highly energy consuming and detrimental to the product quality. Nowadays, power ultrasound (US) technic has been considered as an emerging and promising technology for industrial food processing. Most of published works dealing with drying food assisted by US have studied the effect of ultrasonic pre-treatment prior to air-drying on food and the airborne US conditions during dehydration. In this work a new approach was tested taking in to account drying time and two quality parameters of mango slices dehydrated by convection assisted by 20 KHz power US applied directly using a holed plate as product support and sound transmitting surface. During the drying of mango (Mangifera indica L.) slices (ca. 6.5 g, 0.006 m height and 0.040 m diameter), their weight was recorded every hour until final moisture content (10.0±1.0 % wet basis) was reached. After previous tests, optimization of three drying parameters - frequencies (2, 5 and 8 minutes each half-hour), air temperature (50-55-60⁰C) and power (45-70-95W)- was attempted by using a Box–Behnken design under the response surface methodology for the optimal drying time, color parameters and rehydration rate of dried samples. Assays involved 17 experiments, including a quintuplicate of the central point. Dried samples with and without US application were packed in individual high barrier plastic bags under vacuum, and then stored in the dark at 8⁰C until their analysis. All drying assays and sample analysis were performed in triplicate. US drying experimental data were fitted with nine models, among which the Verna model resulted in the best fit with R2 > 0.9999 and reduced χ2 ≤ 0.000001. Significant reductions in drying time were observed for the assays that used lower frequency and high US power. At 55⁰C, 95 watts and 2 min/30 min of sonication, 10% moisture content was reached in 211 min, as compared with 320 min for the same test without the use of US (blank). Rehydration rates (RR), defined as the ratio of rehydrated sample weight to that of dry sample and measured, was also larger than those of blanks and, in general, the higher the US power, the greater the RR. The direct contact and intermittent US treatment of mango slices used in this work improve drying rates and dried fruit rehydration ability. This technique can thus be used to reduce energy processing costs and the greenhouse gas emissions of fruit dehydration.

Keywords: ultrasonic assisted drying, fruit drying, mango slices, contact ultrasonic drying

Procedia PDF Downloads 334

Authors: Peter Shi

Abstract:

Algorithmic trading is a rapidly expanding domain within quantitative finance, constituting a substantial portion of trading volumes in the US financial market. The demand for rigorous and robust mathematical theories underpinning these trading algorithms is ever-growing. In this study, the author establishes a new stock market model that integrates the Efficient Market Hypothesis and the statistical arbitrage. The model, for the first time, finds probabilistic relations between the rational price and the market price in terms of the conditional expectation. The theory consequently leads to a mathematical justification of the old market adage: buy-low and sell-high. The thresholds for “low” and “high” are precisely derived using a max-min operation on Bayes’s error. This explicit connection harmonizes the Efficient Market Hypothesis and Statistical Arbitrage, demonstrating their compatibility in explaining market dynamics. The amalgamation represents a pioneering contribution to quantitative finance. The study culminates in comprehensive numerical tests using historical market data, affirming that the “buy-low” and “sell-high” algorithm derived from this theory significantly outperforms the general market over the long term in four out of six distinct market environments.

Keywords: efficient market hypothesis, behavioral finance, Bayes' decision, algorithmic trading, risk control, stock market

Procedia PDF Downloads 61