Search results for: decision tree modeling

Authors: Meng-Fei Cheng, Wei-Lun Chen, Han-Chang Ma, Chi-Che Tsai

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Recent K–12 Taiwan Science Education Curriculum Guideline emphasize the essential role of modeling curriculum in science learning; however, few modeling curricula have been designed and adopted in current science teaching. Therefore, this study aims to develop modeling curriculum on electric circuits to investigate any learning difficulties students have with modeling curriculum and further enhance modeling teaching. This study was conducted with 44 10th-grade students in Central Taiwan. Data collection included a students’ understanding of models in science (SUMS) survey that explored the students' epistemology of scientific models and modeling and a complex circuit problem to investigate the students’ modeling abilities. Data analysis included the following: (1) Paired sample t-tests were used to examine the improvement of students’ modeling abilities and conceptual understanding before and after the curriculum was taught. (2) Paired sample t-tests were also utilized to determine the students’ modeling abilities before and after the modeling activities, and a Pearson correlation was used to understand the relationship between students’ modeling abilities during the activities and on the posttest. (3) ANOVA analysis was used during different stages of the modeling curriculum to investigate the differences between the students’ who developed microscopic models and macroscopic models after the modeling curriculum was taught. (4) Independent sample t-tests were employed to determine whether the students who changed their models had significantly different understandings of scientific models than the students who did not change their models. The results revealed the following: (1) After the modeling curriculum was taught, the students had made significant progress in both their understanding of the science concept and their modeling abilities. In terms of science concepts, this modeling curriculum helped the students overcome the misconception that electric currents reduce after flowing through light bulbs. In terms of modeling abilities, this modeling curriculum helped students employ macroscopic or microscopic models to explain their observed phenomena. (2) Encouraging the students to explain scientific phenomena in different context prompts during the modeling process allowed them to convert their models to microscopic models, but it did not help them continuously employ microscopic models throughout the whole curriculum. The students finally consistently employed microscopic models when they had help visualizing the microscopic models. (3) During the modeling process, the students who revised their own models better understood that models can be changed than the students who did not revise their own models. Also, the students who revised their models to explain different scientific phenomena tended to regard models as explanatory tools. In short, this study explored different strategies to facilitate students’ modeling processes as well as their difficulties with the modeling process. The findings can be used to design and teach modeling curricula and help students enhance their modeling abilities.

Keywords: electric circuits, modeling curriculum, science learning, scientific model

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Authors: G. Sava, I. Popa, T. Sava, A. Ion, M. Ilie, C. Manailescu, A. Robu

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At the RoAMS laboratory in Bucharest we have looked for a head-to-head meeting between AMS radiocarbon dating and dendrochronology dating, aiming to point out and explain any differences or similarities that might appear between their output results. As a subject of this investigation, we have fixed our attention on a sequence of tree rings spanning on a period of 700 years, starting with 1000 AD. The samples were collected from the northern Romanian territory within Moldavia region, and were provided by the ‘Marin Dracea - National Institute for Research and Development in Forestry’. All the 23 single ring wood samples were radiocarbon dated using alpha-cellulose extraction, followed by graphitization in an AGE3 installation. A wiggle matching procedure was applied to reduce the radiocarbon uncertainties for the calibrated ages. The results showed a good agreement on 3 out of 4 wood cores, the age-shifting of one of the wood cores being interpreted as an uncertain dendrochronology matching, which was further corrected.

Keywords: wiggle matching, tree-ring radiocarbon dating, dendrochronology, AMS radiocarbon dating, radiocarbon dating in Romania

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Authors: Sofia F. Franco, Jacob Macdonald

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This paper estimates the value of urban public street trees and their complementary and substitution value with other broader urban amenities and dis-amenities via the residential housing market. We estimate a lower bound value on a city’s tree amenities under instrumental variable and geographic regression discontinuity approaches with an application to Lisbon, Portugal. For completeness, we also explore how urban trees and in particular public street trees impact house prices across the city. Finally, we jointly analyze the planting and maintenance costs and benefits of urban street trees. The estimated value of all public trees in Lisbon is €8.84M. When considering specifically trees planted alongside roads and in public squares, the value is €6.06M or €126.64 per tree. This value is conditional on the distribution of trees in terms of their broader density, with higher effects coming from the overall greening of larger areas of the city compared to the greening of the direct neighborhood. Detrimental impacts are found when the number of trees is higher near street canyons, where they may exacerbate the stagnation of air pollution from traffic. Urban street trees also have important spillover benefits due to pollution mitigation around €6.21 million, or an additional €129.93 per tree. There are added benefits of €26.32 and €28.58 per tree in terms of flooding and heat mitigation, respectively. With significant resources and policies aimed at urban greening, the value obtained is shown to be important for discussions on the benefits of urban trees as compared to mitigation and abatement costs undertaken by a municipality.

Keywords: urban public goods, urban street trees, spatial boundary discontinuities, geospatial and remote sensing methods

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Authors: Alireza Dantism

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Presently, describing the function of a protein sequence is one of the most common problems in biology. Usually, this problem can be facilitated by studying the three-dimensional structure of proteins. In the absence of a protein structure, comparative modeling often provides a useful three-dimensional model of the protein that is dependent on at least one known protein structure. Comparative modeling predicts the three-dimensional structure of a given protein sequence (target) mainly based on its alignment with one or more proteins of known structure (templates). Comparative modeling consists of four main steps 1. Similarity between the target sequence and at least one known template structure 2. Alignment of target sequence and template(s) 3. Build a model based on alignment with the selected template(s). 4. Prediction of model errors 5. Optimization of the built model There are many computer programs and web servers that automate the comparative modeling process. One of the most important advantages of these servers is that it makes comparative modeling available to both experts and non-experts, and they can easily do their own modeling without the need for programming knowledge, but some other experts prefer using programming knowledge and do their modeling manually because by doing this they can maximize the accuracy of their modeling. In this study, a web-based tool has been designed to predict the tertiary structure of proteins using PHP and Python programming languages. This tool is called EasyModel. EasyModel can receive, according to the user's inputs, the desired unknown sequence (which we know as the target) in this study, the protein sequence file (template), etc., which also has a percentage of similarity with the primary sequence, and its third structure Predict the unknown sequence and present the results in the form of graphs and constructed protein files.

Keywords: structural bioinformatics, protein tertiary structure prediction, modeling, comparative modeling, modeller

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Authors: A. Porshnev, A. Zaporozhtchuk

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Motivation regarded as a key factor of labor turnover, is especially important for volunteers working on an altruistic basis in NGO. Despite the motivational letter, candidate selection depends on the impression of the selection committee, which can be subject to human bias. We expect that structured and unstructured information provided in motivation letters could be used to improve candidate selection procedures. In our paper, we perform qualitative and quantitative analysis of 2280 motivation letters, create logistic regression, and build a decision tree to improve selection procedures. Our analysis showed that motivation factors are significant and enable human resources department to forecast labor turnover and provide extra information to demographic, professional and timing questions. In spite of the average level of accuracy the model demonstrates the selection procedures of company of under consideration can be improved. We also discuss interrelation between answers to open and closed motivation questions, recommend changes in motivational letter templates to ensure more relevant information about applicants and further steps to create more accurate model.

Keywords: decision trees, logistic regression, model, motivational letter, non-governmental organization, retention, turnover

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Authors: Jihye Jeon

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This paper analyzes the conceptual framework of three statistical methods, multiple regression, path analysis, and structural equation models. When establishing research model of the statistical modeling of complex social phenomenon, it is important to know the strengths and limitations of three statistical models. This study explored the character, strength, and limitation of each modeling and suggested some strategies for accurate explaining or predicting the causal relationships among variables. Especially, on the studying of depression or mental health, the common mistakes of research modeling were discussed.

Keywords: multiple regression, path analysis, structural equation models, statistical modeling, social and psychological phenomenon

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Authors: Siqi Qiu, Yijian Zheng, Xin Guo Ming

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In many reliability and risk analysis, failures of components are supposed to be independent. However, in reality, the ignorance of failure dependencies among components may render the results of reliability and risk analysis incorrect. There are two principal ways to incorporate failure dependencies in system reliability and risk analysis: implicit and explicit methods. In the implicit method, failure dependencies can be modeled by joint probabilities, correlation values or conditional probabilities. In the explicit method, certain types of dependencies can be modeled in a fault tree as mutually independent basic events for specific component failures. In this paper, explicit and implicit methods based on BDD will be proposed to evaluate the reliability of systems considering failure dependencies. The obtained results prove the equivalence of the proposed implicit and explicit methods. It is found that the consideration of failure dependencies decreases the reliability of systems. This observation is intuitive, because more components fail due to failure dependencies. The consideration of failure dependencies helps designers to reduce the dependencies between components during the design phase to make the system more reliable.

Keywords: reliability assessment, risk assessment, failure dependencies, binary decision diagram

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Authors: Kang Woobang

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The base of the column is not only a support but also the embodiment of profound symbolism full of cosmic energy. Finding the full jars from which various energy emanate at the Notre Dame of Paris and Saint-Germain-des-Pres in France, the author was so shocked. As the column is cosmic tree, from the Full Jar full with cosmic energy emerges the cosmic tree composed of shaft and capital.

Keywords: full picher or jar, transcendental or supernatural birth from yonggi, yonggimun, yonggissak

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Authors: Yassine Messaoud, Gordon Nigh, Faouzi Messaoud, Han Chen

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The effect of climate change on tree growth in boreal and temperate forests has received increased interest in the context of global warming. However, most studies were conducted in small areas and with a limited number of tree species. Here, we examined the height growth responses of seventeen tree species to climate change in Western North America. 37009 stands from forest inventory databases in Canada and USA with varying establishment date were selected. Dominant and co-dominant trees from each stand were sampled to determine top tree height at 50 years breast height age. Height was related to historical mean annual and summer temperatures, annual and summer Palmer Drought Severity Index, tree establishment date, slope, aspect, soil fertility as determined by the rate of carbon organic matter decomposition (carbon/nitrogen), geographic locations (latitude, longitude, and elevation), species range (coastal, interior, and both ranges), shade tolerance and leaf form (needle leaves, deciduous needle leaves, and broadleaves). Climate change had mostly a positive effect on tree height growth. The results explained 62.4% of the height growth variance. Since 1880, height growth increase was greater for coastal, high shade tolerant, and broadleaf species. Height growth increased more on steep slopes and high soil fertility soils. Greater height growth was mostly observed at the leading range and upward. Conversely, some species showed the opposite pattern probably due to the increase of drought (coastal Mediterranean area), precipitation and cloudiness (Alaska and British Columbia) and peculiarity (higher latitudes-lower elevations and vice versa) of western North America topography. This study highlights the role of the species ecological amplitude and traits, and geographic locations as the main factors determining the growth response and its magnitude to the recent global climate change.

Keywords: Height growth, global climate change, species range, species characteristics, species ecological amplitude, geographic locations, western North America

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Authors: Leonard Maaya, Michel Meulders, Martina Vandebroek

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Discrete Choice Experiments (DCE) are used to investigate how product attributes affect decision-makers’ choices. In DCEs, choice situations consisting of several alternatives are presented from which choice-makers select the preferred alternative. Standard multinomial logit models based on random utility theory can be used to estimate the utilities for the attributes. The overarching principle in these models is that respondents understand and use all the attributes when making choices. However, studies suggest that respondents sometimes ignore some attributes (commonly referred to as Attribute Non-Attendance/ANA). The choice modeling literature presents ANA as a static process, i.e., respondents’ ANA behavior does not change throughout the experiment. However, respondents may ignore attributes due to changing factors like availability of information on attributes, learning/fatigue in experiments, etc. We develop a dynamic mixture latent Markov model to model changes in ANA when information on attributes is provided. The model is illustrated on e-consumers’ webshop choices. The results indicate that the dynamic ANA model describes the behavioral changes better than modeling the impact of information using changes in parameters. Further, we find that providing information on attributes leads to an increase in the attendance probabilities for the investigated attributes.

Keywords: choice models, discrete choice experiments, dynamic models, e-commerce, statistical modeling

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Authors: Zahid Ullah, Atlas Khan

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This abstract focuses on the advancements in mathematical modeling and optimization techniques that play a crucial role in enhancing the efficiency, reliability, and performance of these systems. In this era of rapidly evolving technology, mathematical modeling and optimization offer powerful tools to tackle the complex challenges faced by control, signal processing, and energy systems. This abstract presents the latest research and developments in mathematical methodologies, encompassing areas such as control theory, system identification, signal processing algorithms, and energy optimization. The abstract highlights the interdisciplinary nature of mathematical modeling and optimization, showcasing their applications in a wide range of domains, including power systems, communication networks, industrial automation, and renewable energy. It explores key mathematical techniques, such as linear and nonlinear programming, convex optimization, stochastic modeling, and numerical algorithms, that enable the design, analysis, and optimization of complex control and signal processing systems. Furthermore, the abstract emphasizes the importance of addressing real-world challenges in control, signal processing, and energy systems through innovative mathematical approaches. It discusses the integration of mathematical models with data-driven approaches, machine learning, and artificial intelligence to enhance system performance, adaptability, and decision-making capabilities. The abstract also underscores the significance of bridging the gap between theoretical advancements and practical applications. It recognizes the need for practical implementation of mathematical models and optimization algorithms in real-world systems, considering factors such as scalability, computational efficiency, and robustness. In summary, this abstract showcases the advancements in mathematical modeling and optimization techniques for control, signal processing, and energy systems. It highlights the interdisciplinary nature of these techniques, their applications across various domains, and their potential to address real-world challenges. The abstract emphasizes the importance of practical implementation and integration with emerging technologies to drive innovation and improve the performance of control, signal processing, and energy.

Keywords: mathematical modeling, optimization, control systems, signal processing, energy systems, interdisciplinary applications, system identification, numerical algorithms

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Authors: Amirhossein Mahlouji

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In current business market, the concept of integrating environmental sustainability into long-term as well as routine operations is becoming a prevailing trend. Therefore, several stimuli are helping organization to move toward environmental sustainability. The concept of green supply chain management can help provide a strategic framework to develop a customized sustainability roadmap for each organization. In this regard, this paper is mainly focused on presenting a strategic decision making framework that will assist top level decision-making issues. This decision-making tool is based on literature and practice in the area of environmentally conscious business practices. The goal of this paper will be on the components and parameters of green supply chain management and how they serve as a baseline for the decision framework. Later, the applicability of a multi-input multi-output decision model (MIMO), will be analyzed as the analytical network process, within the green supply chain.

Keywords: Multi-attribute, Green Supply Chain, Environmental, Sustainability

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Authors: Ouzayr Rabhi, Ibtissam Arrassen

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To provide a complete analysis of the organization and to help decision-making, leaders need to have relevant data; Data Warehouses (DW) are designed to meet such needs. However, designing DW is not trivial and there is no formal method to derive a multidimensional schema from heterogeneous databases. In this article, we present a Model-Driven based approach concerning the design of data warehouses. We describe a multidimensional meta-model and also specify a set of transformations starting from a Unified Modeling Language (UML) metamodel. In this approach, the UML metamodel and the multidimensional one are both considered as a platform-independent model (PIM). The first meta-model is mapped into the second one through transformation rules carried out by the Query View Transformation (QVT) language. This proposal is validated through the application of our approach to generating a multidimensional schema of a Balanced Scorecard (BSC) DW. We are interested in the BSC perspectives, which are highly linked to the vision and the strategies of an organization.

Keywords: data warehouse, meta-model, model-driven architecture, transformation, UML

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Authors: Martha E. Aguiar-Barrera, Humberto Gutierrez-Pulido, Veronica Vargas-Alejo

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Identifying and modeling random phenomena is a fundamental cognitive process to understand and transform reality. Recognizing situations governed by chance and giving them a scientific interpretation, without being carried away by beliefs or intuitions, is a basic training for citizens. Hence the importance of generating teaching-learning processes, supported using technology, paying attention to model creation rather than only executing mathematical calculations. In order to develop the student's knowledge about basic probability distributions and decision making; in this work a model eliciting activity (MEA) is reported. The intention was applying the Model and Modeling Perspective to design an activity related to civil engineering that would be understandable for students, while involving them in its solution. Furthermore, the activity should imply a decision-making challenge based on sample data, and the use of the computer should be considered. The activity was designed considering the six design principles for MEA proposed by Lesh and collaborators. These are model construction, reality, self-evaluation, model documentation, shareable and reusable, and prototype. The application and refinement of the activity was carried out during three school cycles in the Probability and Statistics class for Civil Engineering students at the University of Guadalajara. The analysis of the way in which the students sought to solve the activity was made using audio and video recordings, as well as with the individual and team reports of the students. The information obtained was categorized according to the activity phase (individual or team) and the category of analysis (sample, linearity, probability, distributions, mechanization, and decision-making). With the results obtained through the MEA, four obstacles have been identified to understand and apply the binomial distribution: the first one was the resistance of the student to move from the linear to the probabilistic model; the second one, the difficulty of visualizing (infering) the behavior of the population through the sample data; the third one, viewing the sample as an isolated event and not as part of a random process that must be viewed in the context of a probability distribution; and the fourth one, the difficulty of decision-making with the support of probabilistic calculations. These obstacles have also been identified in literature on the teaching of probability and statistics. Recognizing these concepts as obstacles to understanding probability distributions, and that these do not change after an intervention, allows for the modification of these interventions and the MEA. In such a way, the students may identify themselves the erroneous solutions when they carrying out the MEA. The MEA also showed to be democratic since several students who had little participation and low grades in the first units, improved their participation. Regarding the use of the computer, the RStudio software was useful in several tasks, for example in such as plotting the probability distributions and to exploring different sample sizes. In conclusion, with the models created to solve the MEA, the Civil Engineering students improved their probabilistic knowledge and understanding of fundamental concepts such as sample, population, and probability distribution.

Keywords: linear model, models and modeling, probability, randomness, sample

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Authors: Dimitrios J. Dimitriou, Maria F. Sartzetaki

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Transport infrastructure assets are key components of the national asset portfolio. The decision to invest in a new infrastructure in transports could take from a few years to some decades. This is mainly because of the need to reserve and spent many capitals, the long payback period, the number of the stakeholders involved in decision process and –many times- the investment and business risks are high. Therefore, the decision assessment framework is an essential challenge linked with the key decision factors meet the stakeholder expectations highlighting project trade-offs, financial risks, business uncertainties and market limitations. This paper examines the decision process for new transport infrastructure projects in cross border regions, where a wide range of stakeholders with different expectation is involved. According to a consequences analysis systemic approach, the relationship of transport infrastructure development, economic system development and stakeholder expectation is analyzed. Adopting the on system of system methodological approach, the decision making framework, variables, inputs and outputs are defined, highlighting the key shareholder’s role and expectations. The application provides the methodology outputs presenting the proposed decision framework for a strategic railway project in north Greece deals with the upgrade of the existing railway corridor connecting Greece, Turkey and Bulgaria.

Keywords: decision making, system of system, cross-border, infrastructure project

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Authors: Nguyen J. M., Lucas G., Ruan S., Digonnet H., Antonioli D.

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Introduction: Deep Learning (DL) is a very powerful tool for analyzing image data. But for tabular data, it cannot compete with machine learning methods like XGBoost. The research question becomes: can tabular data be transformed into images that can be analyzed by simple CNNs (Convolutional Neuron Networks)? Will DL be the absolute tool for data classification? All current solutions consist in repositioning the variables in a 2x2 matrix using their correlation proximity. In doing so, it obtains an image whose pixels are the variables. We implement a technology, DeepNIC, that offers the possibility of obtaining an image for each variable, which can be analyzed by simple CNNs. Material and method: The 'ROP' (Regression OPtimized) model is a binary and atypical decision tree whose nodes are managed by a new artificial neuron, the Neurop. By positioning an artificial neuron in each node of the decision trees, it is possible to make an adjustment on a theoretically infinite number of variables at each node. From this new decision tree whose nodes are artificial neurons, we created the concept of a 'Random Forest of Perfect Trees' (RFPT), which disobeys Breiman's concepts by assembling very large numbers of small trees with no classification errors. From the results of the RFPT, we developed a family of 10 statistical information criteria, Nguyen Information Criterion (NICs), which evaluates in 3 dimensions the predictive quality of a variable: Performance, Complexity and Multiplicity of solution. A NIC is a probability that can be transformed into a grey level. The value of a NIC depends essentially on 2 super parameters used in Neurops. By varying these 2 super parameters, we obtain a 2x2 matrix of probabilities for each NIC. We can combine these 10 NICs with the functions AND, OR, and XOR. The total number of combinations is greater than 100,000. In total, we obtain for each variable an image of at least 1166x1167 pixels. The intensity of the pixels is proportional to the probability of the associated NIC. The color depends on the associated NIC. This image actually contains considerable information about the ability of the variable to make the prediction of Y, depending on the presence or absence of other variables. A basic CNNs model was trained for supervised classification. Results: The first results are impressive. Using the GSE22513 public data (Omic data set of markers of Taxane Sensitivity in Breast Cancer), DEEPNic outperformed other statistical methods, including XGBoost. We still need to generalize the comparison on several databases. Conclusion: The ability to transform any tabular variable into an image offers the possibility of merging image and tabular information in the same format. This opens up great perspectives in the analysis of metadata.

Keywords: tabular data, CNNs, NICs, DeepNICs, random forest of perfect trees, classification

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Authors: Venkatesh Pulletikurthi, Karthik B. Ariyur, Luciano Castillo

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The system identification from the turbulence wakes will lead to the tactical advantage to prepare and also, to predict the trajectory of the opponents’ movements. A low-order modeling technique, POD, is used to predict the object based on the wake pattern and compared with pre-trained image recognition neural network (NN) to classify the wake patterns into objects. It is demonstrated that low-order modeling, POD, is able to predict the objects better compared to pretrained NN by ~30%.

Keywords: the bluff body wakes, low-order modeling, neural network, system identification

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Authors: Ahmet Parlak, Celalettin Bilgen

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In order to create an effective early warning system, Identification of the risks, preparation and carrying out risk modeling of risk scenarios, taking into account the shortcomings of the old disaster scenarios should be used to improve the system. In the light of this, the importance of risk modeling in creating an effective early warning system is understood. In the scope of TAFRISK project risk modeling trend analysis report on risk modeling developed and a demonstration was conducted for Risk Modeling for flood and mass movements. For risk modeling R&D, studies have been conducted to determine the information, and source of the information, to be gathered, to develop algorithms and to adapt the current algorithms to Turkey’s conditions for determining the risk score in the high disaster risk areas. For each type of the disaster; Disaster Deficit Index (DDI), Local Disaster Index (LDI), Prevalent Vulnerability Index (PVI), Risk Management Index (RMI) have been developed as disaster indices taking danger, sensitivity, fragility, and vulnerability, the physical and economic damage into account in the appropriate scale of the respective type.

Keywords: disaster, hazard, risk modeling, sensor

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Authors: C. F. Popoola, K. Akinsanya, S. B. Babarinde, D. A. Farinde

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This study investigated published financial statement as correlate of investment decision among commercial bank stakeholders in Nigeria. A correlation research design was used in the study. 180 users of published financial statement were purposively sampled from Lagos and Ibadan. Data generated were analyzed using Pearson correlation and regression. The findings of the study revealed that, balance sheet is negatively related with investment decision (r=-.483; p < .01) while income statement (r= .249; p < .001), notes on the account (r= .230; p < .001), cash flow statement (r= .202; p < .001), value added statement (r= .328; p < .001) and five-year financial summary (r= .191 ;p < .01) are positively related with investment decision. Findings also revealed that components of published financial statement significantly predicted good investment decision (R2= .983; F(5,175)=284.5; p < .05) for commercial bank stakeholders. Therefore, it was suggested that Nigeria banks and professional bodies should instigate programs that will increase the knowledge of stakeholders on published financial statement.

Keywords: commercial banks, financial statement, income statement, investment decision, stakeholders

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Authors: Mladen Djuric, Ivan Janicijevic, Sasa Lazarevic

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One of the organizational issues is how to develop and shape decision making and knowledge management systems which will continually avoid traps of both paralyses by analyses“ and extinction by instinct“, the concepts that are a kind of tolerant limits anti-patterns which define what we can call decision making and knowledge management patterns control zone. This paper discusses potentials for development of a core base for recognizing, capturing, and analyzing anti-patterns in the educational organization, thus creating a space for improving decision making and knowledge management processes in education.

Keywords: anti-patterns, decision making, education, knowledge management

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Authors: Haifa Ben Saber, Mourad Elloumi

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In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of constant rows with a group of columns. This kind of clustering is called biclustering. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed. We introduce a new algorithm called, Enumerative tree (EnumTree) for biclustering of binary microarray data. is an algorithm adopting the approach of enumerating biclusters. This algorithm extracts all biclusters consistent good quality. The main idea of ​​EnumLat is the construction of a new tree structure to represent adequately different biclusters discovered during the process of enumeration. This algorithm adopts the strategy of all biclusters at a time. The performance of the proposed algorithm is assessed using both synthetic and real DNA micryarray data, our algorithm outperforms other biclustering algorithms for binary microarray data. Biclusters with different numbers of rows. Moreover, we test the biological significance using a gene annotation web tool to show that our proposed method is able to produce biologically relevent biclusters.

Keywords: DNA microarray, biclustering, gene expression data, tree, datamining.

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Authors: Zeynep Sener, Mehtap Dursun

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Supplier evaluation and selection is one of the most important components of an effective supply chain management system. Due to the expanding competition in healthcare, selecting the right medical device suppliers offers great potential for increasing quality while decreasing costs. This paper proposes a fuzzy decision making approach for medical supplier selection. A real-world medical device supplier selection problem is presented to illustrate the application of the proposed decision methodology.

Keywords: fuzzy decision making, fuzzy multiple objective programming, medical supply chain, supplier selection

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Authors: Mark Joseph Quinto, Roan Beronilla, Guiller Damian, Eliza Camaso, Ronaldo Alberto

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Forest inventories are essential to assess the composition, structure and distribution of forest vegetation that can be used as baseline information for management decisions. Classical forest inventory is labor intensive and time-consuming and sometimes even dangerous. The use of Light Detection and Ranging (LiDAR) in forest inventory would improve and overcome these restrictions. This study was conducted to determine the possibility of using LiDAR derived data in extracting high accuracy forest biophysical parameters and as a non-destructive method for forest status analysis of San Manual, Pangasinan. Forest resources extraction was carried out using LAS tools, GIS, Envi and .bat scripts with the available LiDAR data. The process includes the generation of derivatives such as Digital Terrain Model (DTM), Canopy Height Model (CHM) and Canopy Cover Model (CCM) in .bat scripts followed by the generation of 17 composite bands to be used in the extraction of forest classification covers using ENVI 4.8 and GIS software. The Diameter in Breast Height (DBH), Above Ground Biomass (AGB) and Carbon Stock (CS) were estimated for each classified forest cover and Tree Count Extraction was carried out using GIS. Subsequently, field validation was conducted for accuracy assessment. Results showed that the forest of San Manuel has 73% Forest Cover, which is relatively much higher as compared to the 10% canopy cover requirement. On the extracted canopy height, 80% of the tree’s height ranges from 12 m to 17 m. CS of the three forest covers based on the AGB were: 20819.59 kg/20x20 m for closed broadleaf, 8609.82 kg/20x20 m for broadleaf plantation and 15545.57 kg/20x20m for open broadleaf. Average tree counts for the tree forest plantation was 413 trees/ha. As such, the forest of San Manuel has high percent forest cover and high CS.

Keywords: carbon stock, forest inventory, LiDAR, tree count

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Authors: Meixia Zhang, Chengjun Ji, Wenxuan Han

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Many wood traits are important functional attributes for tree species, connected with resource competition among species, community dynamics, and ecosystem functions. Large variations in these traits exist among taxonomic categories, but variation in these traits between gymnosperms and angiosperms is still poorly documented. This paper explores the systematic differences in 12 traits between the two tree categories and the potential effects of environmental factors and life form. Based on a database of wood traits for major gymnosperm and angiosperm tree species across China, the values of 12 wood traits and their driving factors in gymnosperms vs. angiosperms were compared. The results are summarized below: i) Means of wood traits were all significantly lower in gymnosperms than in angiosperms. ii) Air-dried density (ADD) and tangential shrinkage coefficient (TSC) reflect the basic information of wood traits for gymnosperms, while ADD and radial shrinkage coefficient (RSC) represent those for angiosperms, providing higher explanation power when used as the evaluation index of wood traits. iii) For both gymnosperm and angiosperm species, life form exhibits the largest explanation rate for large-scale spatial patterns of ADD, TSC (RSC), climatic factors the next, and edaphic factors have the least effect, suggesting that life form is the dominant factor controlling spatial patterns of wood traits. Variations in the magnitude and key traits between gymnosperms and angiosperms and the same dominant factors might indicate the evolutionary divergence and convergence in key functional traits among woody plants.

Keywords: allometry, functional traits, phylogeny, shrinkage coefficient, wood density

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Authors: J. Becker, R. Arnold

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The design of distribution logistics has a decisive impact on a company's logistics costs and performance. Hence, such solutions make an essential contribution to corporate success. This article describes a decision support system for analyzing the potential of distribution logistics in terms of logistics costs and performance. In contrast to previous procedures of business process re-engineering (BPR), this method maps distribution logistics holistically under variable distribution structures. Combined with qualitative measures the decision support system will contribute to a more efficient design of distribution logistics.

Keywords: decision support system, distribution logistics, potential analyses, supply chain management

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Authors: Jacob Livingston Slosser, Naja Holten Moller, Thomas Troels Hildebrandt, Henrik Palmer Olsen

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In this paper, we propose a model for human-AI interaction in public administration that involves legal decision-making. Inspired by Alan Turing’s test for machine intelligence, we propose a way of institutionalizing a continuous working relationship between man and machine that aims at ensuring both good legal quality and higher efficiency in decision-making processes in public administration. We also suggest that our model enhances the legitimacy of using AI in public legal decision-making. We suggest that case loads in public administration could be divided between a manual and an automated decision track. The automated decision track will be an algorithmic recommender system trained on former cases. To avoid unwanted feedback loops and biases, part of the case load will be dealt with by both a human case worker and the automated recommender system. In those cases an experienced human case worker will have the role of an evaluator, choosing between the two decisions. This model will ensure that the algorithmic recommender system is not compromising the quality of the legal decision making in the institution. It also enhances the legitimacy of using algorithmic decision support because it provides justification for its use by being seen as superior to human decisions when the algorithmic recommendations are preferred by experienced case workers. The paper outlines in some detail the process through which such a model could be implemented. It also addresses the important issue that legal decision making is subject to legislative and judicial changes and that legal interpretation is context sensitive. Both of these issues requires continuous supervision and adjustments to algorithmic recommender systems when used for legal decision making purposes.

Keywords: administrative law, algorithmic decision-making, decision support, public law

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Authors: Jean Berger, Nassirou Lo, Martin Noel

Abstract:

Perfectly suited for natural or man-made emergency and disaster management situations such as flood, earthquakes, tornadoes, or tsunami, multi-target search path planning for a team of rescue agents is known to be computationally hard, and most techniques developed so far come short to successfully estimate optimality gap. A novel mixed-integer linear programming (MIP) formulation is proposed to optimally solve the multi-target multi-agent discrete search and rescue (SAR) path planning problem. Aimed at maximizing cumulative probability of successful target detection, it captures anticipated feedback information associated with possible observation outcomes resulting from projected path execution, while modeling agent discrete actions over all possible moving directions. Problem modeling further takes advantage of network representation to encompass decision variables, expedite compact constraint specification, and lead to substantial problem-solving speed-up. The proposed MIP approach uses CPLEX optimization machinery, efficiently computing near-optimal solutions for practical size problems, while giving a robust upper bound obtained from Lagrangean integrality constraint relaxation. Should eventually a target be positively detected during plan execution, a new problem instance would simply be reformulated from the current state, and then solved over the next decision cycle. A computational experiment shows the feasibility and the value of the proposed approach.

Keywords: search path planning, search and rescue, multi-agent, mixed-integer linear programming, optimization

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Authors: Hilal Naimi, Amelbahahouda Adamou-Mitiche, Lahcene Mitiche

Abstract:

The main problem in the area of medical imaging has been image denoising. The most defying for image denoising is to secure data carrying structures like surfaces and edges in order to achieve good visual quality. Different algorithms with different denoising performances have been proposed in previous decades. More recently, models focused on deep learning have shown a great promise to outperform all traditional approaches. However, these techniques are limited to the necessity of large sample size training and high computational costs. This research proposes a denoising approach basing on LDTCWT (Lifting Dual Tree Complex Wavelet Transform) using Hybrid Thresholding with Wiener filter to enhance the quality image. This research describes the LDTCWT as a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). To develop this approach, a hybrid thresholding function is modeled by integrating the Wiener filter into the thresholding function.

Keywords: lifting wavelet transform, image denoising, dual tree complex wavelet transform, wavelet shrinkage, wiener filter

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Authors: Marc Toutain, Pascale Leconte, Antoine Gauthier

Abstract:

Introduction: Eating Disorders (ED), such as anorexia, bulimia, and binge eating, are psychiatric illnesses that mostly affect young people. The main symptoms concern eating (restriction, excessive food intake) and weight control behaviors (laxatives, vomiting). Psychological comorbidities (depression, executive function disorders, etc.) and problematic behaviors toward physical activity (PA) are commonly associated with ED. Acquaintances on ED risk factors are still lacking, and more community sample studies are needed to improve prevention and early detection. To our knowledge, studies are needed to specifically investigate the link between ED risk level, PA, and psychological risk factors in a community sample of adolescents. The aim of this study is to assess the relation between ED risk level, exercise (type, frequency, and motivations for engaging in exercise), and psychological factors based on the Jacobi risk factors model. We suppose that a high risk of ED will be associated with the practice of high caloric cost PA, motivations oriented to weight and shape control, and psychological disturbances. Method: An online survey destined for students has been sent to several middle schools and colleges in northwest France. This survey combined several questionnaires, the Eating Attitude Test-26 assessing ED risk; the Exercise Motivation Inventory–2 assessing motivations toward PA; the Hospital Anxiety and Depression Scale assessing anxiety and depression, the Contour Drawing Rating Scale; and the Body Esteem Scale assessing body dissatisfaction, Rosenberg Self-esteem Scale assessing self-esteem, the Exercise Dependence Scale-Revised assessing PA dependence, the Multidimensional Assessment of Interoceptive Awareness assessing interoceptive awareness and the Frost Multidimensional Perfectionism Scale assessing perfectionism. Machine learning analysis will be performed in order to constitute groups with a tree-based model clustering method, extract risk profile(s) with a bootstrap method comparison, and predict ED risk with a prediction method based on a decision tree-based model. Expected results: 1044 complete records have already been collected, and the survey will be closed at the end of May 2022. Records will be analyzed with a clustering method and a bootstrap method in order to reveal risk profile(s). Furthermore, a predictive tree decision method will be done to extract an accurate predictive model of ED risk. This analysis will confirm typical main risk factors and will give more data on presumed strong risk factors such as exercise motivations and interoceptive deficit. Furthermore, it will enlighten particular risk profiles with a strong level of proof and greatly contribute to improving the early detection of ED and contribute to a better understanding of ED risk factors.

Keywords: eating disorders, risk factors, physical activity, machine learning

Procedia PDF Downloads 61

Authors: Muhammad Awais

Abstract:

Study on the nesting biology of the House Crow Corvus splendens was conducted at Hazara University, Garden Campus (125 acres), Mansehra during the 2013 breeding season (June to September). Details about nest locations, tree characteristics, nest and egg characteristics were recorded. Mean nest density of House Crow was 2.4 nests/ acre. Mean tree and nest height were 14.8±6.30 and 11.8±5.42m. Mean tree canopy spread 9.5±2.48m. Mean maximum and minimum nest diameters were 42.3±2.08 and 39.0±1.73cm respectively while maximum and minimum diameters of nest cup were 15.6±1.52 and 13.3±1.15cm respectively. Nest depth and nest cup depth were measured 19.3±2.08 and 8.3±1.15cm respectively. Mean nest weight was 1.4±0.24 kg. Mean clutch size was 4.0 (ranged 1–6). Mean egg length was 38.6±0.69mm, breadth 26.0±0.69mm, egg volume 13.3±0.83cm3 and egg shape index 1.42±0.83. Mean egg weight was 12.3±0.70g. Egg and nest success was calculated 55.1% and 69.0%. Hatchlings and fledglings produced per nest were 2.20 and 1.44 respectively. Main reasons for reproductive failures were unhatched eggs, poor nest construction, bad weather conditions and observer’s disturbance.

Keywords: breeding, Corvus splendens, fledglings, Hazara university, house crow, Mansehra, populus orientalis

Procedia PDF Downloads 379