Search results for: housing price prediction

Authors: Nato Kutaladze, George Mikuchadze, Giorgi Sokhadze

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Precipitation forecasts are one of the most demanding applications in numerical weather prediction (NWP). Georgia, as the whole Caucasian region, is characterized by very complex topography. The country territory is prone to flash floods and mudflows, quantitative precipitation estimation (QPE) and quantitative precipitation forecast (QPF) at any leading time are very important for Georgia. In this study, advanced research weather forecasting model’s skill in QPF is investigated over Georgia’s territory. We have analyzed several convection parameterization and microphysical scheme combinations for different rainy episodes and heavy rainy phenomena. We estimate errors and biases in accumulated 6 h precipitation using different spatial resolution during model performance verification for 12-hour and 24-hour lead time against corresponding rain gouge observations and satellite data. Various statistical parameters have been calculated for the 8-month comparison period, and some skills of model simulation have been evaluated. Our focus is on the formation and organization of convective precipitation systems in a low-mountain region. Several problems in connection with QPF have been identified for mountain regions, which include the overestimation and underestimation of precipitation on the windward and lee side of the mountains, respectively, and a phase error in the diurnal cycle of precipitation leading to the onset of convective precipitation in model forecasts several hours too early.

Keywords: extremal dependence index, false alarm, numerical weather prediction, quantitative precipitation forecasting

Procedia PDF Downloads 133

Authors: Kristin Prentice, Carri Hand

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As the Canadian population ages and chronic health conditions continue to escalate, older adults will require various types of housing, such as long term care or retirement homes. Moving to a new home may require a change in leisure activities and social networks, which could be challenging to maintain identity and create a sense of home. Leisure has been known to help older adults maintain or increase their quality of life and life satisfaction and may help older adults in moving to new homes. Sense of home and identity within older adults' transitions to new homes are concepts that may also relate to leisure engagement. Literature is scant regarding the role of leisure in older adults moving to new homes and how the sense of home and identity inter-relate. This study aims to explore how leisure may play a role in older adults' transitioning to new homes, including how sense of home and identity inter-relate. An ethnographic approach will be used to understand the culture of older adults transitioning to new homes. This study will involve older adults who have recently relocated to a mid-sized city in Ontario, Canada. The study will focus on the older adult’s interactions with and connections to their home environment through leisure. Data collection will take place via video-conferencing and will include a narrative interview and two other interviews to discuss an activity diary of leisure engagement pre and post move and mental maps to capture spaces where participants engaged in leisure. Participants will be encouraged to share photographs of leisure engagement taken inside and outside their home to help understand the social spaces the participants refer to in their activity diaries and mental maps. Older adults attempt to adjust to their new homes by maintaining their identity, developing a sense of home through creating attachment to place, and maintaining social networks, all of which have been linked to engaging in leisure. This research will provide insight into the role of leisure in this transition process and the extent that the home and community can contribute to aiding their transition to the new home. This research will contribute to existing literature on the inter-relationships of leisure, sense of home, and identity and how they relate to older adults moving to new homes. This research also has potential for influencing policy and practice for meeting the housing needs of older adults.

Keywords: leisure, older adults, transition, identity

Procedia PDF Downloads 100

Authors: Javier E. García-Gallo, Nelson J. Fonseca-Ruiz, John F. Duitama-Munoz

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Sepsis is a syndrome that occurs with physiological and biochemical abnormalities induced by severe infection and carries a high mortality and morbidity, therefore the severity of its condition must be interpreted quickly. After patient admission in an intensive care unit (ICU), it is necessary to synthesize the large volume of information that is collected from patients in a value that represents the severity of their condition. Traditional severity of illness scores seeks to be applicable to all patient populations, and usually assess in-hospital mortality. However, the use of machine learning techniques and the data of a population that shares a common characteristic could lead to the development of customized mortality prediction scores with better performance. This study presents the development of a score for the one-year mortality prediction of the patients that are admitted to an ICU with a sepsis diagnosis. 5650 ICU admissions extracted from the MIMICIII database were evaluated, divided into two groups: 70% to develop the score and 30% to validate it. Comorbidities, demographics and clinical information of the first 24 hours after the ICU admission were used to develop a mortality prediction score. LASSO (least absolute shrinkage and selection operator) and SGB (Stochastic Gradient Boosting) variable importance methodologies were used to select the set of variables that make up the developed score; each of this variables was dichotomized and a cut-off point that divides the population into two groups with different mean mortalities was found; if the patient is in the group that presents a higher mortality a one is assigned to the particular variable, otherwise a zero is assigned. These binary variables are used in a logistic regression (LR) model, and its coefficients were rounded to the nearest integer. The resulting integers are the point values that make up the score when multiplied with each binary variables and summed. The one-year mortality probability was estimated using the score as the only variable in a LR model. Predictive power of the score, was evaluated using the 1695 admissions of the validation subset obtaining an area under the receiver operating characteristic curve of 0.7528, which outperforms the results obtained with Sequential Organ Failure Assessment (SOFA), Oxford Acute Severity of Illness Score (OASIS) and Simplified Acute Physiology Score II (SAPSII) scores on the same validation subset. Observed and predicted mortality rates within estimated probabilities deciles were compared graphically and found to be similar, indicating that the risk estimate obtained with the score is close to the observed mortality, it is also observed that the number of events (deaths) is indeed increasing as the outcome go from the decile with the lowest probabilities to the decile with the highest probabilities. Sepsis is a syndrome that carries a high mortality, 43.3% for the patients included in this study; therefore, tools that help clinicians to quickly and accurately predict a worse prognosis are needed. This work demonstrates the importance of customization of mortality prediction scores since the developed score provides better performance than traditional scoring systems.

Keywords: intensive care, logistic regression model, mortality prediction, sepsis, severity of illness, stochastic gradient boosting

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Tobias Strübing, Fatemeh Ziaeetabar

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Humans usually manipulate objects with their hands. To represent these actions in a simple and understandable way, we need to use a semantic framework. For this purpose, the Semantic Event Chain (SEC) method has already been presented which is done by consideration of touching and non-touching relations between manipulated objects in a scene. This method was improved by a computational model, the so-called enriched Semantic Event Chain (eSEC), which incorporates the information of static (e.g. top, bottom) and dynamic spatial relations (e.g. moving apart, getting closer) between objects in an action scene. This leads to a better action prediction as well as the ability to distinguish between more actions. Each eSEC manipulation descriptor is a huge matrix with thirty rows and a massive set of the spatial relations between each pair of manipulated objects. The current eSEC framework has so far only been used in the category of manipulation actions, which eventually involve two hands. Here, we would like to extend this approach to a whole body action descriptor and make a conjoint activity representation structure. For this purpose, we need to do a statistical analysis to modify the current eSEC by summarizing while preserving its features, and introduce a new version called Enhanced eSEC or (e2SEC). This summarization can be done from two points of the view: 1) reducing the number of rows in an eSEC matrix, 2) shrinking the set of possible semantic spatial relations. To achieve these, we computed the importance of each matrix row in an statistical way, to see if it is possible to remove a particular one while all manipulations are still distinguishable from each other. On the other hand, we examined which semantic spatial relations can be merged without compromising the unity of the predefined manipulation actions. Therefore by performing the above analyses, we made the new e2SEC framework which has 20% fewer rows, 16.7% less static spatial and 11.1% less dynamic spatial relations. This simplification, while preserving the salient features of a semantic structure in representing actions, has a tremendous impact on the recognition and prediction of complex actions, as well as the interactions between humans and robots. It also creates a comprehensive platform to integrate with the body limbs descriptors and dramatically increases system performance, especially in complex real time applications such as human-robot interaction prediction.

Keywords: enriched semantic event chain, semantic action representation, spatial relations, statistical analysis

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Authors: Shaloom Mbambu Kabeya, Léonard Kabeya Mukeba

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Churches, cathedrals and castles beaten from antiquity to modern times were relevant from that time to the present day, and preserved as cultural heritage. Our predecessors as François 1er1, Michelangelo2, and Giotto3 left us traces. Gustave Eiffel4, Hector Guimard5 did not decrease their time to show modernization (evolution) in architecture. Plagiarism is a brake on architectural development, construction works of spirits is necessary architecture. This work explains the relationship between ancient and modern architecture. It also shows the power of mathematics in modern architecture.

Keywords: architectural modernization, heritage, mathematical architecture, materials

Procedia PDF Downloads 579

Authors: Emre Ozaslan, Bulent Acar, Mehmet Ali Guler

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Unidirectional composites are very popular structural materials used in aerospace, marine, energy and automotive industries thanks to their superior material properties. However, the mechanical behavior of composite materials is more complicated than isotropic materials because of their anisotropic nature. Also, a stress concentration availability on the structure, like a hole, makes the problem further complicated. Therefore, enormous number of tests require to understand the mechanical behavior and strength of composites which contain stress concentration. Accurate finite element analysis and analytical models enable to understand mechanical behavior and predict the strength of composites without enormous number of tests which cost serious time and money. In this study, unidirectional Carbon/Epoxy composite specimens with central circular hole were investigated in terms of stress concentration factor and strength prediction. The composite specimens which had different specimen wide (W) to hole diameter (D) ratio were tested to investigate the effect of hole size on the stress concentration and strength. Also, specimens which had same specimen wide to hole diameter ratio, but varied sizes were tested to investigate the size effect. Finite element analysis was performed to determine stress concentration factor for all specimen configurations. For quasi-isotropic laminate, it was found that the stress concentration factor increased approximately %15 with decreasing of W/D ratio from 6 to 3. Point stress criteria (PSC), inherent flaw method and progressive failure analysis were compared in terms of predicting the strength of specimens. All methods could predict the strength of specimens with maximum %8 error. PSC was better than other methods for high values of W/D ratio, however, inherent flaw method was successful for low values of W/D. Also, it is seen that increasing by 4 times of the W/D ratio rises the failure strength of composite specimen as %62.4. For constant W/D ratio specimens, all the strength prediction methods were more successful for smaller size specimens than larger ones. Increasing the specimen width and hole diameter together by 2 times reduces the specimen failure strength as %13.2.

Keywords: failure, strength, stress concentration, unidirectional composites

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Authors: Osayande Pascal Omondiagbe, Sherlock a Licorish

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Stack Overflow is a popular community question and answer portal which is used by practitioners to solve technology-related challenges during software development. Previous studies have shown that this forum is becoming a substitute for official software programming languages documentation. While tools have looked to aid developers by presenting interfaces to explore Stack Overflow, developers often face challenges searching through many possible answers to their questions, and this extends the development time. To this end, researchers have provided ways of predicting acceptable Stack Overflow answers by using various modeling techniques. However, less interest is dedicated to examining the performance and quality of typically used modeling methods, and especially in relation to models’ and features’ complexity. Such insights could be of practical significance to the many practitioners that use Stack Overflow. This study examines the performance and quality of various modeling methods that are used for predicting acceptable answers on Stack Overflow, drawn from 2014, 2015 and 2016. Our findings reveal significant differences in models’ performance and quality given the type of features and complexity of models used. Researchers examining classifiers’ performance and quality and features’ complexity may leverage these findings in selecting suitable techniques when developing prediction models.

Keywords: feature selection, modeling and prediction, neural network, random forest, stack overflow

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Authors: Hasan Taştan, Ayşen Ciravoğlu

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The rapid growth of the human population and the environmental degradation associated with increased consumption of resources raises concerns on sustainability. Social sustainability constitutes one of the three dimensions of sustainability together with environmental and economic dimensions. Even though there is not an agreement on what social sustainability consists of, it is a well known fact that it necessitates user participation. The fore, this study aims to observe and analyze the role of user participation on social sustainability. In this paper, the links between user participation and indicators of social sustainability have been searched. In order to achieve this, first of all a literature review on social sustainability has been done; accordingly, the information obtained from researches has been used in the evaluation of the projects conducted in the developing countries considering user participation. These examples are taken as role models with pros and cons for the development of the checklist for the evaluation of the case studies. Furthermore, a case study over the post earthquake residential settlements in Turkey have been conducted. The case study projects are selected considering different building scales (differing number of residential units), scale of the problem (post-earthquake settlements, rehabilitation of shanty dwellings) and the variety of users (differing socio-economic dimensions). Decisionmaking, design, building and usage processes of the selected projects and actors of these processes have been investigated in the context of social sustainability. The cases include: New Gourna Village by Hassan Fathy, Quinta Monroy dwelling units conducted in Chile by Alejandro Aravena and Beyköy and Beriköy projects in Turkey aiming to solve the problem of housing which have appeared after the earthquake happened in 1999 have been investigated. Results of the study possible links between social sustainability indicators and user participation and links between user participation and the peculiarities of place. Results are compared and discussed in order to find possible solutions to form social sustainability through user participation. Results show that social sustainability issues depend on communities' characteristics, socio-economic conditions and user profile but user participation has positive effects on some social sustainability indicators like user satisfaction, a sense of belonging and social stability.

Keywords: housing projects, residential areas, social sustainability, user participation

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Authors: Arkinzhan Mametov, Alexey Abilov

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Strong earthquakes are dangerous natural phenomena that lead to the destruction of entire cities and the death of a large number of people. The recent strong earthquakes in Turkey and in a number of other states have shown that as a result of them, there are significant human casualties and huge destruction. The city of Almaty is located in the foothill basin of the Trans-Ili Alatau of the Tien Shan Mountain system, in a zone with 9–10-point seismicity. Almaty (formerly Verniy) was founded in 1856 and, since that period, has experienced two catastrophic earthquakes - in 1887 and 1911, which led almost to the complete destruction of the city. Since that time, according to seismologists, the city has been annually exposed to small seismic impacts of 2-3 points. This forced the subsequent search for ways to protect buildings and the public through the use of earthquake-resistant structures and materials, limiting the number of stores of buildings and increasing gaps between them, which was carried out quite consistently and since 1957. However, at present, it is necessary to state a number of violations, primarily of the urban development plan – the placement of high-density multi-stores commercial housing in the urban environment, bypassing the existing regulations and standards in the city. Their appearance contributes to a greater concentration of residents transport in a limited area, which can lead to harmful consequences during powerful earthquakes. The experience of eliminating the consequences of catastrophic earthquakes shows that an important factor in reducing human losses is timely technical and medical assistance to victims of earthquakes, the elimination of blockages, provision of temporary housing and evacuation of the population, especially in winter. In cities located in areas with high seismicity, it is necessary to ensure strict compliance with the requirements of urban development regulations, taking into account the entire complex of planning and organizational measures to minimize the destruction of buildings and human casualties.

Keywords: high seismic zones, urban planning regulations, special standards for planing, minimizing the human casualties

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Authors: Laxuman Thakuri

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Despite the large number of in-depth case studies focused on policy analysis, institutional arrangement, and collective action of common property resource management; how the local institutions take the pricing decision of forest products in community forest management and what kinds of effects produce it, the answers of these questions are largely silent among the policy-makers and researchers alike. The study examined how the local institutions take the pricing decision of forest products in the lowland community forestry of Nepal and how the decisions affect to equitable distribution of benefits and livelihood improvement which are also objectives of Nepalese community forestry. The study assumes that forest products pricing decisions have multiple effects on equitable distribution and livelihood improvement in the areas having heterogeneous socio-economic conditions. The dissertation was carried out at four community forests of lowland, Nepal that has characteristics of high value species, matured-experience of community forest management and better record-keeping system of forest products production, pricing and distribution. The questionnaire survey, individual to group discussions and direct field observation were applied for data collection from the field, and Lorenz curve, gini-coefficient, χ²-text, and SWOT (Strong, Weak, Opportunity, and Threat) analysis were performed for data analysis and results interpretation. The dissertation demonstrates that the low pricing strategy of high-value forest products was supposed crucial to increase the access of socio-economically weak households, and to and control over the important forest products such as timber, but found counter productive as the strategy increased the access of socio-economically better-off households at higher rate. In addition, the strategy contradicts to collect a large-scale community fund and carry out livelihood improvement activities as per the community forestry objectives. The crucial part of the study is despite the fact of low pricing strategy; the timber alone contributed large part of community fund collection. The results revealed close relation between pricing decisions and livelihood objectives. The action research result shows that positive price discrimination can slightly reduce the prevailing inequality and increase the fund. However, it lacks to harness the full price of forest products and collects a large-scale community fund. For broader outcomes of common property resource management in terms of resource sustainability, equity, and livelihood opportunity, the study suggests local institutions to harness the full price of resource products with respect to the local market.

Keywords: community, equitable, forest, livelihood, socioeconomic, Nepal

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Authors: S. Farzad, M. Ali Mandegari, J. F. Görgens

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The South African Sugar Industry, which has a significant impact on the national economy, is currently facing problems due to increasing energy price and low global sugar price. The available bagasse is already combusted in low-efficiency boilers of the sugar mills while bagasse is generally recognized as a promising feedstock for second generation bioethanol production. Establishment of biorefinery annexed to the existing sugar mills, as an alternative for the revitalization of sugar industry producing biofuel and electricity has been proposed and considered in this study. Since the scale is an important issue in the feasibility of the technology, this study has taken into account a typical sugar mill with 300 ton/hr sugar cane capacity. The biorefinery simulation is carried out using Aspen PlusTM V8.6, in which the sugar mill’s power and steam demand has been considered. Hence, sugar mills in South Africa can be categorized as highly efficient, efficient, and not efficient with steam consumption of 33, 40, and 60 tons of steam per ton of cane and electric power demand of 10 MW; three different scenarios are studied. The sugar cane bagasse and tops/trash are supplied to the biorefinery process and the wastes/residues (mostly lignin) from the process are burnt in the CHP plant in order to produce steam and electricity for the biorefinery and sugar mill as well. Considering the efficient sugar mill, the CHP plant has generated 5 MW surplus electric powers, but the obtained energy is not enough for self-sufficiency of the plant (Biorefinery and Sugar mill) due to lack of 34 MW heat. One of the advantages of second generation biorefinery is its low impact on the environment and carbon footprint, thus the plant should be self-sufficient in energy without using fossil fuels. For this reason, a portion of fresh bagasse should be sent to the CHP plant to meet the energy requirements. An optimization procedure was carried out to find out the appropriate portion to be burnt in the combustor. As a result, 20% of the bagasse is re-routed to the combustor which leads to 5 tons of LP Steam and 8.6 MW electric power surpluses.

Keywords: biorefinery, sugarcane bagasse, sugar mill, energy analysis, bioethanol

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Authors: Surajit Bhattacharya, Daniel Veltri, Atit A. Patel, Daniel N. Cox

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miRNAs have emerged as key post-transcriptional regulators of gene expression, however identification of biologically-relevant target genes for this epigenetic regulatory mechanism remains a significant challenge. To address this knowledge gap, we have developed a novel tool in R, Intra-miR-ExploreR, that facilitates integrated discovery of miRNA targets by incorporating target databases and novel target prediction algorithms, using statistical methods including Pearson and Distance Correlation on microarray data, to arrive at high confidence intragenic miRNA target predictions. We have explored the efficacy of this tool using Drosophila melanogaster as a model organism for bioinformatics analyses and functional validation. A number of putative targets were obtained which were also validated using qRT-PCR analysis. Additional features of the tool include downloadable text files containing GO analysis from DAVID and Pubmed links of literature related to gene sets. Moreover, we are constructing interaction maps of intragenic miRNAs, using both micro array and RNA-seq data, focusing on neural tissues to uncover regulatory codes via which these molecules regulate gene expression to direct cellular development.

Keywords: miRNA, miRNA:mRNA target prediction, statistical methods, miRNA:mRNA interaction network

Procedia PDF Downloads 487

Authors: Yongseok Kim, Jeong-Min Lee, Hyunwoo Song, Junghan Yun, Jungin Byun, Jae-Mean Koo, Chang-Sung Seok

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Thermal barrier coating(TBC) is applied for gas turbine components to protect the components from extremely high temperature condition. Since metallic substrate cannot endure such severe condition of gas turbines, delamination of TBC can cause failure of the system. Thus, delamination life of TBC is one of the most important issues for designing the components operating at high temperature condition. Thermal stress caused by thermally grown oxide(TGO) layer is known as one of the major failure mechanisms of TBC. Thermal stress by TGO mainly occurs at the interface between TGO layer and ceramic top coat layer, and it is strongly influenced by the thickness and shape of TGO layer. In this study, Isothermal oxidation is conducted on coin-type TBC specimens prepared by APS(air plasma spray) method. After the isothermal oxidation at various temperature and time condition, the thickness and shape(rumpling shape) of the TGO is investigated, and the test data is processed by numerical analysis. Finally, the test data is arranged into a mathematical prediction model with two variables(temperature and exposure time) which can predict the thickness and rumpling shape of TGO.

Keywords: thermal barrier coating, thermally grown oxide, thermal stress, isothermal oxidation, numerical analysis

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Authors: Xiaobo Liu, Rakesh Mishra, Yun Zhang

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Continuous monitoring of forest canopy height with large coverage is essential for obtaining forest carbon stocks and emissions, quantifying biomass estimation, analyzing vegetation coverage, and determining biodiversity. LiDAR can be used to collect accurate woody vegetation structure such as canopy height. However, LiDAR’s coverage is usually limited because of its high cost and limited maneuverability, which constrains its use for dynamic and large area forest canopy monitoring. On the other hand, optical satellite images, like Sentinel-2, have the ability to cover large forest areas with a high repeat rate, but they do not have height information. Hence, exploring the solution of integrating LiDAR data and Sentinel-2 images to enlarge the coverage of forest canopy height prediction and increase the prediction repeat rate has been an active research topic in the environmental remote sensing community. In this study, we explore the potential of training a Random Forest Regression (RFR) model and a Convolutional Neural Network (CNN) model, respectively, to develop two predictive models for predicting and validating the forest canopy height of the Acadia Forest in New Brunswick, Canada, with a 10m ground sampling distance (GSD), for the year 2018 and 2021. Two 10m airborne LiDAR-derived canopy height models, one for 2018 and one for 2021, are used as ground truth to train and validate the RFR and CNN predictive models. To evaluate the prediction performance of the trained RFR and CNN models, two new predicted canopy height maps (CHMs), one for 2018 and one for 2021, are generated using the trained RFR and CNN models and 10m Sentinel-2 images of 2018 and 2021, respectively. The two 10m predicted CHMs from Sentinel-2 images are then compared with the two 10m airborne LiDAR-derived canopy height models for accuracy assessment. The validation results show that the mean absolute error (MAE) for year 2018 of the RFR model is 2.93m, CNN model is 1.71m; while the MAE for year 2021 of the RFR model is 3.35m, and the CNN model is 3.78m. These demonstrate the feasibility of using the RFR and CNN models developed in this research for predicting large-coverage forest canopy height at 10m spatial resolution and a high revisit rate.

Keywords: remote sensing, forest canopy height, LiDAR, Sentinel-2, artificial intelligence, random forest regression, convolutional neural network

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Authors: Roseline Oghogho Osaseri, Osaseri E. I.

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Lassa fever is an epidemic hemorrhagic fever caused by the Lassa virus, an extremely virulent arena virus. This highly fatal disorder kills 10% to 50% of its victims, but those who survive its early stages usually recover and acquire immunity to secondary attacks. One of the major challenges in giving proper treatment is lack of fast and accurate diagnosis of the disease due to multiplicity of symptoms associated with the disease which could be similar to other clinical conditions and makes it difficult to diagnose early. This paper proposed an Adaptive Neuro Fuzzy Inference System (ANFIS) for the prediction of Lass Fever. In the design of the diagnostic system, four main attributes were considered as the input parameters and one output parameter for the system. The input parameters are Temperature on admission (TA), White Blood Count (WBC), Proteinuria (P) and Abdominal Pain (AP). Sixty-one percent of the datasets were used in training the system while fifty-nine used in testing. Experimental results from this study gave a reliable and accurate prediction of Lassa fever when compared with clinically confirmed cases. In this study, we have proposed Lassa fever diagnostic system to aid surgeons and medical healthcare practictionals in health care facilities who do not have ready access to Polymerase Chain Reaction (PCR) diagnosis to predict possible Lassa fever infection.

Keywords: anfis, lassa fever, medical diagnosis, soft computing

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Authors: Gianvito Colucci, Valeria Di Cosmo, Matteo Nicoli, Orsola Maria Robasto, Laura Savoldi

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The hydrogen value chain deployment is likely to be boosted in the near term by the energy security measures planned by European countries to face the recent energy crisis. In this context, some countries are recognized to have a crucial role in the geopolitics of hydrogen as importers, consumers and exporters. According to the European Hydrogen Backbone Initiative, Italy would be part of one of the 5 corridors that will shape the European hydrogen market. However, the set targets are very ambitious and require large investments to rapidly develop effective hydrogen policies: in this regard, scenario analysis is becoming increasingly important to support energy planning, and energy system optimization models appear to be suitable tools to quantitively carry on that kind of analysis. The work aims to assess the competitiveness of hydrogen in contributing to the Italian energy security in the coming years, under different price and import conditions, using the energy system model TEMOA-Italy. A wide spectrum of hydrogen technologies is included in the analysis, covering the production, storage, delivery, and end-uses stages. National production from fossil fuels with and without CCS, as well as electrolysis and import of low-carbon hydrogen from North Africa, are the supply solutions that would compete with other ones, such as natural gas, biomethane and electricity value chains, to satisfy sectoral energy needs (transport, industry, buildings, agriculture). Scenario analysis is then used to study the competition under different price and import conditions. The use of TEMOA-Italy allows the work to catch the interaction between the economy and technological detail, which is much needed in the energy policies assessment, while the transparency of the analysis and of the results is ensured by the full accessibility of the TEMOA open-source modeling framework.

Keywords: energy security, energy system optimization models, hydrogen, natural gas, open-source modeling, scenario analysis, TEMOA

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Authors: Zeynep Tanriverdi̇

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Sustainable architecture is a design approach that provides healthy, comfortable, safe, clean space production as well as utilizes minimum resources for efficient and economical use of natural resources and energy. Traditional houses located in rural areas are sustainable structures built at the design and implementation stage in accordance with the climatic environmental data of the region and also effectively using natural energy resources. The fact that these structures are located in an earthquake geography like Türkiye brings their earthquake resistance to the agenda. Since the construction of these structures, which contain the architectural and technological cultural knowledge of the past, is shaped according to the characteristics of the regions where they are located, their resistance to earthquakes also differs. Analyses in rural areas after the earthquake show that there are light-damaged structures that can survive, severely damaged structures, and completely destroyed structures. In this regard, experts can implement repair, consolidation, and reconstruction applications, respectively. While simple repair interventions are carried out in accordance with the original data in traditional houses that have shown great resistance to earthquakes, reinforcement work blended with new technologies can be applied in damaged structures. In reconstruction work, a wide variety of applications can be seen with the possibilities of modern technologies. In rural areas experiencing earthquakes around the world, there are experimental new housing applications that are renewable, environmentally friendly, and sustainable with modern construction techniques in the light of scientific data. With these new residences, it is aimed to create earthquake-resistant, economical, healthy, and pain-relieving therapy spaces for people whose daily lives have been interrupted by disasters. In this study, the preservation of high earthquake-prone rural areas will be discussed through the knowledge transfer of traditional architecture and also permanent housing practices using new sustainable technologies to improve the area. In this way, it will be possible to keep losses to a minimum with sustainable, reliable applications prepared for the worst aspects of the disaster situation and to establish a link between the knowledge of the past and the new technologies of the future.

Keywords: sustainability, conservation, traditional construction systems and materials, new technologies, earthquake resistance

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Authors: Xiang-Yao Zheng, Jen-Cheng Wang, Joe-Air Jiang

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Improving the line ampacity while using existing power grids is an important issue that electricity dispatchers are now facing. Using the information provided by the dynamic thermal rating (DTR) of transmission lines, an overhead power grid can operate safely. However, dispatchers usually lack real-time DTR information. Thus, this study proposes a long-short-term memory (LSTM)-based method, which is one of the neural network models. The LSTM-based method predicts the DTR of lines using the weather data provided by Central Weather Bureau (CWB) of Taiwan. The possible thermal bottlenecks at different locations along the line and the margin of line ampacity can be real-time determined by the proposed LSTM-based prediction method. A case study that targets the 345 kV power grid of TaiPower in Taiwan is utilized to examine the performance of the proposed method. The simulation results show that the proposed method is useful to provide the information for the smart grid application in the future.

Keywords: electricity dispatch, line ampacity prediction, dynamic thermal rating, long-short-term memory neural network, smart grid

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Authors: Carlos Theran, Yohn Parra Bautista, Victor Adankai, Richard Alo, Jimwi Liu, Clement G. Yedjou

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Alzheimer's disease (AD) is a neurodegenerative disorder primarily characterized by deteriorating cognitive functions. AD has gained relevant attention in the last decade. An estimated 24 million people worldwide suffered from this disease by 2011. In 2016 an estimated 40 million were diagnosed with AD, and for 2050 is expected to reach 131 million people affected by AD. Therefore, detecting and confirming AD at its different stages is a priority for medical practices to provide adequate and accurate treatments. Recently, Machine Learning (ML) models have been used to study AD's stages handling missing values in multiclass, focusing on the delineation of Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and normal cognitive (CN). But, to our best knowledge, robust performance information of these models and the missing data analysis has not been presented in the literature. In this paper, we propose studying the performance of five different machine learning models for AD's stages multiclass prediction in terms of accuracy, precision, and F1-score. Also, the analysis of three imputation methods to handle the missing value problem is presented. A framework that integrates ML model for AD's stages multiclass prediction is proposed, performing an average accuracy of 84%.

Keywords: alzheimer's disease, missing value, machine learning, performance evaluation

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Authors: Armando Cartenì, Ilaria Henke

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The perceived public transport quality is an important driver that influences both customer satisfaction and mobility choices. The competition among transport operators needs to improve the quality of the services and identify which attributes are perceived as relevant by passengers. Among the “traditional” public transport quality attributes there are, for example: travel and waiting time, regularity of the services, and ticket price. By contrast, there are some “non-conventional” attributes that could significantly influence customer satisfaction jointly with the “traditional” ones. Among these, the beauty/aesthetics of the transport terminals (e.g. rail station and bus terminal) is probably one of the most impacting on user perception. Starting from these considerations, the point stressed in this paper was if (and how munch) the travel experience of the overall travel (e.g. how long is the travel, how many transport modes must be used) influences the perception of the public transport quality. The aim of this paper was to investigate the weight of the terminal quality (e.g. aesthetic, comfort and service offered) within the overall travel experience. The case study was the extra-urban Italian bus network. The passengers of the major Italian terminal bus were interviewed and the analysis of the results shows that about the 75% of the travelers, are available to pay up to 30% more for the ticket price for having a high quality terminal. A travel experience effect was observed: the average perceived transport quality varies with the characteristic of the overall trip. The passengers that have a “long trip” (travel time greater than 2 hours) perceived as “low” the overall quality of the trip even if they pass through a high quality terminal. The opposite occurs for the “short trip” passengers. This means that if a traveler passes through a high quality station, the overall perception of that terminal could be significantly reduced if he is tired from a long trip. This result is important and if confirmed through other case studies, will allow to conclude that the “travel experience impact" must be considered as an explicit design variable for public transport services and planning.

Keywords: transportation planning, sustainable mobility, decision support system, discrete choice model, design problem

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Authors: Mahmood Niroobakhsh

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This article deals with special structuralism approaches to explain a certain kind of social problem. Widespread presence of poverty is a reminder of deep-rooted unresolved problems of social relations. The expected role from an individual for the social system recognizes poverty derived from an interrelated social structure. By the time, enabled to act on his role in the course of social interaction, reintegration of the poor in society may take place. Poverty and housing type are reflections of the underlying social structure, primarily structure’s elements, systemic interrelations, and the overall strength or weakness of that structure. Poverty varies based on social structure in that the stronger structures are less likely to produce poverty.

Keywords: absolute poverty, relative poverty, social structure, urban poverty

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Authors: A. Merzouki, N. Haddaoui

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Polymers have a large reputation as electric insulators. These materials are characterized by weak weight, reduced price and a large domain of physical and chemical properties. They conquered new application domains that were until a recent past the exclusivity of metals. In this work, we used some composite materials (polymers/conductive fillers), as electrodes and we try to cover them with metallic lead layers in order to use them as courant collector grids in lead-acid battery plates.

Keywords: electrodeposition, polymer composites, carbon black, acetylene black

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Authors: Khalid Kaddoura, Ahmed Assad, Tarek Zayed

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Sewer pipelines are prone to deterioration over-time. In fact, their deterioration does not follow a fixed downward pattern. This is in fact due to the defects that propagate through their service life. Sewer pipeline defects are categorized into distinct groups. However, the main two groups are the structural and operational defects. By definition, the structural defects influence the structural integrity of the sewer pipelines such as deformation, cracks, fractures, holes, etc. However, the operational defects are the ones that affect the flow of the sewer medium in the pipelines such as: roots, debris, attached deposits, infiltration, etc. Yet, the process for each defect to emerge follows a cause and effect relationship. Deformation, which is the change of the sewer pipeline geometry, is one type of an influencing defect that could be found in many sewer pipelines due to many surrounding factors. This defect could lead to collapse if the percentage exceeds 15%. Therefore, it is essential to predict the deformation percentage before confronting such a situation. Accordingly, this study will predict the percentage of the deformation defect in sewer pipelines adopting the multiple regression analysis. Several factors will be considered in establishing the model, which are expected to influence the defamation defect severity. Besides, this study will construct a time-based curve to understand how the defect would evolve overtime. Thus, this study is expected to be an asset for decision-makers as it will provide informative conclusions about the deformation defect severity. As a result, inspections will be minimized and so the budgets.

Keywords: deformation, prediction, regression analysis, sewer pipelines

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Authors: Aminah Muchdar, Nuraeni, Eddy

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The aims of the research are: (1) to verify the cropsyst plant model of experimental data in the field of soybean plants and (2) to predict planting time and potential yield soybean plant with the use of cropsyst model. This research is divided into several stages: (1) first calibration stage which conducted in the field from June until September 2015.(2) application models stage, where the data obtained from calibration in the field will be included in cropsyst models. The required data models are climate data, ground data/soil data,also crop genetic data. The relationship between the obtained result in field with simulation cropsyst model indicated by Efficiency Index (EF) which the value is 0,939.That is showing that cropsyst model is well used. From the calculation result RRMSE which the value is 1,922%.That is showing that comparative fault prediction results from simulation with result obtained in the field is 1,92%. The conclusion has obtained that the prediction of soybean planting time cropsyst based models that have been made valid for use. and the appropriate planting time for planting soybeans mainly on rain-fed land is at the end of the rainy season, in which the above study first planting time (June 2, 2015) which gives the highest production, because at that time there was still some rain. Tanggamus varieties more resistant to slow planting time cause the percentage decrease in the yield of each decade is lower than the average of all varieties.

Keywords: soybean, Cropsyst, calibration, efficiency Index, RRMSE

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Authors: M. F. Wu, F. T. Cheng, C. S. Wu, M. C. Tan

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It is a critical time to upgrade technology and increase value added with manufacturing skills developing and management strategies that will highly satisfy the customers need in the precision machinery global market. In recent years, the supply side, each precision machinery manufacturers in each country are facing the pressures of price reducing from the demand side voices that pushes the high-end precision machinery manufacturers adopts low-cost and high-quality strategy to retrieve the market. Because of the trend of the global market, the manufacturers must take price reducing strategies and upgrade technology of low-end machinery for differentiations to consolidate the market. By using six key success factors (KSFs), customer perceived value, customer satisfaction, customer service, product design, product effectiveness and machine structure quality are causal conditions to explore the impact of competitive advantage of the enterprise, such as overall profitability and product pricing power. This research uses key success paths (KSPs) approach and f/s QCA software to explore various combinations of causal relationships, so as to fully understand the performance level of KSFs and business objectives in order to achieve competitive advantage. In this study, the combination of a causal relationships, are called Key Success Paths (KSPs). The key success paths guide the enterprise to achieve the specific outcomes of business. The findings of this study indicate that there are thirteen KSPs to achieve the overall profitability, sixteen KSPs to achieve the product pricing power and seventeen KSPs to achieve both overall profitability and pricing power of the enterprise. The KSPs provide the directions of resources integration and allocation, improve utilization efficiency of limited resources to realize the continuous vision of the enterprise.

Keywords: precision machinery industry, key success factors (KSFs), key success paths (KSPs), overall profitability, product pricing power, competitive advantages

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Authors: Vinod Kumar, Surjit Angra

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The objective of this theoretical study is to develop simple design formulas for the prediction of minimum film thickness and maximum mean film temperature rise in lightly loaded high-speed rolling/sliding lubricated elliptical contacts incorporating starvation effect. Herein, the reported numerical analysis focuses on thermoelastohydrodynamically lubricated rolling/sliding elliptical contacts, considering the Newtonian rheology of lubricant for wide range of operating parameters, namely load characterized by Hertzian pressure (PH = 0.01 GPa to 0.10 GPa), rolling speed (>10 m/s), slip parameter (S varies up to 1.0), and ellipticity ratio (k = 1 to 5). Starvation is simulated by systematically reducing the inlet supply. This analysis reveals that influences of load, rolling speed, and level of starvation are significant on the minimum film thickness. However, the maximum mean film temperature rise is strongly influenced by slip in addition to load, rolling speed, and level of starvation. In the presence of starvation, reduction in minimum film thickness and increase in maximum mean film temperature are observed. Based on the results of this study, empirical relations are developed for the prediction of dimensionless minimum film thickness and dimensionless maximum mean film temperature rise at the contacts in terms of various operating parameters.

Keywords: starvation, lubrication, elliptical contact, traction, minimum film thickness

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Authors: Zeynep Küçükakça, Nezaket Parlak, Mesut Gür, Tahsin Engin, Hasan Küçük

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In the current research, the single phase fluid flow and heat transfer characteristics are experimentally investigated. The experiments are conducted to cover transition zone for the Reynolds numbers ranging from 100 to 4800 by fused silica and stainless steel microtubes having diameters of 103-180 µm. The applicability of the Logarithmic Mean Temperature Difference (LMTD) method is revealed and an experimental method is developed to calculate the heat transfer coefficient. Heat transfer is supplied by a water jacket surrounding the microtubes and heat transfer coefficients are obtained by LMTD method. The results are compared with data obtained by the correlations available in the literature in the study. The experimental results indicate that the Nusselt numbers of microtube flows do not accord with the conventional results when the Reynolds number is lower than 1000. After that, the Nusselt number approaches the conventional theory prediction. Moreover, the scaling effects in micro scale such as axial conduction, viscous heating and entrance effects are discussed. On the aspect of fluid characteristics, the friction factor is well predicted with conventional theory and the conventional friction prediction is valid for water flow through microtube with a relative surface roughness less than about 4 %.

Keywords: microtube, laminar flow, friction factor, heat transfer, LMTD method

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Authors: John Dorrell, Matthew Ambrosia, Abilash

Abstract:

This paper employs econometric analysis to quantify the potential profit wind farms can receive by allocating excess wind energy to power bitcoin mining machines. Cryptocurrency mining consumes a substantial amount of electricity worldwide, and wind energy produces a significant amount of energy that is lost because of the intermittent nature of the resource. Supply does not always match consumer demand. By combining the weaknesses of these two technologies, we can improve efficiency and a sustainable path to mine cryptocurrencies. This paper uses historical wind energy from the ERCOT network in Texas and cryptocurrency data from 2000-2021, to create 4-year return on investment projections. Our research model incorporates the price of bitcoin, the price of the miner, the hash rate of the miner relative to the network hash rate, the block reward, the bitcoin transaction fees awarded to the miners, the mining pool fees, the cost of the electricity and the percentage of time the miner will be running to demonstrate that wind farms generate enough excess energy to mine bitcoin profitably. Excess wind energy can be used as a financial battery, which can utilize wasted electricity by changing it into economic energy. The findings of our research determine that wind energy producers can earn profit while not taking away much if any, electricity from the grid. According to our results, Bitcoin mining could give as much as 1347% and 805% return on investment with the starting dates of November 1, 2021, and November 1, 2022, respectively, using wind farm curtailment. This paper is helpful to policymakers and investors in determining efficient and sustainable ways to power our economic future. This paper proposes a practical solution for the problem of crypto mining energy consumption and creates a more sustainable energy future for Bitcoin.

Keywords: bitcoin, mining, economics, energy

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Authors: Reza Dinarvand, Mahdi Sadeghian, Somaye Sadeghian

Abstract:

Prediction of the ultimate bearing capacity of piles (Qu) is one of the basic issues in geotechnical engineering. So far, several methods have been used to estimate Qu, including the recently developed artificial intelligence methods. In recent years, optimization algorithms have been used to minimize artificial network errors, such as colony algorithms, genetic algorithms, imperialist competitive algorithms, and so on. In the present research, artificial neural networks based on colonial competition algorithm (ANN-ICA) were used, and their results were compared with other methods. The results of laboratory tests of short piles in clayey soils with parameters such as pile diameter, pile buried length, eccentricity of load and undrained shear resistance of soil were used for modeling and evaluation. The results showed that ICA-based artificial neural networks predicted lateral bearing capacity of short piles with a correlation coefficient of 0.9865 for training data and 0.975 for test data. Furthermore, the results of the model indicated the superiority of ICA-based artificial neural networks compared to back-propagation artificial neural networks as well as the Broms and Hansen methods.

Keywords: artificial neural network, clayey soil, imperialist competition algorithm, lateral bearing capacity, short pile

Procedia PDF Downloads 131