Search results for: artificial life

Authors: Shuai Zhao, Shalendra D. Sharma, Jin Xu

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This research uses advanced machine learning models to learn a number of hypotheses regarding political executives. Specifically, it analyses the impact these powerful leaders have on economic growth by using leaders’ data from the Archigos database from 1835 to the end of 2015. The data is processed by the AutoGluon, which was developed by Amazon. Automated Machine Learning (AutoML) and AutoGluon can automatically extract features from the data and then use multiple classifiers to train the data. Use a linear regression model and classification model to establish the relationship between leaders and economic growth (GDP per capita growth), and to clarify the relationship between their characteristics and economic growth from a machine learning perspective. Our work may show as a model or signal for collaboration between the fields of statistics and artificial intelligence (AI) that can light up the way for political researchers and economists.

Keywords: comparative politics, political executives, leaders’ characteristics, artificial intelligence

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Authors: Z. H. Safari, M. Anbia, G. H. Kouzegari, R. Amirkhani

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Toluene and Nitro derivatives are widely used in industry particularly in various defense applications. Tri-nitro-toluene derivative is a powerful basic explosive material that is a basis upon which to compare equivalent explosive power of similar materials. The aim of this paper is to measure the explosive power of these hazardous substances in fuels having different shelf-life and therefore optimizing their storage and maintenance. The methodology involves measuring the amounts of di- nitro- toluene and tri-nitro-toluene in the aged samples at 90 ° C by gas chromatography. Results show no significant difference in the concentration of the TNT compound over a given time while there was a significant difference in DNT compound over the same period. The underlying reason is attributed to the simultaneous production of the material with destruction of stabilizer.

Keywords: dinitrotoluene, trinitrotoluene, double-base solid propellants, artificial aging

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Authors: Sultan Ghaleb Aldaihani

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- The environment in which educational leadership takes place is becoming increasingly complex due to factors like globalization and rapid technological change. - This is creating a "leadership gap" where the complexity of the environment outpaces the ability of leaders to effectively respond. - Educational leadership involves guiding teachers and the broader school system towards improved student learning and achievement. 2. Implications of Artificial Intelligence (AI) in Educational Leadership: - AI has great potential to enhance education, such as through intelligent tutoring systems and automating routine tasks to free up teachers. - AI can also have significant implications for educational leadership by providing better information and data-driven decision-making capabilities. - Computer-adaptive testing can provide detailed, individualized data on student learning that leaders can use for instructional decisions and accountability. 3. Enhancing Decision-Making Processes: - Statistical models and data mining techniques can help identify at-risk students earlier, allowing for targeted interventions. - Probability-based models can diagnose students likely to drop out, enabling proactive support. - These data-driven approaches can make resource allocation and decision-making more effective. 4. Improving Efficiency and Productivity: - AI systems can automate tasks and change processes to improve the efficiency of educational leadership and administration. - Integrating AI can free up leaders to focus more on their role's human, interactive elements.

Keywords: Education, Leadership, Technology, Artificial Intelligence

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Authors: Habtemariam Alemu

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It has been known that the performance of position and force control is highly affected by both robot dynamic and environment stiffness uncertainties. In this paper, joint space hybrid force and position control strategy with self-selecting matrix using artificial neural network compensator is proposed. The objective of the work is to improve controller robustness by applying a neural network technique in order to compensate the effect of uncertainties in the robot model. Simulation results for a 6 degree of freedom (6-DoF) manipulator and different types of environments showed the effectiveness of the suggested approach. 6-DoF Puma 560 family robot manipulator is chosen as industrial robot and its efficient dynamic model is designed using Matlab/SimMechanics library.

Keywords: robot manipulator, force/position control, artificial neural network, Matlab/Simulink

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Authors: P. Karimi, A. H. Khedmati Bazkiaei

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The main goal of this study is to test differential neural network as a controller of smart structure and is to enumerate its advantages and disadvantages in comparison with other controllers. In this study, the smart structure has been considered as a Euler Bernoulli cantilever beam and it has been tried that it be under control with the use of vibration neural network resulting from movement. Also, a linear observer has been considered as a reference controller and has been compared its results. The considered vibration charts and the controlled state have been recounted in the final part of this text. The obtained result show that neural observer has better performance in comparison to the implemented linear observer.

Keywords: smart material, on-line differential artificial neural network, active control, finite element method

Procedia PDF Downloads 195

Authors: Hooman Torabifard

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In the last few years, with the progress of technology and computers and artificial intelligence entry into all kinds of scientific and industrial fields, the lifestyles of human life have changed and in general, the way of humans live on earth has many changes and development. Until now, some of the changes has occurred in the context of digital images and image processing and still continues. However, besides all the benefits, there have been disadvantages. One of these disadvantages is the multiplicity of images with high volume and data; the focus of this paper is on improving and developing a method for summarizing and enhancing the productivity of these images. The general method used for this purpose in this paper consists of a set of methods based on data obtained from image processing and using the PSO (Particle swarm optimization) algorithm. In the remainder of this paper, the method used is elaborated in detail.

Keywords: image summarization, particle swarm optimization, image threshold, image processing

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Authors: Rodrigo Aguiar, Adelino Ferreira

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Road traffic accidents are the leading cause of unnatural death and injuries worldwide, representing a significant problem of road safety. In this context, the use of artificial intelligence with advanced machine learning techniques has gained prominence as a promising approach to predict traffic accidents. This article investigates the application of machine learning algorithms to develop traffic accident frequency prediction models. Models are evaluated based on performance metrics, making it possible to do a comparative analysis with traditional prediction approaches. The results suggest that machine learning can provide a powerful tool for accident prediction, which will contribute to making more informed decisions regarding road safety.

Keywords: machine learning, artificial intelligence, frequency of accidents, road safety

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Authors: Olga Leontjeva

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In the age of Artificial Intelligence (AI), some organization management approaches need to be adapted or changed. Human Resource Management (HRM) is a part of organization management that is under the managers' focus nowadays, because AI integration into organization activities brings some HRM-connected challenges. The topic became more significant during the crises of many organizations in the world caused by the coronavirus pandemic (COVID-19). The paper presents an approach, which will be used for the study that is going to be focused on the various case analysis. The author of the future study will analyze the cases of the organizations from Latvia and Spain that are grouped by the size, type of activity and area of business. The information for the cases will be collected through structured interviews and online surveys. The main result presented is the questionnaire developed that will be used for the study as well as the definition and description of sampling. The first round of the survey will be based on convenience sampling that is the main limitation of the study. To conclude, the approach developed will help to collect valid data if the organizations participating in the survey are ready to share their cases in depth, so the researchers could draw the right conclusions and generalize compared organizations’ cases. The questionnaire developed for the survey is applicable for both written online data collection as well as for the interviews. The case analysis will help to identify some HRM challenges that are connected to AI integration into organization activities such as management of different generation employees and their training peculiarities.

Keywords: age of artificial intelligence, case analysis, generation Y and Z employees, human resource management

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Authors: Ali Abdelnabi, Nermeen Hamza

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Coral calcium is a boasting natural product and dietary supplement which is considered a source of alkaline calcium carbonate, this study is a comparative study, comparing the remineralization effect of the new product of coral calcium with that of nano-hydroxyapatite. Methodology: a total of 35 extracted molars were collected, examined and sectioned to obtain 70 sound enamel discs, all discs were numbered and examined by scanning electron microscope coupled with Energy Dispersive Analysis of X-rays(EDAX) for mineral content, subjected to artificial caries, and mineral content was re-measured, discs were divided into seven groups according to the remineralizing agent used, where groups 1 to 3 used 10%, 20%, 30% nanohydroxyapatite gel respectively, groups 4 to 6 used 10%, 20%, 30% coral calcium gel and group 7 with no remineralizing agent (control group). All groups were re-examined by EDAX after remineralization; data were calculated and tabulated. Results: All groups showed a statistically significant drop in calcium level after artificial caries; all groups showed a statistically significant rise in calcium content after remineralization except for the control group; groups 1 and 5 showed the highest increase in calcium level after remineralization. Conclusion: coral calcium can be considered a comparative product to nano-hydroxyapatite regarding the remineralization of enamel initial carious lesions.

Keywords: artificial caries, coral calcium, nanohydroxyapatite, re-mineralization

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Authors: Prachi Desai, Ankita Gandhi, Mitali Acharya

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Rainfall forecast is mainly used for predictions of rainfall in a specified area and determining their future rainfall conditions. Rainfall is always a global issue as it affects all major aspects of one's life. Agricultural, fisheries, forestry, tourism industry and other industries are widely affected by these conditions. The studies have resulted in insufficient availability of water resources and an increase in water demand in the near future. We already have a new forecast system that uses the deep Convolutional Neural Network (CNN) to forecast monthly rainfall and climate changes. We have also compared CNN against Artificial Neural Networks (ANN). Machine Learning techniques that are used in rainfall predictions include ARIMA Model, ANN, LR, SVM etc. The dataset on which we are experimenting is gathered online over the year 1901 to 20118. Test results have suggested more realistic improvements than conventional rainfall forecasts.

Keywords: ANN, CNN, supervised learning, machine learning, deep learning

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Authors: Tipu Sultan

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The proposed research intends to explore the gender role attitudes and work-life balance among dual-earner couples in Pakistan. With the increase of female labor force participation in Pakistan, the trend of dual-earner couples has been increased than ever before. This new trend of dual-earner families has significantly affected the personal life of dual-earner couples. Due to major change in household structures, the traditions and the routine activities are in continuous transition. Balancing work and family life is more complex in the patriarchal society of Pakistan because of the social expectations of gender roles. A dichotomous behavioral reflection is being observed in Pakistani society. The one group of people having an egalitarian attitude are supporting the new gender roles of females, whereas the other group of people having a traditional mindset is still in the favor of patriarchy. Therefore, gender roles are re-evaluated, and it would be more interesting to raise questions on the interplay of new gender roles and work-life balance among dual-earners. The semi-structured interview guide will be utilized to explore gender role attitudes, ideal and in-practice gender roles, experiences of work-life imbalances/balances, possible strategies to create a balance between work and family life among dual-earner couples.

Keywords: dual-earner couples, gender role attitudes, Pakistan, work-life balance

Procedia PDF Downloads 131

Authors: Aurea C. L. Lacerda, Paulo R. H. Moreno, Bruna M. P. Vianna, Cristina H. R. Serra, Airton Martin, André R. Baby, Vladi O. Consiglieri, Telma M. Kaneko

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The modified Franz cell is the most widely used model for in vitro permeation studies, however it still presents some disadvantages. Thus, some alternative methods have been developed such as Skin PAMPA, which is a bio- artificial membrane that has been applied for skin penetration estimation of xenobiotics based on HT permeability model consisting. Skin PAMPA greatest advantage is to carry out more tests, in a fast and inexpensive way. The membrane system mimics the stratum corneum characteristics, which is the primary skin barrier. The barrier properties are given by corneocytes embedded in a multilamellar lipid matrix. This layer is the main penetration route through the paracellular permeation pathway and it consists of a mixture of cholesterol, ceramides, and fatty acids as the dominant components. However, there is no consensus on the membrane composition. The objective of this work was to compare the performance among different bio-artificial membranes for studying the permeation in skin PAMPA system. Material and methods: In order to mimetize the lipid composition`s present in the human stratum corneum six membranes were developed. The membrane composition was equimolar mixture of cholesterol, ceramides 1-O-C18:1, C22, and C20, plus fatty acids C20 and C24. The membrane integrity assay was based on the transport of Brilliant Cresyl Blue, which has a low permeability; and Lucifer Yellow with very poor permeability and should effectively be completely rejected. The membrane characterization was performed using Confocal Laser Raman Spectroscopy, using stabilized laser at 785 nm with 10 second integration time and 2 accumulations. The membrane behaviour results on the PAMPA system were statistically evaluated and all of the compositions have shown integrity and permeability. The confocal Raman spectra were obtained in the region of 800-1200 cm-1 that is associated with the C-C stretches of the carbon scaffold from the stratum corneum lipids showed similar pattern for all the membranes. The ceramides, long chain fatty acids and cholesterol in equimolar ratio permitted to obtain lipid mixtures with self-organization capability, similar to that occurring into the stratum corneum. Conclusion: The artificial biological membranes studied for Skin PAMPA showed to be similar and with comparable properties to the stratum corneum.

Keywords: bio-artificial membranes, comparison, confocal Raman, skin PAMPA

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Authors: Fiúza A., Góis J. Leite M., Braga H., Lima A., Jorge P., Moutela P., Martins L., Futuro A.

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Lepidolite is an important lithium mineral that, to the author’s best knowledge, has not been used to produce lithium hydroxide, necessary for energy conversion to electric vehicles. Alkaline leaching of lithium concentrates allows the establishment of a production diagram avoiding most of the environmental drawbacks that are associated with the usage of acid reagents. The tested processes involve a pretreatment by digestion at high temperatures with additives, followed by leaching at hot atmospheric pressure. The solutions obtained must be compatible with solutions from the leaching of spodumene concentrates, allowing the development of a common treatment diagram, an important accomplishment for the feasible exploitation of Portuguese resources. Statistical programming and interpretation techniques are used to minimize the laboratory effort required by conventional approaches and also allow phenomenological comprehension.

Keywords: artificial intelligence, tailings free process, ferroelectric electrolyte battery, life cycle assessment

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Authors: Seon Soon Choi

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The principal purpose of this paper is to find the influence of maximum fatigue load on the probabilistic aspect of fatigue crack propagation life at a specified grown crack in magnesium alloys. The experiments of fatigue crack propagation are carried out in laboratory air under different conditions of the maximum fatigue loads to obtain the fatigue crack propagation data for the statistical analysis. In order to analyze the probabilistic aspect of fatigue crack propagation life, the goodness-of fit test for probability distribution of the fatigue crack propagation life at a specified grown crack is implemented through Anderson-Darling test. The good probability distribution of the fatigue crack propagation life is also verified under the conditions of the maximum fatigue loads.

Keywords: fatigue crack propagation life, magnesium alloys, maximum fatigue load, probability

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Authors: Frank Kuebler, Rolf Steinhilper

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Artificial neural networks (ANN) as well as Design of Experiments (DOE) based regression analysis (RA) are mainly used for modeling of complex systems. Both methodologies are commonly applied in process and quality control of manufacturing processes. Due to the fact that resource efficiency has become a critical concern for manufacturing companies, these models needs to be extended to predict resource-consumption of manufacturing processes. This paper describes an approach to use neural networks as well as DOE based regression analysis for predicting resource consumption of manufacturing processes and gives a comparison of the achievable results based on an industrial case study of a turning process.

Keywords: artificial neural network, design of experiments, regression analysis, resource efficiency, manufacturing process

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Authors: Mirna Febriani

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The use of stainless steel wires in the field of dentistry is widely used, especially for orthodontic and prosthodontic treatment using stainless steel wire. The oral cavity is the ideal environment for corrosion, which can be caused by saliva. Prevention of corrosion on stainless steel wires can be done by using an organic or non-organic corrosion inhibitor. One of the organic inhibitors that can be used to prevent corrosion is the leaves of breadfruit. The method used for this research using Atomic Absorption Spectrophotometric test. The results showed that the difference of chromium ion releases on soaking in saliva and breadfruit leaf extracts on days 1, 3, 7 and 14. Statically calculation with independent T-test with p < 0,05 showed the significant difference. The conclusion of this study shows that breadfruit leaf extract can inhibit the corrosion rate of stainless steel wires.

Keywords: chromium ion, stainless steel, artificial saliva, breadfruit leaf

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Authors: Seyed Sadegh Naseralavi, Najmeh Bemani

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In this paper, different concrete codes including America, New Zealand, Mexico, Italy, India, Canada, Hong Kong, Euro Code and Britain are compared with the Iranian concrete design code. First, by using Adaptive Neuro Fuzzy Inference System (ANFIS), the codes having the most correlation with the Iranian ninth issue of the national regulation are determined. Consequently, two anticipated methods are used for comparing the codes: Artificial Neural Network (ANN) and Multi-variable regression. The results show that ANN performs better. Predicting is done by using only tensile steel ratio and with ignoring the compression steel ratio.

Keywords: adaptive neuro fuzzy inference system, anticipate method, artificial neural network, concrete design code, multi-variable regression

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Authors: M. Meddi, A. Boufekane

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The Mitidja coastal groundwater which extends over an area of 1450 km2 is a strategic resource in the Algiers region. The high dependence of the regional economy on the use of this groundwater forces us to have recourse to its artificial recharge from the Wadi El Harrach in its upstream part. This system of artificial recharge has shown its effectiveness in the development of water resource mentioned in the succeeding works in several regions of the world. The objective of this study is to: Increase the reserves of water inputs by infiltration, raise the water level and its good quality in wells and boreholes, reduce losses to the sea, and address seawater intrusion by maintaining balance in the freshwater-saltwater interface in the downstream part of the groundwater basin. After analyzing the situation, it was noticed that a qualitative monitoring of the Wadi water for the groundwater recharge has to be done. For this purpose, we proceeded during three successive years (2010, 2011, and 2012) to the monthly sampling of water in the upstream part of the Wadi El Harrach for chemical analysis. The variation of the sediment transport concentration will be also measured. This monitoring aims to characterize the water quality and avoid clogging in the proposed recharge area. The results of these analyses showed the good chemical quality according to the analyses we performed in the laboratory during the three years, but they are too loaded with suspended matters. We noticed that these fine particles come from the grinding of limestone of sandpit located upstream of the area of the proposed recharge system. This problem can be solved by a water supply upstream of sandpit. For the recharge, we propose the method of using two wells for dual use, which means that it can be used for water supply and extraction. This solution is inexpensive in our case and could easily be used as wells are already drilled in the upstream part. This solution increases over time the piezometric level and also reduce groundwater contamination by saltwater in the downstream part.

Keywords: water quality, artificial groundwater recharge, Mitidja, North Algeria

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Authors: Yasaman Sanayei, Alireza Bahiraie

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This paper presents a systematic methodology based on the application of artificial neural networks for sequencing batch reactor (SBR). The SBR is a fill-and-draw biological wastewater technology, which is specially suited for nutrient removal. Employing reactive dye by Sphingomonas paucimobilis bacteria at sequence batch reactor is a novel approach of dye removal. The influent COD, MLVSS, and reaction time were selected as the process inputs and the effluent COD and BOD as the process outputs. The best possible result for the discrete pole parameter was a= 0.44. In orderto adjust the parameters of ANN, the Levenberg-Marquardt (LM) algorithm was employed. The results predicted by the model were compared to the experimental data and showed a high correlation with R2> 0.99 and a low mean absolute error (MAE). The results from this study reveal that the developed model is accurate and efficacious in predicting COD and BOD parameters of the dye-containing wastewater treated by SBR. The proposed modeling approach can be applied to other industrial wastewater treatment systems to predict effluent characteristics. Note that SBR are normally operated with constant predefined duration of the stages, thus, resulting in low efficient operation. Data obtained from the on-line electronic sensors installed in the SBR and from the control quality laboratory analysis have been used to develop the optimal architecture of two different ANN. The results have shown that the developed models can be used as efficient and cost-effective predictive tools for the system analysed.

Keywords: artificial neural network, COD removal, SBR, Sphingomonas paucimobilis

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Authors: Shang-Sin Shiu, Shu-I Chin, Hsiu-Ju Chen, Ru-Yu Lien

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The Hospice and Palliative Care Act has undergone three revisions, making it less challenging for terminal patients to withdraw life support systems. However, the adequacy of care before withdraw is a crucial factor in end-of-life medical treatment. The author observed that intensive care unit (ICU) nursing staff often rely on simple flowcharts or word of mouth, leading to inadequate preparation and failure to meet patient needs before withdraw. This results in confusion or hesitation among those executing the process. Therefore, there is a motivation to improve the withdraw of patient care processes, establish standardized procedures, ensure the accuracy of removal execution, enhance end-of-life care self-efficacy for nursing staff, and improve the overall quality of care. The investigation identified key issues: the lack of applicable guidelines for ICU care for withdraw from life-sustaining, insufficient education and training on withdraw and end-of-life care, scattered locations of withdraw-related tools, and inadequate self-efficacy in withdraw from life-sustaining care. Solutions proposed include revising withdraw care processes and guidelines, integrating tools and locations, conducting educational courses, and forming support groups. After the project implementation, the accuracy of removal cognition improved from 78% to 96.5%, self-efficacy in end-of-life care after removal increased from 54.7% to 93.1%, and the correctness of care behavior progressed from 27.7% to 97.8%. It is recommended to regularly conduct courses on removing life support system care and grief consolation to enhance the quality of end-of-life care.

Keywords: the intensive care unit (ICU) patients, nursing staff, withdraw life support systems, self-efficacy

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Authors: Fatima Z. Cherhabil, Mammar Sedrati, Sonia-Sabrina Bendib‎

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In order to support the continued growth, critical latency of ‎IoT ‎applications, and ‎various obstacles of traditional data centers, ‎mobile edge ‎computing (MEC) has ‎emerged as a promising solution that extends cloud data-processing and decision-making to edge devices. ‎By adopting a MEC structure, IoT applications could be executed ‎locally, on ‎an edge server, different fog nodes, or distant cloud ‎data centers. However, we are ‎often ‎faced with wanting to optimize conflicting criteria such as ‎minimizing energy ‎consumption of limited local capabilities (in terms of CPU, RAM, storage, bandwidth) of mobile edge ‎devices and trying to ‎keep ‎high performance (reducing ‎response time, increasing throughput and service availability) ‎at the same ‎time‎. Achieving one goal may affect the other, making task offloading (TO), ‎resource allocation (RA), and service placement (SP) complex ‎processes. ‎It is a nontrivial multi-objective optimization ‎problem ‎to study the trade-off between conflicting criteria. ‎The paper provides a survey on different TO, SP, and RA recent multi-‎objective optimization (MOO) approaches used in edge computing environments, particularly artificial intelligent (AI) ones, to satisfy various objectives, constraints, and dynamic conditions related to IoT applications‎.

Keywords: mobile edge computing, multi-objective optimization, artificial ‎intelligence ‎approaches, task offloading, resource allocation, ‎ service placement

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Authors: Zhi-Jun Lu, Qi Lu, Meng Wu, Qian Xiang, Jun Gu

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The analysis of perforated steel members is a 3D problem in nature, therefore the traditional analytical expressions for the ultimate load of thin-walled steel sections cannot be used for the perforated steel member design. In this study, finite element method (FEM) and artificial neural network (ANN) were used to simulate the process of stub column tests based on specific codes. Results show that compared with those of the FEM model, the ultimate load predictions obtained from ANN technique were much closer to those obtained from the physical experiments. The ANN model for the solving the hard problem of complex steel perforated sections is very promising.

Keywords: artificial neural network (ANN), finite element method (FEM), perforated sections, thin-walled Steel, ultimate load

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Authors: Shankar Kambhampaty, Harish Rohan Kambhampaty

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Data in IT systems in enterprises has been growing at a phenomenal pace. This has provided opportunities to run analytics to gather intelligence on key business parameters that enable them to provide better products and services to customers. While there are several artificial intelligence (AI/ML) and business intelligence (BI) tools and technologies available in the marketplace to run analytics, there is a need for an integrated view when developing intelligent solutions in enterprises. This paper progressively elaborates a reference model for enterprise solutions, builds an integrated view of data, information, and intelligence components, and presents a reference architecture for intelligent enterprise solutions. Finally, it applies the reference architecture to an insurance organization. The reference architecture is the outcome of experience and insights gathered from developing intelligent solutions for several organizations.

Keywords: architecture, model, intelligence, artificial intelligence, business intelligence, AI, BI, ML, analytics, enterprise

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Authors: Z. Zerdoumi, D. Benatia, , D. Chicouche

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This paper investigates the application of artificial neural network to the problem of nonlinear channel equalization. The difficulties caused by channel distortions such as inter symbol interference (ISI) and nonlinearity can overcome by nonlinear equalizers employing neural networks. It has been shown that multilayer perceptron based equalizer outperform significantly linear equalizers. We present a multilayer perceptron based equalizer with decision feedback (MLP-DFE) trained with the back propagation algorithm. The capacity of the MLP-DFE to deal with nonlinear channels is evaluated. From simulation results it can be noted that the MLP based DFE improves significantly the restored signal quality, the steady state mean square error (MSE), and minimum Bit Error Rate (BER), when comparing with its conventional counterpart.

Keywords: Artificial Neural Network, signal restoration, Nonlinear Channel equalization, equalization

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Authors: M. Ben Slimene, M.-S. Ouali

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Asset management of urban infrastructures faces a multitude of challenges that need to be overcome to obtain a reliable measurement of performances. Predicting the performance of slowly aging systems is one of those challenges, which helps the asset manager to investigate specific failure modes and to undertake the appropriate maintenance and rehabilitation interventions to avoid catastrophic failures as well as to optimize the maintenance costs. This article presents a methodology for modeling the deterioration of slowly degrading assets based on an operating history. It consists of extracting degradation profiles by grouping together assets that exhibit similar degradation sequences using an unsupervised classification technique derived from artificial intelligence. The obtained clusters are used to build the performance prediction models. This methodology is applied to a sample of a stormwater drainage culvert dataset.

Keywords: artificial Intelligence, clustering, culvert, regression model, slow degradation

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Authors: Amit R. Bhende, G. K. Awari

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Remaining useful life (RUL) prediction is one of key technologies to realize prognostics and health management that is being widely applied in many industrial systems to ensure high system availability over their life cycles. The present work proposes a data-driven method of RUL prediction based on multiple health state assessment for rolling element bearings. Bearing degradation data at three different conditions from run to failure is used. A RUL prediction model is separately built in each condition. Feed forward back propagation neural network models are developed for prediction modeling.

Keywords: bearing degradation data, remaining useful life (RUL), back propagation, prognosis

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Authors: Reggie Chandra

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Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

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Authors: Wasan Ismail Khalil, Hisham Khalid Ahmed, Zainab Ali

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Structural Lightweight Aggregate Concrete (SLWAC) has been developed in recent years because it reduces the dead load, cost, thermal conductivity and coefficient of thermal expansion of the structure. So SLWAC has the advantage of being a relatively green building material. Lightweight Aggregate (LWA) is either occurs as natural material such as pumice, scoria, etc. or as artificial material produced from different raw materials such as expanded shale, clay, slate, etc. The use of SLWAC in Iraq is limited due to the lack in natural LWA. The existence of Iraqi clay deposit with different types and characteristics leads to the idea of producing artificial expanded clay aggregate. The main aim in this work is to present of the properties of artificial LWA produced in the laboratory. Available local bentonite clay which occurs in the Western region of Iraq was used as raw material to produce the LWA. Sodium silicate as liquid industrial waste material from glass plant was mixed with bentonite clay in mix proportion 1:1 by weight. The manufacturing method of the lightweight aggregate including, preparation and mixing of clay and sodium silicate, burning of the mixture in the furnace at the temperature between 750-800˚C for two hours, and finally gradually cooling process. The produced LWA was then crushed to small pieces then screened on standard sieve series and prepared with grading which conforms to the specifications of LWA. The maximum aggregate size used in this investigation is 10 mm. The chemical composition and the physical properties of the produced LWA are investigated. The results indicate that the specific gravity of the produced LWA is 1.5 with the density of 543kg/m3 and water absorption of 20.7% which is in conformity with the international standard of LWA. Many trail mixes were carried out in order to produce LWAC containing the artificial LWA produced in this research. The selected mix proportion is 1:1.5:2 (cement: sand: aggregate) by weight with water to cement ratio of 0.45. The experimental results show that LWAC has oven dry density of 1720 kg/m3, water absorption of 8.5%, the thermal conductivity of 0.723 W/m.K and compressive strength of 23 N/mm2. The SLWAC produced in this research can be used in the construction of different thermal insulated buildings and masonry units. It can be concluded that the SLWA produced in this study contributes to sustainable development by, using industrial waste materials, conserving energy, enhancing the thermal and structural efficiency of concrete.

Keywords: expanded clay, lightweight aggregate, structural lightweight aggregate concrete, sustainable

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Authors: Moslem Taheri Soodejani, Mohammad Hassan Lotfi

Abstract:

Introduction: The outbreak of Covid-19 disease is an international public health concern. Therefore, the analysis of information related to mortality and disability due to COVID-19 is considered important, so the present study was designed and conducted with the aim of assessing COVID-19 Disability-Adjusted Life Years (DALYs) in Yazd. Methods: In Yazd province, all suspected cases of Covid-19 that would be referred to central hospitals in order to get confirmed through PCR or CT scan tests were recruited to our study. The fatality data of Covid- 19 was gathered from the forensic medicine organization. The Disability-Adjusted Life Years (DALYs) combines in one measure years of life lost (YLL), the loss of healthy life due to premature mortality and years of life lived with disability (YLD), the loss of healthy life because of disease and disability. Results: The total burden of COVID-19 was 23,472 years. The number of years lost due to premature death was 23385 and the number of years of life with disability due to COVID-19 was estimated to be 87 years. The disease burden was 12992 years for men and 10480 years for women. The overall incidence of COVID-19 was 1411 per 100,000, of which 1419 in men and 1402 in women per 100,000. Conclusion: The outbreak of the COVID-19 pandemic affected a large population and the residents of Yazd Province lost many years of their lives due to this disease.

Keywords: DALY, covid- 19, Yazd, Iran

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Authors: E. Vazquez-Santacruz, M. Gamboa-Zuniga

Abstract:

In this paper, we show a methodology for bodies classification in lying state using HOG descriptors and pressures sensors positioned in a matrix form (14 x 32 sensors) on the surface where bodies lie down. it will be done in real time. Our system is embedded in a care robot that can assist the elderly patient and medical staff around to get a better quality of life in and out of hospitals. Due to current technology a limited number of sensors is used, wich results in low-resolution data array, that will be used as image of 14 x 32 pixels. Our work considers the problem of human posture classification with few information (sensors), applying digital process to expand the original data of the sensors and so get more significant data for the classification, however, this is done with low-cost algorithms to ensure the real-time execution.

Keywords: real-time classification, sensors, robots, health care, elderly patients, artificial intelligence

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