Search results for: time prediction algorithms

Authors: Vesna Karapetkovska Hristova, M. Ayaz Ahmad, Julijana Tomovska, Biljana Bogdanova Popov, Blagojce Najdovski

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The average total concentrations of heavy metals; (cadmium [Cd], copper [Cu], nickel [Ni], lead [Pb], and zinc [Zn]) were analyzed in soil and vegetables samples collected from the different region of Macedonia during the years 2010-2012. Basic soil properties such as pH, organic matter and clay content were also included in the study. The average concentrations of Cd, Cu, Ni, Pb, Zn in the A horizon (0-30 cm) of agricultural soils were as follows, respectively: 0.25, 5.3, 6.9, 15.2, 26.3 mg kg-1 of soil. We have found that neural networking model can be considered as a tool for prediction and spatial analysis of the processes controlling the metal transfer within the soil-and vegetables. The predictive ability of such models is well over 80% as compared to 20% for typical regression models. A radial basic function network reflects good predicting accuracy and correlation coefficients between soil properties and metal content in vegetables much better than the back-propagation method. Neural Networking / soft computing can support the decision-making processes at different levels, including agro ecology, to improve crop management based on monitoring data and risk assessment of metal transfer from soils to vegetables.

Keywords: soft computing approach, total concentrations, heavy metals, agricultural soils

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Authors: Mohammed Jourdani, Hamid Mounir, Abdellatif El Marjani

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Proton exchange membrane fuel cells (PEMFCs) have been developed as a promising power source for transportation and stationary applications, and power devices for computers and mobile telephones. This paper discusses and summarizes the latest developments of materials and remaining challenges of PEMFC. The different contributions to the material of all components and the efficiencies are analyzed. Many technical advances are introduced to increase the PEMFC fuel cell efficiency and life time for transportation, stationary and portable utilization. By the last years the total cost of this system is decreasing. However, the remaining challenges that need to be overcome mean that it will be several years before full commercialization can take place.

Keywords: PEMFC fuel cell, materials, recent development, efficiency, life time, commercialization possibility

Procedia PDF Downloads 291

Authors: Kritiyaporn Kunsook

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Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), decision tree, support vector machines (SVMs), Naïve Bayes, and ensemble classifier by voting are powerful data driven methods that are relatively less widely used in the mapping of technique of system, and thus have not been comparatively evaluated together thoroughly in this field. The performances of a series of MLAs, ANNs, decision tree, SVMs, Naïve Bayes, and ensemble classifier by voting in technique of hydroponic systems prospectively modeling are compared based on the accuracy of each model. Classification of hydroponic systems only covers the test samples from vegetables grown with Nutrient film technique (NFT) and Deep flow technique (DFT). The feature, which are the characteristics of vegetables compose harvesting height width, temperature, require light and color. The results indicate that the classification performance of the ANNs is 98%, decision tree is 98%, SVMs is 97.33%, Naïve Bayes is 96.67%, and ensemble classifier by voting is 98.96% algorithm respectively.

Keywords: artificial neural networks, decision tree, support vector machines, naïve Bayes, ensemble classifier by voting

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Authors: Stelios A. Mitilineos, Nicolas-Alexander Tatlas, Georgia Korompili, Lampros Kokkalas, Stelios M. Potirakis

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Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is a widespread chronic disease that mostly remains undetected, mainly due to the fact that it is diagnosed via polysomnography which is a time and resource-intensive procedure. Screening the disease’s symptoms at home could be used as an alternative approach in order to alert individuals that potentially suffer from OSAHS without compromising their everyday routine. Since snoring is usually linked to OSAHS, developing a snore detector is appealing as an enabling technology for screening OSAHS at home using ubiquitous equipment like commodity microphones (included in, e.g., smartphones). In this context, this study developed a snore detection tool and herein present the approach and selection of specific sound features that discriminate snoring vs. environmental sounds, as well as the performance of the proposed tool. Furthermore, a Real-Time Snore Detector (RTSD) is built upon the snore detection tool and employed in whole-night sleep sound recordings resulting to a large dataset of snoring sound excerpts that are made freely available to the public. The RTSD may be used either as a stand-alone tool that offers insight to an individual’s sleep quality or as an independent component of OSAHS screening applications in future developments.

Keywords: obstructive sleep apnea hypopnea syndrome, apnea screening, snoring detection, machine learning, neural networks

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Authors: Ilaria Lucrezia Amerise

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Electricity markets throughout the world have undergone substantial changes. Accurate, reliable, clear and comprehensible modeling and forecasting of different variables (loads and prices in the first instance) have achieved increasing importance. In this paper, we describe the actual state of the art focusing on reg-SARMA methods, which have proven to be flexible enough to accommodate the electricity price/load behavior satisfactory. More specifically, we will discuss: 1) The dichotomy between point and interval forecasts; 2) The difficult choice between stochastic (e.g. climatic variation) and non-deterministic predictors (e.g. calendar variables); 3) The confrontation between modelling a single aggregate time series or creating separated and potentially different models of sub-series. The noteworthy point that we would like to make it emerge is that prices and loads require different approaches that appear irreconcilable even though must be made reconcilable for the interests and activities of energy companies.

Keywords: interval forecasts, time series, electricity prices, reg-SARIMA methods

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Authors: Gautam Kumar Saharia, Sagardeep Talukdar, Riki Dutta, Sudipta Nandy

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The physics informed neural network (PINN) method opens up an approach for numerically solving nonlinear partial differential equations leveraging fast calculating speed and high precession of modern computing systems. We construct the PINN based on strong universal approximation theorem and apply the initial-boundary value data and residual collocation points to weekly impose initial and boundary condition to the neural network and choose the optimization algorithms adaptive moment estimation (ADAM) and Limited-memory Broyden-Fletcher-Golfard-Shanno (L-BFGS) algorithm to optimize learnable parameter of the neural network. Next, we improve the PINN with a weighted loss function to obtain both the bright and dark soliton solutions of Fokas-Lenells equation (FLE). We find the proposed scheme of adjustable weight coefficients into PINN has a better convergence rate and generalizability than the basic PINN algorithm. We believe that the PINN approach to solve the partial differential equation appearing in nonlinear optics would be useful to study various optical phenomena.

Keywords: deep learning, optical Soliton, neural network, partial differential equation

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Authors: Randall E. Osborne, Seth A. Doty

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As participation in organized sports increases, so does the risk of sustaining an athletic injury. These unfortunate injuries result in missed time from practice and, inevitably, the field of competition. Recovery time plays a pivotal role in the overall rehabilitation of the athlete. With time and rehabilitation, an athlete’s physical injury can be properly treated. However, there seem to be few measures assessing psychological recovery from injury. Although an athlete has been cleared to return to play, there may still be lingering doubt about their injury. Overall, there is a vast difference between being physically cleared to play and being psychologically ready to return to play. Certain personality traits might serve as predictors of an individual’s rate of psychological recovery from an injury. The purpose of this research study is to explore the correlations between athletes’ personality and their recovery from an athletic injury, specifically, examining how locus of control has been utilized through other studies and can be beneficial to the current study. Additionally, this section will examine the link between hardiness and coping strategies. In the current study, mental toughness is being tested, but it is important to determine the link between these two concepts. Hardiness and coping strategies are closely related and can play a major role in an athlete’s mental toughness. It is important to examine competitive trait anxiety to illustrate perceived anxiety during athletic competition. The Big 5 and Social Support will also be examined in conjunction with recovery from athletic injury. Athletic injury is a devastating and common occurrence that can happen in any sport. Injured athletes often require resources and treatment to be able to return to the field of play. Athletes become more involved with physical and mental treatment as the length of recovery time increases. It is very reasonable to assume that personality traits would be predictive of athlete recovery from injury. The current study investigated the potential relationship between personality traits and recovery time; more specifically, the personality traits of locus of control, hardiness, social support, competitive trait anxiety, and the “Big 5” personality traits. Results indicated that athletes with a higher internal locus of control tend to report being physically ready to return to play and “ready” to return to play faster than those with an external locus of control. Additionally, Openness to Experience (among the Big 5 personality dimensions) was also related to the speed of return to play.

Keywords: athlete, injury, personality, readiness to play, recovery

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Authors: Emna Benmohamed, Hela Ltifi, Mounir Ben Ayed

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Bayesian Network (BN) is one of the most efficient classification methods. It is widely used in several fields (i.e., medical diagnostics, risk analysis, bioinformatics research). The BN is defined as a probabilistic graphical model that represents a formalism for reasoning under uncertainty. This classification method has a high-performance rate in the extraction of new knowledge from data. The construction of this model consists of two phases for structure learning and parameter learning. For solving this problem, the K2 algorithm is one of the representative data-driven algorithms, which is based on score and search approach. In addition, the integration of the expert's knowledge in the structure learning process allows the obtainment of the highest accuracy. In this paper, we propose a hybrid approach combining the improvement of the K2 algorithm called K2 algorithm for Parents and Children search (K2PC) and the expert-driven method for learning the structure of BN. The evaluation of the experimental results, using the well-known benchmarks, proves that our K2PC algorithm has better performance in terms of correct structure detection. The real application of our model shows its efficiency in the analysis of the phosphate laundry effluents' impact on the watershed in the Gafsa area (southwestern Tunisia).

Keywords: Bayesian network, classification, expert knowledge, structure learning, surface water analysis

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Authors: Pavlo Selyshchev, Tetiana Didenko

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Via formalism of the Complex systems and in the framework of the climb - glide model a theoretical approach to describe the influence of irradiation on transient creep of metals. We consider metal under such stress and conditions of irradiation at which creep is determined by dislocation motion that consists in climb and glide. It is shown that there are qualitatively different regimes of a creep as a result of irradiation. Simulation and analysis of this phenomenon are performed. The time dependence of creep rate of metal under an irradiation is theoretically obtained. The conditions of zero minimums of the creep-rate existence as well as the times of their appearance are determined. The changing of the position of creep-rate dips in the conditions of the temperature exposure change is investigated. The obtained results are compared with the experimentally observed dependence of the creep rate on time.

Keywords: creep, climb and glide of dislocations, irradiation, non-linear feed-back, point defects

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Authors: Gerard Van Es

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In this study, the use of a high resolution surface laser scanner to assess the micro texture of runway surfaces was investigated experimentally. Micro texture is one of the important surface components that helps to provide high braking friction between aircraft tires and a wet runway surface. Algorithms to derive different parameters that characterise micro texture was developed. Surface scans with a high resolution laser scanner were conducted on 40 different runway (like) surfaces. For each surface micro texture parameters were calculated from the laser scan data. These results were correlated with results obtained from a British pendulum tester that was used on the same surface. Results obtained with the British pendulum tester are generally considered to be indicative for the micro texture related friction characteristics. The results show that a meaningful correlation can be found between different parameters that characterise micro texture obtained with the laser scanner and the British pendulum tester results. Surface laser scanners are easier to operate and give more consistent results than a British pendulum tester. Therefore for airport operators surface laser scanners can be a useful tool to determine if their runway becomes slippery when wet due to a smooth micro texture.

Keywords: runway friction, micro texture, aircraft braking performance, slippery runways

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Authors: M. Khalid, W. Rashmi, L. L. Kwan

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This work provides an overview on the experimental and numerical simulations of various nanofluids and their flow and heat transfer behavior. It was further extended to study the effect of nanoparticle concentration, fluid flow rates and thermo-physical properties on the heat transfer enhancement of Al2O3/water nanofluid in a turbulent flow circular conduit using ANSYS FLUENT™ 14.0. Single-phase approximation (homogeneous model) and two-phase (mixture and Eulerian) models were used to simulate the nanofluid flow behavior in the 3-D horizontal pipe. The numerical results were further validated with experimental correlations reported in the literature. It was found that heat transfer of nanofluids increases with increasing particle volume concentration and Reynolds number, respectively. Results showed good agreement (~9% deviation) with the experimental correlations, especially for a single-phase model with constant properties. Among two-phase models, mixture model (~14% deviation) showed better prediction compared to Eulerian-dispersed model (~18% deviation) when temperature independent properties were used. Non-drag forces were also employed in the Eulerian two-phase model. However, the two-phase mixture model with temperature dependent nanofluid properties gave slightly closer agreement (~12% deviation).

Keywords: nanofluid, CFD, heat transfer, forced convection, circular conduit

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Authors: Ergün Şakalar, Şeyma Özçirak Ergün, Emrah Yalazi̇, Emine Altinkaya, Cengiz Ataşoğlu

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In recent years, food allergies which cause serious health problems affect to public health around the world. Foodstuffs which contain allergens are either intentionally used as ingredients or are encased as contaminant in food products. The prevalence of clinical allergy to peanuts and nuts is estimated at about 0.4%-1.1% of the adult population, representing the allergy to pistachio the 7% of the cases of tree nut causing allergic reactions. In order to protect public health and enforce the legislation, methods for sensitive analysis of pistachio and peanut contents in food are required. Pea, pistachio and peanut are used together, to reduce the cost in food production such as baklava, snack foods.DNA technology-based methods in food analysis are well-established and well-roundedtools for species differentiation, allergen detection. Especially, the probe-based TaqMan real-time PCR assay can amplify target DNA with efficiency, specificity, and sensitivity.In this study, pistachio, peanut and pea were finely ground and three separate series of triplet mixtures containing 0.1, 1, 10, 100, 1000, 10,000 and 100,000 mg kg-1 of each sample were prepared for each series, to a final weight of 100 g. DNA from reference samples and industrial products was successfully extracted with the GIDAGEN® Multi-Fast DNA Isolation Kit. TaqMan probes were designed for triplex determination of ITS, Ara h 3 and pea lectin genes which are specific regions for identification pistachio, peanut and pea, respectively.The real-time PCR as quantitative detected pistachio, peanut and pea in these mixtures down to the lowest investigated level of 0.1, 0.1 and 1 mg kg-1, respectively. Also, the methods reported here are capable of detecting of as little as 0.001% level of peanut DNA, 0,000001% level of pistachio DNA and 0.000001% level of pea DNA. We accomplish that the quantitative triplex real-time PCR method developed in this study canbe applied to detect pistachio, peanut and peatraces for three allergens at once in commercial food products.

Keywords: allergens, DNA, real-time PCR, TaqMan probe

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Authors: Jaishree Ranganathan, Poonam Rajurkar, Angelina A. Tzacheva, Zbigniew W. Ras

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In today’s world, business often depends on the customer feedback and reviews. Sentiment analysis helps identify and extract information about the sentiment or emotion of the of the topic or document. Attribute selection is a challenging problem, especially with large datasets in actionable pattern mining algorithms. Action Rule Mining is one of the methods to discover actionable patterns from data. Action Rules are rules that help describe specific actions to be made in the form of conditions that help achieve the desired outcome. The rules help to change from any undesirable or negative state to a more desirable or positive state. In this paper, we present a Lexicon based weighted scheme approach to identify emotions from customer feedback data in the area of manufacturing business. Also, we use Rough sets and explore the attribute selection method for large scale datasets. Then we apply Actionable pattern mining to extract possible emotion change recommendations. This kind of recommendations help business analyst to improve their customer service which leads to customer satisfaction and increase sales revenue.

Keywords: actionable pattern discovery, attribute selection, business data, data mining, emotion

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Authors: Priyanka Dargode, Suhas Gore, Manju Sharma, Arvind Lali

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Anaerobic digestion has been widely used for production of methane from different biowastes. However, the complexity of microbial communities involved in the process is poorly understood. The performance of biogas production process concerning the process productivity is closely coupled to its microbial community structure and syntrophic interactions amongst the community members. The present study aims at understanding taxonomic variations occurring in any starter inoculum when acclimatised to different lignocellulosic biomass (LBM) feedstocks relating to time of digestion. The work underlines use of high throughput Next Generation Sequencing (NGS) for validating the changes in taxonomic patterns of microbial communities. Biomethane Potential (BMP) batches were set up with different pretreated and non-pretreated LBM residues using the same microbial consortium and samples were withdrawn for studying the changes in microbial community in terms of its structure and predominance with respect to changes in metabolic profile of the process. DNA of samples withdrawn at different time intervals with reference to performance changes of the digestion process, was extracted followed by its 16S rRNA amplicon sequencing analysis using Illumina Platform. Biomethane potential and substrate consumption was monitored using Gas Chromatography(GC) and reduction in COD (Chemical Oxygen Demand) respectively. Taxonomic analysis by QIIME server data revealed that microbial community structure changes with different substrates as well as at different time intervals. It was observed that biomethane potential of each substrate was relatively similar but, the time required for substrate utilization and its conversion to biomethane was different for different substrates. This could be attributed to the nature of substrate and consequently the discrepancy between the dominance of microbial communities with regards to different substrate and at different phases of anaerobic digestion process. Knowledge of microbial communities involved would allow a rational substrate specific consortium design which will help to reduce consortium adaptation period and enhance the substrate utilisation resulting in improved efficacy of biogas process.

Keywords: amplicon sequencing, biomethane potential, community predominance, taxonomic analysis

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Authors: Valentina Di Maria, Anton Ianakiev

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The use of adhesive anchors for wooden constructions is an efficient technology to connect and design timber members in new timber structures and to rehabilitate the damaged structural members of historical buildings. Due to the lack of standard regulation in this specific area of structural design, designers’ choices are still supported by test analysis that enables knowledge, and the prediction, of the structural behavior of glued in rod joints. The paper outlines an experimental research activity aimed at identifying the tensile resistance capacity of several new adhesive joint prototypes made of epoxy resin, steel bar and timber, Oak and Douglas Fir species. The development of new adhesive connectors has been carried out by using epoxy to glue stainless steel bars into pre-drilled holes, characterized by smooth and rough internal surfaces, in timber samples. The realization of a threaded contact surface using a specific drill bit has led to an improved bond between wood and epoxy. The applied changes have also reduced the cost of the joints’ production. The paper presents the results of this parametric analysis and a Finite Element analysis that enables identification and study of the internal stress distribution in the proposed adhesive anchors.

Keywords: glued in rod joints, adhesive anchors, timber, epoxy, rough contact surface, threaded hole shape

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Authors: Abdurrahman Karacabey

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Threat paradigm has shifted throughout the history. Considering conjuncture of our time, a major threat for humanity is terrorism. Although variety of reasons are influential, financial, and human resources are the vital needs for terrorist groups. It is known that terrorism is a significant term while taking decisions in diplomatic, politic, and military issues. Even though the methods to provide resources for terrorism are quite similar, there are still some differences for deterrent terrorist groups being active in various regions of the globe. Due to social and psychological reasons activists have generally similar excuses to join terrorist groups.At the same time, terrorists’ fiscal activities to secure permanence of terrorism, occupy the politics of the countries. Besides, preventive actions are expensive creating huge burdens in host nation’s economy. This paper elaborates on how ISIS is providing human and economic resources, course of actions to overcome ISIS is on the agenda of all countries.

Keywords: financial resources, human resources, isis, terrorism

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Authors: Arian Hosseini, Mahmudul Hasan

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To address the increasing need for efficient and accurate content moderation, we propose an efficient and lightweight deep classification ensemble structure. Our approach is based on a combination of simple visual features, designed for high-accuracy classification of violent content with low false positives. Our ensemble architecture utilizes a set of lightweight models with narrowed-down color features, and we apply it to both images and videos. We evaluated our approach using a large dataset of explosion and blast contents and compared its performance to popular deep learning models such as ResNet-50. Our evaluation results demonstrate significant improvements in prediction accuracy, while benefiting from 7.64x faster inference and lower computation cost. While our approach is tailored to explosion detection, it can be applied to other similar content moderation and violence detection use cases as well. Based on our experiments, we propose a "think small, think many" philosophy in classification scenarios. We argue that transforming a single, large, monolithic deep model into a verification-based step model ensemble of multiple small, simple, and lightweight models with narrowed-down visual features can possibly lead to predictions with higher accuracy.

Keywords: deep classification, content moderation, ensemble learning, explosion detection, video processing

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Authors: Jean-Francois Rajotte, Martin Sotir, Frank Gouineau

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The installation and management of traffic monitoring devices can be costly from both a financial and resource point of view. It is therefore important to take advantage of in-place infrastructures to extract the most information. Here we show how low-quality urban road traffic images from cameras already available in many cities (such as Montreal, Vancouver, and Toronto) can be used to estimate traffic flow. To this end, we use a pre-trained neural network, developed for object detection, to count vehicles within images. We then compare the results with human annotations gathered through crowdsourcing campaigns. We use this comparison to assess performance and calibrate the neural network annotations. As a use case, we consider six months of continuous monitoring over hundreds of cameras installed in the city of Montreal. We compare the results with city-provided manual traffic counting performed in similar conditions at the same location. The good performance of our system allows us to consider applications which can monitor the traffic conditions in near real-time, making the counting usable for traffic-related services. Furthermore, the resulting annotations pave the way for building a historical vehicle counting dataset to be used for analysing the impact of road traffic on many city-related issues, such as urban planning, security, and pollution.

Keywords: traffic monitoring, deep learning, image annotation, vehicles, roads, artificial intelligence, real-time systems

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Authors: Anurag Reddy Ramireddy, Abrar Ahmed, G. Sourya Sri Harsha, Pushkala Muralidharan

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Supply chains have been developing over time since the inception of commercial trade and barter. The Green Supply Chain Management (GSCM) is a powerful way to differentiate a company from its competitors and it can greatly influence the plan success. With increased awareness to corporate responsibility and the requirement to meet the terms with environmental policy, GSCM is becoming increasingly important for companies. This paper explains the concept of green supply chain management, the difference between conventional supply chain management and green supply management and how GSCM benefits organizations while at the same time supporting a sustainable environment system. An effort has also been made to analyse research already done in this field while exploring the challenges and barriers that organizations face in implementing GSCM practices in their existing systems.

Keywords: corporate social responsibility, green supply chain management, sustainability

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Authors: Meitham Amereh, Mohsen Akbari, Ben Nadler

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Cancer has been recognized as one of the most challenging problems in biology and medicine. Aggressive tumors are a lethal type of cancers characterized by high genomic instability, rapid progression, invasiveness, and therapeutic resistance. Their behavior involves complicated molecular biology and consequential dynamics. Although tremendous effort has been devoted to developing therapeutic approaches, there is still a huge need for new insights into the dark aspects of tumors. As one of the key requirements in better understanding the complex behavior of tumors, mathematical modeling and continuum physics, in particular, play a pivotal role. Mathematical modeling can provide a quantitative prediction on biological processes and help interpret complicated physiological interactions in tumors microenvironment. The pathophysiology of aggressive tumors is strongly affected by the extracellular cues such as stresses produced by mechanical forces between the tumor and the host tissue. During the tumor progression, the growing mass displaces the surrounding extracellular matrix (ECM), and due to the level of tissue stiffness, stress accumulates inside the tumor. The produced stress can influence the tumor by breaking adherent junctions. During this process, the tumor stops the rapid proliferation and begins to remodel its shape to preserve the homeostatic equilibrium state. To reach this, the tumor, in turn, upregulates epithelial to mesenchymal transit-inducing transcription factors (EMT-TFs). These EMT-TFs are involved in various signaling cascades, which are often associated with tumor invasiveness and malignancy. In this work, we modeled the tumor as a growing hyperplastic mass and investigated the effects of mechanical stress from surrounding ECM on tumor invasion. The invasion is modeled as volume-preserving inelastic evolution. In this framework, principal balance laws are considered for tumor mass, linear momentum, and diffusion of nutrients. Also, mechanical interactions between the tumor and ECM is modeled using Ciarlet constitutive strain energy function, and dissipation inequality is utilized to model the volumetric growth rate. System parameters, such as rate of nutrient uptake and cell proliferation, are obtained experimentally. To validate the model, human Glioblastoma multiforme (hGBM) tumor spheroids were incorporated inside Matrigel/Alginate composite hydrogel and was injected into a microfluidic chip to mimic the tumor’s natural microenvironment. The invasion structure was analyzed by imaging the spheroid over time. Also, the expression of transcriptional factors involved in invasion was measured by immune-staining the tumor. The volumetric growth, stress distribution, and inelastic evolution of tumors were predicted by the model. Results showed that the level of invasion is in direct correlation with the level of predicted stress within the tumor. Moreover, the invasion length measured by fluorescent imaging was shown to be related to the inelastic evolution of tumors obtained by the model.

Keywords: cancer, invasion, mathematical modeling, microfluidic chip, tumor spheroids

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Authors: Soofiyan Atar, Adil Shaikh, Sahil Rajpurkar, Pragnesh Bhalala, Aniket Desai, Irfan Siddavatam

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Deep reinforcement learning (deep RL) algorithms leverage the symbolic power of complex controllers by automating it by mapping sensory inputs to low-level actions. Deep RL eliminates the complex robot dynamics with minimal engineering. Deep RL provides high-risk involvement by directly implementing it in real-world scenarios and also high sensitivity towards hyperparameters. Tuning of hyperparameters on a pneumatic quadruped robot becomes very expensive through trial-and-error learning. This paper presents an automated learning control for a pneumatic quadruped robot using sample efficient deep Q learning, enabling minimal tuning and very few trials to learn the neural network. Long training hours may degrade the pneumatic cylinder due to jerk actions originated through stochastic weights. We applied this method to the pneumatic quadruped robot, which resulted in a hopping gait. In our process, we eliminated the use of a simulator and acquired a stable gait. This approach evolves so that the resultant gait matures more sturdy towards any stochastic changes in the environment. We further show that our algorithm performed very well as compared to programmed gait using robot dynamics.

Keywords: model-based reinforcement learning, gait stability, supervised learning, pneumatic quadruped

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Authors: Ali Pourkazemi

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The transient radar method (TRM) is one of the non-destructive methods that was introduced by authors a few years ago. The transient radar method can be classified as a wave-based non destructive testing (NDT) method that can be used in a wide frequency range. Nevertheless, it requires a narrow band, ranging from a few GHz to a few THz, depending on the application. As a time-of-flight and real-time method, TRM can measure the electromagnetic properties of the sample under test not only quickly and accurately, but also blindly. This means that it requires no prior knowledge of the sample under test. For multi-layer structures, TRM is not only able to detect changes related to any parameter within the multi-layer structure but can also measure the electromagnetic properties of each layer and its thickness individually. Although the temperature, humidity, and general environmental conditions may affect the sample under test, they do not affect the accuracy of the Blind TRM algorithm. In this paper, the electromagnetic properties as well as the thickness of the individual building insulation materials - as a single-layer structure - are measured experimentally. Finally, the correlation between the reflection coefficients and some other technical parameters such as sound insulation, thermal resistance, thermal conductivity, compressive strength, and density is investigated. The sample to be studied is 30 cm x 50 cm and the thickness of the samples varies from a few millimeters to 6 centimeters. This experiment is performed with both biostatic and differential hardware at 10 GHz. Since it is a narrow-band system, high-speed computation for analysis, free-space application, and real-time sensor, it has a wide range of potential applications, e.g., in the construction industry, rubber industry, piping industry, wind energy industry, automotive industry, biotechnology, food industry, pharmaceuticals, etc. Detection of metallic, plastic pipes wires, etc. through or behind the walls are specific applications for the construction industry.

Keywords: transient radar method, blind electromagnetic geometrical parameter extraction technique, ultrafast nondestructive multilayer dielectric structure characterization, electronic measurement systems, illumination, data acquisition performance, submillimeter depth resolution, time-dependent reflected electromagnetic signal blind analysis method, EM signal blind analysis method, time domain reflectometer, microwave, milimeter wave frequencies

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Authors: Hossein Arzani, Masoud Jafari Shalamzari, Z. Arzani

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This paper follows FAO model of suitability analysis. Influential factors affecting extensive grazing were determined and converted into a model. Taleghan rangelands were examined for common types of grazing animals as an example. Advantages and limitations were elicited. All range ecosystems’ components affect range suitability but due to the time and money restrictions, the most important and feasible elements were investigated. From which three sub-models including water accessibility, forage production and erosion sensitivity were considered. Suitable areas in four levels of suitability were calculated using GIS. This suitability modeling approach was adopted due to its simplicity and the minimal time that is required for transforming and analyzing the data sets. Managers could be benefited from the model to devise the measures more wisely to cope with the limitations and enhance the rangelands health and condition.

Keywords: range suitability, land-use, extensive grazing, modeling, land evaluation

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Authors: Friday Godwin Okibe, Elaoyi David Paul, Oladayo Thomas Ojekunle

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In this study, Nitrogen and Phosphorous co-doped Zinc Oxide (NPZ) was prepared through a solvent-free reaction. The NPZ was characterized by Scanning Electron Microscopy (SEM) and Fourier Transform Infrared (FTIR) spectroscopy. The photocatalytic activity of the catalyst was investigated by monitoring the degradation of para-nitrophenol (PNP) under visible light irradiation and the process was optimized using factorial design of experiment. The factors investigated were initial concentration of para-nitrophenol, catalyst loading, pH and irradiation time. The characterization results revealed a successful doping of ZnO by nitrogen and phosphorus and an improvement in the surface morphology of the catalyst. The photo-catalyst exhibited improved photocatalytic activity under visible light by 73.8%. The statistical analysis of the optimization result showed that the model terms were significant at 95% confidence level. Interactions plots revealed that irradiation time was the most significant factor affecting the degradation process. The cube plots of the interactions of the variables showed that an optimum degradation efficiency of 66.9% was achieved at 10mg/L initial PNP concentration, 0.5g catalyst loading, pH 7 and 150 minutes irradiation time.

Keywords: nitrogen and phosphorous co-doped Zno, p-nitrophenol, photocatalytic degradation, optimization, factorial design of experimental

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Authors: Maryam Kalkatechi

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The application of algorithms in architecture has been realized as geometric forms which are increasingly being used by architecture firms. The abstraction of ideas in a formulated algorithm is not possible. There is still a gap between design innovation and final built in prescribed formulas, even the most aesthetical realizations. This paper presents the application of erudite design process to conceptualize biomimetic paradigms in architecture. The process is customized to material and tectonics. The first part of the paper outlines the design process elements within four biomimetic pre-concepts. The pre-concepts are chosen from plants family. These include the pine leaf, the dandelion flower; the cactus flower and the sun flower. The choice of these are related to material qualities and natural pattern of the tectonics of these plants. It then focuses on four versions of tectonic comprehension of one of the biomimetic pre-concepts. The next part of the paper discusses the implementation of STEAM in higher education in architecture. This is shown by the relations within the design process and the manifestation of the thinking processes. The A in the SETAM, in this case, is only achieved by the design process, an engaging event as a performing arts, in which the conceptualization and development is realized in final built.

Keywords: biomimetic paradigm, erudite design process, tectonic, STEAM (Science, Technology, Engineering, Arts, Mathematic)

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Authors: Yeghnem Redha, Guerroudj Hicham Zakaria, Hanifi Hachemi Amar Lemiya, Meftah Sid Ahmed, Tounsi Abdelouahed, Adda Bedia El Abbas

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Creep deformation of concrete is often responsible for excessive deflection at service loads which can compromise the performance of elements within a structure. Although laboratory test may be undertaken to determine the deformation properties of concrete, these are time-consuming, often expensive and generally not a practical option. Therefore, relatively simple empirically design code models are relied to predict the creep strain. This paper reviews the accuracy of creep and shrinkage predictions of reinforced concrete (RC) shear walls structures strengthened with carbon fibre reinforced polymer (CFRP) sheets, which is characterized by a widthwise varying fibre volume fraction. This review is yielded by CEB-FIB MC90 model. The time-dependent behavior was investigated to analyze their static behavior. In the numerical formulation, the adherents and the adhesives are all modelled as shear wall elements, using the mixed finite element method. Several tests were used to dem¬onstrate the accuracy and effectiveness of the proposed method. Numerical results from the present analysis are presented to illustrate the significance of the time-dependency of the lateral displacements.

Keywords: RC shear walls strengthened, CFRP sheets, creep and shrinkage, CEB-FIP MC90 model, finite element method, static behavior

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Authors: Strahinja Z. Kovačević, Lidija R. Jevrić, Sanja O. Podunavac Kuzmanović

Abstract:

The relationship between antibacterial activity of eighteen different substituted benzimidazole derivatives and their molecular characteristics was studied using chemometric QSAR (Quantitative Structure–Activity Relationships) approach. QSAR analysis has been carried out on inhibitory activity towards Staphylococcus aureus, by using molecular descriptors, as well as minimal inhibitory activity (MIC). Molecular descriptors were calculated from the optimized structures. Principal component analysis (PCA) followed by hierarchical cluster analysis (HCA) and multiple linear regression (MLR) was performed in order to select molecular descriptors that best describe the antibacterial behavior of the compounds investigated, and to determine the similarities between molecules. The HCA grouped the molecules in separated clusters which have the similar inhibitory activity. PCA showed very similar classification of molecules as the HCA, and displayed which descriptors contribute to that classification. MLR equations, that represent MIC as a function of the in silico molecular descriptors were established. The statistical significance of the estimated models was confirmed by standard statistical measures and cross-validation parameters (SD = 0.0816, F = 46.27, R = 0.9791, R2CV = 0.8266, R2adj = 0.9379, PRESS = 0.1116). These parameters indicate the possibility of application of the established chemometric models in prediction of the antibacterial behaviour of studied derivatives and structurally very similar compounds.

Keywords: antibacterial, benzimidazole, molecular descriptors, QSAR

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Authors: Nur Mohammad Ali, Md. Shafiqul Alam, Jayanta Bhusan Deb, Nowrin Sharmin

Abstract:

The past ten years have seen significant expansion in the aviation sector, which during the previous five years has steadily pushed emerging countries closer to economic independence. It is crucial to accurately forecast the potential demand for air travel to make long-term financial plans. To forecast market demand for low-cost passenger carriers, this study suggests working with low-cost airlines, airports, consultancies, and governmental institutions' strategic planning divisions. The study aims to develop an artificial intelligence-based methods, notably fuzzy inference systems (FIS), to determine the most accurate forecasting technique for domestic low-cost carrier demand in Bangladesh. To give end users real-world applications, the study includes nine variables, two sub-FIS, and one final Mamdani Fuzzy Inference System utilizing a graphical user interface (GUI) made with the app designer tool. The evaluation criteria used in this inquiry included mean square error (MSE), accuracy, precision, sensitivity, and specificity. The effectiveness of the developed air passenger demand prediction FIS is assessed using 240 data sets, and the accuracy, precision, sensitivity, specificity, and MSE values are 90.83%, 91.09%, 90.77%, and 2.09%, respectively.

Keywords: aviation industry, fuzzy inference system, membership function, graphical user interference

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Authors: Dilsad Akal

Abstract:

Aim: Companies communicate with each other and with their costumers via call centers. Call centers are defined as stressful because of their uncertain working hours, inadequate relief time, performance based system and heavy workload. In literature, this sector is defined as risky as mining sector by means of health and safety. The aim of this research is to enlight the relatively dark area. Subject and Methods: The collection of data for this study completed during April-May 2015 for the two selected call centers in different parts of Turkey. The applied question mostly investigated the health conditions of call center workers. Electromagnetic field measurements were completed at the same time with applying the question poll. The ratio of employee accessibility noted as 73% for the first call center and 87% for the second. Results: The results of electromagnetic field measurements were as between 371 V/m-32 V/m for the first location and between 370 V/m-61 V/m for the second. The general complaints of the employees for both workplaces can be counted as; inadequate relief time, inadequate air conditioning, disturbance, poor thermal conditions, inadequate or extreme lighting. Furthermore, musculoskeletal discomfort, stress, ear and eye discomfort are main health problems of employees. Conclusion: The measured values and the responses to the question poll were found parallel with the other similar research results in literature. At the end of this survey, a risk map of workplace was prepared in terms of safety and health at work in general and some suggestions for resolution were provided.

Keywords: call center, health and safety, electromagnetic field, risk map

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Authors: Anshul, Rajni Kalia, Sachin Kumar

Abstract:

Introduction: The costoclavicular approach, a modification to the infraclavicular approach, has been described for anesthesia for upper limb surgeries. Material And Methods: In this randomized and single-blind study, fourty patients undergoing emergency/elective upper limb surgery were allocated to two groups. Group C and S received ultrasound-guided Costoclavicular block and Supraclavicular block, respectively, with 20 ml 0.5 % ropivacaine with 8 mg dexamethasone under strict asepsis. The primary outcome assessed was the total duration of sensory and motor block in the postoperative period. Secondary outcomes were to compare the time taken to perform the procedure, block characteristics in terms of onset of motor and sensory blockade, the efficacy of analgesia with respect to the time of administration of the first rescue analgesic dose with both the blocks and note the side effects pertaining to either of the blocks. Results: The mean total duration of sensory and motor blockade was longer in group C vs. group S (p=0.002 and 0.024, respectively). The mean duration to perform a block in group S was more than in group C (p=0.012). The mean onset of sensory and motor Blockade Time in group S was more than in group C (p<0.001 and <0.001, respectively). The mean duration to perform a block in group S was more than in group C (p=0.012). Conclusion: The costoclavicular approach is better than supraclavicular in terms of rapid execution, faster onset of sensory-motor blockade, prolonged postoperative analgesia and similar PONV and safety profile.

Keywords: costoclavicular, supraclavicular, ropivacaine, dexamethasone

Procedia PDF Downloads 55