Search results for: machine performance

Authors: Fahem Belkacemi, Lyes Ouali

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Maintenance strategies and assessments continue to be a major concern for companies today. The socio-economic bets of their competitiveness are closely linked to the activities and quality of maintenance. This work deals with a study of a preventive maintenance plan to improve the availability of the production line within SAMSUNG HOME APPLIANCE “SAMHA”, Setif, Algeria. First, we applied the method of analysis of failure modes, their impact, and criticality to reduce downtime and identification of the most critical elements. Finally, to improve the availability of the production line, we carried out a study of the current preventive maintenance plan in the production line workshop at the company level and according to the history sheet of machine failures. We proposed a preventive maintenance plan to improve the availability of the production line.

Keywords: preventive maintenance, DRUM line, AMDEC, availability

Procedia PDF Downloads 47

Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

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Authors: Muhammet Baldan, Emel Timuçin

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Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems.

Keywords: solubility, random forest, molecular descriptors, maccs keys

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Authors: Caroline Provost, Francois Dumont

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Production of insects for human and animal consumption is an increasingly important activity in Canada. Protein production is more efficient and less harmful to the environment using insect rearing compared to the impact of traditional livestock, poultry and fish farms. Insects are rich in essential amino acids, essential fatty acids and trace elements. Thus, insect-based products could be used as a food supplement for livestock and domestic animals and may even find their way into the diets of high performing athletes or fine dining. Nevertheless, several parameters remain to be determined to ensure efficient and profitable production that meet the potential of these sectors. This project proposes to improve the production processes, rearing diets and processing methods for three species with valuable gastronomic and nutritional potential: the common mealworms (Tenebrio molitor), the small mealworm (Alphitobius diaperinus), and the giant mealworm (Zophobas morio). The general objective of the project is to acquire specific knowledge for mass rearing of insects dedicated to animal and human consumption in order to respond to current market opportunities and meet a growing demand for these products. Mass rearing of the three species of mealworm was produced to provide the individuals needed for the experiments. Mealworms eat flour from different cereals (e.g. wheat, barley, buckwheat). These cereals vary in their composition (protein, carbohydrates, fiber, vitamins, antioxidant, etc.), but also in their purchase cost. Seven different diets were compared to optimize the yield of the rearing. Diets were composed of cereal flour (e.g. wheat, barley) and were either mixed or left alone. Female fecundity, larvae mortality and growing curves were observed. Some flour diets have positive effects on female fecundity and larvae performance while each mealworm was found to have specific diet requirements. Trade-offs between mealworm performance and costs need to be considered. Experiments on the effect of flour composition on several parameters related to performance and nutritional and gastronomic value led to the identification of a more appropriate diet for each mealworm.

Keywords: mass rearing, mealworm, human consumption, diet

Procedia PDF Downloads 133

Authors: Ron Cottam, Nils Langloh, Willy Ranson, Roger Vounckx

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We start by formally describing the requirements for environmental-reaction survival computation in a natural temporally-demanding medium, and develop this into a more general model of the evolutionary context as a computational machine. The effect of this development is to replace deterministic logic by a modified form which exhibits a continuous range of dimensional fractal diffuseness between the isolation of perfectly ordered localization and the extended communication associated with nonlocality as represented by pure causal chaos. We investigate the appearance of life and consciousness in the derived general model, and propose a representation of Nature within which all localizations have the character of quasi-quantal entities. We compare our conclusions with Heisenberg’s uncertainty principle and nonlocal teleportation, and maintain that computability is the principal influence on evolution in the model we propose.

Keywords: computability, evolution, life, localization, modeling, nonlocality

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Authors: Abbas Al-Refaie

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This paper aims at improving the performance of the tableting process using statistical quality control and fuzzy goal programming. The tableting process was studied. Statistical control tools were used to characterize the existing process for three critical responses including the averages of a tablet’s weight, hardness, and thickness. At initial process factor settings, the estimated process capability index values for the tablet’s averages of weight, hardness, and thickness were 0.58, 3.36, and 0.88, respectively. The L₉ array was utilized to provide experimentation design. Fuzzy goal programming was then employed to find the combination of optimal factor settings. Optimization results showed that the process capability index values for a tablet’s averages of weight, hardness, and thickness were improved to 1.03, 4.42, and 1.42, respectively. Such improvements resulted in significant savings in quality and production costs.

Keywords: fuzzy goal programming, control charts, process capability, tablet optimization

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Authors: Ayush Satija, Ritu Raj

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Escalating demand for elevated structures as a remedy for traffic congestion has led to a surge in the construction of viaducts and bridges predominantly employing prestressed beams. However, post-tensioned I-girder superstructures are gaining traction for their attributes like structural efficiency, cost-effectiveness, and easy construction. Recently, Ultra-high-performance fiber-reinforced concrete (UHPFRC) has emerged as a revolutionary material in reshaping conventional infrastructure engineering. UHPFRC offers exceptional properties including high compressive and tensile strength, alongside enhanced durability. Its adoption in bridges yields benefits, notably a remarkable strength-to-weight ratio enabling the design of lighter and slender structural elements, enhancing functionality and sustainability. Despite its myriad advantages, integration of UHPFRC in construction is still evolving, hindered by factors like cost, material availability, and design standardization. Consequently, there's a need to assess the feasibility of substituting ordinary concrete (OC) with UHPFRC in bridges, focusing on economic considerations. This research undertakes an efficacy study between post-tensioned I-girders fabricated from UHPFRC and OC, evaluating cost parameters associated with concrete production, reinforcement, and erection. The study reveals that UHPFRC becomes economically viable for spans exceeding 40.0m. This shift in cost-effectiveness is attributed to factors like reduced girder depth, elimination of un-tensioned steel, diminished need for shear reinforcement and decreased erection costs.

Keywords: post tensioned I girder, superstructure, ultra-high-performance fiber reinforced concrete, ordinary concrete

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Authors: Richard Okyere Boadu, Judith Obiri-Yeboah, Kwame Adu Okyere Boadu, Nathan Kumasenu Mensah, Grace Amoh-Agyei

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Routine health information system (RHIS) quality assurance has become an important issue, not only because of its significance in promoting a high standard of patient care but also because of its impact on government budgets for the maintenance of health services. A routine health information system comprises healthcare data collection, compilation, storage, analysis, report generation, and dissemination on a routine basis in various healthcare settings. The data from RHIS give a representation of health status, health services, and health resources. The sources of RHIS data are normally individual health records, records of services delivered, and records of health resources. Using reliable information from routine health information systems is fundamental in the healthcare delivery system. Quality assurance practices are measures that are put in place to ensure the health data that are collected meet required quality standards. Routine health information system quality assurance practices ensure that data that are generated from the system are fit for use. This study considered quality assurance practices in the RHIS processes. Methods: A cross-sectional study was conducted in eight health facilities in Tarkwa Sub-Municipal Health Service in the western region of Ghana. The study involved routine quality assurance practices among the 90 health staff and management selected from facilities in Tarkwa Sub-Municipal who collected or used data routinely from 24th December 2019 to 20th January 2020. Results: Generally, Tarkwa Sub-Municipal health service appears to practice quality assurance during data collection, compilation, storage, analysis and dissemination. The results show some achievement in quality control performance in report dissemination (77.6%), data analysis (68.0%), data compilation (67.4%), report compilation (66.3%), data storage (66.3%) and collection (61.1%). Conclusions: Even though the Tarkwa Sub-Municipal Health Directorate engages in some control measures to ensure data quality, there is a need to strengthen the process to achieve the targeted percentage of performance (90.0%). There was a significant shortfall in quality assurance practices performance, especially during data collection, with respect to the expected performance.

Keywords: quality assurance practices, assessment of routine health information system quality, routine health information system, data quality

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Authors: Ahmed Khalil

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The discoveries in material sciences create an impulse in renewable energy transmission. Application techniques become more accessible by applied sciences. Variety of materials, application methods, and performance analyzing techniques can convert daily life functions to energy sources. These functions not only include natural sources like sun, wind, or water but also comprise the motion of tools used by human beings. In line with this, vehicles' motion, speed and weights come to the scene as energy sources together with piezoelectric nano-generators beneath the roads. Numerous application examples are put forward with repeated average performance, versus the differentiating challenges depending on geography and project conditions. Such holistic approach provides way for feed backs on research and improvement process of nano-generators beneath asphalt roads. This paper introduces the specific application methods of piezoelectric nano-generator beneath asphalt roads of Ahmadi Township in Kuwait.

Keywords: nano-generator pavements, piezoelectric, renewable energy, transducer

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Authors: Nouha Khediri, Mohammed Ben Ammar, Monji Kherallah

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Recently, facial emotion recognition (FER) has become increasingly essential to understand the state of the human mind. Accurately classifying emotion from the face is a challenging task. In this paper, we present a facial emotion recognition approach named CV-FER, benefiting from deep learning, especially CNN and VGG16. First, the data is pre-processed with data cleaning and data rotation. Then, we augment the data and proceed to our FER model, which contains five convolutions layers and five pooling layers. Finally, a softmax classifier is used in the output layer to recognize emotions. Based on the above contents, this paper reviews the works of facial emotion recognition based on deep learning. Experiments show that our model outperforms the other methods using the same FER2013 database and yields a recognition rate of 92%. We also put forward some suggestions for future work.

Keywords: CNN, deep-learning, facial emotion recognition, machine learning

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Authors: Yang Li, Xiaorong Guan, Cheng Xu

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In order to design a suitable control scheme for human extremity exoskeleton, the interaction force control scheme with traditional PI controller was presented, and the simulation study of the electromechanical system of the human extremity exoskeleton was carried out by using a MATLAB/Simulink module. By analyzing the simulation calculation results, it was shown that the traditional PI controller is not very suitable for every movement speed of human body. So, at last the fuzzy self-adaptive PI controller was presented to solve this problem. Eventually, the superiority and feasibility of the fuzzy self-adaptive PI controller was proved by the simulation results and experimental results.

Keywords: human extremity exoskeleton, interaction force control scheme, simulation study, fuzzy self-adaptive pi controller, man-machine coordinated walking, bear payload

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Authors: Ping-Hsiung Wang, Kuo-Chun Chang

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There has been a common trend worldwide in the seismic design and evaluation of bridges towards the performance-based method where the lateral displacement or the displacement ductility of bridge column is regarded as an important indicator for performance assessment. However, the seismic response of a bridge to an earthquake is a combined result of cyclic displacements and accumulated energy dissipation, causing damage to the bridge, and hence the lateral displacement (ductility) alone is insufficient to tell its actual seismic performance. This study aims to propose a damage-based seismic design and evaluation method for reinforced concrete bridges on the basis of the newly developed capacity-based inelastic displacement spectra. The capacity-based inelastic displacement spectra that comprise an inelastic displacement ratio spectrum and a corresponding damage state spectrum was constructed by using a series of nonlinear time history analyses and a versatile, smooth hysteresis model. The smooth model could take into account the effects of various design parameters of RC bridge columns and correlates the column’s strength deterioration with the Park and Ang’s damage index. It was proved that the damage index not only can be used to accurately predict the onset of strength deterioration, but also can be a good indicator for assessing the actual visible damage condition of column regardless of its loading history (i.e., similar damage index corresponds to similar actual damage condition for the same designed columns subjected to very different cyclic loading protocols as well as earthquake loading), providing a better insight into the seismic performance of bridges. Besides, the computed spectra show that the inelastic displacement ratio for far-field ground motions approximately conforms to the equal displacement rule when structural period is larger than around 0.8 s, but that for near-fault ground motions departs from the rule in the whole considered spectral regions. Furthermore, the near-fault ground motions would lead to significantly greater inelastic displacement ratio and damage index than far-field ground motions and most of the practical design scenarios cannot survive the considered near-fault ground motion when the strength reduction factor of bridge is not less than 5.0. Finally, the spectrum formula is presented as a function of structural period, strength reduction factor, and various column design parameters for far-field and near-fault ground motions by means of the regression analysis of the computed spectra. And based on the developed spectrum formula, a design example of a bridge is presented to illustrate the proposed damage-based seismic design and evaluation method where the damage state of the bridge is used as the performance objective.

Keywords: damage index, far-field, near-fault, reinforced concrete bridge, seismic design and evaluation

Procedia PDF Downloads 115

Authors: Yang Fu, Jaime Rodrigo Navarro, Jose F. Monserrat, Faiza Bouchmal, Oscar Carrasco Quilis

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Cellular Vehicle to Everything (C-V2X) is considered a promising solution for future autonomous driving. From Release 16 to Release 17, the Third Generation Partnership Project (3GPP) has introduced the definitions and services for 5G New Radio (NR) V2X. Experience from previous generations has shown that establishing a simulator for C-V2X communications is an essential preliminary step to achieve reliable and stable communication links. This paper proposes a complete framework of a link-level simulator based on the 3GPP specifications for the Physical Sidelink Share Channel (PSSCH) of the 5G NR Physical Layer (PHY). In this framework, several algorithms in the receiver part, i.e., sliding window in channel estimation and Minimum Mean Square Error (MMSE)-based equalization, are developed. Finally, the performance of the developed PSSCH receiver is validated through extensive simulations under different assumptions.

Keywords: C-V2X, channel estimation, link-level simulator, sidelink, 3GPP

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Authors: Tina Vuko, Marija Maretić, Marko Čular

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The aim of this study is to analyze the role and effectiveness of internal mechanism (audit committee) of corporate governance on credit institutions performance in Croatia. Based on research objective, sample of 78 credit institutions listed on Zagreb Stock Exchange, from 2007 to 2012, has been collected and efficiency index of audit committee (EIAC) has been created. Based on the sample and created EIAC, conclusions are as follows: audit committees of credit institutions have medium efficiency, based on EIAC measurement; there is a significant difference in audit committee effectiveness, in observed period; there is no positive relationship between audit committee effectiveness and credit institution performance; there is a significant difference between level of audit committee effectiveness and audit firm type. Future research should contain increased number of elements in EIAC creation and increased sample, for all obligators who need to establish audit committee.

Keywords: corporate governance, audit committee, financial institutions, efficiency index of audit committee

Procedia PDF Downloads 299

Authors: Raied, Badr Al-Fulaiti, E. I. El-Shafey

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Cyclodextrin (CD) derivatives (αCD, βCD, ϒCD and hp-βCD) were successfully immobilized on silica gel surface via epichlorohydrin as a cross linker. The ratio of silica to CD was optimized in preliminary experiments based on best performance of iodine adsorption capacity. Selected adsorbents with ratios of silica to CD derivatives, in this study, include Si-αCD (3:2), Si-βCD (4:1), Si-ϒCD (4:1) and Si-hp-βCD (4:1). The adsorption of iodine (I2/KI) solution was investigated in terms of initial pH, contact time, iodine concentration and temperature. No significant variations was noticed for iodine adsorption at different pH values, thus, initial pH 6 was selected for further studies. Equilibrium adsorption was reached faster on Si-hp-βCD than other adsorbents with kinetic adsorption data fitting well pseudo second order model. Activation energy (Ea) was found to be in the range of 12.7 - 23.4 kJ/mol. Equilibrium adsorption data were found to fit well the Langmuir adsorption model with lower uptake as temperature rises. Iodine uptake follows the order: Si-hp-βCD (714 mg/g) >Si-αCD (625 mg/g) >Si-βCD (555.6 mg/g)> Si-ϒCD (435 mg/g). Thermodynamic study showed that iodine adsorption is exothermic and spontaneous. Adsorbents reuse exhibited excellent performance for iodine adsorption with a decrease in iodine uptake of ~ 2- 4 % in the third adsorption cycle.

Keywords: adsorption, iodine, silica, cyclodextrin, functionalization, epichlorohydrin

Procedia PDF Downloads 114

Authors: Mohd Ritzman Abdul Karim, Zhaohui Huang

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A current focus in seismic engineering practice is the development of seismic design approach that focuses on the performance-based design. Performance-based design aims to design the structures to achieve specified performance based on the damage limit states. This damage limit is more restrictive limit than life safety and needs to be carefully estimated to avoid damage in piers due to failure in transverse reinforcement. In this paper, a different perspective of damage limit states has been explored by integrating two damage control material limit state, concrete and reinforcement by introduced parameters such as expected yield stress of transverse reinforcement where peak tension strain prior to bar buckling is introduced in a recent study. The different perspective of damage limit states with modified yield displacement and the modified plastic-hinge length is used in order to predict damage-control target displacement for reinforced concreate (RC) bridge pier. Three-dimensional (3D) finite element (FE) model has been developed for estimating damage target displacement to validate proposed damage limit states. The result from 3D FE analysis was validated with experimental study found in the literature. The validated model then was applied to predict the damage target displacement for RC bridge pier and to validate the proposed study. The tensile strain on reinforcement and compression on concrete were used to determine the predicted damage target displacement and compared with the proposed study. The result shows that the proposed damage limit states were efficient in predicting damage-control target displacement consistent with FE simulations.

Keywords: damage-control target displacement, damage limit states, reinforced concrete bridge pier, yield displacement

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Authors: Mohsen Maadani, Seyed Ahmad Motamedi

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The use of wireless technology in industrial networks has gained vast attraction in recent years. In this paper, we have thoroughly analyzed the effect of contention window (CW) size on the performance of IEEE 802.11-based industrial wireless networks (IWN), from delay and reliability perspective. Results show that the default values of CWmin, CWmax, and retry limit (RL) are far from the optimum performance due to the industrial application characteristics, including short packet and noisy environment. An adaptive CW algorithm (payload-dependent) has been proposed to minimize the average delay. Finally a simple, but effective CW and RL setting has been proposed for industrial applications which outperforms the minimum-average-delay solution from maximum delay and jitter perspective, at the cost of a little higher average delay. Simulation results show an improvement of up to 20%, 25%, and 30% in average delay, maximum delay and jitter respectively.

Keywords: average delay, contention window, distributed coordination function (DCF), jitter, industrial wireless network (IWN), maximum delay, reliability, retry limit

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Authors: M. Y. Kang, Euntaek Yoon, H. D. Choi

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To calculate the full energy (FE) absorption peak efficiency for arbitrary volume sample, we developed and verified the EXVol (Efficiency calculator for EXtended Voluminous source) code which is based on effective solid angle method. EXVol is possible to describe the source area as a non-uniform three-dimensional (x, y, z) source. And decompose and set it into several sets of volume units. Users can equally divide (x, y, z) coordinate system to calculate the detection efficiency at a specific position of a cylindrical volume source. By determining the detection efficiency for differential volume units, the total radiative absolute distribution and the correction factor of the detection efficiency can be obtained from the nondestructive measurement of the source. In order to check the performance of the EXVol code, Si ingot of 20 cm in diameter and 50 cm in height were used as a source. The detector was moved at the collimation geometry to calculate the detection efficiency at a specific position and compared with the experimental values. In this study, the performance of the EXVol code was extended to obtain the detection efficiency distribution at a specific position in a large volume source.

Keywords: attenuation, EXVol, detection efficiency, volume source

Procedia PDF Downloads 171

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 103

Authors: Moez ul Hassan, Bushra Noman, Sarmad Hameed, Shahab Mehmood, Asma Bashir

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The importance of Noble prize winning “Patch Clamp Technique” is well documented. However, Patch Clamp Technique is very expensive and hence hinders research in developing countries. In this paper, detection, processing and recording of ultra low current from induced cells by using transimpedence amplifier is described. The sensitivity of the proposed amplifier is in the range of femto amperes (fA). Capacitive-feedback is used with active load to obtain a 20MΩ transimpedance gain. The challenging task in designing includes achieving adequate performance in gain, noise immunity and stability. The circuit designed by the authors was able to measure current in the rangeof 300fA to 100pA. Adequate performance shown by the amplifier with different input current and outcome result was found to be within the acceptable error range. Results were recorded using LabVIEW 8.5®for further research.

Keywords: drug discovery, ionic current, operational amplifier, patch clamp

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Authors: Laid Fekih

Abstract:

Background: Depression affects people in all communities across the world and in all aspects of their lives. Its spread varies from one country to another, can happen at any age and get rid of it is not easy. There is no clear policy in Algeria's higher education institutions to detect and treat these disorders or pay particular attention to those at risk. Identifying the prevalence of depression among Algerian students, its correlation with different variables, and studying gender differences in the light of a range of variables is necessary to develop an appropriate plan to raise the level of hope and love of life among students. Method: Random samples of 1500 University of Tlemcen students (967 girls and 533 boys), aged 19 to 24 years completed a self-administered questionnaire that included Beck's Depression Inventory ®-II (BDI®-II), (School Health Promotion: The Mood part), Other questions included in this survey focused on demographic characteristics including gender, age and year of study, academic performance (Annual Average Score (0-20) AAS), were examined. Results: The rate of depression (moderate, severe and extreme) varied from 03% to 13% among university students in Tlemcen University. There was no difference in the rates of depression in male and female students, which means that male and female students do have similar rates of depression. The rate of depression in the first-year of the study shows a higher score relative to students of other years. Depression has a negative relationship with academic performance, which means that depressed students have many difficulties in academic tasks at university. Conclusion: Depression among university students is an important center of interest in the world, not only because of the ease with which they can be followed, or the difficulties encountered during their studies and their technical courses but for the link between the level of depression and the quality of care of mental health services, especially if many students with mood and emotional problems don't meet the criteria for psychotherapy.

Keywords: depression, epidemiology, university students, academic performance

Procedia PDF Downloads 127

Authors: Mohd Zamri Osman, Mohd Aizaini Maarof, Mohd Foad Rohani

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Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.

Keywords: color space, neural network, random forest, skin detection, statistical feature

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Authors: M. Soltani, K. Pakzad, A. Haghigh-Khiyabani, M. Alavi, R. Naderi, M. Castex

Abstract:

Effect of probiotic Bactocell (Pediococcus acidilactici) as a supplementary diet was studied on post-larvae 12-15 of white leg shrimp (Litopenaeus vannamei) (150000 PL/0.5 h pond, average body weight=0.02 g) growth performance under farm condition for 102 days at water quality parameters consisting of temperature at 30.5-36οC, dissolved oxygen 4.1-6.6 mg/l, salinity 40-54 g/l, turbidity 35-110 cm, ammonia 0.1-0.8 mg/l and nitrite 0.1-0.9 mg/l. Also, the resistance level of the treated shrimps was assessed against a virulent strain of Vibrio paraheamolyticus as intramuscular injection route at 1.4 x 106 cells/shrimp. Significantly higher growth rate (11.3±1.54 g) and lower feed conversion ratio (1.1) were obtained in shrimps fed diets supplemented with Bactocell at 350 g/ tone feed compared to other treatments of 250 g Bactocell/ton feed (10.8±2 g, 1.3), 500 g Bactocell/ton feed (10.3±1.7 g, 1.3) and untreated control (10.1±2 g, 1.4). Also, thermal growth coefficient (0.057%) and protein efficiency ratio (2.13) were significantly improved in shrimps fed diets supplemented with Bactocell at 350 g/ton feed compare to other groups. Shrimps fed diet supplemented with Bactocell at 350 g/tone feed showed significantly higher protein content (72.56%) in their carcass composition than treatments of 250 g/ton feed (65.9%), 500 g/ton feed (67.5%) and control group (65.9%), while the carcass contents of moisture, lipid and ash in all shrimp groups were not significantly affected by different concentrations of Bactocell. No mortality occurred in the experimentally infected shrimps fed with Bactocell at 500 g/tone feed after 7 hours post-challenge with V. parahemolyticus. The mortality levels of 100%, 40%, 50% and 70% were obtained in shrimps fed with 0.0, 500 g/tone feed, 350 g/ton feed and 250 g/ton feed, respectively 14 hours post-infection. Also, the cumulative mortalities were achieved in 100%, 92% and 81% in shrimps few with Bactocell at 500 g/ton feed, 250 g/ton feed and 350 g/ton feed, respectively.

Keywords: litopenaeus vannamei, vibrio paraheamolyticus, pediococcus acidilactici, growth performance, bactocell

Procedia PDF Downloads 661

Authors: Neda Nabid, Iman Hajirasouliha, Sanaz Shirinbar

Abstract:

One of the most challenging issues in earthquake engineering is to find effective ways to reduce earthquake forces and damage to structural and non-structural elements under strong earthquakes. While friction dampers are the most efficient systems to improve the seismic performance of substandard structures, their optimum design is a challenging task. This research aims to find more appropriate slip load distribution pattern for efficient design of friction panels. Non-linear dynamic analyses are performed on 3, 5, 10, 15, and 20-story RC frame using Drain-2dx software to find the appropriate range of slip loads and investigate the effects of different distribution patterns (cantilever, uniform, triangle, and reverse triangle) under six different earthquake records. The results indicate that using triangle load distribution can significantly increase the energy dissipation capacity of the frame and reduce the maximum inter-storey drift, and roof displacement.

Keywords: friction panels, slip load, distribution patterns, RC frames, energy dissipation

Procedia PDF Downloads 417

Authors: Gajanan Bhat, Vincent Kandagor, Daniel Prather, Ramesh Bhave

Abstract:

Polyetherimide (PEI), an engineering plastic with very high glass transition temperature and excellent chemical and thermal stability, has been processed into a controlled porosity filter media of varying pore size, performance, and surface characteristics. A special grade of the PEI was processed by melt blowing to produce microfiber nonwovens suitable as filter media. The resulting microfiber webs were characterized to evaluate their structure and properties. The fiber webs were further modified by hot pressing, a post processing technique, which reduces the pore size in order to improve the barrier properties of the resulting membranes. This ongoing research has shown that PEI can be a good candidate for filter media requiring high temperature and chemical resistance with good mechanical properties. Also, by selecting the appropriate processing conditions, it is possible to achieve desired filtration performance from this engineering plastic.

Keywords: nonwovens, melt blowing, polyehterimide, filter media, microfibers

Procedia PDF Downloads 303

Authors: Xiang Zhang, David Rey, S. Travis Waller

Abstract:

Stochastic User Equilibrium (SUE) model is a widely used traffic assignment model in transportation planning, which is regarded more advanced than Deterministic User Equilibrium (DUE) model. However, a problem exists that the performance of the SUE model depends on its error term parameter. The objective of this paper is to propose a systematic method of determining the appropriate error term parameter value for the SUE model. First, the significance of the parameter is explored through a numerical example. Second, the parameter calibration method is developed based on the Logit-based route choice model. The calibration process is realized through multiple nonlinear regression, using sequential quadratic programming combined with least square method. Finally, case analysis is conducted to demonstrate the application of the calibration process and validate the better performance of the SUE model calibrated by the proposed method compared to the SUE models under other parameter values and the DUE model.

Keywords: parameter calibration, sequential quadratic programming, stochastic user equilibrium, traffic assignment, transportation planning

Procedia PDF Downloads 280

Authors: Hadi Zarafshani, Shidvash Vakilipour, Shahin Teimori, Sara Barati

Abstract:

In the present study, the aerodynamic performance of a rigid two-dimensional pitching bio-inspired corrugate airfoil was numerically investigated at Reynolds number of 14000. The Open Field Operations And Manipulations (OpenFOAM) computational fluid dynamic tool is used to solve flow governing equations numerically. The k-ω SST turbulence model with low Reynolds correction (k-ω SST LRC) and the pimpleDyMFOAM solver are utilized to simulate the flow field around pitching bio-airfoil. The lift and drag coefficients of the airfoil are calculated at reduced frequencies k=1.24-4.96 and the angular amplitude of A=5^°-20^°. Results show that in a fixed reduced frequency, the absolute value of the sectional lift and drag coefficients increase with increasing pitching amplitude. In a fixed angular amplitude, the absolute value of the lift and drag coefficients increase as the pitching reduced frequency increases.

Keywords: bio-inspired pitching airfoils, OpenFOAM, low Reynolds k-ω SST model, lift and drag coefficients

Procedia PDF Downloads 174

Authors: Ahmed N. Shmroukh, Gamal Tag Abdel-Jaber, Rashed D. Aldughpassi

Abstract:

The feasibility of improving the performance of the proposed solar still unit which operated in very hot climate is investigated numerically and verified with experimental data. This solar desalination unit with proposed auxiliary device as seawater preheating system using petrol based textherm oil was used to produce pure fresh water from seawater. The effective evaporation area of basin is about 1 m². The unit was tested in two main operation modes which are normal and with seawater preheating system. The results showed that, there is good agreement between the theoretical data and the experimental data; this means that the numerical model can be accurately dependable for predicting the proposed solar still performance and design parameters. The results also showed that the fresh water productivity of the solar still in the modified preheating case which is higher than normal case, leads to an increase in productivity of 42%.

Keywords: improving productivity, seawater desalination, solar stills, theoretical model

Procedia PDF Downloads 126

Authors: Murat Sarı, Emin Gundogar, Mumtaz Ipek

Abstract:

Nowadays, in a furniture sector, competition of market share (portion) and production variety and changeability enforce the firm to reengineer operations on manufacturing line to increase the productivity. In this study, spring mattress manufacturing line of the furniture manufacturing firm is analyzed analytically. It’s intended to search and find the bottlenecks of production to balance the semi-finished material flow. There are four base points required to investigate in bottleneck elimination process. These are bottlenecks of Method, Material, Machine and Man (work force) resources, respectively. Mentioned bottlenecks are investigated and varied scenarios are created for recruitment of manufacturing system. Probable near optimal alternatives are determined by system models built in Arena simulation software.

Keywords: bottleneck search, buffer stock, furniture sector, simulation

Procedia PDF Downloads 347

Authors: Nebi Gedik

Abstract:

One of the significant and continual public health problems in the world is breast cancer. Early detection is very important to fight the disease, and mammography has been one of the most common and reliable methods to detect the disease in the early stages. However, it is a difficult task, and computer-aided diagnosis (CAD) systems are needed to assist radiologists in providing both accurate and uniform evaluation for mass in mammograms. In this study, a multiresolution statistical method to classify mammograms as normal and abnormal in digitized mammograms is used to construct a CAD system. The mammogram images are represented by wave atom transform, and this representation is made by certain groups of coefficients, independently. The CAD system is designed by calculating some statistical features using each group of coefficients. The classification is performed by using support vector machine (SVM).

Keywords: wave atom transform, statistical features, multi-resolution representation, mammogram

Procedia PDF Downloads 209