Search results for: efficiency classification

Authors: Alma Ramić, Mirjana Skočibušić, Renata Odžak, Tomica Hrenar, Ines Primožič

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In an attempt to identify a new class of antimicrobial agents, the antimicrobial potential of Cinchona alkaloid derivatives was evaluated. The bark of the Cinchona trees is the source of a variety of alkaloids, among which the best known are quinine, quinidine, cinchonine and cinchonidine. They are very useful as organocatalysts in stereoselective synthesis. On the other hand, quinine is traditionally used in the treatment of malaria. Furthermore, Cinchona alkaloids possess various analgesic, anti-inflammatory and anti–arrhythmic properties as well. In this work we present the synthesis of twenty quaternary derivatives of pseudo−enantiomeric Cinchona alkaloid derivatives to evaluate their antibacterial activity. Quaternization of quinuclidine moiety was carried out with groups diverse in their size. The structures of compounds were systematically modified to obtain drug-like properties with proper physical and chemical properties and avoiding toxophore. All compounds were prepared in good yields and were characterized by standard analytical spectroscopy methods (1D and 2D NMR, IR, MS). The antibacterial activities of all compounds were evaluated against series of recent clinical isolates of antibiotic susceptible Gram-positive and resistant Gram-negative pathogens by determining their zone of inhibition and minimum inhibitory concentrations. All compounds showed good to strong broad-spectrum activity, equivalent or better in comparison with standard antibiotics used. Furthermore, seven compounds exhibited significant antibacterial efficiency against Gram-negative isolates. To visualize the results, principal component analysis was used as an additional classification tool. Cytotoxicity of compounds with different cell lines in human cell culture was determined. Based on these results, substituted quaternary Cinchona scaffold can be considered as promising new class of antimicrobials and further investigations should be performed. Supported by Croatian Science Foundation, Project No 3775 ADESIRE.

Keywords: antibacterial efficiency, cinchona alkaloids, cytotoxicity, pseudo‐enantiomers

Procedia PDF Downloads 153

Authors: Sulayman Adrees Mohammed, Ahmed Faraj Abd Alsameea

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The ongoing hindrance for Libyan oil and gas companies is the persistent challenge of eradicating maintenance program failures that result in exorbitant costs and production setbacks. Accordingly, this research is prompted to introduce the concept of lean philosophy in maintenance, which aims to eliminate waste and enhance productivity in maintenance procedures through the identification and differentiation of value-adding (VA) and non-value-adding (NVA) activities. The purpose of this paper was to explore and describe the benefits that can be gained by adopting the Lean philosophy towards the enhancement of maintenance programs' efficiency from theoretical perspectives. The oil industry maintenance community in Libya now has an introduced tool by which they can effectively evaluate their maintenance program functionality and reduce the areas of non-value added activities within maintenance, thereby enhancing the availability of the equipment and the capacity of the oil and gas facilities.

Keywords: efficiency, lean philosophy, Libyan oil and gas scenario, maintenance programs

Procedia PDF Downloads 109

Authors: Kishore K. Pochampally

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The success of a supply chain heavily relies on the efficiency of the suppliers involved. In this paper, we propose a neural network approach to evaluate the efficiency of a supplier, which is being considered for inclusion in a supply chain, using the available linguistic (fuzzy) data of suppliers that already exist in the supply chain. The approach is carried out in three phases, as follows: In phase one, we identify criteria for evaluation of the supplier of interest. Then, in phase two, we use performance measures of already existing suppliers to construct a neural network that gives weights (importance values) of criteria identified in phase one. Finally, in phase three, we calculate the overall rating of the supplier of interest. The following are the major findings of the research conducted for this paper: (i) linguistic (fuzzy) ratings of suppliers such as 'good', 'bad', etc., can be converted (defuzzified) to numerical ratings (1 – 10 scale) using fuzzy logic so that those ratings can be used for further quantitative analysis; (ii) it is possible to construct and train a multi-level neural network in order to determine the weights of the criteria that are used to evaluate a supplier; and (iii) Borda’s rule can be used to group the weighted ratings and calculate the overall efficiency of the supplier.

Keywords: fuzzy data, neural network, supplier, supply chain

Procedia PDF Downloads 114

Authors: Ranjit Kumar Sahu, Ramakar Jha

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Urban Water Management is the practice of managing freshwater, waste water, and storm water as components of a basin-wide management plan. It builds on existing water supply and sanitation considerations within an urban settlement by incorporating urban water management within the scope of the entire river basin. The pervasive problems generated by urban development have prompted, in the present work, to study the spatial extent of urbanization in Golden Triangle of Odisha connecting the cities Bhubaneswar (20.2700° N, 85.8400° E), Puri (19.8106° N, 85.8314° E) and Konark (19.9000° N, 86.1200° E)., and patterns of periodic changes in urban development (systematic/random) in order to develop future plans for (i) urbanization promotion areas, and (ii) urbanization control areas. Remote Sensing, using USGS (U.S. Geological Survey) Landsat8 maps, supervised classification of the Urban Sprawl has been done for during 1980 - 2014, specifically after 2000. This Work presents the following: (i) Time series analysis of Hydrological data (ground water and rainfall), (ii) Application of SWMM (Storm Water Management Model) and other soft computing techniques for Urban Water Management, and (iii) Uncertainty analysis of model parameters (Urban Sprawl and correlation analysis). The outcome of the study shows drastic growth results in urbanization and depletion of ground water levels in the area that has been discussed briefly. Other relative outcomes like declining trend of rainfall and rise of sand mining in local vicinity has been also discussed. Research on this kind of work will (i) improve water supply and consumption efficiency (ii) Upgrade drinking water quality and waste water treatment (iii) Increase economic efficiency of services to sustain operations and investments for water, waste water, and storm water management, and (iv) engage communities to reflect their needs and knowledge for water management.

Keywords: Storm Water Management Model (SWMM), uncertainty analysis, urban sprawl, land use change

Procedia PDF Downloads 425

Authors: Soha A. Bahanshal, Byung G. Kim

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Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor.

Keywords: machine learning, prediction, classification, hybrid fuzzy weighted k-nearest neighbor, diabetic hospital readmission

Procedia PDF Downloads 186

Authors: Mahmoud I. Syam

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Stratified double median ranked set sampling (SDMRSS) method is suggested for estimating the population mean. The SDMRSS is compared with the simple random sampling (SRS), stratified simple random sampling (SSRS), and stratified ranked set sampling (SRSS). It is shown that SDMRSS estimator is an unbiased of the population mean and more efficient than SRS, SSRS, and SRSS. Also, by SDMRSS, we can increase the efficiency of mean estimator for specific value of the sample size. SDMRSS is applied on real life examples, and the results of the example agreed the theoretical results.

Keywords: efficiency, double ranked set sampling, median ranked set sampling, ranked set sampling, stratified

Procedia PDF Downloads 247

Authors: Atwari Rawani, Suresh Prasad Sharma, K. D. P. Singh

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In this paper, an analytical investigation based on energy and exergy analysis of the parabolic trough collector (PTC) with alternate clockwise and counter-clockwise twisted tape inserts in the absorber tube has been presented. For fully developed flow under quasi-steady state conditions, energy equations have been developed in order to analyze the rise in fluid temperature, thermal efficiency, entropy generation and exergy efficiency. Also the effect of system and operating parameters on performance have been studied. A computer program, based on mathematical models is developed in C++ language to estimate the temperature rise of fluid for evaluation of performances under specified conditions. For numerical simulations four different twist ratio, x = 2,3,4,5 and mass flow rate 0.06 kg/s to 0.16 kg/s which cover the Reynolds number range of 3000 - 9000 is considered. This study shows that twisted tape inserts when used shows great promise for enhancing the performance of PTC. Results show that for x=1, Nusselt number/heat transfer coefficient is found to be 3.528 and 3.008 times over plain absorber of PTC at mass flow rate of 0.06 kg/s and 0.16 kg/s respectively; while corresponding enhancement in thermal efficiency is 12.57% and 5.065% respectively. Also the exergy efficiency has been found to be 10.61% and 10.97% and enhancement factor is 1.135 and 1.048 for same set of conditions.

Keywords: exergy efficiency, twisted tape ratio, turbulent flow, useful heat gain

Procedia PDF Downloads 173

Authors: Bhekisipho Twala, Mantepu Masetshaba, Ramapulana Nkoana

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Multiple classifier systems combine several individual classifiers to deliver a final classification decision. However, an increasingly controversial question is whether such systems can outperform the single best classifier, and if so, what form of multiple classifiers system yields the most significant benefit. Also, multi-target tracking detection using multiple sensors is an important research field in mobile techniques and military applications. In this paper, several multiple classifiers systems are evaluated in terms of their ability to predict a system’s failure or success for multi-sensor target tracking tasks. The Bristol Eden project dataset is utilised for this task. Experimental and simulation results show that the human activity identification system can fulfill requirements of target tracking due to improved sensors classification performances with multiple classifier systems constructed using boosting achieving higher accuracy rates.

Keywords: single classifier, ensemble learning, multi-target tracking, multiple classifiers

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Authors: Young-Kwan Choi, Gang-Wook Shin, Sung-Taek Hong

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Water supply system consumes large amount of power load during water treatment and transportation of purified water. Many energy conserving high efficiency materials such as DC motor and LED light have recently been introduced to water supply system for energy conservation. This paper performed empirical analysis on BLDC, AC motors, and comparatively analyzed the change in power according to DC power supply ratio in order to conserve energy of a next-generation water treatment system called vertical water treatment system. In addition, a DC distribution system linked with photovoltaic generation was simulated to analyze the energy conserving effect of DC load.

Keywords: vertical water treatment system, DC power supply, energy efficiency, BLDC

Procedia PDF Downloads 503

Authors: Vitor M. Vilas Boas, Cleison D. Silva, Gustavo S. Mafra, Alexandre Trofino Neto

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Motor imagery (MI) based brain-computer interfaces (BCI) uses event-related (de)synchronization (ERS/ ERD), typically recorded using electroencephalography (EEG), to translate brain electrical activity into control commands. To mitigate undesirable artifacts and noise measurements on EEG signals, methods based on band-pass filters defined by a specific frequency band (i.e., 8 – 30Hz), such as the Infinity Impulse Response (IIR) filters, are typically used. Spatial techniques, such as Common Spatial Patterns (CSP), are also used to estimate the variations of the filtered signal and extract features that define the imagined motion. The CSP effectiveness depends on the subject's discriminative frequency, and approaches based on the decomposition of the band of interest into sub-bands with smaller frequency ranges (SBCSP) have been suggested to EEG signals classification. However, despite providing good results, the SBCSP approach generally increases the computational cost of the filtering step in IM-based BCI systems. This paper proposes the use of the Fast Fourier Transform (FFT) algorithm in the IM-based BCI filtering stage that implements SBCSP. The goal is to apply the FFT algorithm to reduce the computational cost of the processing step of these systems and to make them more efficient without compromising classification accuracy. The proposal is based on the representation of EEG signals in a matrix of coefficients resulting from the frequency decomposition performed by the FFT, which is then submitted to the SBCSP process. The structure of the SBCSP contemplates dividing the band of interest, initially defined between 0 and 40Hz, into a set of 33 sub-bands spanning specific frequency bands which are processed in parallel each by a CSP filter and an LDA classifier. A Bayesian meta-classifier is then used to represent the LDA outputs of each sub-band as scores and organize them into a single vector, and then used as a training vector of an SVM global classifier. Initially, the public EEG data set IIa of the BCI Competition IV is used to validate the approach. The first contribution of the proposed method is that, in addition to being more compact, because it has a 68% smaller dimension than the original signal, the resulting FFT matrix maintains the signal information relevant to class discrimination. In addition, the results showed an average reduction of 31.6% in the computational cost in relation to the application of filtering methods based on IIR filters, suggesting FFT efficiency when applied in the filtering step. Finally, the frequency decomposition approach improves the overall system classification rate significantly compared to the commonly used filtering, going from 73.7% using IIR to 84.2% using FFT. The accuracy improvement above 10% and the computational cost reduction denote the potential of FFT in EEG signal filtering applied to the context of IM-based BCI implementing SBCSP. Tests with other data sets are currently being performed to reinforce such conclusions.

Keywords: brain-computer interfaces, fast Fourier transform algorithm, motor imagery, sub-band common spatial patterns

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Authors: Amanda T. Badaró, Douglas F. Barbin, Sofia T. Garcia, Maria Teresa P. S. Clerici, Amanda R. Ferreira

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Food fortification is the intentional addition of a nutrient in a food matrix and has been widely used to overcome the lack of nutrients in the diet or increasing the nutritional value of food. Fortified food must meet the demand of the population, taking into account their habits and risks that these foods may cause. Wheat and its by-products, such as semolina, has been strongly indicated to be used as a food vehicle since it is widely consumed and used in the production of other foods. These products have been strategically used to add some nutrients, such as fibers. Methods of analysis and quantification of these kinds of components are destructive and require lengthy sample preparation and analysis. Therefore, the industry has searched for faster and less invasive methods, such as Near-Infrared Spectroscopy (NIR). NIR is a rapid and cost-effective method, however, it is based on indirect measurements, yielding high amount of data. Therefore, NIR spectroscopy requires calibration with mathematical and statistical tools (Chemometrics) to extract analytical information from the corresponding spectra, as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). PCA is well suited for NIR, once it can handle many spectra at a time and be used for non-supervised classification. Advantages of the PCA, which is also a data reduction technique, is that it reduces the data spectra to a smaller number of latent variables for further interpretation. On the other hand, LDA is a supervised method that searches the Canonical Variables (CV) with the maximum separation among different categories. In LDA, the first CV is the direction of maximum ratio between inter and intra-class variances. The present work used a portable infrared spectrometer (NIR) for identification and classification of pure and fiber-fortified semolina samples. The fiber was added to semolina in two different concentrations, and after the spectra acquisition, the data was used for PCA and LDA to identify and discriminate the samples. The results showed that NIR spectroscopy associate to PCA was very effective in identifying pure and fiber-fortified semolina. Additionally, the classification range of the samples using LDA was between 78.3% and 95% for calibration and 75% and 95% for cross-validation. Thus, after the multivariate analysis such as PCA and LDA, it was possible to verify that NIR associated to chemometric methods is able to identify and classify the different samples in a fast and non-destructive way.

Keywords: Chemometrics, fiber, linear discriminant analysis, near-infrared spectroscopy, principal component analysis, semolina

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Authors: Anum Ali, Kashaf ad Dooja, Asif Saleem

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The future smart cities trend will be towards Internet of Things (IoT); IoT creates dynamic connections in a ubiquitous manner. Smart cities offer ease and flexibility for daily life matters. By using small devices that are connected to cloud servers based on IoT, network traffic between these devices is growing exponentially, whose security is a concerned issue, since ratio of cyber attack may make the network traffic vulnerable. This paper discusses the latest machine learning approaches in related work further to tackle the increasing rate of cyber attacks, machine learning algorithm is applied to IoT-based network traffic data. The proposed algorithm train itself on data and identify different sections of devices interaction by using supervised learning which is considered as a classifier related to a specific IoT device class. The simulation results clearly identify the attacks and produce fewer false detections.

Keywords: IoT, traffic security, deep learning, classification

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Authors: Bahram Talebjedi, Emily Earl, Mina Hoorfar

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One of the issues that arises with microscale lab-on-a-chip technology is that the laminar flow within the microchannels limits the mixing of fluids. To combat this, micromixers have been introduced as a means to try and incorporate turbulence into the flow to better aid the mixing process. This study presents an electroosmotic micromixer that balances vortex generation and degeneration with the inlet flow velocity to greatly increase the mixing efficiency. A comprehensive parametric study was performed to evaluate the role of the relevant parameters on the mixing efficiency. It was observed that the suggested micromixer is perfectly suited for biological applications due to its low pressure drop (below 10 Pa) and low shear rate. The proposed micromixer with optimized working parameters is able to attain a mixing efficiency of 95% in a span of 0.5 seconds using a frequency of 10 Hz, a voltage of 0.7 V, and an inlet velocity of 0.366 mm/s.

Keywords: microfluidics, active mixer, pulsed AC electroosmosis flow, micromixer

Procedia PDF Downloads 138

Authors: Ali Mamizadeh, Ires Iskender, Saeid Aghaei

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Energy is one of the important issues with high priority property in the world. Energy demand is rapidly increasing depending on the growing population and industry. The useable energy sources in the world will be insufficient to meet the need for energy. Therefore, the efficient and economical usage of energy sources is getting more importance. In a survey conducted among electric consuming machines, the electrical machines are consuming about 40% of the total electrical energy consumed by electrical devices and 96% of this consumption belongs to induction motors. Induction motors are the workhorses of industry and have very large application areas in industry and urban systems like water pumping and distribution systems, steel and paper industries and etc. Monitoring and the control of the motors have an important effect on the operating performance of the motor, driver selection and replacement strategy management of electrical machines. The sensorless monitoring system for monitoring and calculating efficiency of induction motors are studied in this study. The equivalent circuit of IEEE is used in the design of this study. The terminal current and voltage of induction motor are used in this motor to measure the efficiency of induction motor. The motor nameplate information and the measured current and voltage are used in this system to calculate accurately the losses of induction motor to calculate its input and output power. The efficiency of the induction motor is monitored online in the proposed method without disconnecting the motor from the driver and without adding any additional connection at the motor terminal box. The proposed monitoring system measure accurately the efficiency by including all losses without using torque meter and speed sensor. The monitoring system uses embedded architecture and does not need to connect to a computer to measure and log measured data. The conclusion regarding the efficiency, the accuracy and technical and economical benefits of the proposed method are presented. The experimental verification has been obtained on a 3 phase 1.1 kW, 2-pole induction motor. The proposed method can be used for optimal control of induction motors, efficiency monitoring and motor replacement strategy.

Keywords: induction motor, efficiency, power losses, monitoring, embedded design

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Authors: Abdullah Işıktaş, Kevser Dincer

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The use of HEPA filters for air conditioning systems in clean rooms tends to increase progressively in pharmaceutical, food stuff industries and in hospitals. There are two standards widely used for HEPA filters; the EN 1822 standards published by the European Union, CEN (European Committee for Standardization) and the US based IEST standard (Institute of Environmental Sciences and Technology. Both standards exhibit some differences in the definitions of efficiency and its measurement methods. While IEST standard defines efficiency at the grit diameter of 0.3 µm, the EN 1822 standard takes MPPS (Most Penetrating Particle Size) as the basis of its definition. That is, the most difficult grit size to catch up. On the other hand, while IEST suggests that photometer and grit counters be used for filter testing, in EN 1822 standard, only the grit (grain) counters are recommended for that purpose. In this study, powder holding capacities of H13 and H14 grade materials under the EN 779 standard are investigated experimentally by using activated carbon. Measurements were taken on an experimental set up based on the TS 932 standard. Filter efficiency was measured by injecting test powder at amounts predetermined in the standards into the filters at certain intervals. The data obtained showed that the powder holding capacities of the activated carbon filter are high enough to yield efficiency of around 90% and that the H13 and H14 filters exhibit high efficiency suitable for the standard used.

Keywords: activated carbon filters, HEPA filters, powder holding capacities, air conditioning systems

Procedia PDF Downloads 244

Authors: Ali Ulvi Uzer

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Data reduction is an important topic in the field of pattern recognition applications. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts. The Principal Component Analysis (PCA) method is frequently used for data reduction. The Support Vector Machine (SVM) method is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data, the algorithm outputs an optimal hyperplane which categorizes new examples. This study offers a hybrid approach that uses the PCA for data reduction and Support Vector Machines (SVM) for classification. In order to detect the accuracy of the suggested system, two boreholes taken from the soil sample was used. The classification accuracies for this dataset were obtained through using ten-fold cross-validation method. As the results suggest, this system, which is performed through size reduction, is a feasible system for faster recognition of dataset so our study result appears to be very promising.

Keywords: feature selection, sequential forward selection, support vector machines, soil sample

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Authors: Tatiana Tunon, Gottfried Pestal

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Government agencies responsible for the management of fisheries resources publish many types of grey literature, and these materials are increasingly accessible to the public on agency websites. However, scope and quality vary considerably, and end-users need meta-data about the report series when deciding whether to use the information (e.g. apply the methods, include the results in a systematic review), or when prioritizing materials for archiving (e.g. library holdings, reference databases). A proposed taxonomy for these report series was developed based on a review of 41 report series from 6 government agencies in 4 countries (Canada, New Zealand, Scotland, and United States). Each report series was categorized according to multiple criteria describing peer-review process, content, and purpose. A robust classification tree was then fitted to these descriptions, and the resulting taxonomic groups were used to compare agency output from 4 countries using reports available in their online repositories.

Keywords: classification tree, fisheries, government, grey literature

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Authors: Ping Tan, Xiaomeng Su, Yi Shen

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The motion intention in the motor imagery braincomputer interface is identified by classifying the event-related desynchronization (ERD) and event-related synchronization ERS characteristics of sensorimotor rhythm (SMR) in EEG signals. When the subject imagines different limbs or different parts moving, the rhythm components and bandwidth will change, which varies from person to person. How to find the effective sensorimotor frequency band of subjects is directly related to the classification accuracy of brain-computer interface. To solve this problem, this paper proposes a Minimum Distance to Riemannian Mean Classification method based on Non-Uniform Filter Banks. During the training phase, the EEG signals are decomposed into multiple different bandwidt signals by using multiple band-pass filters firstly; Then the spatial covariance characteristics of each frequency band signal are computered to be as the feature vectors. these feature vectors will be classified by the MDRM (Minimum Distance to Riemannian Mean) method, and cross validation is employed to obtain the effective sensorimotor frequency bands. During the test phase, the test signals are filtered by the bandpass filter of the effective sensorimotor frequency bands, and the extracted spatial covariance feature vectors will be classified by using the MDRM. Experiments on the BCI competition IV 2a dataset show that the proposed method is superior to other classification methods.

Keywords: non-uniform filter banks, motor imagery, brain-computer interface, minimum distance to Riemannian mean

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Authors: Margarida Plana

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The end-users behavior has been identified in the last years as one of the main responsible for the success degree of the energy efficiency improvements. It is essential to create tools to limit their impact on the final consumption. This paper is focused on presenting the results of the analysis developed on the basis of real projects’ data in order to quantify the impact of end-users behavior. The analysis is focused on how the behavior of building’s occupants can vary the achievement of the energy savings targets and how they can be limited. The results obtained show that the management systems are one of the main tools available to control and limit the end-users interaction with the equipment operation. In fact, the results will present the management systems as ‘a must’ on any energy efficiency project.

Keywords: end-users impacts, energy efficiency, energy savings, management systems

Procedia PDF Downloads 261

Authors: Dercan Gencbas, Hatice Bebis, Sue Moorhead

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In the nursing process, many of the nursing classification systems were created to be used in international. From these, NANDA-I, Nursing Outcomes Classification (NOC) and Nursing Interventions Classification (NIC). In this direction, the main objective of this study is to establish a model for caregivers in hospitals and communities in Turkey and to ensure that nursing outputs are assessed by NOC-based measures. There are many scales to measure Urinary Incontinence (UI), which is very common in children, in old age, vaginal birth, NOC scales are ideal for use in the nursing process for comprehensive and holistic assessment, with surveys available. For this reason, the purpose of this study is to evaluate the validity of the NOC outputs and indicators used for UI NANDA-I. This research is a methodological study. In addition to the validity of scale indicators in the study, how much they will contribute to recovery after the nursing intervention was assessed by experts. Scope validations have been applied and calculated according to Fehring 1987 work model. According to this, nursing inclusion criteria and scores were determined. For example, if experts have at least four years of clinical experience, their score was 4 points or have at least one year of the nursing classification system, their score was 1 point. The experts were a publication experience about nursing classification, their score was 1 point, or have a doctoral degree in nursing, their score was 2 points. If the expert has a master degree, their score was 1 point. Total of 55 experts rated Fehring as a “senior degree” with a score of 90 according to the expert scoring. The nursing interventions to be applied were asked to what extent these indicators would contribute to recovery. For coverage validity tailored to Fehring's model, each NOC and NOC indicator from specialists was asked to score between 1-5. Score for the significance of indicators was from 1=no precaution to 5=very important. After the expert opinion, these weighted scores obtained for each NOC and NOC indicator were classified as 0.8 critical, 0.8 > 0.5 complements, > 0.5 are excluded. In the NANDA-I / NOC / NIC system (guideline), 5 NOCs proposed for nursing diagnoses for UI were proposed. These outputs are; Urinary Continence, Urinary Elimination, Tissue Integrity, Self CareToileting, Medication Response. After the scales are translated into Turkish, the weighted average of the scores obtained from specialists for the coverage of all 5 NOCs and the contribution of nursing initiatives exceeded 0.8. After the opinions of the experts, 79 of the 82 indicators were calculated as critical, 3 of the indicators were calculated as supplemental. Because of 0.5 > was not obtained, no substance was removed. All NOC outputs were identified as valid and usable scales in Turkey. In this study, five NOC outcomes were verified for the evaluation of the output of individuals who have received nursing knowledge of UI and variant types. Nurses in Turkey can benefit from the outputs of the NOC scale to perform the care of the elderly incontinence.

Keywords: nursing outcomes, content validity, nursing diagnosis, urinary incontinence

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Authors: W. S. Li, S. T. Yang, H. C. Cheng

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The power conversion efficiency (PCE) of the perovskite solar cells has been achieved by inserting vertically-aligned ZnO nanowires (NWs) between the cathode and the active layer and shows better solar cells performance. Perovskite solar cells have drawn significant attention due to the superb efficiency and low-cost fabrication process. In this experiment, ZnO nanowires are used as the electron transport layer (ETL) due to its low temperature process. The main idea of this thesis is utilizing the 3D structures of the hydrothermally-grown ZnO nanowires to increase the junction area to improve the photovoltaic performance of the perovskite solar cells. The infiltration and the surface coverage of the perovskite precursor solution changed as tuning the length of the ZnO nanowires. It is revealed that the devices with ZnO nanowires of 150 nm demonstrated the best PCE of 8.46 % under the AM 1.5G illumination (100 mW/cm2).

Keywords: hydrothermally-grown ZnO nanowires, perovskite solar cells, low temperature process, pinholes

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Authors: Gizem Eser Erdek

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This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

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Authors: Sibel Kurt

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In this study, with the use of Goffee and Jones’s corporate culture classification and the scale of this classification, there aimed to analyze a company’s corporate culture which does not have a public relations or communication department. First of all, the type of corporate culture in the company had been determined. Then it questioned if there are subcultures which formed according to demographics or the department of work. In the survey questionnaire, there are 53 questions total. 6 of these questions are about demographics, and 47 of them are about corporate culture. 152 personnel of the company had answered the survey, and the data have been evaluated according to frequency, descriptive, and compare means tests. The type of corporate culture of the company was determined as the 'communal' from the typology of Goffee and Jones in the positive form. There are no subcultures in the company which bases on the demographics, but only one subculture has determined according to the department of work. As a result, the absence of public relations department, personnel’s low level of awareness about corporate culture, and the lack of information between management and employees has been revealed.

Keywords: corporate culture, subculture, public relations, organizational communication

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Authors: T. Garlan, I. Gabelotaud, S. Lucas, E. Marchès

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Production of a global sedimentological seabed map has been initiated in 1995 to provide the necessary tool for searches of aircraft and boats lost at sea, to give sedimentary information for nautical charts, and to provide input data for acoustic propagation modelling. This original approach had already been initiated one century ago when the French hydrographic service and the University of Nancy had produced maps of the distribution of marine sediments of the French coasts and then sediment maps of the continental shelves of Europe and North America. The current map of the sediment of oceans presented was initiated with a UNESCO's general map of the deep ocean floor. This map was adapted using a unique sediment classification to present all types of sediments: from beaches to the deep seabed and from glacial deposits to tropical sediments. In order to allow good visualization and to be adapted to the different applications, only the granularity of sediments is represented. The published seabed maps are studied, if they present an interest, the nature of the seabed is extracted from them, the sediment classification is transcribed and the resulted map is integrated in the world map. Data come also from interpretations of Multibeam Echo Sounder (MES) imagery of large hydrographic surveys of deep-ocean. These allow a very high-quality mapping of areas that until then were represented as homogeneous. The third and principal source of data comes from the integration of regional maps produced specifically for this project. These regional maps are carried out using all the bathymetric and sedimentary data of a region. This step makes it possible to produce a regional synthesis map, with the realization of generalizations in the case of over-precise data. 86 regional maps of the Atlantic Ocean, the Mediterranean Sea, and the Indian Ocean have been produced and integrated into the world sedimentary map. This work is permanent and permits a digital version every two years, with the integration of some new maps. This article describes the choices made in terms of sediment classification, the scale of source data and the zonation of the variability of the quality. This map is the final step in a system comprising the Shom Sedimentary Database, enriched by more than one million punctual and surface items of data, and four series of coastal seabed maps at 1:10,000, 1:50,000, 1:200,000 and 1:1,000,000. This step by step approach makes it possible to take into account the progresses in knowledge made in the field of seabed characterization during the last decades. Thus, the arrival of new classification systems for seafloor has improved the recent seabed maps, and the compilation of these new maps with those previously published allows a gradual enrichment of the world sedimentary map. But there is still a lot of work to enhance some regions, which are still based on data acquired more than half a century ago.

Keywords: marine sedimentology, seabed map, sediment classification, world ocean

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Authors: M. G. Dirodi, G. Gugliotta, C. Leonardi

Abstract:

The member states shall establish zones and agglomerations throughout their territory to assess and manage air quality in order to comply with European directives. In Italy decree 155/2010, transposing Directive 2008/50/EC on ambient air quality and cleaner air for Europe, merged into a single act the previous provisions on ambient air quality assessment and management, including those resulting from the implementation of Directive 2004/107/EC relating to arsenic, cadmium, nickel, mercury, and polycyclic aromatic hydrocarbons in ambient air. Decree 155/2010 introduced stricter rules for identifying zones on the basis of the characteristics of the territory in spite of considering pollution levels, as it was in the past. The implementation of such new criteria has reduced the great variability of the previous zoning, leading to a significant reduction of the total number of zones and to a complete and uniform ambient air quality assessment and management throughout the Country. The present document is related to the new zones definition in Italy according to Decree 155/2010. In particular, the paper contains the description and the analysis of the outcome of zoning and classification.

Keywords: zones, agglomerations, air quality assessment, classification

Procedia PDF Downloads 330

Authors: Khaoula Hidouri, Ali Benhmidene, Bechir Chouachi

Abstract:

The purification of brackish seawater becomes a necessity and not a choice against demographic and industrial growth especially in third world countries. Two models can be used in this work: simple solar still and simple solar still coupled with a heat pump. In this research, the productivity of water by Simple Solar Distiller (SSD) and Simple Solar Distiller Hybrid Heat Pump (SSDHP) was determined by the orientation, the use of heat pump, the simple or double glass cover. The productivity can exceed 1.2 L/m²h for the SSDHP and 0.5 L/m²h for SSD model. The result of the global efficiency is determined for two models SSD and SSDHP give respectively 30%, 50%. The internal efficiency attained 35% for SSD and 60% of the SSDHP models. Convective heat coefficient can be determined by attained 2.5 W/m²°C and 0.5 W/m²°C respectively for SSDHP and SSD models.

Keywords: productivity, efficiency, convective heat coefficient, SSD model, SSDHPmodel

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Authors: Almahd Alshereef, Ahmed Alkilany, Hammad Said, Azuraliza Abu Bakar

Abstract:

From time to time, electrical power grid is directed by the National Electricity Operator to conduct load shedding, which involves hours' power outages on the area of this study, Southern Electrical Grid of Libya (SEGL). Load shedding is conducted in order to alleviate pressure on the National Electricity Grid at times of peak demand. This approach has chosen a set of categories to study load-shedding problem considering the effect of the demand priorities on the operation of the power system during emergencies. Classification of category region for load shedding problem is solved by a new algorithm (the harmony algorithm) based on the "random generation list of category region", which is a possible solution with a proximity degree to the optimum. The obtained results prove additional enhancements compared to other heuristic approaches. The case studies are carried out on SEGL.

Keywords: optimization, harmony algorithm, load shedding, classification

Procedia PDF Downloads 396

Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

Abstract:

Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR.

Keywords: time-frequency plane, atomic decomposition, envelope sampling, Gabor atoms, matching pursuit, sparse dictionary learning, sparse autoencoder

Procedia PDF Downloads 289

Authors: Ruedee Niyomrath

Abstract:

The purposes of this research is aim to build the energy saving stove for stew coconut sugar. The research started from explores ceramic raw materials in local area, create the appropriate mixture of ceramic raw materials for construction material of stove, and make it by ceramic process. It includes design and build the energy saving stove, experiment the efficiency of energy saving stove as to thermal efficiency, energy saving, performance of time, and energy cost efficiency, transfer the knowledge for community, stove manufacturers, and technicians. The findings must be useful to the coconut sugar enterprises producing, to reduce the cost of production, preserve natural resources, and environments.

Keywords: ceramic raw material, energy saving stove, stove design, performance of stove, stove for stew coconut sugar

Procedia PDF Downloads 361

Authors: Hamzeh Awad

Abstract:

Disability is considered to be a worldwide complex phenomenon which rising at a phenomenal rate and caused by many different factors. Chronic diseases such as cardiovascular disease and diabetes can lead to mobility disability in particular and disability in general. The ICF is an integrative bio-psycho-social model of functioning and disability and considered by the World Health Organization (WHO) to be a reference for disability classification using its categories and core set to classify disorder’s functional limitations. Specialist programs at Sultan Bin Abdul Aziz Humanitarian City (SBAHC) are providing both inpatient and outpatient services have started to implement the ICF and use it as a problem solving tool in Rehab. Diabetes is leading contributing factor for disability and considered epidemic in several Gulf countries including the Kingdom of Saudi Arabia (KSA), where its prevalence continues to increase dramatically. Metabolic disorders, mainly diabetes are not well covered in Rehab field. The purpose of this study is present to research and clinical rehabilitation field of DREAM and ICF as a framework in clinical and research setting in Rehab service. Also, shed the light on using the ICF as problem solving tool at SBAHC. There are synergies between disability causes and wider public health priorities in relation to both chronic disease and disability prevention. Therefore, there is a need for strong advocacy and understanding of the role of ICF as a reference in Rehab settings in Middle East if we wish to seize the opportunity to reverse current trends of acquired disability in the region.

Keywords: international classification of functioning, disability and health (ICF), prototype, rehabilitation and diabetes

Procedia PDF Downloads 351