Search results for: Optical Network Unit

Authors: Mamta Thakur, Dr. Shashi Mawar, Ms. Levis Murry, Dr. D.k.sharma

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Aim: The aim of this study is to develop nephrology nursing procedure protocol after clinical audit of current nephrology nursing practices. Materials and methods: This descriptive observational study was conducted on 40 nurses who were working in Nephrology Department of AIIMS, New Delhi to observe their current practices to assess the existing gaps in the practice. The nurses were enrolled through total enumerative sampling. Sociodemographic profile of nurses and clinical profile for site of procedure was collected. Observation checklist was formed on the basis of standard nursing practices, which included 7 dimensions for hemodialysis procedure and 3 dimensions for procedure of assisting renal biopsy. Based on the gaps identified, nephrology nursing procedure protocol will be developed. Nurses were observed during two shifts, and each nurse was observed once. Scoring of items were done in each dimension, and for acceptable practices, nurses have to score ≥80% in each dimension. Results: Data was analyzed using descriptive statistics. Majority of nurses (73.7%) in nephrology ward and (80.9%) in hemodialysis unit have not undergone any special training in nephrology. Most of nurses (80.9%) followed the acceptable nursing practices for procedure of connection for hemodialysis. None of nurses followed the acceptable level (≥80%) of nursing practices for the procedure of predialysis assessment, the procedure for site preparation, during dialysis assessment and post dialysis assessment. None (100%) showed the acceptable level of nursing practices for all the dimensions of assisting renal biopsy procedure. Nephrology nursing procedure protocol was developed by researcher following a rigorous process, and this will reduce the gaps in the nursing practice. Conclusion: Clinical audit found that there were gaps in the existing nursing practices compared to the standardised nursing practices for procedure of hemodialysis and assisting renal biopsy, and these gaps have been addressed by the development of the protocol.

Keywords: nursing practice, nephrology nursing procedure, nursing protocol, renal biopsy, hemodialysis

Procedia PDF Downloads 107

Authors: M. Kowalski, M. Życzkowski, M. Karol

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Current quantum key distribution systems (QKD) offer low bit rate of up to single MHz. Compared to conventional optical fiber links with multiple GHz bitrates, parameters of recent QKD systems are significantly lower. In the article we present the conception of application of the Compressed Sensing method for compression of quantum information. The compression methodology as well as the signal reconstruction method and initial results of improving the throughput of quantum information link are presented.

Keywords: quantum key distribution systems, fiber optic system, compressed sensing

Procedia PDF Downloads 699

Authors: Mohammad Yahaghifar

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Nowadays Wireless system designers have been facing the continuously increasing demand for high data rates and mobility required by new wireless applications. Evolving future communication network generation cellular wireless networks are envisioned to overcome the fundamental challenges of existing cellular networks, for example, higher data rates, excellent end-to-end performance, and user coverage in hot-spots and crowded areas with lower latency,energy consumption and cost per information transfer. In this paper we propose a potential cellular architecture that separates indoor and outdoor scenarios and discuss various promising technologies for future wireless communication systemssystems, such as massive MIMO, energy-efficient communications,cognitive radio networks, and visible light communications and we disscuse about 5G that is next generation of wireless networks.

Keywords: future challenges in networks, cellur architecture, visible light communication, 5G wireless technologies, spatial modulation, massiva mimo, cognitive radio network, green communications

Procedia PDF Downloads 491

Authors: Ho Yeon Park, Kyoung-Jae Kim

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The purpose of this study is to investigate whether friendship in social media can be an important factor in recommender system through social scientific analysis of friendship in popular social media such as Facebook and Twitter. For this purpose, this study analyzes data on friendship in real social media using component analysis and clique analysis among sub-group analysis in social network analysis. In this study, we propose an algorithm to reflect the results of sub-group analysis on the recommender system. The key to this algorithm is to ensure that recommendations from users in friendships are more likely to be reflected in recommendations from users. As a result of this study, outcomes of various subgroup analyzes were derived, and it was confirmed that the results were different from the results of the existing recommender system. Therefore, it is considered that the results of the subgroup analysis affect the recommendation performance of the system. Future research will attempt to generalize the results of the research through further analysis of various social data.

Keywords: sub-group analysis, social media, social network analysis, recommender systems

Procedia PDF Downloads 369

Authors: Eric Lacoste

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Laboratory animals are currently widely used as a model of human pathologies in dermatology such as atopic dermatitis (AD). These models provide a better understanding of the pathophysiology of this complex and multifactorial disease, the discovery of potential new therapeutic targets and the testing of the efficacy of new therapeutics. However, confirmation of the correct development of AD is mainly based on histology from skin biopsies requiring invasive surgery or euthanasia of the animals, plus slicing and staining protocols. However, there are currently accessible imaging technologies such as Optical Coherence Tomography (OCT), which allows non-invasive visualization of the main histological structures of the skin (like stratum corneum, epidermis, and dermis) and assessment of the dynamics of the pathology or efficacy of new treatments. Briefly, female immunocompetent hairless mice (SKH1 strain) were sensitized and challenged topically on back and ears for about 4 weeks. Back skin and ears thickness were measured using calliper at 3 occasions per week in complement to a macroscopic evaluation of atopic dermatitis lesions on back: erythema, scaling and excoriations scoring. In addition, OCT was performed on the back and ears of animals. OCT allows a virtual in-depth section (tomography) of the imaged organ to be made using a laser, a camera and image processing software allowing fast, non-contact and non-denaturing acquisitions of the explored tissues. To perform the imaging sessions, the animals were anesthetized with isoflurane, placed on a support under the OCT for a total examination time of 5 to 10 minutes. The results show a good correlation of the OCT technique with classical HES histology for skin lesions structures such as hyperkeratosis, epidermal hyperplasia, and dermis thickness. This OCT imaging technique can, therefore, be used in live animals at different times for longitudinal evaluation by repeated measurements of lesions in the same animals, in addition to the classical histological evaluation. Furthermore, this original imaging technique speeds up research protocols, reduces the number of animals and refines the use of the laboratory animal.

Keywords: atopic dermatitis, mouse model, oxzolone model, histology, imaging

Procedia PDF Downloads 139

Authors: Yang Zhang, Jian He

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Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper introduces sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for coronary heart disease prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network.

Keywords: Bi-LSTM, CHD, ECG, ResNet, sliding window

Procedia PDF Downloads 95

Authors: E. Behmanesh, J. Pannek

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The distribution-allocation problem is known as one of the most comprehensive strategic decision. In real-world cases, it is impossible to solve a distribution-allocation problem in traditional ways with acceptable time. Hence researchers develop efficient non-traditional techniques for the large-term operation of the whole supply chain. These techniques provide near-optimal solutions particularly for large scales test problems. This paper, presents an integrated supply chain model which is flexible in the delivery path. As the solution methodology, we apply a memetic algorithm with a novelty in population presentation. To illustrate the performance of the proposed memetic algorithm, LINGO optimization software serves as a comparison basis for small size problems. In large size cases that we are dealing with in the real world, the Genetic algorithm as the second metaheuristic algorithm is considered to compare the results and show the efficiency of the memetic algorithm.

Keywords: integrated logistics network, flexible path, memetic algorithm, genetic algorithm

Procedia PDF Downloads 378

Authors: Nataša Kalajdžija, Strahinja Kovačević, Davor Lončar, Sanja Podunavac Kuzmanović, Lidija Jevrić

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In order to investigate the relationship between retention and structure, a quantitative Structure Retention Relationships (QSRRs) study was applied for the prediction of retention times of a set of 23 secoestrane derivatives in a reversed-phase thin-layer chromatography. After the calculation of molecular descriptors, a suitable set of molecular descriptors was selected by using step-wise multiple linear regressions. Artificial Neural Network (ANN) method was employed to model the nonlinear structure-activity relationships. The ANN technique resulted in 5-6-1 ANN model with the correlation coefficient of 0.98. We found that the following descriptors: Critical pressure, total energy, protease inhibition, distribution coefficient (LogD) and parameter of lipophilicity (miLogP) have a significant effect on the retention times. The prediction results are in very good agreement with the experimental ones. This approach provided a new and effective method for predicting the chromatographic retention index for the secoestrane derivatives investigated.

Keywords: lipophilicity, QSRR, RP TLC retention, secoestranes

Procedia PDF Downloads 463

Authors: Akram Rasheed

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Background: Simulation in the health care field has been increasingly used over the last years in the training of resuscitation and life support practices. It has shown the advantage of improving the decision-making and technical skills through deliberate practice and return demonstration. Local Problem: This article reports on the integration of simulation-based training (SBT) in the training program about proper team dynamics and leadership skills during cardiopulmonary resuscitation (CPR) in the intensive care unit (ICU). Method and Intervention: Training of 180 critical care nurses was conducted using SBT between 1st January and 30th 2020. We had conducted 15 workshops, with the integration of SBT using high fidelity manikins and using demonstration and return-demonstration approach to train the nursing staff about proper team dynamics and leadership skills during CPR. Results: After completing the SBT session, all 180 nurses completed the evaluation form. The majority of evaluation items were rated over 95% for the effectiveness of the education; four items were less than 95% (88–94%). Lower rated items considered training and practice time, improved competency, and commitment to apply to learn. The team dynamics SBT was evaluated as an effective means to improve team dynamics and leadership skills during CPR in the intensive care unit (ICU). Conclusion: The use of simulation-based training to improve team dynamics and leadership skills is an effective method for better patient management during CPR. Besides skills competency, closed-loop communication, clear messages, clear roles, and assignments, knowing one’s limitations, knowledge sharing, constructive interventions, re-evaluating and summarizing, and mutual respect are all important concepts that should be considered during team dynamics training. However, participants reported the need for a repeated practice opportunity to build competency.

Keywords: cardiopulmonary resuscitation, high fidelity manikins, simulation-based training, team dynamics

Procedia PDF Downloads 145

Authors: Xiang Shijie, Zhou Dong, Tian Dan

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This paper focuses on the application of real-time semantic segmentation technology in complex road condition recognition, aiming to address the critical issue of how to improve segmentation accuracy while ensuring real-time performance. Semantic segmentation technology has broad application prospects in fields such as autonomous vehicle navigation and remote sensing image recognition. However, current real-time semantic segmentation networks face significant technical challenges and optimization gaps in balancing speed and accuracy. To tackle this problem, this paper conducts an in-depth study and proposes an innovative Guided Image Reconstruction Module. By resampling high-resolution images into a set of low-resolution images, this module effectively reduces computational complexity, allowing the network to more efficiently extract features within limited resources, thereby improving the performance of real-time segmentation tasks. In addition, a dual-branch network structure is designed in this paper to fully leverage the advantages of different feature layers. A novel Hybrid Attention Mechanism is also introduced, which can dynamically capture multi-scale contextual information and effectively enhance the focus on important features, thus improving the segmentation accuracy of the network in complex road condition. Compared with traditional methods, the proposed model achieves a better balance between accuracy and real-time performance and demonstrates competitive results in road condition segmentation tasks, showcasing its superiority. Experimental results show that this method not only significantly improves segmentation accuracy while maintaining real-time performance, but also remains stable across diverse and complex road conditions, making it highly applicable in practical scenarios. By incorporating the Guided Image Reconstruction Module, dual-branch structure, and Hybrid Attention Mechanism, this paper presents a novel approach to real-time semantic segmentation tasks, which is expected to further advance the development of this field.

Keywords: hybrid attention mechanism, image reconstruction, real-time, road status recognition

Procedia PDF Downloads 30

Authors: R. Rajabpour, N. Talebbeydokhti, M. H. Ahmadi

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In recent years, new techniques for solving complex problems in engineering are proposed. One of these techniques is JPSO algorithm. With innovative changes in the nature of the jump algorithm JPSO, it is possible to construct a graph-based solution with a new algorithm called G-JPSO. In this paper, a new algorithm to solve the optimal control problem Fletcher-Powell and optimal control of pumps in water distribution network was evaluated. Optimal control of pumps comprise of optimum timetable operation (status on and off) for each of the pumps at the desired time interval. Maximum number of status on and off for each pumps imposed to the objective function as another constraint. To determine the optimal operation of pumps, a model-based optimization-simulation algorithm was developed based on G-JPSO and JPSO algorithms. The proposed algorithm results were compared well with the ant colony algorithm, genetic and JPSO results. This shows the robustness of proposed algorithm in finding near optimum solutions with reasonable computational cost.

Keywords: G-JPSO, operation, optimization, pumping station, water distribution networks

Procedia PDF Downloads 406

Authors: Magdi M. Nabi, Ding-Li Yu

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This paper presents a neural network based model predictive control (MPC) strategy to control a strongly exothermic reaction with complicated nonlinear kinetics given by Chylla-Haase polymerization reactor that requires a very precise temperature control to maintain product uniformity. In the benchmark scenario, the operation of the reactor must be guaranteed under various disturbing influences, e.g., changing ambient temperatures or impurity of the monomer. Such a process usually controlled by conventional cascade control, it provides a robust operation, but often lacks accuracy concerning the required strict temperature tolerances. The predictive control strategy based on the RBF neural model is applied to solve this problem to achieve set-point tracking of the reactor temperature against disturbances. The result shows that the RBF based model predictive control gives reliable result in the presence of some disturbances and keeps the reactor temperature within a tight tolerance range around the desired reaction temperature.

Keywords: Chylla-Haase reactor, RBF neural network modelling, model predictive control, semi-batch reactors

Procedia PDF Downloads 471

Authors: Rahele Mesbah, Nic Van Der Wee, Manja Koenders, Erik Giltay, Albert Van Hemert, Max De Leeuw

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Introduction: Patients with bipolar disorder (BD), characterized by depressive and manic episodes, often suffer from cognitive dysfunction. An up-to-date meta-analysis of functional Magnetic Resonance Imaging (fMRI) studies examining cognitive function in BD is lacking. Objective: The aim of the current fMRI meta-analysis is to investigate brain functioning of bipolar patients compared with healthy subjects within three domains of emotion processing, reward processing, and working memory. Method: Differences in brain regions activation were tested within whole-brain analysis using the activation likelihood estimation (ALE) method. Separate analyses were performed for each cognitive domain. Results: A total of 50 fMRI studies were included: 20 studies used an emotion processing (316 BD and 369 HC) task, 9 studies a reward processing task (215 BD and 213 HC), and 21 studies used a working memory task (503 BD and 445 HC). During emotion processing, BD patients hyperactivated parts of the left amygdala and hippocampus as compared to HC’s, but showed hypoactivation in the inferior frontal gyrus (IFG). Regarding reward processing, BD patients showed hyperactivation in part of the orbitofrontal cortex (OFC). During working memory, BD patients showed increased activity in the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). Conclusions: This meta-analysis revealed evidence for activity disturbances in several brain areas involved in the cognitive functioning of BD patients. Furthermore, most of the found regions are part of the so-called fronto-limbic network which is hypothesized to be affected as a result of BD candidate genes' expression.

Keywords: cognitive functioning, fMRI analysis, bipolar disorder, fronto-limbic network

Procedia PDF Downloads 469

Authors: Raeed Alanazi

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Introduction: Nursing students have a crucial role to play in the inhibition of infectious diseases and, therefore, must be trained in infection control and prevention modules prior to entering clinical settings. Simulations have been found to have a positive impact on infection control skills and the use of standard precautions. Aim: The purpose of this study was to use the four sources of self-efficacy in explaining the level of clinical self-efficacy and adherence to infection control practices in Saudi nursing students during simulation practice. Method: A cross-sectional design with convenience sampling was used. This study was conducted in all Saudi nursing schools, with a total number of 197 students participated in this study. Three scales were used simulation self- efficacy Scale (SSES), the four sources of self-efficacy scale (SSES), and Compliance with Standard Precautions Scale (CSPS). Multiple linear regression was used to test the use of the four sources of self-efficacy (SSES) in explaining level of clinical self-efficacy and adherence to infection control in nursing students. Results: The vicarious experience subscale (p =.044) was statistically significant. The regression model indicated that for every one unit increase in vicarious experience (observation and reflection in simulation), the participants’ adherence to infection control increased by .13 units (β =.22, t = 2.03, p =.044). In addition, the regression model indicated that for every one unit increase in education level, the participants’ adherence to infection control increased by 1.82 units (beta=.34= 3.64, p <.001). Also, the mastery experience subscale (p <.001) and vicarious experience subscale (p = .020) were shared significant associations with clinical self-efficacy. Conclusion: The findings of this research support the idea that simulation-based learning can be a valuable teaching-learning method to help nursing students develop clinical competence, which is essential in providing quality and safe nursing care.

Keywords: simulation-based learning, clinical self-efficacy, infection control, nursing students

Procedia PDF Downloads 75

Authors: Lu Yang, Meiyang Song, Xiang Yu, Wenhao Zhou, Chuntao Feng

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To address the problem that the FM continuous-wave radar (FMCW) extracts human vital sign signals which are susceptible to noise interference and low reconstruction accuracy, a new detection scheme for the sign signals is proposed. Firstly, an improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) algorithm is applied to decompose the radar-extracted thoracic signals to obtain several intrinsic modal functions (IMF) with different spatial scales, and then the IMF components are optimized by a BP neural network improved by immune genetic algorithm (IGA). The simulation results show that this scheme can effectively separate the noise and accurately extract the respiratory and heartbeat signals and improve the reconstruction accuracy and signal-to-noise ratio of the sign signals.

Keywords: frequency modulated continuous wave radar, ICEEMDAN, BP neural network, vital signs signal

Procedia PDF Downloads 172

Authors: J. Nisar, S. Halim

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The additional relationships i.e., start-to-start, finish-to-finish, and start-to-finish, between activity in Precedence Diagram Method (PDM) provides a more flexible schedule than traditional Critical Path Method (CPM). But, changing the duration of critical activities in the PDM network will have an anomalous effect on the critical path and the project completion date. In this study, we classified the critical activities in two groups i.e., 1. activity on single critical path and 2. activity on multi-critical paths, and six classes i.e., normal, reverse, neutral, perverse, decrease-reverse and increase-normal, based on their effects on project duration in PDM. Furthermore, we determined the maximum float of time by which the duration each type of critical activities can be changed without effecting the project duration. This study would help the project manager to clearly understand the behavior of each critical activity on critical path, and he/she would be able to change the project duration by shortening or lengthening activities based on project budget and project deadline.

Keywords: construction management, critical path method, project scheduling network, precedence diagram method

Procedia PDF Downloads 228

Authors: Gurukiran Veerapur, Nytik Birudavolu, Seetharam U. N., Chandravva Hebbi, R. Praneeth Reddy

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This work presents a robust approach for the recognition of characters in Telugu and Kannada, two South Indian scripts with structural similarities in characters. To recognize the characters exhaustive datasets are required, but there are only a few publicly available datasets. As a result, we decided to create a dataset for one language (source language),train the model with it, and then test it with the target language.Telugu is the target language in this work, whereas Kannada is the source language. The suggested method makes use of Canny edge features to increase character identification accuracy on pictures with noise and different lighting. A dataset of 45,150 images containing printed Kannada characters was created. The Nudi software was used to automatically generate printed Kannada characters with different writing styles and variations. Manual labelling was employed to ensure the accuracy of the character labels. The deep learning models like CNN (Convolutional Neural Network) and Visual Attention neural network (VAN) are used to experiment with the dataset. A Visual Attention neural network (VAN) architecture was adopted, incorporating additional channels for Canny edge features as the results obtained were good with this approach. The model's accuracy on the combined Telugu and Kannada test dataset was an outstanding 97.3%. Performance was better with Canny edge characteristics applied than with a model that solely used the original grayscale images. The accuracy of the model was found to be 80.11% for Telugu characters and 98.01% for Kannada words when it was tested with these languages. This model, which makes use of cutting-edge machine learning techniques, shows excellent accuracy when identifying and categorizing characters from these scripts.

Keywords: base characters, modifiers, guninthalu, aksharas, vattakshara, VAN

Procedia PDF Downloads 55

Authors: Akhil Dubey, Rajnesh Singh

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In modern days there occur many rapid modifications in field of ad hoc network. These modifications create many revolutionary changes in the routing. Predictive energy efficient routing is inspired on the bee’s behavior of swarm intelligence. Predictive routing improves the efficiency of routing in the energetic point of view. The main aim of this routing is the minimum energy consumption during communication and maximized intermediate node’s remaining battery power. This routing is based on food searching behavior of bees. There are two types of bees for the exploration phase the scout bees and for the evolution phase forager bees use by this routing. This routing algorithm computes the energy consumption, fitness ratio and goodness of the path. In this paper we review the literature related with predictive routing, presenting modified routing and simulation result of this algorithm comparison with artificial bee colony based routing schemes in MANET and see the results of path fitness and probability of fitness.

Keywords: mobile ad hoc network, artificial bee colony, PEEBR, modified predictive routing

Procedia PDF Downloads 418

Authors: Loganathan Kumaresan, Velusamy Chidambaram, Arumugam Velayutham Karthikeyani, Alex Cheru Pulikottil, Madhusudan Sau, Gurpreet Singh Kapur, Sankara Sri Venkata Ramakumar

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Environmentally driven regulations throughout the world stipulate dramatic improvements in the quality of transportation fuels and refining operations. The exhaust gases like CO, NOx, and SOx from stationary sources (e.g., refinery) and motor vehicles contribute to a large extent for air pollution. The refining industry is under constant environmental pressure to achieve more rigorous standards on sulphur content in the fuel used in the transportation sector and other off-gas emissions. Fluid catalytic cracking unit (FCCU) is a major secondary process in refinery for gasoline and diesel production. CO-combustion promoter additive and gasoline sulphur reduction (GSR) additive are catalytic systems used in FCCU to assist the combustion of CO to CO₂ in the regenerator and regulate sulphur in gasoline faction respectively along with main FCC catalyst. Effectiveness of these catalysts is governed by the active metal used, its dispersion, the type of base material employed, and retention characteristics of additive in FCCU such as attrition resistance and density. The challenge is to have a high-density microsphere catalyst support for its retention and high activity of the active metals as these catalyst additives are used in low concentration compare to the main FCC catalyst. The present paper discusses in the first part development of high dense microsphere of nanocrystalline alumina by hydro-thermal method for CO combustion promoter application. Performance evaluation of additive was conducted under simulated regenerator conditions and shows CO combustion efficiency above 90%. The second part discusses the efficacy of a co-precipitation method for the generation of the active crystalline spinels of Zn, Mg, and Cu with aluminium oxides as an additive. The characterization and micro activity test using heavy combined hydrocarbon feedstock at FCC unit conditions for evaluating gasoline sulphur reduction activity are studied. These additives were characterized by X-Ray Diffraction, NH₃-TPD & N₂ sorption analysis, TPR analysis to establish structure-activity relationship. The reaction of sulphur removal mechanisms involving hydrogen transfer reaction, aromatization and alkylation functionalities are established to rank GSR additives for their activity, selectivity, and gasoline sulphur removal efficiency. The sulphur shifting in other liquid products such as heavy naphtha, light cycle oil, and clarified oil were also studied. PIONA analysis of liquid product reveals 20-40% reduction of sulphur in gasoline without compromising research octane number (RON) of gasoline and olefins content.

Keywords: hydrothermal, nanocrystalline, spinel, sulphur reduction

Procedia PDF Downloads 102

Authors: R. B. Knudsen, O. T. Rasmussen, R. A. Alphinas

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The reason for conducting this research is to develop an algorithm that is capable of classifying news articles from the automobile industry, according to the competitive actions that they entail, with the use of Text Mining (TM) methods. It is needed to test how to properly preprocess the data for this research by preparing pipelines which fits each algorithm the best. The pipelines are tested along with nine different classification algorithms in the realm of regression, support vector machines, and neural networks. Preliminary testing for identifying the optimal pipelines and algorithms resulted in the selection of two algorithms with two different pipelines. The two algorithms are Logistic Regression (LR) and Artificial Neural Network (ANN). These algorithms are optimized further, where several parameters of each algorithm are tested. The best result is achieved with the ANN. The final model yields an accuracy of 0.79, a precision of 0.80, a recall of 0.78, and an F1 score of 0.76. By removing three of the classes that created noise, the final algorithm is capable of reaching an accuracy of 94%.

Keywords: Artificial Neural network, Competitive dynamics, Logistic Regression, Text classification, Text mining

Procedia PDF Downloads 128

Authors: Jin Liang, James H. Liu, Ti-Jun Xiao

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It is known that there exist many physical phenomena where abrupt or impulsive changes occur either in the system dynamics, for example, ad-hoc network, or in the input forces containing impacts, for example, the bombardment of space antenna by micrometeorites. There are many other examples such as ultra high-speed optical signals over communication networks, the collision of particles, inventory control, government decisions, interest changes, changes in stock price, etc. These are impulsive phenomena. Hence, as a combination of the traditional initial value problems and the short-term perturbations whose duration can be negligible in comparison with the duration of the process, the systems with impulsive conditions (i.e., impulsive systems) are more realistic models for describing the impulsive phenomenon. Such a situation is also suitable for the delay systems, which include some of the past states of the system. So far, there have been a lot of research results in the study of impulsive systems with delay both in finite and infinite dimensional spaces. In this paper, we investigate the periodicity of solutions to the nonautonomous impulsive evolution equations with infinite delay in Banach spaces, where the coefficient operators (possibly unbounded) in the linear part depend on the time, which are impulsive systems in infinite dimensional spaces and come from the optimal control theory. It was indicated that the study of periodic solutions for these impulsive evolution equations with infinite delay was challenging because the fixed point theorems requiring some compactness conditions are not applicable to them due to the impulsive condition and the infinite delay. We are happy to report that after detailed analysis, we are able to combine the techniques developed in our previous papers, and some new ideas in this paper, to attack these impulsive evolution equations and derive periodic solutions. More specifically, by virtue of the related transition operator family (evolution family), we present a Poincaré operator given by the nonautonomous impulsive evolution system with infinite delay, and then show that the operator is a condensing operator with respect to Kuratowski's measure of non-compactness in a phase space by using an Amann's lemma. Finally, we derive periodic solutions from bounded solutions in view of the Sadovskii fixed point theorem. We also present a relationship between the boundedness and the periodicity of the solutions of the nonautonomous impulsive evolution system. The new results obtained here extend some earlier results in this area for evolution equations without impulsive conditions or without infinite delay.

Keywords: impulsive, nonautonomous evolution equation, optimal control, periodic solution

Procedia PDF Downloads 258

Authors: Zhidong Zhang, Yingchen Yang

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In this study, a diagnostic assessment model for Mastery Engineering Learning was established based on a group of undergraduate students who studied in an engineering course. A diagnostic assessment model can examine both students' learning process and report achievement results. One very unique characteristic is that the diagnostic assessment model can recognize the errors and anything blocking students in their learning processes. The feedback is provided to help students to know how to solve the learning problems with alternative strategies and help the instructor to find alternative pedagogical strategies in the instructional designs. Dynamics is a core course in which is a common course being shared by several engineering programs. This course is a very challenging for engineering students to solve the problems. Thus knowledge acquisition and problem-solving skills are crucial for student success. Therefore, developing an effective and valid assessment model for student learning are of great importance. Diagnostic assessment is such a model which can provide effective feedback for both students and instructor in the mastery of engineering learning.

Keywords: diagnostic assessment, mastery learning, engineering, bayesian network model, learning processes

Procedia PDF Downloads 155

Authors: Tibor Ajtai, Máté Pintér, Noémi Utry, Gergely Kiss-Albert, Andrea Palágyi, László Manczinger, Csaba Vágvölgyi, Gábor Szabó, Zoltán Bozóki

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According to the best knowledge of the authors, here we experimentally demonstrate first, a quantified correlation between the real-time measured optical feature of the ambient and the off-line measured toxicity data. Finally, using these correlations we are presenting a novel methodology for real time characterisation of ambient toxicity based on the multi wavelength aerosol phase photoacoustic measurement. Ambient carbonaceous particulate matter is one of the most intensively studied atmospheric constituent in climate science nowadays. Beyond their climatic impact, atmospheric soot also plays an important role as an air pollutant that harms human health. Moreover, according to the latest scientific assessments ambient soot is the second most important anthropogenic emission source, while in health aspect its being one of the most harmful atmospheric constituents as well. Despite of its importance, generally accepted standard methodology for the quantitative determination of ambient toxicology is not available yet. Dominantly, ambient toxicology measurement is based on the posterior analysis of filter accumulated aerosol with limited time resolution. Most of the toxicological studies are based on operational definitions using different measurement protocols therefore the comprehensive analysis of the existing data set is really limited in many cases. The situation is further complicated by the fact that even during its relatively short residence time the physicochemical features of the aerosol can be masked significantly by the actual ambient factors. Therefore, decreasing the time resolution of the existing methodology and developing real-time methodology for air quality monitoring are really actual issues in the air pollution research. During the last decades many experimental studies have verified that there is a relation between the chemical composition and the absorption feature quantified by Absorption Angström Exponent (AAE) of the carbonaceous particulate matter. Although the scientific community are in the common platform that the PhotoAcoustic Spectroscopy (PAS) is the only methodology that can measure the light absorption by aerosol with accurate and reliable way so far, the multi-wavelength PAS which are able to selectively characterise the wavelength dependency of absorption has become only available in the last decade. In this study, the first results of the intensive measurement campaign focusing the physicochemical and toxicological characterisation of ambient particulate matter are presented. Here we demonstrate the complete microphysical characterisation of winter time urban ambient including optical absorption and scattering as well as size distribution using our recently developed state of the art multi-wavelength photoacoustic instrument (4λ-PAS), integrating nephelometer (Aurora 3000) as well as single mobility particle sizer and optical particle counter (SMPS+C). Beyond this on-line characterisation of the ambient, we also demonstrate the results of the eco-, cyto- and genotoxicity measurements of ambient aerosol based on the posterior analysis of filter accumulated aerosol with 6h time resolution. We demonstrate a diurnal variation of toxicities and AAE data deduced directly from the multi-wavelength absorption measurement results.

Keywords: photoacoustic spectroscopy, absorption Angström exponent, toxicity, Ames-test

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Authors: Innocent Uzougbo Onwuegbuzie

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Wireless Sensor Networks (WSNs) are crucial for numerous applications, yet they face significant challenges due to resource constraints such as limited power and memory. Traditional routing algorithms like Dijkstra, Ad hoc On-Demand Distance Vector (AODV), and Bellman-Ford, while effective in path establishment and discovery, are not optimized for the unique demands of WSNs due to their large memory footprint and power consumption. This paper introduces the Adaptive Energy-Aware Routing (AEAR) model, a solution designed to address these limitations. AEAR integrates reactive route discovery, localized decision-making using geographic information, energy-aware metrics, and dynamic adaptation to provide a robust and efficient routing strategy. We present a detailed comparative analysis using a dataset of 50 sensor nodes, evaluating power consumption, memory footprint, and path cost across AEAR, Dijkstra, AODV, and Bellman-Ford algorithms. Our results demonstrate that AEAR significantly reduces power consumption and memory usage while optimizing path weight. This improvement is achieved through adaptive mechanisms that balance energy efficiency and link quality, ensuring prolonged network lifespan and reliable communication. The AEAR model's superior performance underlines its potential as a viable routing solution for energy-constrained WSN environments, paving the way for more sustainable and resilient sensor network deployments.

Keywords: wireless sensor networks (WSNs), adaptive energy-aware routing (AEAR), routing algorithms, energy, efficiency, network lifespan

Procedia PDF Downloads 41

Authors: Jhe-Hao Huang, Kun-Da Wu, Wei-Cheng Shih, Jui-Pin Hung

Abstract:

This study presented the investigation on the dynamic characteristics of a spindle tool system by experimental and finite element modeling approaches. As well known facts, the machining stability is greatly determined by the dynamic characteristics of the spindle tool system. Therefore, understanding the factors affecting dynamic behavior of a spindle tooling system is a prerequisite in dominating the final machining performance of machine tool system. To this purpose, a physical spindle unit was employed to assess the dynamic characteristics by vibration tests. Then, a three-dimensional finite element model of a high-speed spindle system integrated with tool holder was created to simulate the dynamic behaviors. For modeling the angular contact bearings, a series of spring elements were introduced between the inner and outer rings. The spring constant can be represented by the contact stiffness of the rolling bearing based on Hertz theory. The interface characteristic between spindle nose and tool holder taper can be quantified from the comparison of the measurements and predictions. According to the results obtained from experiments and finite element predictions, the vibration behavior of the spindle is dominated by the bending deformation of the spindle shaft in different modes, which is further determined by the stiffness of the bearings in spindle housing. Also, the spindle unit with tool holder shows a different dynamic behavior from that of spindle without tool holder. This indicates the interface property between tool holder and spindle nose plays an dominance on the dynamic characteristics the spindle tool system. Overall, the dynamic behaviors the spindle with and without tool holder can be successfully investigated through the finite element model proposed in this study. The prediction accuracy is determined by the modeling of the rolling interface of ball bearings in spindles and the interface characteristics between tool holder and spindle nose. Besides, identifications of the interface characteristics of a ball bearing and spindle tool holder are important for the refinement of the spindle tooling system to achieve the optimum machining performance.

Keywords: contact stiffness, dynamic characteristics, spindle, tool holder interface

Procedia PDF Downloads 302

Authors: Yan Fang, Jie Yin, Bihu Wu

Abstract:

The interaction between climate change and tourism is one of the most promising research areas of recent decades. In this paper, a scientometric analysis of 976 academic publications between 1990 and 2015 related to climate change and tourism is presented in order to characterize the intellectual landscape by identifying and visualizing the evolution of the collaboration network, the co-citation network, and emerging trends of citation burst and keyword co-occurrence. The results show that the number of publications in this field has increased rapidly and it has become an interdisciplinary and multidisciplinary topic. The research areas are dominated by Australia, USA, Canada, New Zealand, and European countries, which have the most productive authors and institutions. The hot topics of climate change and tourism research in recent years are further identified, including the consequences of climate change for tourism, necessary adaptations, the vulnerability of the tourism industry, tourist behaviour and demand in response to climate change, and emission reductions in the tourism sector. The work includes an in-depth analysis of a major forum of climate change and tourism to help readers to better understand global trends in this field in the past 25 years.

Keywords: climate change, tourism, scientometrics, CiteSpace

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Authors: Rahul Malhotra, Anurag Sharma

Abstract:

Fiber based access networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This paper is targeted to show the simultaneous delivery of triple play service (data, voice, and video). The comparative investigation and suitability of various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be accommodated decreases due to increase in bit error rate.

Keywords: FTTH, quad play, play service, access networks, data rate

Procedia PDF Downloads 420

Authors: Shankaran Sitarama

Abstract:

New Product Development involves team members coming together and working in teams to come up with innovative solutions to problems, resulting in new products. Thus, a core attribute of a successful NPD team is their creativity and innovation. They need to be creative as a group, generating a breadth of ideas and innovative solutions that solve or address the problem they are targeting and meet the user’s needs. They also need to be very efficient in their teamwork as they work through the various stages of the development of these ideas, resulting in a POC (proof-of-concept) implementation or a prototype of the product. There are two distinctive traits that the teams need to have, one is ideational creativity, and the other is effective and efficient teamworking. There are multiple types of tensions that each of these traits cause in the teams, and these tensions reflect in the team dynamics. Ideational conflicts arising out of debates and deliberations increase the collective knowledge and affect the team creativity positively. However, the same trait of challenging each other’s viewpoints might lead the team members to be disruptive, resulting in interpersonal tensions, which in turn lead to less than efficient teamwork. Teams that foster and effectively manage these creative tensions are successful, and teams that are not able to manage these tensions show poor team performance. In this paper, it explore these tensions as they result in the team communication social network and propose a Creative Tension Balance index along the lines of Degree of Balance in social networks that has the potential to highlight the successful (and unsuccessful) NPD teams. Team communication reflects the team dynamics among team members and is the data set for analysis. The emails between the members of the NPD teams are processed through a semantic analysis algorithm (LSA) to analyze the content of communication and a semantic similarity analysis to arrive at a social network graph that depicts the communication amongst team members based on the content of communication. This social network is subjected to traditional social network analysis methods to arrive at some established metrics and structural balance analysis metrics. Traditional structural balance is extended to include team interaction pattern metrics to arrive at a creative tension balance metric that effectively captures the creative tensions and tension balance in teams. This CTB (Creative Tension Balance) metric truly captures the signatures of successful and unsuccessful (dissonant) NPD teams. The dataset for this research study includes 23 NPD teams spread out over multiple semesters and computes this CTB metric and uses it to identify the most successful and unsuccessful teams by classifying these teams into low, high and medium performing teams. The results are correlated to the team reflections (for team dynamics and interaction patterns), the team self-evaluation feedback surveys (for teamwork metrics) and team performance through a comprehensive team grade (for high and low performing team signatures).

Keywords: team dynamics, social network analysis, new product development teamwork, structural balance, NPD teams

Procedia PDF Downloads 83

Authors: Tushar K. Routh

Abstract:

If inputs are engineered in certain manners, they can influence deep neural networks’ (DNN) performances by facilitating misclassifications, a phenomenon well-known as adversarial attacks that question networks’ vulnerability. Recent studies have unfolded the relationship between vulnerability of such networks with their complexity. In this paper, the distinctive influence of additional convolutional layers at the decision boundaries of several DNN architectures was investigated. Here, to engineer inputs from widely known image datasets like MNIST, Fashion MNIST, and Cifar 10, we have exercised One Step Spectral Attack (OSSA) and Fast Gradient Method (FGM) techniques. The aftermaths of adding layers to the robustness of the architectures have been analyzed. For reasoning, separation width from linear class partitions and local geometry (curvature) near the decision boundary have been examined. The result reveals that model complexity has significant roles in adjusting relative distances from margins, as well as the local features of decision boundaries, which impact robustness.

Keywords: DNN robustness, decision boundary, local curvature, network complexity

Procedia PDF Downloads 82

Authors: Merouane Boudjerda, Bénina Touaibia, Mustapha K Mihoubi

Abstract:

In Algeria, water resources play a crucial role in economic development. But over the last decades, they are relatively limited and gradually decreasing to the detriment of agriculture. The agricultural irrigation is the primary water consuming sector followed by the domestic and industrial sectors. The research presented in this paper focuses on the optimization of irrigation water demand. Dynamic Programming-Neural Network (DPNN) method is applied to investigate reservoir optimization. The optimal operation rule is formulated to minimize the gap between water release and water irrigation demand. As a case study, Boukerdane dam’s reservoir system in North of Algeria has been selected to examine our proposed optimization model. The application of DPNN method allowed increasing the satisfaction rate (SR) from 34% to 60%. In addition, the operation rule generated showed more reliable and resilience operation for the examined case study.

Keywords: water management, agricultural demand, Boukerdane dam, dynamic programming, artificial neural network

Procedia PDF Downloads 136