Search results for: trans-european transport network

Authors: Yu Zhang, Ye Tian, Fang Ye Yixuan Kang

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The capacity of conventional cellular networks has reached its upper bound and it can be well handled by introducing femtocells with low-cost and easy-to-deploy. Spectrum interference issue becomes more critical in peace with the value-added multimedia services growing up increasingly in two-tier cellular networks. Spectrum allocation is one of effective methods in interference mitigation technology. This paper proposes a game-theory-based on OFDMA downlink spectrum allocation aiming at reducing co-channel interference in two-tier femtocell networks. The framework is formulated as a non-cooperative game, wherein the femto base stations are players and frequency channels available are strategies. The scheme takes full account of competitive behavior and fairness among stations. In addition, the utility function reflects the interference from the standpoint of channels essentially. This work focuses on co-channel interference and puts forward a negative logarithm interference function on distance weight ratio aiming at suppressing co-channel interference in the same layer network. This scenario is more suitable for actual network deployment and the system possesses high robustness. According to the proposed mechanism, interference exists only when players employ the same channel for data communication. This paper focuses on implementing spectrum allocation in a distributed fashion. Numerical results show that signal to interference and noise ratio can be obviously improved through the spectrum allocation scheme and the users quality of service in downlink can be satisfied. Besides, the average spectrum efficiency in cellular network can be significantly promoted as simulations results shown.

Keywords: femtocell networks, game theory, interference mitigation, spectrum allocation

Procedia PDF Downloads 147

Authors: Motahareh Fadaei

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Wireless sensor networks that are used to monitor a special environment, are formed from a large number of sensor nodes. The role of these sensors is to sense special parameters from ambient and to make connection. In these networks, the most important challenge is the management of energy usage. Clustering is one of the methods that are broadly used to face this challenge. In this paper, a distributed clustering protocol based on learning automata is proposed for underwater wireless sensor networks. The proposed algorithm that is called LA-Clustering forms clusters in the same energy level, based on the energy level of nodes and the connection radius regardless of size and the structure of sensor network. The proposed approach is simulated and is compared with some other protocols with considering some metrics such as network lifetime, number of alive nodes, and number of transmitted data. The simulation results demonstrate the efficiency of the proposed approach.

Keywords: clustering, energy consumption‎, learning automata, underwater sensor networks

Procedia PDF Downloads 307

Authors: Ana Hol

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We live in a constantly changing environment where technology has become an integral component of our day to day life. We rely heavily on mobile devices, we search for data via web, we utilise smart home sensors to create the most suited ambiences and we utilise applications to shop, research, communicate and share data. Heavy reliance on technology therefore is creating new connections between STEM (Science, Technology, Engineering and Mathematics) fields which in turn rises a question of what the STEM education of the future should be like? This study was based on the reviews of the six Australian Information Systems students who undertook an international study tour to India where they were given an opportunity to network, communicate and meet local students, staff and business representatives and from them learn about the local business implementations, local customs and regulations. Research identifies that if we are to continue to implement and utilise electronic devices on the global scale, such as for example implement smart cars that can smoothly cross borders, we will need the workforce that will have the knowledge about the cars themselves, their parts, roads and transport networks, road rules, road sensors, road monitoring technologies, graphical user interfaces, movement detection systems as well as day to day operations, legal rules and regulations of each region and country, insurance policies, policing and processes so that the wide array of sensors can be controlled across country’s borders. In conclusion, it can be noted that allowing students to learn about the local conditions, roads, operations, business processes, customs and values in different countries is giving students a cutting edge advantage as such knowledge cannot be transferred via electronic sources alone. However once understanding of each problem or project is established, multidisciplinary innovative STEM projects can be smoothly conducted.

Keywords: STEM, curiosity, innovation, advancements

Procedia PDF Downloads 190

Authors: Jungyeol Hong, Dongjoo Park

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The primary purpose of this study is to develop a methodological framework to predict daily subway ridership at a station-level and to examine the association between subway ridership and bus demand incorporating commercial business facility in the vicinity of each subway station. The socio-economic characteristics, land-use, and built environment as factors may have an impact on subway ridership. However, it should be considered not only the endogenous relationship between bus and subway demand but also the characteristics of commercial business within a subway station’s sphere of influence, and integrated transit network topology. Regarding a statistical approach to estimate subway ridership at a station level, therefore it should be considered endogeneity and heteroscedastic issues which might have in the subway ridership prediction model. This study focused on both discovering the impacts of bus demand, commercial business characteristics, and network topology on subway ridership and developing more precise subway ridership estimation accounting for its statistical bias. The spatial scope of the study covers entire Seoul city in South Korea and includes 243 stations with the temporal scope set at twenty-four hours with one-hour interval time panels each. The data for subway and bus ridership was collected Seoul Smart Card data from 2015 and 2016. Three-Stage Least Square(3SLS) approach was applied to develop daily subway ridership model as capturing the endogeneity and heteroscedasticity between bus and subway demand. Independent variables incorporating in the modeling process were commercial business characteristics, social-economic characteristics, safety index, transit facility attributes, and dummies for seasons and time zone. As a result, it was found that bus ridership and subway ridership were endogenous each other and they had a significantly positive sign of coefficients which means one transit mode could increase another transportation mode’s ridership. In other words, two transit modes of subway and bus have a mutual relationship instead of the competitive relationship. The commercial business characteristics are the most critical dimension among the independent variables. The variables of commercial business facility rate in the paper containing six types; medical, educational, recreational, financial, food service, and shopping. From the model result, a higher rate in medical, financial buildings, shopping, and food service facility lead to increment of subway ridership at a station, while recreational and educational facility shows lower subway ridership. The complex network theory was applied for estimating integrated network topology measures that cover the entire Seoul transit network system, and a framework for seeking an impact on subway ridership. The centrality measures were found to be significant and showed a positive sign indicating higher centrality led to more subway ridership at a station level. The results of model accuracy tests by out of samples provided that 3SLS model has less mean square error rather than OLS and showed the methodological approach for the 3SLS model was plausible to estimate more accurate subway ridership. Acknowledgement: This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT (2017R1C1B2010175).

Keywords: subway ridership, bus ridership, commercial business characteristic, endogeneity, network topology

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Authors: Sagir M. Yusuf, Chris Baber

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We describe issues bedeviling the coordination of heterogeneous (different sensors carrying agents) multi-agent missions such as belief conflict, situation reasoning, etc. We applied Bayesian and agents' presumptions inferential reasoning to solve the outlined issues with the heterogeneous multi-agent belief variation and situational-base reasoning. Bayesian Belief Network (BBN) was used in modeling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases from agents’ missions were used in training the BBN using gradient descent and expectation-maximization algorithms. The output network is a well-trained BBN for making inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness and solve agents’ sensor conflicts.

Keywords: distributed constraint optimization problem, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence

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Authors: L. S. Chathurika

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Early prediction of student performance is an important factor to be gained academic excellence. Whatever the study stream in secondary education, students lay the foundation for higher studies during the first year of their degree or diploma program in Sri Lanka. The information technology (IT) field has certain improvements in the education domain by selecting specialization areas to show the talents and skills of students. These specializations can be software engineering, network administration, database administration, multimedia design, etc. After completing the first-year, students attempt to select the best path by considering numerous factors. The purpose of this experiment is to predict the best study path using machine learning algorithms. Five classification algorithms: decision tree, support vector machine, artificial neural network, Naïve Bayes, and logistic regression are selected and tested. The support vector machine obtained the highest accuracy, 82.4%. Then affecting features are recognized to select the best study path.

Keywords: algorithm, classification, evaluation, features, testing, training

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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

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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

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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

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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 367

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

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Authors: Hassiba Benguergoura, Kamal Chanane, Sâad Moulay

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Pervaporation (PV), a membrane-based separation technology, has gained much attention because of its energy saving capability and low-cost, especially for separation of azeotropic or close-boiling liquid mixtures. There are two crucial issues for industrial application of pervaporation process. The first is developing membrane material and tailoring membrane structure to obtain high pervaporation performances. The second is modeling pervaporation transport to better understand of the above-mentioned structure–pervaporation relationship. Many models were proposed to predict the mass transfer process, among them, solution-diffusion model is most widely used in describing pervaporation transport including preferential sorption, diffusion and evaporation steps. For modeling pervaporation transport, the permeation flux, which depends on the solubility and diffusivity of components in the membrane, should be obtained first. Traditionally, the solubility was calculated according to the Flory–Huggins theory. Separation of the benzene (Bz)/cyclohexane (Cx) mixture is industrially significant. Numerous papers have been focused on the Bz/Cx system to assess the PV properties of membrane materials. Membranes with both high permeability and selectivity are desirable for practical application. Several new polymers have been prepared to get both high permeability and selectivity. Styrene-butadiene rubbers (SBR), dense membranes cross-linked by chloromethylation were used in the separation of benzene/cyclohexane mixtures. The impact of chloromethylation reaction as a new method of cross-linking SBR on the pervaporation performance have been reported. In contrast to the vulcanization with sulfur, the cross-linking takes places on styrene units of polymeric chains via a methylene bridge. The partial pervaporative (PV) fluxes of benzene/cyclohexane mixtures in styrene-butadiene rubber (SBR) were predicted using Fick's first law. The predicted partial fluxes and the PV separation factor agreed well with the experimental data by integrating Fick's law over the benzene concentration. The effects of feed concentration and operating temperature on the predicted permeation flux by this proposed model are investigated. The predicted permeation fluxes are in good agreement with experimental data at lower benzene concentration in feed, but at higher benzene concentration, the model overestimated permeation flux. The predicted and experimental permeation fluxes all increase with operating temperature increasing. Solvent sorption levels for benzene/ cyclohexane mixtures in a SBR membrane were determined experimentally. The results showed that the solvent sorption levels were strongly affected by the feed composition. The Flory- Huggins equation generates higher R-square coefficient for the sorption selectivity.

Keywords: benzene, cyclohexane, pervaporation, permeation, sorption modeling, SBR

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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

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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 456

Authors: Emmanuelle Maria, Pierre Crançon, Gaëtane Lespes

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Colloids are ubiquitous in the environment and are known to play a major role in enhancing the transport of trace elements, thus being an important vector for contaminants dispersion. Colloids study and characterization are necessary to improve our understanding of the fate of pollutants in the environment. However, in stream water and groundwater, colloids are often very poorly concentrated. It is therefore necessary to pre-concentrate colloids in order to get enough material for analysis, while preserving their initial structure. Many techniques are used to extract and/or pre-concentrate the colloidal phase from bulk aqueous phase, but yet there is neither reference method nor estimation of the impact of these different techniques on the colloids structure, as well as the bias introduced by the separation method. In the present work, we have tested and compared several methods of colloidal phase extraction/pre-concentration, and their impact on colloids properties, particularly their size distribution and their elementary composition. Ultrafiltration methods (frontal, tangential and centrifugal) have been considered since they are widely used for the extraction of colloids in natural waters. To compare these methods, a ‘synthetic groundwater’ was used as a reference. The size distribution (obtained by Field-Flow Fractionation (FFF)) and the chemical composition of the colloidal phase (obtained by Inductively Coupled Plasma Mass Spectrometry (ICPMS) and Total Organic Carbon analysis (TOC)) were chosen as comparison factors. In this way, it is possible to estimate the pre-concentration impact on the colloidal phase preservation. It appears that some of these methods preserve in a more efficient manner the colloidal phase composition while others are easier/faster to use. The choice of the extraction/pre-concentration method is therefore a compromise between efficiency (including speed and ease of use) and impact on the structural and chemical composition of the colloidal phase. In perspective, the use of these methods should enhance the consideration of colloidal phase in the transport of pollutants in environmental assessment studies and forensics.

Keywords: chemical composition, colloids, extraction, preconcentration methods, size distribution

Procedia PDF Downloads 205

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 447

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

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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

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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

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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

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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

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Authors: Makhosazana Dubazana

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Many South Africans commuters use minibus taxis daily and are connected to the informal transport network through metro centres informally known as Taxi Ranks. Taxi ranks form part of an economic nexus for many informal traders, connecting them to commuters, their prime clientele. They work along designated areas along the periphery of the taxi rank and in between taxi lanes. Informal traders are therefore at risk of adverse health effects associated with the inhalation of exhaust fumes from minibus taxis. Of the exhaust emissions, benzene, toluene, ethylbenzene and xylenes (BTEX) have high toxicity. Purpose: The purpose of this study was to conduct a Human Health Risk Assessment for informal traders, looking at their exposure to BTEX compounds. Methods: The study was conducted in a subsection of a taxi rank which is representative of the entire taxi rank. This subsection has a daily average of 400 minibus taxi moving through it and an average of 60 informal traders working in it. In the health risk assessment, a questionnaire was conducted to understand the occupational behaviour of the informal traders. This was used to deduce the exposure scenarios and sampling locations. Three sampling campaigns were run for an average of 10 hours each covering the average working hours of traders. A gas chronographer was used for collecting continues ambient air samples at 15 min intervals. Results: Over the three sampling days, the average concentrations were, 8.46ppb, 0.63 ppb, 1.27ppb and 1.0ppb for benzene, toluene, ethylbenzene, and xylene respectively. The average cancer risk is 9.46E-03. In several cases, they were incidences of unacceptable risk for the cumulative exposure of all four BTEX compounds. Conclusion: This study adds to the body of knowledge on the Human Health Risk effects of urban BTEX pollution, furthermore focusing on the impact of urban BTEX on high risk personal such as informal traders, in Southern Africa.

Keywords: human health risk assessment, informal traders, occupational risk, urban BTEX

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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

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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

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Authors: Tania Viju, Bimal P., Naseer M. A.

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This study aims to develop a conceptual framework for the decision support system (DSS), that helps the decision-makers to dynamically choose appropriate safety measures for each identified accident blackspot. An accident blackspot is a segment of road where the frequency of accident occurrence is disproportionately greater than other sections on roadways. According to a report by the World Bank, India accounts for the highest, that is, eleven percent of the global death in road accidents with just one percent of the world’s vehicles. Hence in 2015, the Ministry of Road Transport and Highways of India gave prime importance to the rectification of accident blackspots. To enhance road traffic safety and reduce the traffic accident rate, effectively identifying and rectifying accident blackspots is of great importance. This study helps to understand and evaluate the existing methods in accident blackspot identification and prediction that are used around the world and their application in Indian roadways. The decision support system, with the help of IoT, ICT and smart systems, acts as a management and planning tool for the government for employing efficient and cost-effective rectification strategies. In order to develop a decision criterion, several factors in terms of quantitative as well as qualitative data that influence the safety conditions of the road are analyzed. Factors include past accident severity data, occurrence time, light, weather and road conditions, visibility, driver conditions, junction type, land use, road markings and signs, road geometry, etc. The framework conceptualizes decision-making by classifying blackspot stretches based on factors like accident occurrence time, different climatic and road conditions and suggesting mitigation measures based on these identified factors. The decision support system will help the public administration dynamically manage and plan the necessary safety interventions required to enhance the safety of the road network.

Keywords: decision support system, dynamic management, road accident blackspots, road safety

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Authors: Yan Fang, Jie Yin, Bihu Wu

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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: Louise Ödlund, Danica Djuric Ilic

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Sustainable development includes many challenges related to energy use, such as (1) developing flexibility on the demand side of the electricity systems due to an increased share of intermittent electricity sources (e.g., wind and solar power), (2) overcoming economic challenges related to an increased share of renewable energy in the transport sector, (3) increasing efficiency of the biomass use, (4) increasing utilization of industrial excess heat (e.g., approximately two thirds of the energy currently used in EU is lost in the form of excess and waste heat). The European Commission has been recognized DH technology as of essential importance to reach sustainability. Flexibility in the fuel mix, and possibilities of industrial waste heat utilization, combined heat, and power (CHP) production and energy recovery through waste incineration, are only some of the benefits which characterize DH technology. The aim of this study is to provide an overview of the possible business strategies which would enable DH to have an important role in future sustainable energy systems. The methodology used in this study is a systematic literature review. The study includes a systematic approach where DH is seen as a part of an integrated system that consists of transport , industrial-, and electricity sectors as well. The DH technology can play a decisive role in overcoming the sustainability challenges related to our energy use. The introduction of biofuels in the transport sector can be facilitated by integrating biofuel and DH production in local DH systems. This would enable the development of local biofuel supply chains and reduce biofuel production costs. In this way, DH can also promote the development of biofuel production technologies that are not yet developed. Converting energy for running the industrial processes from fossil fuels and electricity to DH (above all biomass and waste-based DH) and delivering excess heat from industrial processes to the local DH systems would make the industry less dependent on fossil fuels and fossil fuel-based electricity, as well as the increasing energy efficiency of the industrial sector and reduce production costs. The electricity sector would also benefit from these measures. Reducing the electricity use in the industry sector while at the same time increasing the CHP production in the local DH systems would (1) replace fossil-based electricity production with electricity in biomass- or waste-fueled CHP plants and reduce the capacity requirements from the national electricity grid (i.e., it would reduce the pressure on the bottlenecks in the grid). Furthermore, by operating their central controlled heat pumps and CHP plants depending on the intermittent electricity production variation, the DH companies may enable an increased share of intermittent electricity production in the national electricity grid.

Keywords: energy system, district heating, sustainable business strategies, sustainable development

Procedia PDF Downloads 161

Authors: Shankaran Sitarama

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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

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Authors: Nayak Amar, Turner Naomi, Gobina Edward

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The Scottish Higher Education Institutes must report their scope 1 & 2 emissions, whereas reporting scope 3 is optional. Scope 3 is indirect emissions which embodies a significant component of total carbon footprint and therefore it is important to record, measure and report it accurately. Robert Gordon University (RGU) reported only business travel, grid transmission and distribution, water supply and transport, and recycling scope 3 emissions. This study estimates the RGUs total scope 3 emissions by comparing it with a similar HEI in scale. The scope 3 emission reporting of sixteen Scottish HEI was studied. Glasgow Caledonian University was identified as the nearest neighbour by comparing its students' full time equivalent, staff full time equivalent, research-teaching split, budget, and foundation year. Apart from the peer, data was also collected from the Higher Education Statistics Agency database. RGU reported emissions from business travel, grid transmission and distribution, water supply, and transport and recycling. This study estimated RGUs scope 3 emissions from procurement, student-staff commute, and international student trip. The result showed that RGU covered only 11% of the scope 3 emissions. The major contributor to scope 3 emissions were procurement (48%), student commute (21%), international student trip (16%), and staff commute (4%). The estimated scope 3 emission was more than 14 times the reported emissions. This study has shown the relative importance of each scope 3 emissions source, which gives a guideline for the HEIs, on where to focus their attention to capture maximum scope 3 emissions. Moreover, it has demonstrated that it is possible to estimate the scope 3 emissions with limited data.

Keywords: HEI, university, emission calculations, scope 3 emissions, emissions reporting

Procedia PDF Downloads 90

Authors: Tushar K. Routh

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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 63