Search results for: transportation networks

Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

Procedia PDF Downloads 84

Authors: Junhang Luo

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With the transformation of spatial structure and the deepening of urban development, the demand for a better life and the concerns for social resources equities of residents are increasing. As an important social resource, park plays an essential role in building environmentally sustainable cities. Thus, it is important to examine park accessibility for low-income and how it works in relative deprivation, so as to provide all residents with equitable services. Using the network and buffer methods of GIS, this paper analyzes urban park accessibility for low-income residents in Chongqing, China. And then conduct a satisfaction evaluation of park resource accessibility with low-incomes through questionnaire surveys from deprivation dimensions. Results show that the level of park accessibility in Chongqing varies significantly and the degree of relative deprivation is relatively high. Public transportation convenience improves and the number of community park increases contribute positively to improving park accessibility and alleviating the relative deprivation of public resources. Combined with the innovation pattern of social governance in China, it suggests that urban park accessibility needs to be jointly governed and optimized by multiple social resources from the government to the public, and the service efficiency needs the index system and planning standards according to local conditions to improve quality and promote equity. At the same time, building a perfect park system and complete legislation assurance system will also play a positive role in ensuring that all residents can enjoy the urban public space more fairly, especially low-income groups.

Keywords: urban park, accessibility, relative deprivation, GIS network analysis, chongqing

Procedia PDF Downloads 143

Authors: Akshaya P., Priyanjali Prabhkaran

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The paper examines the formulation of strategies to develop a comprehensive model of city level water utility management to addressing urban sanitation issues. The water is prime life sustaining natural resources and nature’s gifts to all living beings on the earth multiple urban sanitation issues are addressed in the supply of water in a city. Many of these urban sanitation issues are linked to population expansion and economic inequity. Increased usage of water and the development caused water scarcity. The lack of water supply results increases the chance of unhygienic situations in the cities. In this study, the urban sanitation issues are identified with respect to water supply and utility management. The study compared based on their best practices and initiatives. From this, best practices and initiatives identify suitable sustainable measures to address water supply issues in the city level. The paper concludes with the listed provision that should be considered suitable measures for water supply and utility management in city level to address the urban sanitation issues.

Keywords: water, benchmarking water supply, water supply networks, water supply management

Procedia PDF Downloads 96

Authors: Ayodele Adekunle Faiyetole, Olumide Olawale Jegede

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This paper presents the experimental design and needs justifications for a localized intelligent parking system (L-IPS), ideal for quasi-close communities with increasing vehicular volume that depends on limited or constant parking facilities. For a constant supply in parking facilities, the demand for an increasing vehicular volume could lead to poor time conservation or extended travel time, traffic congestion or impeded mobility, and safety issues. Increased negative environmental and economic externalities are other associated and consequent downsides of disparities in demand and supply. This L-IPS is designed using a microcontroller, ultrasonic sensors, LED indicators, such that the current status, in terms of parking spots availability, can be known from the main entrance to the community or a parking zone on a LCD screen. As an advanced traffic management system (ATMS), the L-IPS is designed to resolve aspects of infrastructure-to-driver (I2D) communication and parking detection issues. Thus, this L-IPS can act as a timesaver for users by helping them know the availability of parking spots. Providing on-time, informed routing, to a next preference or seamless moving to berth on the available spot on a proximate facility as the case may be. Its use could also increase safety and increase mobility, and fuel savings and costs, therefore, reducing negative environmental and economic externalities due to transportation systems.

Keywords: intelligent parking systems, localized intelligent parking system, intelligent transport systems, advanced traffic management systems, infrastructure-to-drivers communication

Procedia PDF Downloads 160

Authors: Rasha A.Ibrahim El-Ghazawy, Ashraf M. El-Saeed, Heusin El-Shafey, M. Abdel-Raheim, Maher A. El-Sockary

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Two thermotropic liquid crystalline curing agents based on abietic acid with different mesogens (LCC1 and LCC2) were synthesized for producing thermally stable liquid crystal networks suitable for high performance epoxy coatings. Differential scanning calorimetry (DSC) and polarized optical microscope (POM) was used to identify the liquid crystal phase transformation temperatures and texture, respectively. POM micro graphs for both LCCs revealing cholesteric texture. A multifunctional epoxy resin with two abietic acid moieties was also synthesized. Dynamic mechanical (DMA) and thermogravimetric (TGA) analyses show that the fully bio-based cured epoxies by either LCCs possess high glass transition temperature (Tg), high modulus (G`) and improved thermal stability. The chemical structure of the synthesized LCCs and epoxy resin was investigated through FTIR and 1HNMR spectroscopic techniques.

Keywords: abietic acid, dynamic mechanical analysis, epoxy resin, liquid crystal, thermo gravimetric analysis

Procedia PDF Downloads 351

Authors: Mehrzad Soltani, Pegah Rezaei

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In the endeavor to construct cities that are not only thriving but also environmentally responsible, effective urban planning and architectural design assume paramount significance. The focal point of this pursuit is the harnessing of urban renewable energy. By embracing sustainable practices such as the integration of solar panels into the urban landscape and the establishment of smart grids, cities are poised to confront head-on the dual challenge of surging energy demands and pressing environmental concerns. Urban renewable energy solutions offer a multifaceted approach to these issues. Firstly, they usher in a clean and sustainable source of energy, reducing the cities' ecological footprint while ensuring a continuous power supply. This transition to eco-friendly energy is also intrinsically linked to enhanced spatial utilization, thereby streamlining the efficiency of urban areas. Moreover, it spurs the adoption of sustainable transportation alternatives, diminishing the reliance on fossil fuels and mitigating air pollution. However, the significance of integrating renewable energy solutions transcends the realm of urban sustainability. It embodies a holistic approach towards creating cities that harmoniously coexist with the natural environment while catering to the needs and aspirations of their inhabitants. In essence, prioritizing sustainability in urban planning and architectural design has evolved from a choice to a necessity, one that not only safeguards the cities' well-being but also fosters a better quality of life for their residents. Thus, it is imperative that we acknowledge the transformative potential of these innovations as we pave the way towards the cities of the future.

Keywords: sustainability, smart grids, solar panel, urban planning, environmental concerns

Procedia PDF Downloads 75

Authors: Praveen Kumar, Nitin Kumar, Hemant Kumar

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The modernization of computer technology and commercial computational fluid dynamic (CFD) simulation has given better detailed results as compared to experimental investigation techniques. CFD techniques are widely used in different field due to its flexibility and performance. Evaluation of pipeline erosion is complex phenomenon to solve by numerical arithmetic technique, whereas CFD simulation is an easy tool to resolve that type of problem. Erosion wear behaviour due to solid–liquid mixture in the slurry pipeline has been investigated using commercial CFD code in FLUENT. Multi-phase Euler-Lagrange model was adopted to predict the solid particle erosion wear in 22.5° pipe bend for the flow of bottom ash-water suspension. The present study addresses erosion prediction in three dimensional 22.5° pipe bend for two-phase (solid and liquid) flow using finite volume method with standard k-ε turbulence, discrete phase model and evaluation of erosion wear rate with varying velocity 2-4 m/s. The result shows that velocity of solid-liquid mixture found to be highly dominating parameter as compared to solid concentration, density, and particle size. At low velocity, settling takes place in the pipe bend due to low inertia and gravitational effect on solid particulate which leads to high erosion at bottom side of pipeline.

Keywords: computational fluid dynamics (CFD), erosion, slurry transportation, k-ε Model

Procedia PDF Downloads 398

Authors: Ahmed Al-Adaileh, Souheil Khaddaj

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Outcomes of exhaustion of natural resources started influencing each spirit on this planet. Energy is an essential factor in this aspect. To restore the circumstance to the appropriate track, all attempts must focus on two fundamental branches: producing electricity from clean and renewable reserves and decreasing the overall unnecessary consumption of energy. The focal point of this paper will be on lessening the power consumption in the household's segment. This paper is an attempt to give a clear understanding of a framework called Reduction of Energy Consumption in Household Sector (RECHS) and how it should help householders to reduce their power consumption by substituting their household appliances, turning-off the appliances when stand-by modus is detected, and scheduling their appliances operation periods. Technically, the framework depends on utilizing Z-Wave compatible plug-ins which will be connected to the usual house devices to gauge and control them remotely and semi-automatically. The suggested framework underpins numerous quality characteristics, for example, integrability, scalability, security and adaptability.

Keywords: smart energy management systems, internet of things, wireless mesh networks, microservices, cloud computing, big data

Procedia PDF Downloads 178

Authors: Revanth Rallapalli

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Manually dispensing solids and powders can be difficult as it requires gradually pour and check the amount on the scale to be dispensed. Current systems are manual and non-continuous in nature and are user-dependent and difficult to control powder dispensation. Recurrent dosing of powdered medicines in precise amounts quickly and accurately has been an all-time challenge. Various new powder dispensing mechanisms are being designed to overcome these challenges. A battery-operated screw conveyor mechanism is being innovated to overcome the above problems faced. These inventions are numerically evaluated at the concept development level by employing Computational Fluid Dynamics (CFD) of gas-solids multiphase flow systems. CFD has been very helpful in development of such devices saving time and money by reducing the number of prototypes and testing. Furthermore, this paper describes a simulation of powder dispensation from the trocar’s end by considering the powder as secondary flow in air, is simulated by using the technique called Dense Discrete Phase Model incorporated with Kinetic Theory of Granular Flow (DDPM-KTGF). By considering the volume fraction of powder as 50%, the transportation of powder from the inlet side to trocar’s end side is done by rotation of the screw conveyor. Thus, the performance is calculated for a 1-sec time frame in an unsteady computation manner. This methodology will help designers in developing design concepts to improve the dispensation and also at the effective area within a quick turnaround time frame.

Keywords: DDPM-KTGF, gas-solids multiphase flow, screw conveyor, Unsteady

Procedia PDF Downloads 171

Authors: Francesco Buccafurri, Celeste Romolo

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Nowadays phishing is the most frequent starting point of cyber-attack vectors. Phishing is implemented both via email and social network messages. While a wide scientific literature exists which addresses the problem of contrasting email spam-phishing, no specific countermeasure has been so far proposed for phishing included into private messages of social network platforms. Unfortunately, the problem is severe. This paper proposes an approach against social network phishing, based on a non invasive collaborative information-sharing approach which leverages blockchain. The detection method works by filtering candidate messages, by distilling them by means of a distance-preserving hash function, and by publishing hashes over a public blockchain through a trusted smart contract (thus avoiding denial of service attacks). Phishing detection exploits social information embedded into social network profiles to identify similar messages belonging to disjoint contexts. The main contribution of the paper is to introduce a new approach to contrasting the problem of social network phishing, which, despite its severity, received little attention by both research and industry.

Keywords: phishing, social networks, information sharing, blockchain

Procedia PDF Downloads 317

Authors: Yana Snegireva

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Carbonate reservoirs are known for their heterogeneity, resulting from various geological processes such as diagenesis and fracturing. These complexities may cause great challenges in understanding fluid flow behavior and predicting the production performance of naturally fractured reservoirs. The investigation of carbonate reservoirs is crucial, as many petroleum reservoirs are naturally fractured, which can be difficult due to the complexity of their fracture networks. This can lead to geological uncertainties, which are important for global petroleum reserves. The problem outlines the key challenges in carbonate reservoir modeling, including the accurate representation of fractures and their connectivity, as well as capturing the impact of fractures on fluid flow and production. Traditional reservoir modeling techniques often oversimplify fracture networks, leading to inaccurate predictions. Therefore, there is a need for a modern approach that can capture the complexities of carbonate reservoirs and provide reliable predictions for effective reservoir management and production optimization. The modern approach to carbonate reservoir modeling involves the utilization of the hybrid fracture modeling approach, including the discrete fracture network (DFN) method and implicit fracture network, which offer enhanced accuracy and reliability in characterizing complex fracture systems within these reservoirs. This study focuses on the application of the hybrid method in the Nepsko-Botuobinskaya anticline of the Eastern Siberia field, aiming to prove the appropriateness of this method in these geological conditions. The DFN method is adopted to model the fracture network within the carbonate reservoir. This method considers fractures as discrete entities, capturing their geometry, orientation, and connectivity. But the method has significant disadvantages since the number of fractures in the field can be very high. Due to limitations in the amount of main memory, it is very difficult to represent these fractures explicitly. By integrating data from image logs (formation micro imager), core data, and fracture density logs, a discrete fracture network (DFN) model can be constructed to represent fracture characteristics for hydraulically relevant fractures. The results obtained from the DFN modeling approaches provide valuable insights into the East Siberia field's carbonate reservoir behavior. The DFN model accurately captures the fracture system, allowing for a better understanding of fluid flow pathways, connectivity, and potential production zones. The analysis of simulation results enables the identification of zones of increased fracturing and optimization opportunities for reservoir development with the potential application of enhanced oil recovery techniques, which were considered in further simulations on the dual porosity and dual permeability models. This approach considers fractures as separate, interconnected flow paths within the reservoir matrix, allowing for the characterization of dual-porosity media. The case study of the East Siberia field demonstrates the effectiveness of the hybrid model method in accurately representing fracture systems and predicting reservoir behavior. The findings from this study contribute to improved reservoir management and production optimization in carbonate reservoirs with the use of enhanced and improved oil recovery methods.

Keywords: carbonate reservoir, discrete fracture network, fracture modeling, dual porosity, enhanced oil recovery, implicit fracture model, hybrid fracture model

Procedia PDF Downloads 66

Authors: Asmir Gogic, Aljo Mujcic, Sandra Ibric, Nermin Suljanovic

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Ubiquity of natural disasters during last few decades have risen serious questions towards the prediction of such events and human safety. Every disaster regardless its proportion has a precursor which is manifested as a disruption of some environmental parameter such as temperature, humidity, pressure, vibrations and etc. In order to anticipate and monitor those changes, in this paper we propose an overall system for disaster prediction and monitoring, based on wireless sensor network (WSN). Furthermore, we introduce a modified and simplified WSN routing protocol built on the top of the trickle routing algorithm. Routing algorithm was deployed using the bluetooth low energy protocol in order to achieve low power consumption. Performance of the WSN network was analyzed using a real life system implementation. Estimates of the WSN parameters such as battery life time, network size and packet delay are determined. Based on the performance of the WSN network, proposed system can be utilized for disaster monitoring and prediction due to its low power profile and mesh routing feature.

Keywords: bluetooth low energy, disaster prediction, mesh routing protocols, wireless sensor networks

Procedia PDF Downloads 372

Authors: Umar Albalawi, Sang C. Suh, Jinoh Kim

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As internet continues to expand its usage with an enormous number of applications, cyber-threats have significantly increased accordingly. Thus, accurate detection of malicious traffic in a timely manner is a critical concern in today’s Internet for security. One approach for intrusion detection is to use Machine Learning (ML) techniques. Several methods based on ML algorithms have been introduced over the past years, but they are largely limited in terms of detection accuracy and/or time and space complexity to run. In this work, we present a novel method for intrusion detection that incorporates a set of supervised learning algorithms. The proposed technique provides high accuracy and outperforms existing techniques that simply utilizes a single learning method. In addition, our technique relies on partial flow information (rather than full information) for detection, and thus, it is light-weight and desirable for online operations with the property of early identification. With the mid-Atlantic CCDC intrusion dataset publicly available, we show that our proposed technique yields a high degree of detection rate over 99% with a very low false alarm rate (0.4%).

Keywords: intrusion detection, supervised learning, traffic classification, computer networks

Procedia PDF Downloads 336

Authors: Samuel B. Dulay, Sandra Julich, Herbert Tomaso, Ciara K. O'Sullivan

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An early, rapid and definite detection for the presence of biowarfare agents, pathogens, viruses and toxins is required in different situations which include civil rescue and security units, homeland security, military operations, public transportation securities such as airports, metro and railway stations due to its harmful effect on the human population. In this work, an electrochemical genosensor array that allows simultaneous detection of different biowarfare agents within an integrated microsystem that provides an easy handling of the technology which combines a microfluidics setup with a multiplexing genosensor array has been developed and optimised for the following targets: Bacillus anthracis, Brucella abortis and melitensis, Bacteriophage lambda, Francisella tularensis, Burkholderia mallei and pseudomallei, Coxiella burnetii, Yersinia pestis, and Bacillus thuringiensis. The electrode array was modified via co-immobilisation of a 1:100 (mol/mol) mixture of a thiolated probe and an oligoethyleneglycol-terminated monopodal thiol. PCR products from these relevant biowarfare agents were detected reproducibly through a sandwich assay format with the target hybridised between a surface immobilised probe into the electrode and a horseradish peroxidase-labelled secondary reporter probe, which provided an enzyme based electrochemical signal. The potential of the designed microsystem for multiplexed genosensor detection and cross-reactivity studies over potential interfering DNA sequences has demonstrated high selectivity using the developed platform producing high-throughput.

Keywords: biowarfare agents, genosensors, multipled detection, microsystem

Procedia PDF Downloads 263

Authors: Bo Zhang, Ellen S. Wilschut, Dehlia M. C. Willemsen, Marieke H. Martens

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With the rapid development of intelligent transportation systems, automated platooning of trucks is drawing increasing interest for its beneficial effects on safety, energy consumption and traffic flow efficiency. Nevertheless, one major challenge lies in the safe transition of control from the automated system back to the human drivers, especially when they have been inattentive after a long period of highly automated driving. In this study, we investigated driver take-over time after a system initiated request to leave the platooning system Virtual Tow Bar in a non-critical scenario. 22 professional truck drivers participated in the truck driving simulator experiment, and each was instructed to drive under three experimental conditions before the presentation of the take-over request (TOR): driver ready (drivers were instructed to monitor the road constantly), driver not-ready (drivers were provided with a tablet) and eye-shut. The results showed significantly longer take-over time in both driver not-ready and eye-shut conditions compared with the driver ready condition. Further analysis revealed hand movement time as the main factor causing long response time in the driver not-ready condition, while in the eye-shut condition, gaze reaction time also influenced the total take-over time largely. In addition to comparing the means, large individual differences can be found especially in two driver, not attentive conditions. The importance of a personalized driver readiness predictor for a safe transition is concluded.

Keywords: driving simulation, highly automated driving, take-over time, transition of control, truck platooning

Procedia PDF Downloads 242

Authors: Shayan Mohajer Hamidi

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Federated Learning (FL) has emerged as a promising paradigm for training machine learning models on distributed data without the need for centralized data aggregation. In the realm of wireless communications, FL has the potential to leverage the vast amounts of data generated by wireless devices to improve model performance and enable intelligent applications. However, the fairness aspect of FL in wireless communications remains largely unexplored. This abstract presents an idea for fair federated learning in wireless communications, addressing the challenges of imbalanced data distribution, privacy preservation, and resource allocation. Firstly, the proposed approach aims to tackle the issue of imbalanced data distribution in wireless networks. In typical FL scenarios, the distribution of data across wireless devices can be highly skewed, resulting in unfair model updates. To address this, we propose a weighted aggregation strategy that assigns higher importance to devices with fewer samples during the aggregation process. By incorporating fairness-aware weighting mechanisms, the proposed approach ensures that each participating device's contribution is proportional to its data distribution, thereby mitigating the impact of data imbalance on model performance. Secondly, privacy preservation is a critical concern in federated learning, especially in wireless communications where sensitive user data is involved. The proposed approach incorporates privacy-enhancing techniques, such as differential privacy, to protect user privacy during the model training process. By adding carefully calibrated noise to the gradient updates, the proposed approach ensures that the privacy of individual devices is preserved without compromising the overall model accuracy. Moreover, the approach considers the heterogeneity of devices in terms of computational capabilities and energy constraints, allowing devices to adaptively adjust the level of privacy preservation to strike a balance between privacy and utility. Thirdly, efficient resource allocation is crucial for federated learning in wireless communications, as devices operate under limited bandwidth, energy, and computational resources. The proposed approach leverages optimization techniques to allocate resources effectively among the participating devices, considering factors such as data quality, network conditions, and device capabilities. By intelligently distributing the computational load, communication bandwidth, and energy consumption, the proposed approach minimizes resource wastage and ensures a fair and efficient FL process in wireless networks. To evaluate the performance of the proposed fair federated learning approach, extensive simulations and experiments will be conducted. The experiments will involve a diverse set of wireless devices, ranging from smartphones to Internet of Things (IoT) devices, operating in various scenarios with different data distributions and network conditions. The evaluation metrics will include model accuracy, fairness measures, privacy preservation, and resource utilization. The expected outcomes of this research include improved model performance, fair allocation of resources, enhanced privacy preservation, and a better understanding of the challenges and solutions for fair federated learning in wireless communications. The proposed approach has the potential to revolutionize wireless communication systems by enabling intelligent applications while addressing fairness concerns and preserving user privacy.

Keywords: federated learning, wireless communications, fairness, imbalanced data, privacy preservation, resource allocation, differential privacy, optimization

Procedia PDF Downloads 61

Authors: Stefanie Siebenhütter

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This paper aims to reveal criterions involved in the process of identity-forming among multilingual minority language speakers in Northeastern Thailand and in the capital Bangkok. Using sociolinguistic interviews and questionnaires, it is asked which factors are important for speakers and how they define their identity by their interactions socially as well as linguistically. One key question to answer is how sociolinguistic factors may force or diminish the process of forming social identity of multilingual minority speakers. However, the motivation for specific language use is rarely overt to the speaker’s themselves as well as to others. Therefore, identifying the intentions included in the process of identity construction is to approach by scrutinizing speaker’s behavior and attitudes. Combining methods used in sociolinguistics and social psychology allows uncovering the tools for identity construction that ethnic Kui uses to range themselves within a multilingual setting. By giving an overview of minority speaker’s language use in context of the specific border near multilingual situation and asking how speakers construe identity within this spatial context, the results exhibit some of the subtle and mostly unconscious criterions involved in the ongoing process of identity construction.

Keywords: social identity, identity construction, minority language, multilingualism, social networks, social boundaries

Procedia PDF Downloads 251

Authors: Stefano Zappacosta, Cristiano Bove, Maria Carmela Marinelli, Paola di Lauro, Katarina Spasenovic, Lorenzo Ostano, Giuseppe Aiello, Marco Pietrosanto

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This paper proposes a novel neural network approach for assessing flood hazard mapping. The core of the model is a machine learning component fed by frequency ratios, namely statistical correlations between flood event occurrences and a selected number of topographic properties. The proposed hybrid model can be used to classify four different increasing levels of hazard. The classification capability was compared with the flood hazard mapping River Basin Plans (PAI) designed by the Italian Institute for Environmental Research and Defence, ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale). The study area of Piemonte, an Italian region, has been considered without loss of generality. The frequency ratios may be used as a standalone block to model the flood hazard mapping. Nevertheless, the mixture with a neural network improves the classification power of several percentage points, and may be proposed as a basic tool to model the flood hazard map in a wider scope.

Keywords: flood modeling, hazard map, neural networks, hydrogeological risk, flood risk assessment

Procedia PDF Downloads 161

Authors: Abe D. Desta

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This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

Procedia PDF Downloads 103

Authors: Musab Isah

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This paper presents a longitudinal analysis of Internet speed data in the Gulf Cooperation Council (GCC) region, focusing on the most populous cities of each of the six countries – Riyadh, Saudi Arabia; Dubai, UAE; Kuwait City, Kuwait; Doha, Qatar; Manama, Bahrain; and Muscat, Oman. The study utilizes data collected from the Measurement Lab (M-Lab) infrastructure over a five-year period from January 1, 2019, to December 31, 2023. The analysis includes downstream and upstream throughput data for the cities, covering significant events such as the launch of 5G networks in 2019, COVID-19-induced lockdowns in 2020 and 2021, and the subsequent recovery period and return to normalcy. The results showcase substantial increases in Internet speeds across the cities, highlighting improvements in both download and upload throughput over the years. All the GCC countries have achieved above-average Internet speeds that can conveniently support various online activities and applications with excellent user experience.

Keywords: internet data science, internet performance measurement, throughput analysis, internet speed, measurement lab, network diagnostic tool

Procedia PDF Downloads 41

Authors: Eugene Stepanov, Arkadi Ponossov

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Many mathematical models used in biological and other applications are ill-posed. The reason for that is the nature of differential equations, where the nonlinearities are assumed to be step functions, which is done to simplify the analysis. Prominent examples are switched systems arising from gene regulatory networks and neural field equations. This simplification leads, however, to theoretical and numerical complications. In the presentation, it is proposed to apply the theory of hybrid feedback controls to regularize the problem. Roughly speaking, one attaches a finite state control (‘automaton’), which follows the trajectories of the original system and governs its dynamics at the points of ill-posedness. The construction of the automaton is based on the classification of the attractors of the specially designed adjoint dynamical system. This ‘hybridization’ is shown to regularize the original switched system and gives rise to efficient hybrid numerical schemes. Several examples are provided in the presentation, which supports the suggested analysis. The method can be of interest in other applied fields, where differential equations contain step-like nonlinearities.

Keywords: hybrid feedback control, ill-posed problems, singular perturbation analysis, step-like nonlinearities

Procedia PDF Downloads 227

Authors: Monica Racha, Siva Chandrasekaran, Alex Stojcevski

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The engineering programs focus on promoting students' personal and professional development by ensuring that students acquire technical and professional competencies during four-year studies. The traditional engineering laboratory provides an opportunity for students to "practice by doing," and laboratory facilities aid them in obtaining insight and understanding of their discipline. Due to rapid technological advancements and the current COVID-19 outbreak, the traditional labs were transforming into virtual learning environments. Aim: To better understand the limitations of the physical laboratory, this research study aims to use a Machine Learning (ML) algorithm that interfaces with the Augmented Reality HoloLens and predicts the image behavior to classify and detect the electronic components. The automated electronic components error classification and detection automatically detect and classify the position of all components on a breadboard by using the ML algorithm. This research will assist first-year undergraduate engineering students in conducting laboratory practices without any supervision. With the help of HoloLens, and ML algorithm, students will reduce component placement error on a breadboard and increase the efficiency of simple laboratory practices virtually. Method: The images of breadboards, resistors, capacitors, transistors, and other electrical components will be collected using HoloLens 2 and stored in a database. The collected image dataset will then be used for training a machine learning model. The raw images will be cleaned, processed, and labeled to facilitate further analysis of components error classification and detection. For instance, when students conduct laboratory experiments, the HoloLens captures images of students placing different components on a breadboard. The images are forwarded to the server for detection in the background. A hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm will be used to train the dataset for object recognition and classification. The convolution layer extracts image features, which are then classified using Support Vector Machine (SVM). By adequately labeling the training data and classifying, the model will predict, categorize, and assess students in placing components correctly. As a result, the data acquired through HoloLens includes images of students assembling electronic components. It constantly checks to see if students appropriately position components in the breadboard and connect the components to function. When students misplace any components, the HoloLens predicts the error before the user places the components in the incorrect proportion and fosters students to correct their mistakes. This hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm automating electronic component error classification and detection approach eliminates component connection problems and minimizes the risk of component damage. Conclusion: These augmented reality smart glasses powered by machine learning provide a wide range of benefits to supervisors, professionals, and students. It helps customize the learning experience, which is particularly beneficial in large classes with limited time. It determines the accuracy with which machine learning algorithms can forecast whether students are making the correct decisions and completing their laboratory tasks.

Keywords: augmented reality, machine learning, object recognition, virtual laboratories

Procedia PDF Downloads 123

Authors: R. Bernaoui, P. Ohly

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Our research shows the use of the mobile phone that consolidates the relationship between veterinarians, and that between breeders and veterinarians. On the other hand it asserts that the tool in question is a means of economic development. The results of our survey reveal a positive return to the veterinary community, which shows that the mobile phone has become an effective means of sustainable development through the transfer of a rapid and punctual information inheritance via social networks; including many Internet applications. Our results show that almost all veterinarians use the mobile phone for interprofessional communication. We therefore believe that the use of the mobile phone by livestock operators has greatly improved the working conditions, just as the use of this tool contributes to a better management of the exploitation as long as it allows limit travel but also save time. These results show that we are witnessing a growth in the use of mobile telephony technologies that impact is as much in terms of sustainable development. Allowing access to information, especially technical information, the mobile phone, and Information and Communication of Technology (ICT) in general, give livestock sector players not only security, by limiting losses, but also an efficiency that allows them a better production and productivity.

Keywords: algeria, breeder-veterinarian, digital heritage, networking

Procedia PDF Downloads 109

Authors: Xinyuan Liu, Xiaojun Jing, Yuan He, Junsheng Mu

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Aspect-based Sentiment Analysis (ABSA) has attracted much attention due to its capacity to determine the sentiment polarity of the certain aspect in a sentence. In previous works, great significance of the interaction between aspect and sentence has been exhibited in ABSA. In consequence, a Multi-Level Attentional Networks (MLAN) is proposed. MLAN consists of four parts: Embedding Layer, Encoding Layer, Multi-Level Attentional (MLA) Layers and Final Prediction Layer. Among these parts, MLA Layers including Aspect Level Attentional (ALA) Layer and Interactive Attentional (ILA) Layer is the innovation of MLAN, whose function is to focus on the important information and obtain multiple levels’ attentional weighted representation of aspect and sentence. In the experiments, MLAN is compared with classical TD-LSTM, MemNet, RAM, ATAE-LSTM, IAN, AOA, LCR-Rot and AEN-GloVe on SemEval 2014 Dataset. The experimental results show that MLAN outperforms those state-of-the-art models greatly. And in case study, the works of ALA Layer and ILA Layer have been proven to be effective and interpretable.

Keywords: deep learning, aspect-based sentiment analysis, attention, natural language processing

Procedia PDF Downloads 127

Authors: Ming-Jong Yao, Chin-Sum Shui, Chih-Han Wang

Abstract:

This paper is developed based on a real-world decision scenario that an industrial gas company that applies the Vendor Managed Inventory model and supplies liquid oxygen with a self-operated heterogeneous vehicle fleet to hospitals in nearby cities. We name it as a Joint Replenishment and Heterogeneous Vehicle Routing Problem with Cyclical Schedule and formulate it as a non-linear mixed-integer linear programming problem which simultaneously determines the length of the planning cycle (PC), the length of the replenishment cycle and the dates of replenishment for each customer and the vehicle routes of each day within PC, such that the average daily operation cost within PC, including inventory holding cost, setup cost, transportation cost, and overtime labor cost, is minimized. A solution method based on genetic algorithm, embedded with an encoding and decoding mechanism and local search operators, is then proposed, and the hash function is adopted to avoid repetitive fitness evaluation for identical solutions. Numerical experiments demonstrate that the proposed solution method can effectively solve the problem under different lengths of PC and number of customers. The method is also shown to be effective in determining whether the company should expand the storage capacity of a customer whose demand increases. Sensitivity analysis of the vehicle fleet composition shows that deploying a mixed fleet can reduce the daily operating cost.

Keywords: cyclic inventory routing problem, joint replenishment, heterogeneous vehicle, genetic algorithm

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Authors: Noritaka Shigei, Kazuto Matsumoto, Yoshiki Nakashima, Hiromi Miyajima

Abstract:

Although Mobile Wireless Sensor Networks (MWSNs), which consist of mobile sensor nodes (MSNs), can cover a wide range of observation region by using a small number of sensor nodes, they need to construct a network to collect the sensing data on the base station by moving the MSNs. As an effective method, the network construction method based on Virtual Rails (VRs), which is referred to as VR method, has been proposed. In this paper, we propose two types of effective techniques for the VR method. They can prolong the operation time of the network, which is limited by the battery capabilities of MSNs and the energy consumption of MSNs. The first technique, an effective arrangement of VRs, almost equalizes the number of MSNs belonging to each VR. The second technique, an adaptive movement method of MSNs, takes into account the residual energy of battery. In the simulation, we demonstrate that each technique can improve the network lifetime and the combination of both techniques is the most effective.

Keywords: mobile sensor node, relay of sensing data, residual energy, virtual rail, wireless sensor network

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Authors: N. Z. van Hierden, Q. Yu, F. Gauvin

Abstract:

Cement manufacturing is, because of its production process, among the highest contributors to CO₂ emissions worldwide. As cement is one of the major components in 3D printed concrete, achieving sustainability and carbon neutrality can be particularly challenging. To improve the sustainability of 3D printed materials, different CO₂-reducing strategies can be used, each one with a distinct level of impact and complexity. In this work, we focus on the development of these sustainable mixtures and finding alternatives. Promising alternatives for cement and clinker replacement include the use of recycled building materials, amongst which (calcined) bricks and roof tiles. To study the potential of recycled clay-based building materials, the application of calcinated clay itself is studied as well. Compared to cement, the calcination temperature of clay-based materials is significantly lower, resulting in reduced CO₂ output. Reusing these materials is therefore a promising solution for utilizing waste streams while simultaneously reducing the cement content in 3D concrete mixtures. In addition, waste streams can be locally sourced, thereby reducing the emitted CO₂ during transportation. In this research, various alternative binders are examined, such as calcined clay blends (LC3) from recycled tiles and bricks, or locally obtained clay resources. Using various experiments, a high potential for mix designs including these resources has been shown with respect to material strength, while sustaining decent printability and buildability. Therefore, the defined strategies are promising and can lead to a more sustainable, low-CO₂ mixture suitable for 3D printing while using accessible materials.

Keywords: cement replacement, 3DPC, circular building materials, calcined clay, CO₂ reduction

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Authors: Nasim Hossein Hamzeh, Abbas Ranjbar Saadat Abadi, Maggie Chel Gee Ooi, Steven Soon-Kai Kong, Christian Opp

Abstract:

Dust storm is one of the most important natural disasters in the world, where the Middle East suffers frequently due to the existence of the dust belt region. As a country in the Middle East, Iran mostly is affected by the dust storms from some internal and also external dust sources, mostly originating from deserts in Iraq, Syria, and Saudi Arabia. In this study, some severe Shamal dust storms were investigated in Southwest Iran. The measured 〖PM〗_10 reached up to 834 μg m-3 in some stations in west Iran and Iran-Iraq borders, while the measured 〖PM〗_10 reached up to 4947 μg m-3 SW stations in northern shores of the Persian Gulf. During these severe dust storms, a low-level jet was observed at 930hPa atmospheric level in north Iraq and south Iraq. the jet core and its width were about 16 ms-1 and 100 km, respectively, in the cases where it is located in the NW regions of Iraq and northeastern Syria (at 35°N and 40-41°E), So the jet was stronger at higher latitudes (34°N - 35°N) than at lower latitudes (32°N). Therefore, suitable conditions have been created for lifting of dust sources located in northwestern Iraq and northeastern Syria. The topography surrounding the Mesopotamia and north of the Persian Gulf play a major role in the development of the Low-Level Jet through the interaction of meteorological conditions and mountain forcing. Also, the output of CALIPSO satellite images show dust rising to higher than 5 km in these dust cases, that confirming the influence of Shamal wind on the dust storm occurrence.

Keywords: dust storm, shamal wind, the persian gulf, southwest Iran

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Authors: Anita Kushwaha

Abstract:

We propose a new evolutionary computational model called Artificial Reproduction System which is based on the complex process of meiotic reproduction occurring between male and female cells of the living organisms. Artificial Reproduction System is an attempt towards a new computational intelligence approach inspired by the theoretical reproduction mechanism, observed reproduction functions, principles and mechanisms. A reproductive organism is programmed by genes and can be viewed as an automaton, mapping and reducing so as to create copies of those genes in its off springs. In Artificial Reproduction System, the binding mechanism between male and female cells is studied, parameters are chosen and a network is constructed also a feedback system for self regularization is established. The model then applies Mendel’s law of inheritance, allele-allele associations and can be used to perform data analysis of imbalanced data, multivariate, multiclass and big data. In the experimental study Artificial Reproduction System is compared with other state of the art classifiers like SVM, Radial Basis Function, neural networks, K-Nearest Neighbor for some benchmark datasets and comparison results indicates a good performance.

Keywords: bio-inspired computation, nature- inspired computation, natural computing, data mining

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Authors: B. Karthik Vasan, Rachil Maliwal, Somnath Basu

Abstract:

Generation of solid waste is a major problem confronting the iron and steel industry around the world. Disposal of untreated wastes is no longer a viable solution in view of the environmental regulations becoming more and more stringent, as well as an increase in community awareness about the long-term hazards of indiscriminate waste disposal. The current work explores the possibility of converting some of the ‘problematic’ solid wastes generated during steel manufacturing operations, viz. dust from primary steelmaking, iron ore handling, and flux calcination processes, into value-added products instead of environmentally hazardous disposal practices. It was possible to develop a synthetic calcium ferrite, which helped to enhance the dissolution of calcined basic fluxes (e.g. CaO) and reduce the overall energy consumption during steel making. This, in turn, increased process efficiency and reduced greenhouse gas emissions. The preliminary results from laboratory-scale experiments clearly demonstrate the potential of utilizing these ‘waste materials’ that are generated in-house in iron and steel manufacturing plants. The energy required for synthesis of the ferrite may be reduced further by partially utilizing the waste heat from the exhaust gases. In the longer run, it would result in significant financial benefits due to reduced dependence on purchased fluxes. The synthesized ferrite is non-hygroscopic and this provides an additional benefit during its storage and transportation, relative to calcined lime (CaO) that is widely used as a basic flux across the steel making industry.

Keywords: calcium ferrite, flux, slag formation, solid waste

Procedia PDF Downloads 204