Search results for: intelligent tuning

Authors: Maad Kamal Al-Anni

Abstract:

This study is about an algorithmic dependence of Artificial Neural Network on Multilayer Perceptron (MPL) pertaining to the classification and clustering presentations for Mobile Adhoc Network vulnerabilities. Moreover, mobile ad hoc network (MANET) is ubiquitous intelligent internetworking devices in which it has the ability to detect their environment using an autonomous system of mobile nodes that are connected via wireless links. Security affairs are the most important subject in MANET due to the easy penetrative scenarios occurred in such an auto configuration network. One of the powerful techniques used for inspecting the network packets is Intrusion Detection System (IDS); in this article, we are going to show the effectiveness of artificial neural networks used as a machine learning along with stochastic approach (information gain) to classify the malicious behaviors in simulated network with respect to different IDS techniques. The monitoring agent is responsible for detection inference engine, the audit data is collected from collecting agent by simulating the node attack and contrasted outputs with normal behaviors of the framework, whenever. In the event that there is any deviation from the ordinary behaviors then the monitoring agent is considered this event as an attack , in this article we are going to demonstrate the  signature-based IDS approach in a MANET by implementing the back propagation algorithm over ensemble-based Traffic Table (TT), thus the signature of malicious behaviors or undesirable activities are often significantly prognosticated and efficiently figured out, by increasing the parametric set-up of Back propagation algorithm during the experimental results which empirically shown its effectiveness  for the ratio of detection index up to 98.6 percentage. Consequently it is proved in empirical results in this article, the performance matrices are also being included in this article with Xgraph screen show by different through puts like Packet Delivery Ratio (PDR), Through Put(TP), and Average Delay(AD).

Keywords: Intrusion Detection System (IDS), Mobile Adhoc Networks (MANET), Back Propagation Algorithm (BPA), Neural Networks (NN)

Procedia PDF Downloads 194

Authors: Poojan Gharat, Haridas Pal, Sharmistha Dutta Choudhury

Abstract:

Tuning photophysical properties of guest dyes through host-guest interactions involving macrocyclic hosts are the attractive research areas since past few decades, as these changes can directly be implemented in chemical sensing, molecular recognition, fluorescence imaging and dye laser applications. Excited state intramolecular proton transfer (ESIPT) is an intramolecular prototautomerization process display by some specific dyes. The process is quite amenable to tunability by the presence of different macrocyclic hosts. The present study explores the interesting effect of p-sulfonatocalix[n]arene (SCXn) and cyclodextrin (CD) hosts on the excited-state prototautomeric equilibrium of Chrysazine (CZ), a model antitumour drug. CZ exists exclusively in its normal form (N) in the ground state. However, in the excited state, the excited N* form undergoes ESIPT along with its pre-existing intramolecular hydrogen bonds, giving the excited state prototautomer (T*). Accordingly, CZ shows a single absorption band due to N form, but two emission bands due to N* and T* forms. Facile prototautomerization of CZ is considerably inhibited when the dye gets bound to SCXn hosts. However, in spite of lower binding affinity, the inhibition is more profound with SCX6 host as compared to SCX4 host. For CD-CZ system, while prototautomerization process is hindered by the presence of β-CD, it remains unaffected in the presence of γCD. Reduction in the prototautomerization process of CZ by SCXn and βCD hosts is unusual, because T* form is less dipolar in nature than the N*, hence binding of CZ within relatively hydrophobic hosts cavities should have enhanced the prototautomerization process. At the same time, considering the similar chemical nature of two CD hosts, their effect on prototautomerization process of CZ would have also been similar. The atypical effects on the prototautomerization process of CZ by the studied hosts are suggested to arise due to the partial inclusion or external binding of CZ with the hosts. As a result, there is a strong possibility of intermolecular H-bonding interaction between CZ dye and the functional groups present at the portals of SCXn and βCD hosts. Formation of these intermolecular H-bonds effectively causes the pre-existing intramolecular H-bonding network within CZ molecule to become weak, and this consequently reduces the prototautomerization process for the dye. Our results suggest that rather than the binding affinity between the dye and host, it is the orientation of CZ in the case of SCXn-CZ complexes and the binding stoichiometry in the case of CD-CZ complexes that play the predominant role in influencing the prototautomeric equilibrium of the dye CZ. In the case of SCXn-CZ complexes, the results obtained through experimental findings are well supported by quantum chemical calculations. Similarly for CD-CZ systems, binding stoichiometries obtained through geometry optimization studies on the complexes between CZ and CD hosts correlate nicely with the experimental results. Formation of βCD-CZ complexes with 1:1 stoichiometry while formation of γCD-CZ complexes with 1:1, 1:2 and 2:2 stoichiometries are revealed from geometry optimization studies and these results are in good accordance with the observed effects by the βCD and γCD hosts on the ESIPT process of CZ dye.

Keywords: intermolecular proton transfer, macrocyclic hosts, quantum chemical studies, photophysical studies

Procedia PDF Downloads 121

Authors: Getaneh Berie Tarekegn

Abstract:

A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

Procedia PDF Downloads 75

Authors: Leandros A. Maglaras, Jianmin Jiang

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The protection of the national infrastructures from cyberattacks is one of the main issues for national and international security. The funded European Framework-7 (FP7) research project CockpitCI introduces intelligent intrusion detection, analysis and protection techniques for Critical Infrastructures (CI). The paradox is that CIs massively rely on the newest interconnected and vulnerable Information and Communication Technology (ICT), whilst the control equipment, legacy software/hardware, is typically old. Such a combination of factors may lead to very dangerous situations, exposing systems to a wide variety of attacks. To overcome such threats, the CockpitCI project combines machine learning techniques with ICT technologies to produce advanced intrusion detection, analysis and reaction tools to provide intelligence to field equipment. This will allow the field equipment to perform local decisions in order to self-identify and self-react to abnormal situations introduced by cyberattacks. In this paper, an intrusion detection module capable of detecting malicious network traffic in a Supervisory Control and Data Acquisition (SCADA) system is presented. Malicious data in a SCADA system disrupt its correct functioning and tamper with its normal operation. OCSVM is an intrusion detection mechanism that does not need any labeled data for training or any information about the kind of anomaly is expecting for the detection process. This feature makes it ideal for processing SCADA environment data and automates SCADA performance monitoring. The OCSVM module developed is trained by network traces off line and detects anomalies in the system real time. The module is part of an IDS (intrusion detection system) developed under CockpitCI project and communicates with the other parts of the system by the exchange of IDMEF messages that carry information about the source of the incident, the time and a classification of the alarm.

Keywords: cyber-security, SCADA systems, OCSVM, intrusion detection

Procedia PDF Downloads 552

Authors: C. B. Masera, M. Imprialou, L. Budd, C. Morton

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Even though signalised intersections are necessary for urban road traffic management, they can act as bottlenecks and disrupt traffic operations. Interrupted traffic flow causes congestion, delays, stop-and-go conditions (i.e. excessive acceleration/deceleration) and longer journey times. Vehicle and infrastructure connectivity offers the potential to provide improved new services with additional functions of assisting drivers. This paper focuses on one of the applications of vehicle-to-infrastructure communication namely Green Light Optimal Speed Advisory (GLOSA). To assess the effectiveness of GLOSA in the urban road network, an integrated microscopic traffic simulation framework is built into VISSIM software. Vehicle movements and vehicle-infrastructure communications are simulated through the interface of External Driver Model. A control algorithm is developed for recommending an optimal speed that is continuously updated in every time step for all vehicles approaching a signal-controlled point. This algorithm allows vehicles to pass a traffic signal without stopping or to minimise stopping times at a red phase. This study is performed with all connected vehicles at 100% penetration rate. Conventional vehicles are also simulated in the same network as a reference. A straight road segment composed of two opposite directions with two traffic lights per lane is studied. The simulation is implemented under 150 vehicles per hour and 200 per hour traffic volume conditions to identify how different traffic densities influence the benefits of GLOSA. The results indicate that traffic flow is improved by the application of GLOSA. According to this study, vehicles passed through the traffic lights more smoothly, and waiting times were reduced by up to 28 seconds. Average delays decreased for the entire network by 86.46% and 83.84% under traffic densities of 150 vehicles per hour per lane and 200 vehicles per hour per lane, respectively.

Keywords: connected vehicles, GLOSA, intelligent transport systems, vehicle-to-infrastructure communication

Procedia PDF Downloads 171

Authors: O. J. Akinsola, Yinan Zheng, Rose Anorlu, F. T. Ogunsola, Lifang Hou, Robert Leo-Murphy

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Cervical Cancer (CC) is the 2nd most common cancer among women living in low and middle-income countries, with no associated symptoms during formative periods. With the advancement and innovative medical research, there are numerous preventive measures being utilized, but the incidence of cervical cancer cannot be truncated with the application of only screening tests. The mortality associated with this invasive cervical cancer can be nipped in the bud through the important role of early-stage detection. This study research selected an array of different top features selection techniques which was aimed at developing a model that could validly diagnose the risk factors of cervical cancer. A retrospective clinic-based cohort study was conducted on 178 HIV-associated cervical cancer patients in Lagos University teaching Hospital, Nigeria (U54 data repository) in April 2022. The outcome measure was the automated prediction of the HIV-associated cervical cancer cases, while the predictor variables include: demographic information, reproductive history, birth control, sexual history, cervical cancer screening history for invasive cervical cancer. The proposed technique was assessed with R and Python programming software to produce the model by utilizing the classification algorithms for the detection and diagnosis of cervical cancer disease. Four machine learning classification algorithms used are: the machine learning model was split into training and testing dataset into ratio 80:20. The numerical features were also standardized while hyperparameter tuning was carried out on the machine learning to train and test the data. Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN). Some fitting features were selected for the detection and diagnosis of cervical cancer diseases from selected characteristics in the dataset using the contribution of various selection methods for the classification cervical cancer into healthy or diseased status. The mean age of patients was 49.7±12.1 years, mean age at pregnancy was 23.3±5.5 years, mean age at first sexual experience was 19.4±3.2 years, while the mean BMI was 27.1±5.6 kg/m2. A larger percentage of the patients are Married (62.9%), while most of them have at least two sexual partners (72.5%). Age of patients (OR=1.065, p<0.001**), marital status (OR=0.375, p=0.011**), number of pregnancy live-births (OR=1.317, p=0.007**), and use of birth control pills (OR=0.291, p=0.015**) were found to be significantly associated with HIV-associated cervical cancer. On top ten 10 features (variables) considered in the analysis, RF claims the overall model performance, which include: accuracy of (72.0%), the precision of (84.6%), a recall of (84.6%) and F1-score of (74.0%) while LR has: an accuracy of (74.0%), precision of (70.0%), recall of (70.0%) and F1-score of (70.0%). The RF model identified 10 features predictive of developing cervical cancer. The age of patients was considered as the most important risk factor, followed by the number of pregnancy livebirths, marital status, and use of birth control pills, The study shows that data mining techniques could be used to identify women living with HIV at high risk of developing cervical cancer in Nigeria and other sub-Saharan African countries.

Keywords: associated cervical cancer, data mining, random forest, logistic regression

Procedia PDF Downloads 83

Authors: Wei Yao

Abstract:

A steerable and flexible drill is needed in orthopaedic surgery. For example, osteoarthritis is a common condition affecting millions of people for which joint replacement is an effective treatment which improves the quality and duration of life in elderly sufferers. Conventional surgery is not very accurate. Computer navigation and robotics can help increase the accuracy. For example, In Total Hip Arthroplasty (THA), robotic surgery is currently practiced mainly on acetabular side helping cup positioning and orientation. However, femoral stem positioning mostly uses hand-rasping method rather than robots for accurate positioning. The other case for using a flexible drill in surgery is Anterior Cruciate Ligament (ACL) Reconstruction. The majority of ACL Reconstruction failures are primarily caused by technical mistakes and surgical errors resulting from drilling the anatomical bone tunnels required to accommodate the ligament graft. The proposed new steerable drill system will perform orthopedic surgery through curved tunneling leading to better accuracy and patient outcomes. It may reduce intra-operative fractures, dislocations, early failure and leg length discrepancy by making possible a new level of precision. This technology is based on a robotically assisted, steerable, hand-held flexible drill, with a drill-tip tracking device and a multi-modality navigation system. The critical differentiator is that this robotically assisted surgical technology now allows the surgeon to prepare 'patient specific' and more anatomically correct 'curved' bone tunnels during orthopedic surgery rather than drilling straight holes as occurs currently with existing surgical tools. The flexible and steerable drill and its navigation system for femoral milling in total hip arthroplasty had been tested on sawbones to evaluate the accuracy of the positioning and orientation of femoral stem relative to the pre-operative plan. The data show the accuracy of the navigation system is better than traditional hand-rasping method.

Keywords: navigation, robotic orthopedic surgery, steerable drill, tracking

Procedia PDF Downloads 167

Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

Abstract:

Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: electrical disaggregation, DTW, general appliance modeling, event detection

Procedia PDF Downloads 78

Authors: Monish Sengupta, Benjamin Heydecker, Daniel Woodland

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New train control systems rely mainly on Automatic Train Protection (ATP) and Automatic Train Operation (ATO) dynamically to control the speed and hence performance. The ATP and the ATO form the vital element within the CBTC (Communication Based Train Control) and within the ERTMS (European Rail Traffic Management System) system architectures. Reliable and accurate measurement of train location, speed and acceleration are vital to the operation of train control systems. In the past, all CBTC and ERTMS system have deployed a balise or equivalent to correct the uncertainty element of the train location. Typically a CBTC train is allowed to miss only one balise on the track, after which the Automatic Train Protection (ATP) system applies emergency brake to halt the service. This is because the location uncertainty, which grows within the train control system, cannot tolerate missing more than one balise. Balises contribute a significant amount towards wayside maintenance and studies have shown that balises on the track also forms a constraint for future track layout change and change in speed profile.This paper investigates the causes of the location uncertainty that is currently experienced and considers whether it is possible to identify an effective filter to ascertain, in conjunction with appropriate sensors, more accurate speed, distance and location for a CBTC driven train without the need of any external balises. An appropriate sensor fusion algorithm and intelligent sensor selection methodology will be deployed to ascertain the railway location and speed measurement at its highest precision. Similar techniques are already in use in aviation, satellite, submarine and other navigation systems. Developing a model for the speed control and the use of Kalman filter is a key element in this research. This paper will summarize the research undertaken and its significant findings, highlighting the potential for introducing alternative approaches to train positioning that would enable removal of all trackside location correction balises, leading to huge reduction in maintenances and more flexibility in future track design.

Keywords: ERTMS, CBTC, ATP, ATO

Procedia PDF Downloads 410

Authors: Faith Eweluegim Enahoro-Ofagbe

Abstract:

Water is essential for sustaining life. As a limited resource, effective water management is vital. Water scarcity has become more common due to the effects of climate change, land degradation, deforestation, and population growth, especially in rural communities, which are more susceptible to water-related issues such as water shortage, water-borne disease, et c., due to the unsuccessful implementation of water policies and projects in Nigeria. Since rural communities generate the majority of agricultural products, they significantly impact on water management for sustainability. The development of methods to advance this goal for residential and agricultural usage in the present and the future is a challenge for rural residents. This study evaluated rural water supply systems and irrigation management techniques to conserve water in Kwara State, North-Central Nigeria. Suggesting some measures to conserve water resources for sustainability, off-season farming, and socioeconomic security that will remedy water degradation, unemployment which is one of the causes of insecurity in the country, by considering the use of fabricated or locally made irrigation equipment, which are affordable by rural farmers, among other recommendations. Questionnaires were distributed to respondents in the study area for quantitative evaluation of irrigation methods practices. For physicochemical investigation, samples were also gathered from their available water sources. According to the study's findings, 30 percent of farmers adopted intelligent irrigation management techniques to conserve water resources, saving 45% of the water previously used for irrigation. 70 % of farmers practice seasonal farming. Irrigation water is drawn from river channels, streams, and unlined and unprotected wells. 60% of these rural residents rely on private boreholes for their water needs, while 40% rely on government-supplied rural water. Therefore, the government must develop additional water projects, raise awareness, and offer irrigation techniques that are simple to adapt for water management, increasing socio-economic productivity, security, and water sustainability.

Keywords: water resource management, sustainability, irrigation, rural water management, irrigation management technique

Procedia PDF Downloads 111

Authors: J. Martin, A. Nominé, T. Czerwiec, G. Henrion, T. Belmonte

Abstract:

The plasma electrolytic oxidation (PEO) is a particular electrochemical process to produce protective oxide ceramic coatings on light-weight metals (Al, Mg, Ti). When applied to aluminum alloys, the resulting PEO coating exhibit improved wear and corrosion resistance because thick, hard, compact and adherent crystalline alumina layers can be achieved. Several investigations have been carried out to improve the efficiency of the PEO process and one particular way consists in tuning the suitable electrical regime. Despite the considerable interest in this process, there is still no clear understanding of the underlying discharge mechanisms that make possible metal oxidation up to hundreds of µm through the ceramic layer. A key parameter that governs the PEO process is the numerous short-lived micro-discharges (micro-plasma in liquid) that occur continuously over the processed surface when the high applied voltage exceeds the critical dielectric breakdown value of the growing ceramic layer. By using a bipolar pulsed current to supply the electrodes, we previously observed that micro-discharges are delayed with respect to the rising edge of the anodic current. Nevertheless, explanation of the origin of such phenomena is still not clear and needs more systematic investigations. The aim of the present communication is to identify the relationship that exists between this delay and the mechanisms responsible of the oxide growth. For this purpose, the delay of micro-discharges ignition is investigated as the function of various electrical parameters such as the current density (J), the current pulse frequency (F) and the anodic to cathodic charge quantity ratio (R = Qp/Qn) delivered to the electrodes. The PEO process was conducted on Al2214 aluminum alloy substrates in a solution containing potassium hydroxide [KOH] and sodium silicate diluted in deionized water. The light emitted from micro-discharges was detected by a photomultiplier and the micro-discharge parameters (number, size, life-time) were measured during the process by means of ultra-fast video imaging (125 kfr./s). SEM observations and roughness measurements were performed to characterize the morphology of the elaborated oxide coatings while XRD was carried out to evaluate the amount of corundum -Al203 phase. Results show that whatever the applied current waveform, the delay of micro-discharge appearance increases as the process goes on. Moreover, the delay is shorter when the current density J (A/dm2), the current pulse frequency F (Hz) and the ratio of charge quantity R are high. It also appears that shorter delays are associated to stronger micro-discharges (localized, long and large micro-discharges) which have a detrimental effect on the elaborated oxide layers (thin and porous). On the basis of the results, a model for the growth of the PEO oxide layers will be presented and discussed. Experimental results support that a mechanism of electrical charge accumulation at the oxide surface / electrolyte interface takes place until the dielectric breakdown occurs and thus until micro-discharges appear.

Keywords: aluminium, micro-discharges, oxidation mechanisms, plasma electrolytic oxidation

Procedia PDF Downloads 264

Authors: Chih-Jer Lin, Jian-Hong Hou

Abstract:

Industry 4.0 is a vision of integrated industry implemented by artificial intelligent computing, software, and Internet technologies. The main goal of industry 4.0 is to deal with the difficulty owing to competitive pressures in the marketplace. For today’s manufacturing factories, the type of production is changed from mass production (high quantity production with low product variety) to medium quantity-high variety production. To offer flexibility, better quality control, and improved productivity, robot manipulators are used to combine material processing, material handling, and part positioning systems into an integrated manufacturing system. To implement the automated system for sheet metal bending operations, motion planning of a 7-degrees of freedom (DOF) robot is studied in this paper. A virtual reality (VR) environment of a bending cell, which consists of the robot and a bending machine, is established using the virtual robot experimentation platform (V-REP) simulator. For sheet metal bending operations, the robot only needs six DOFs for the pick-and-place or tracking tasks. Therefore, this 7 DOF robot has more DOFs than the required to execute a specified task; it can be called a redundant robot. Therefore, this robot has kinematic redundancies to deal with the task-priority problems. For redundant robots, Pseudo-inverse of the Jacobian is the most popular motion planning method, but the pseudo-inverse methods usually lead to a kind of chaotic motion with unpredictable arm configurations as the Jacobian matrix lose ranks. To overcome the above problem, we proposed a method to formulate the motion planning problems as optimization problem. Moreover, a genetic algorithm (GA) based method is proposed to deal with motion planning of the redundant robot. Simulation results validate the proposed method feasible for motion planning of the redundant robot in an automated sheet-metal bending operations.

Keywords: redundant robot, motion planning, genetic algorithm, obstacle avoidance

Procedia PDF Downloads 146

Authors: Moet Moet Myint lay

Abstract:

Professional learning communities provide ongoing professional development for teachers, where they become learning leaders and actively participate in school improvement. The development of professional knowledge requires a significant focus on professional competence in the work of teachers, and a solid foundation of professional knowledge and skills is necessary for members of society to become intelligent members. Continuing professional development (CPD) plays a vital role in improving educational outcomes, as its importance has been proven over the years. This article explores the need for CPD for teachers in Myanmar and the utility of professional learning communities in improving teacher quality. This study aims to explore a comprehensive understanding of professional learning communities to support the continuing professional development of teacher educators in improving the quality of education. The research questions are: (1) How do teacher educators in Myanmar understand the concept of professional learning communities for continuing professional development? (2) What CPD training is required for all teachers in teachers' colleges? Quantitative research methods were used in this study. Survey data were collected from 50 participants (teacher trainers) from five educational institutions. The analysis shows that professional learning communities when done well, can have a lasting impact on teacher quality. Furthermore, the creation of professional learning communities is the best indicator of professional development in existing education systems. Some research suggests that teacher professional development is closely related to teacher professional skills and school improvement. As a result of the collective learning process, teachers gain a deeper understanding of the subject matter, increase their knowledge, and develop their professional teaching skills. This will help improve student performance and school quality in the future. The lack of clear understanding and knowledge about PLC among school leaders and leads teachers to believe that PLC activities are not beneficial. Lack of time, teacher accountability, leadership skills, and negative attitudes of participating teachers were the most frequently cited challenges in implementing PLCs. As a result of these findings, educators and stakeholders can use them to implement professional learning communities.

Keywords: professional learning communities, continuing professional development, teacher education, competence, school improvement

Procedia PDF Downloads 59

Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

Abstract:

Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: general appliance model, non intrusive load monitoring, events detection, unsupervised techniques;

Procedia PDF Downloads 82

Authors: Xiyue Wang, Shaoning Yan

Abstract:

Cities in China today is faced with an increasingly serious river ecological crisis accompanying with the development of urbanization: waterlogging on account of the fragmented urban natural hydrological system; the limited ecological function of the hydrological system caused by a destruction of water system and waterfront ecological environment. Additionally, the eco-hydrological processes of rivers are affected by various environmental factors, which are more complex in the context of urban environment. Therefore, efficient hydrological monitoring and analysis tools, accurate and visual hydrological and hydraulic models are becoming more important basis for decision-makers and an important way for landscape architects to solve urban hydrological problems, formulating sustainable and forward-looking schemes. The study mainly introduces the river and flood analysis model based on the platform of Autodesk Civil 3D. Taking the Luanhe River in Qian'an City of Hebei Province as an example, the 3D models of the landform, river, embankment, shoal, pond, underground stream and other land features were initially built, with which the water transfer simulation analysis, river floodplain analysis, and river ecology analysis were carried out, ultimately the real-time visualized simulation and analysis of rivers in various hypothetical scenarios were realized. Through the establishment of digital hydrological and hydraulic model, the hydraulic data can be accurately and intuitively simulated, which provides basis for rational water system and benign urban ecological system design. Though, the hydrological and hydraulic model based on Autodesk Civil3D own its boundedness: the interaction between the model and other data and software is unfavorable; the huge amount of 3D data and the lack of basic data restrict the accuracy and application range. The hydrological and hydraulic model based on Autodesk Civil3D platform provides more possibility to access convenient and intelligent tool for urban planning and monitoring, a solid basis for further urban research and design.

Keywords: visualization, hydrological and hydraulic model, Autodesk Civil 3D, urban river

Procedia PDF Downloads 297

Authors: Zhang Dongqing

Abstract:

Concern for students' mental health and the application of intelligent advanced technologies are driving changes in teaching models. The traditional teacher-centered classroom is beginning to transform into a student-centered smart interactive learning environment. Nowadays, smart classrooms are compatible with constructivist learning. This theory emphasizes the role of teachers in the teaching process as helpers and facilitators of knowledge construction, and students learn by interacting with them. The spatial design of classrooms is closely related to the teaching model and should also be developed in the direction of smart classroom design. The goal is to explore the impact of smart classroom layout on student-centered teaching environment and teacher-student interaction under the guidance of constructivist learning theory, by combining the design process and feedback analysis of the smart transformation project on the campus of Tongji University in Shanghai. During the research process, the theoretical basis of constructivist learning was consolidated through literature research and case analysis. The integration and visual field analysis of the traditional and transformed indoor floor plans were conducted using space syntax tools. Finally, questionnaire surveys and interviews were used to collect data. The main conclusions are as followed: flexible spatial layouts can promote students' learning effects and mental health; the interactivity of smart classroom layouts is different and needs to be combined with different teaching models; the public areas of teaching buildings can also improve the interactive learning atmosphere by adding discussion space. This article provides a data-based research basis for improving students' learning effects and mental health, and provides a reference for future smart classroom design.

Keywords: spatial layout, smart classroom, space syntax, renovation, educational environment

Procedia PDF Downloads 72

Authors: Amirreza Fazely Hamedani, Muzzamil Aziz, Philipp Wieder, Ramin Yahyapour

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Software-defined networking in recent years came into the sight of so many network designers as a successor to the traditional networking. Unlike traditional networks where control and data planes engage together within a single device in the network infrastructure such as switches and routers, the two planes are kept separated in software-defined networks (SDNs). All critical decisions about packet routing are made on the network controller, and the data level devices forward the packets based on these decisions. This type of network is vulnerable to DDoS attacks, degrading the overall functioning and performance of the network by continuously injecting the fake flows into it. This increases substantial burden on the controller side, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to the legitimate users. Thus, the protection of this novel network architecture against denial of service attacks is essential. In the world of cybersecurity, attacks and new threats emerge every day. It is essential to have tools capable of managing and analyzing all this new information to detect possible attacks in real-time. These tools should provide a comprehensive solution to automatically detect, predict and prevent abnormalities in the network. Big data encompasses a wide range of studies, but it mainly refers to the massive amounts of structured and unstructured data that organizations deal with on a regular basis. On the other hand, it regards not only the volume of the data; but also that how data-driven information can be used to enhance decision-making processes, security, and the overall efficiency of a business. This paper presents an intelligent big data framework as a solution to handle illegitimate traffic burden on the SDN network created by the numerous DDoS attacks. The framework entails an efficient defence and monitoring mechanism against DDoS attacks by employing the state of the art machine learning techniques.

Keywords: apache spark, apache kafka, big data, DDoS attack, machine learning, SDN network

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Authors: Komal Rathore, Kiesha Pierre, Kyle Cogswell, Aaron Driscoll, Andres Tejada Martinez, Gita Iranipour, Luke Mulford, Aydin Sunol

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Modeling of Biological Wastewater Treatment Plants requires several parameters for kinetic rate expressions, thermo-physical properties, and hydrodynamic behavior. The kinetics and associated mechanisms become complex due to several biological processes taking place in wastewater treatment plants at varying times and spatial scales. A dynamic process model that incorporated the complex model for activated sludge kinetics was developed using the BioWin software platform for an Advanced Wastewater Treatment Plant in Valrico, Florida. Due to the extensive number of tunable parameters, an experimental design was employed for judicious selection of the most influential parameter sets and their bounds. The model was tuned using both the influent and effluent plant data to reconcile and rectify the forecasted results from the BioWin Model. Amount of mixed liquor suspended solids in the oxidation ditch, aeration rates and recycle rates were adjusted accordingly. The experimental analysis and plant SCADA data were used to predict influent wastewater rates and composition profiles as a function of time for extended periods. The lumped dynamic model development process was coupled with Computational Fluid Dynamics (CFD) modeling of the key units such as oxidation ditches in the plant. Several CFD models that incorporate the nitrification-denitrification kinetics, as well as, hydrodynamics was developed and being tested using ANSYS Fluent software platform. These realistic and verified models developed using BioWin and ANSYS were used to plan beforehand the operating policies and control strategies for the biological wastewater plant accordingly that further allows regulatory compliance at minimum operational cost. These models, with a little bit of tuning, can be used for other biological wastewater treatment plants as well. The BioWin model mimics the existing performance of the Valrico Plant which allowed the operators and engineers to predict effluent behavior and take control actions to meet the discharge limits of the plant. Also, with the help of this model, we were able to find out the key kinetic and stoichiometric parameters which are significantly more important for modeling of biological wastewater treatment plants. One of the other important findings from this model were the effects of mixed liquor suspended solids and recycle ratios on the effluent concentration of various parameters such as total nitrogen, ammonia, nitrate, nitrite, etc. The ANSYS model allowed the abstraction of information such as the formation of dead zones increases through the length of the oxidation ditches as compared to near the aerators. These profiles were also very useful in studying the behavior of mixing patterns, effect of aerator speed, and use of baffles which in turn helps in optimizing the plant performance.

Keywords: computational fluid dynamics, flow-sheet simulation, kinetic modeling, process dynamics

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Authors: Taha Benarbia

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The advent of automated and connected vehicles has revolutionized the transportation industry, presenting new opportunities for enhancing the efficiency, safety, and sustainability of our transportation networks. This paper explores the integration of automated and connected vehicles into a smart transportation framework, leveraging the power of deep learning techniques to optimize the overall network performance. The first aspect addressed in this paper is the deployment of automated vehicles (AVs) within the transportation system. AVs offer numerous advantages, such as reduced congestion, improved fuel efficiency, and increased safety through advanced sensing and decisionmaking capabilities. The paper delves into the technical aspects of AVs, including their perception, planning, and control systems, highlighting the role of deep learning algorithms in enabling intelligent and reliable AV operations. Furthermore, the paper investigates the potential of connected vehicles (CVs) in creating a seamless communication network between vehicles, infrastructure, and traffic management systems. By harnessing real-time data exchange, CVs enable proactive traffic management, adaptive signal control, and effective route planning. Deep learning techniques play a pivotal role in extracting meaningful insights from the vast amount of data generated by CVs, empowering transportation authorities to make informed decisions for optimizing network performance. The integration of deep learning with automated and connected vehicles paves the way for advanced transportation network optimization. Deep learning algorithms can analyze complex transportation data, including traffic patterns, demand forecasting, and dynamic congestion scenarios, to optimize routing, reduce travel times, and enhance overall system efficiency. The paper presents case studies and simulations demonstrating the effectiveness of deep learning-based approaches in achieving significant improvements in network performance metrics

Keywords: automated vehicles, connected vehicles, deep learning, smart transportation network

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Authors: Aleksandr S. Demin, Anastasiia V. Ivanova

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Purpose: The current economy tends to increase the influence of digital technologies and diminish the human role in management. However, it is impossible to deny that a person still leads a business with its own set of values and priorities. The article presented aims to incorporate the peculiarities of the national culture and the characteristics of the supply chain using the quantitative values of the national culture obtained by the scholars of comparative management (Hofstede, House, and others). Design/Methodology/Approach: The conducted research is based on the secondary data in the field of cross-country comparison achieved by Prof. Hofstede and received in the GLOBE project. The data mentioned are used to design different aspects of the supply chain both on the cross-functional and inter-organizational levels. The connection between a range of principles in general (roles assignment, customer service prioritization, coordination of supply chain partners) and in comparative management (acknowledgment of the national peculiarities of the country in which the company operates) is shown over economic and mathematical models, mainly linear programming models. Findings: The combination of the team management wheel concept, the business processes of the global supply chain, and the national culture characteristics let a transnational corporation to form a supply chain crew balanced in costs, functions, and personality. To elaborate on an effective customer service policy and logistics strategy in goods and services distribution in the country under review, two approaches are offered. The first approach relies exceptionally on the customer’s interest in the place of operation, while the second one takes into account the position of the transnational corporation and its previous experience in order to accord both organizational and national cultures. The effect of integration practice on the achievement of a specific supply chain goal in a specific location is advised to assess via types of correlation (positive, negative, non) and the value of national culture indices. Research Limitations: The models developed are intended to be used by transnational companies and business forms located in several nationally different areas. Some of the inputs to illustrate the application of the methods offered are simulated. That is why the numerical measurements should be used with caution. Practical Implications: The research can be of great interest for the supply chain managers who are responsible for the engineering of global supply chains in a transnational corporation and the further activities in doing business on the international area. As well, the methods, tools, and approaches suggested can be used by top managers searching for new ways of competitiveness and can be suitable for all staff members who are keen on the national culture traits topic. Originality/Value: The elaborated methods of decision-making with regard to the national environment suggest the mathematical and economic base to find a comprehensive solution.

Keywords: logistics integration, logistics services, multinational corporation, national culture, team management, service policy, supply chain management

Procedia PDF Downloads 106

Authors: Dedie Tooy, Rio Kolibu, Rio Putra, Herry Frits Pinatik, Daniel P. M. Ludong

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This study presents the design and development of a smart greenhouse prototype tailored to optimize Pak choi (Brassica chinensis L.) cultivation in tropical rural climates. Pak choi, a high-demand leafy vegetable in Indonesia, often experiences suboptimal growth due to elevated temperatures and humidity. The objective of this research is to design and develop an intelligent greenhouse to optimize pak choi cultivation in tropical rural climates. The design of a smart greenhouse provides a controlled environment to stabilize these conditions, but managing fluctuating temperature, humidity, and light in tropical regions remains challenging. This system regulates critical environmental factors, including temperature, humidity, irrigation system, and light, creating optimal conditions for Pak Choi. The prototype's effectiveness was evaluated by monitoring growth indicators such as leaf weight, freshness, and moisture content, alongside the consistency of the internal climate compared to external conditions. Results indicate that the smart greenhouse supports superior crop growth, enhances yield quality, and reduces environmental resource consumption. The irrigation control system test was carried out for 40 days. Researchers observed the results of the automatic system working according to the sensor value readings. The results of the temperature control system test work: when the air temperature in the greenhouse is more than 33 degrees, the condensation pump will turn on, and when the temperature is below 32 degrees, the pump will automatically turn itself off. The cycle repeats continuously. The results achieved pak coy can live up to 40 days. As part of our ongoing research, we are actively considering integrating double-layered roofs to improve insulation and reduce external temperature fluctuations, which could further enhance the effectiveness of the smart greenhouse.

Keywords: smart greenhouse, horticulture, rural tropical climate, sustainable agriculture

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Authors: J. C. Colmenares, M. Paszkiewicz-Gawron, D. Lomot, S. R. Pradhan, A. Qayyum

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This work is a report of recent selected experiments of photocatalysis intensification using flow microphotoreactors (fabricated by an ultrasound-based technique) for photocatalytic selective oxidation of benzyl alcohol (BnOH) to benzaldehyde (PhCHO) (in the frame of the concept of lignin valorization), and the proof of concept of intensifying a flow selective photocatalytic oxidation process by acoustic cavitation. The synthesized photocatalysts were characterized by using different techniques such as UV-Vis diffuse reflectance spectroscopy, X-ray diffraction, nitrogen sorption, thermal gravimetric analysis, and transmission electron microscopy. More specifically, the work will be on: a Design and development of metal-containing TiO₂ coated microflow reactor for photocatalytic partial oxidation of benzyl alcohol: The current work introduces an efficient ultrasound-based metal (Fe, Cu, Co)-containing TiO₂ deposition on the inner walls of a perfluoroalkoxy alkanes (PFA) microtube under mild conditions. The experiments were carried out using commercial TiO₂ and sol-gel synthesized TiO₂. The rough surface formed during sonication is the site for the deposition of these nanoparticles in the inner walls of the microtube. The photocatalytic activities of these semiconductor coated fluoropolymer based microreactors were evaluated for the selective oxidation of BnOH to PhCHO in the liquid flow phase. The analysis of the results showed that various features/parameters are crucial, and by tuning them, it is feasible to improve the conversion of benzyl alcohol and benzaldehyde selectivity. Among all the metal-containing TiO₂ samples, the 0.5 at% Fe/TiO₂ (both, iron and titanium, as cheap, safe, and abundant metals) photocatalyst exhibited the highest BnOH conversion under visible light (515 nm) in a microflow system. This could be explained by the higher crystallite size, high porosity, and flake-like morphology. b. Designing/fabricating photocatalysts by a sonochemical approach and testing them in the appropriate flow sonophotoreactor towards sustainable selective oxidation of key organic model compounds of lignin: Ultrasonication (US)-assitedprecipitaion and US-assitedhydrosolvothermal methods were used for the synthesis of metal-oxide-based and metal-free-carbon-based photocatalysts, respectively. Additionally, we report selected experiments of intensification of a flow photocatalytic selective oxidation through the use of ultrasonic waves. The effort of our research is focused on the utilization of flow sonophotocatalysis for the selective transformation of lignin-based model molecules by nanostructured metal oxides (e.g., TiO₂), and metal-free carbocatalysts. A plethora of parameters that affects the acoustic cavitation phenomena, and as a result the potential of sonication were investigated (e.g. ultrasound frequency and power). Various important photocatalytic parameters such as the wavelength and intensity of the irradiated light, photocatalyst loading, type of solvent, mixture of solvents, and solution pH were also optimized.

Keywords: heterogeneous photo-catalysis, metal-free carbonaceous materials, selective redox flow sonophotocatalysis, titanium dioxide

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Authors: Yelba Maria Carrillo

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The focus of this research study was to explore barriers, if any, faced by parents or legal guardians who are of Latinx background and speak Spanish as a primary language or are bilingual speakers of Spanish and English; barriers that limit their understanding of and involvement in their gifted child’s academic life. This study was guided by a qualitative case study design. The primary investigator hosted focus group interviews at a Magnet Middle School in Southern California. The groups consisted of 25 parents, or legal guardians of bilingual (English/Spanish) or former English learner students enrolled in a school serving 6th-8th grades. The primary investigator interviewed Latinx Spanish-speaking parents or legal guardians of gifted students regarding their perception of their child’s giftedness, parental involvement in schools, and fostering their child’s exceptional abilities. Parents and legal guardians described children as creative, intellectual, and highly intelligent. Key themes such as student performance, language proficiency, socio-emotional, and general intellectual ability were strong indicators of giftedness. Barriers such as language and education inhibited parent and legal guardian ability to understand their child’s giftedness, which resulted in their inability to adequately contribute to the development of their children’s talents and advocate for the appropriate services for their children. However, they recognized the importance of being involved in their child’s academic life and the importance of nurturing their ‘dón’ or ‘gift.’ La Familia is the foundation and core of Latinx culture; and, without a strong foundation, children lack guidance, confidence, and awareness to tap into their gifted abilities. Providing Latinx parents with the proper tools and resources to appropriately identify gifted characteristics and traits could lead to early identification and intervention for students in schools and at home.

Keywords: gifted education, gifted Latino students, Latino parent involvement, high ability students

Procedia PDF Downloads 157

Authors: Cristian Cazacu, Bela Kobulniczky

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Even though nowadays the median area of the Bucharest’s Sector 1 owns one of the best reputations in terms of quality of life level, the problems in urban planning from the last twenty years, as well as those related to the urban environment, became more and more obvious and shrill. And all this happened as long as non-compliance with urban and spatial planning laws, corroborated with uncontrolled territorial expansion on certain areas and faulty management of public and private spaces were more acute. The action of all these factors has been felt more and more strongly in the territory in the last twenty years, generating the degradation of the quality of the urban environment and affecting in parallel the general level of the inhabitants¬’ quality of life. Our methodology is based on analyzing a wide range of environmental parameters and it is also based on using advanced resources and skills for mapping planning and environmental dysfunctions as well as the possibility of integrating information into GIS programs, all data sets corroborated with problems related to spatial planning management and inaccuracies of the urbanistic sector. In the end, we managed to obtain a calculated and realistic image of the dysfunctions and a quantitative view of their magnitude in the territory. We also succeeded to create a full general map of the degree of degradation of the urban environment by typologies of urban tissues. Moreover, the methods applied by us can also be used globally to calculate and create realistic images and intelligent maps over the quality of the environment in areas larger than this one. Our study shows that environmental degradation occurred differently in the urban tissues from our study area, depending on several factors, reviewing the faulty way in which the processes of recovery / urban regeneration of the gap in recent years have led to the creation of new territorial dysfunctions. The general, centralized results show that the analyzed space has a much wider range of problems than initially thought, although notoriety and social etiquette place them far above other spaces from the same city of study.

Keywords: environment, GIS, planning, urban tissues

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Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier

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Human motion recognition has been extensively increased in recent years due to its importance in a wide range of applications, such as human-computer interaction, intelligent surveillance, augmented reality, content-based video compression and retrieval, etc. However, it is still regarded as a challenging task especially in realistic scenarios. It can be seen as a general machine learning problem which requires an effective human motion representation and an efficient learning method. In this work, we introduce a descriptor based on Laban Movement Analysis technique, a formal and universal language for human movement, to capture both quantitative and qualitative aspects of movement. We use Discrete Hidden Markov Model (DHMM) for training and classification motions. We improve the classification algorithm by proposing two DHMMs for each motion class to process the motion sequence in two different directions, forward and backward. Such modification allows avoiding the misclassification that can happen when recognizing similar motions. Two experiments are conducted. In the first one, we evaluate our method on a public dataset, the Microsoft Research Cambridge-12 Kinect gesture data set (MSRC-12) which is a widely used dataset for evaluating action/gesture recognition methods. In the second experiment, we build a dataset composed of 10 gestures(Introduce yourself, waving, Dance, move, turn left, turn right, stop, sit down, increase velocity, decrease velocity) performed by 20 persons. The evaluation of the system includes testing the efficiency of our descriptor vector based on LMA with basic DHMM method and comparing the recognition results of the modified DHMM with the original one. Experiment results demonstrate that our method outperforms most of existing methods that used the MSRC-12 dataset, and a near perfect classification rate in our dataset.

Keywords: human motion recognition, motion representation, Laban Movement Analysis, Discrete Hidden Markov Model

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Authors: Mehrzad Soltani, Pegah Rezaei

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In response to the escalating challenges posed by resource scarcity, urban congestion, and the dearth of green spaces, contemporary urban areas have undergone a remarkable transformation into smart cities. This evolution necessitates a strategic and forward-thinking approach to urban development, with the primary objective of diminishing and eventually eradicating dependence on non-renewable energy sources. This steadfast commitment to sustainable development is geared toward the continual enhancement of our global urban milieu, ensuring a healthier and more prosperous environment for forthcoming generations. This transformative vision has been meticulously shaped by an extensive research framework, incorporating in-depth field studies and investigations conducted at both neighborhood and city levels. Our holistic strategy extends its purview to encompass major cities and states, advocating for the realization of exceptional development firmly rooted in the principles of sustainable intelligence. At its core, this approach places a paramount emphasis on stringent pollution control measures, concurrently safeguarding ecological equilibrium and regional cohesion. Central to the realization of this vision is the widespread adoption of environmentally friendly materials and components, championing the cultivation of plant life and harmonious green spaces, and the seamless integration of intelligent lighting and irrigation systems. These systems, including solar panels and solar energy utilization, are deployed wherever feasible, effectively meeting the essential lighting and irrigation needs of these dynamic urban ecosystems. Overall, the transformation of urban areas into smart cities necessitates a holistic and innovative approach to urban development. By actively embracing sustainable intelligence and adhering to strict environmental standards, these cities pave the way for a brighter and more sustainable future, one that is marked by resilient, thriving, and eco-conscious urban communities.

Keywords: smart city, green urban, sustainability, urban management

Procedia PDF Downloads 72

Authors: Merve Begum Terzi, Orhan Arikan, Adnan Abaci, Mustafa Candemir

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Acute myocardial infarction is a major cause of death in the world. Therefore, its fast and reliable diagnosis is a major clinical need. ECG is the most important diagnostic methodology which is used to make decisions about the management of the cardiovascular diseases. In patients with acute myocardial ischemia, temporary chest pains together with changes in ST segment and T wave of ECG occur shortly before the start of myocardial infarction. In this study, a technique which detects changes in ST/T sections of ECG is developed for the early diagnosis of acute myocardial ischemia. For this purpose, a database of real ECG recordings that contains a set of records from 75 patients presenting symptoms of chest pain who underwent elective percutaneous coronary intervention (PCI) is constituted. 12-lead ECG’s of the patients were recorded before and during the PCI procedure. Two ECG epochs, which are the pre-inflation ECG which is acquired before any catheter insertion and the occlusion ECG which is acquired during balloon inflation, are analyzed for each patient. By using pre-inflation and occlusion recordings, ECG features that are critical in the detection of acute myocardial ischemia are identified and the most discriminative features for the detection of acute myocardial ischemia are extracted. A classification technique based on support vector machine (SVM) approach operating with linear and radial basis function (RBF) kernels to detect ischemic events by using ST-T derived joint features from non-ischemic and ischemic states of the patients is developed. The dataset is randomly divided into training and testing sets and the training set is used to optimize SVM hyperparameters by using grid-search method and 10fold cross-validation. SVMs are designed specifically for each patient by tuning the kernel parameters in order to obtain the optimal classification performance results. As a result of implementing the developed classification technique to real ECG recordings, it is shown that the proposed technique provides highly reliable detections of the anomalies in ECG signals. Furthermore, to develop a detection technique that can be used in the absence of ECG recording obtained during healthy stage, the detection of acute myocardial ischemia based on ECG recordings of the patients obtained during ischemia is also investigated. For this purpose, a Gaussian mixture model (GMM) is used to represent the joint pdf of the most discriminating ECG features of myocardial ischemia. Then, a Neyman-Pearson type of approach is developed to provide detection of outliers that would correspond to acute myocardial ischemia. Neyman – Pearson decision strategy is used by computing the average log likelihood values of ECG segments and comparing them with a range of different threshold values. For different discrimination threshold values and number of ECG segments, probability of detection and probability of false alarm values are computed, and the corresponding ROC curves are obtained. The results indicate that increasing number of ECG segments provide higher performance for GMM based classification. Moreover, the comparison between the performances of SVM and GMM based classification showed that SVM provides higher classification performance results over ECG recordings of considerable number of patients.

Keywords: ECG classification, Gaussian mixture model, Neyman–Pearson approach, support vector machine

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Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

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Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

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Authors: Evelyn Osehontue Uroro, Richard Bright, Jing Yang Quek, Krasimir Vasilev

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Bacterial infections have been a challenge both in the public and private sectors. The colonization of bacteria often occurs in medical devices such as catheters, heart valves, respirators, and orthopaedic implants. When biomedical devices are inserted into patients, the deposition of macromolecules such as fibrinogen and immunoglobin on their surfaces makes it easier for them to be prone to bacteria colonization leading to the formation of biofilms. The formation of biofilms on medical devices has led to a series of device-related infections which are usually difficult to eradicate and sometimes cause the death of patients. These infections require surgical replacements along with prolonged antibiotic therapy, which would incur additional health costs. It is, therefore, necessary to prevent device-related infections by inhibiting the formation of biofilms using intelligent technology. Antibiotic resistance of bacteria is also a major threat due to overuse. Different antimicrobial agents have been applied to microbial infections. They include conventional antibiotics like rifampicin. The use of conventional antibiotics like rifampicin has raised concerns as some have been found to have hepatic and nephrotoxic effects due to overuse. Hence, there is also a need for proper delivery of these antibiotics. Different techniques have been developed to encapsulate and slowly release antimicrobial agents, thus reducing host cytotoxicity. Examples of delivery systems are solid lipid nanoparticles, hydrogels, micelles, and polymeric nanoparticles. The different ways by which drugs are released from polymeric nanoparticles include diffusion-based release, elution-based release, and chemical/stimuli-responsive release. Polymeric nanoparticles have gained a lot of research interest as they are basically made from biodegradable polymers. An example of such a biodegradable polymer is polycaprolactone (PCL). PCL degrades slowly by hydrolysis but is often sensitive and responsive to stimuli like enzymes to release encapsulants for antimicrobial therapy. This study presents the synthesis of PCL nanoparticles loaded with rifampicin and the on-demand release of rifampicin for treating staphylococcus aureus infections.

Keywords: enzyme, Staphylococcus aureus, PCL, rifampicin

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Authors: Ghassan Kbar, Shady Aly, Ibrahim Alsharawy, Akshay Bhatia, Nur Alhasan, Ronaldo Enriquez

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The Smart Help for persons with disability (PWD) is a part of the project SMARTDISABLE which aims to develop relevant solution for PWD that target to provide an adequate workplace environment for them. It would support PWD needs smartly through smart help to allow them access to relevant information and communicate with other effectively and flexibly, and smart editor that assist them in their daily work. It will assist PWD in knowledge processing and creation as well as being able to be productive at the work place. The technical work of the project involves design of a technological scenario for the Ambient Intelligence (AmI) - based assistive technologies at the workplace consisting of an integrated universal smart solution that suits many different impairment conditions and will be designed to empower the Physically disabled persons (PDP) with the capability to access and effectively utilize the ICTs in order to execute knowledge rich working tasks with minimum efforts and with sufficient comfort level. The proposed technology solution for PWD will support voice recognition along with normal keyboard and mouse to control the smart help and smart editor with dynamic auto display interface that satisfies the requirements for different PWD group. In addition, a smart help will provide intelligent intervention based on the behavior of PWD to guide them and warn them about possible misbehavior. PWD can communicate with others using Voice over IP controlled by voice recognition. Moreover, Auto Emergency Help Response would be supported to assist PWD in case of emergency. This proposed technology solution intended to make PWD very effective at the work environment and flexible using voice to conduct their tasks at the work environment. The proposed solution aims to provide favorable outcomes that assist PWD at the work place, with the opportunity to participate in PWD assistive technology innovation market which is still small and rapidly growing as well as upgrading their quality of life to become similar to the normal people at the workplace. Finally, the proposed smart help solution is applicable in all workplace setting, including offices, manufacturing, hospital, etc.

Keywords: ambient intelligence, ICT, persons with disability PWD, smart application, SHW

Procedia PDF Downloads 423