Search results for: guided digital learning

Authors: Muhammad Ammar, Talha Ali, Abdul Basit, Bakhtawar Rajput, Zobia Sohail

Abstract:

Music note detection is an area of study for the past few years and has its own influence in music file generation from sheet music. We proposed a method to detect music notes on sheet music using basic thresholding and blob detection. Subsequently, we created a notes dictionary using a semi-supervised learning approach. After notes detection, for each test image, the new symbols are added to the dictionary. This makes the notes detection semi-automatic. The experiments are done on images from a dataset and also on the captured images. The developed approach showed almost 100% accuracy on the dataset images, whereas varying results have been seen on captured images.

Keywords: music note, sheet music, optical music recognition, blob detection, thresholding, dictionary generation

Procedia PDF Downloads 176

Authors: Rubaiat S. Ahmed, Jemer Garrido, Sergey M. Motov

Abstract:

Clinical informatics (CI) enables the transformation of data into a systematic organization that improves the quality of care and the generation of positive health outcomes.Innovative technology through informatics that compiles accurate data on analgesic utilization in the emergency department can enhance pain management in this important clinical setting. We aim to establish a simplified reporting system through CI to examine and assess the analgesic prescribing practices in the EDthrough executing a U.S. federal grant project on opioid reduction initiatives. Queried data points of interest from a level-one trauma ED’s electronic medical records were used to create data sets and develop informational/visual reporting dashboards (on Microsoft Excel and Google Sheets) concerning analgesic usage across several pre-defined parameters and performance metrics using CI. The data was then qualitatively analyzed to evaluate ED analgesic prescribing trends by departmental clinicians and leadership. During a 12-month reporting period (Dec. 1, 2020 – Nov. 30, 2021) for the ongoing project, about 41% of all ED patient visits (N = 91,747) were for pain conditions, of which 81.6% received analgesics in the ED and at discharge (D/C). Of those treated with analgesics, 24.3% received opioids compared to 75.7% receiving opioid alternatives in the ED and at D/C, including non-pharmacological modalities. Demographics showed among patients receiving analgesics, 56.7% were aged between 18-64, 51.8% were male, 51.7% were white, and 66.2% had government funded health insurance. Ninety-one percent of all opioids prescribed were in the ED, with intravenous (IV) morphine, IV fentanyl, and morphine sulfate immediate release (MSIR) tablets accounting for 88.0% of ED dispensed opioids. With 9.3% of all opioids prescribed at D/C, MSIR was dispensed 72.1% of the time. Hydrocodone, oxycodone, and tramadol usage to only 10-15% of the time, and hydromorphone at 0%. Of opioid alternatives, non-steroidal anti-inflammatory drugs were utilized 60.3% of the time, 23.5% with local anesthetics and ultrasound-guided nerve blocks, and 7.9% with acetaminophen as the primary non-opioid drug categories prescribed by ED providers. Non-pharmacological analgesia included virtual reality and other modalities. An average of 18.5 ED opioid orders and 1.9 opioid D/C prescriptions per 102.4 daily ED patient visits was observed for the period. Compared to other specialties within our institution, 2.0% of opioid D/C prescriptions are given by ED providers, compared to the national average of 4.8%. Opioid alternatives accounted for 69.7% and 30.3% usage, versus 90.7% and 9.3% for opioids in the ED and D/C, respectively.There is a pressing need for concise, relevant, and reliable clinical data on analgesic utilization for ED providers and leadership to evaluate prescribing practices and make data-driven decisions. Basic computer software can be used to create effective visual reporting dashboards with indicators that convey relevant and timely information in an easy-to-digest manner. We accurately examined our ED's analgesic prescribing practices using CI through dashboard reporting. Such reporting tools can quickly identify key performance indicators and prioritize data to enhance pain management and promote safe prescribing practices in the emergency setting.

Keywords: clinical informatics, dashboards, emergency department, health informatics, healthcare informatics, medical informatics, opioids, pain management, technology

Procedia PDF Downloads 139

Authors: Ying Han, Yuanxiang Chen, Yongtao Huang, Jia Fu, Kaile Li, Shangjing Lin, Jianguo Yu

Abstract:

In long-haul and high-speed optical transmission system, the orthogonal frequency division multiplexing (OFDM) signal suffers various linear and non-linear impairments. In recent years, researchers have proposed compensation schemes for specific impairment, and the effects are remarkable. However, different impairment compensation algorithms have caused an increase in transmission delay. With the widespread application of deep neural networks (DNN) in communication, multi-impairment compensation based on DNN will be a promising scheme. In this paper, we propose and apply DNN to compensate multi-impairment of 16-QAM coherent optical OFDM signal, thereby improving the performance of the transmission system. The trained DNN models are applied in the offline digital signal processing (DSP) module of the transmission system. The models can optimize the constellation mapping signals at the transmitter and compensate multi-impairment of the OFDM decoded signal at the receiver. Furthermore, the models reduce the peak to average power ratio (PAPR) of the transmitted OFDM signal and the bit error rate (BER) of the received signal. We verify the effectiveness of the proposed scheme for 16-QAM Coherent Optical OFDM signal and demonstrate and analyze transmission performance in different transmission scenarios. The experimental results show that the PAPR and BER of the transmission system are significantly reduced after using the trained DNN. It shows that the DNN with specific loss function and network structure can optimize the transmitted signal and learn the channel feature and compensate for multi-impairment in fiber transmission effectively.

Keywords: coherent optical OFDM, deep neural network, multi-impairment compensation, optical transmission

Procedia PDF Downloads 140

Authors: N. Attari, S. Amziane, M. Chemrouk

Abstract:

A large number of old buildings have been identified as having potentially critical detailing to resist earthquakes. The main reinforcement of lap-spliced columns just above the joint region, discontinuous bottom beam reinforcement, and little or no joint transverse reinforcement are the most critical details of interior beam column joints in such buildings. This structural type constitutes a large share of the building stock, both in developed and developing countries, and hence it represents a substantial exposure. Direct observation of damaged structures, following the Algiers 2003 earthquake, has shown that damage occurs usually at the beam-column joints, with failure in bending or shear, depending on geometry and reinforcement distribution and type. While substantial literature exists for the design of concrete frame joints to withstand this type of failure, after the earthquake many structures were classified as slightly damaged and, being uneconomic to replace them, at least in the short term, suitable means of repairs of the beam column joint area are being studied. Furthermore; there exists a large number of buildings that need retrofitting of the joints before the next earthquake. The paper reports the results of the experimental programme, constituted of three beam-column reinforced concrete joints at a scale of one to three (1/3) tested under the effect of a pre-stressing axial load acting over the column. The beams were subjected at their ends to an alternate cyclic loading under displacement control to simulate a seismic action. Strain and cracking fields were monitored with the help a digital recording camera. Following the analysis of the results, a comparison can be made between the performances in terms of ductility, strength and mode of failure of the different strengthening solution considered.

Keywords: fibre reinforced polymers, joints, reinforced concrete, beam columns

Procedia PDF Downloads 413

Authors: Yahaya Abdullahi, Ben Coetzee, Linda Van Den Berg

Abstract:

The study aimed at establishing the global positioning system (GPS) determined singles match characteristics that act as predictors of successful and less-successful male singles badminton players’ group classification. Twenty-two (22) male single players (aged: 23.39 ± 3.92 years; body stature: 177.11 ± 3.06cm; body mass: 83.46 ± 14.59kg) who represented 10 African countries participated in the study. Players were categorised as successful and less-successful players according to the results of five championships’ of the 2014/2015 season. GPS units (MinimaxX V4.0), Polar Heart Rate Transmitter Belts and digital video cameras were used to collect match data. GPS-related variables were corrected for match duration and independent t-tests, a cluster analysis and a binary forward stepwise logistic regression were calculated. A Receiver Operating Characteristic Curve (ROC) was used to determine the validity of the group classification model. High-intensity accelerations per second were identified as the only GPS-determined variable that showed a significant difference between groups. Furthermore, only high-intensity accelerations per second (p=0.03) and low-intensity efforts per second (p=0.04) were identified as significant predictors of group classification with 76.88% of players that could be classified back into their original groups by making use of the GPS-based logistic regression formula. The ROC showed a value of 0.87. The identification of the last-mentioned GPS-related variables for the attainment of badminton performances, emphasizes the importance of using badminton drills and conditioning techniques to not only improve players’ physical fitness levels but also their abilities to accelerate at high intensities.

Keywords: badminton, global positioning system, match analysis, inertial movement analysis, intensity, effort

Procedia PDF Downloads 187

Authors: Mohammad Reza Ostad Ali Naghi Kashani, Esfanji Elia

Abstract:

Nowadays, the amounts of companies which tend to have an Enterprise Resource Planning (ERP) application are increasing particularly in developing countries like Iran. ERP projects are expensive, time consuming, and complex, in addition the failure rate is high among these projects. It is important to know whether these projects could meet their goals or not. Furthermore, the area which should be improved should be identified. In this paper we made a framework to evaluate ERP projects success implementation. First, based on literature review we made a framework based on BSC model, financial, customer, processes, learning and knowledge, because of the importance of change management it was added to model. Then an organization was divided in three layers. We choose corporate, managerial, and operational levels. Then to find criteria to assess each aspect, we use Delphi method in two rounds. And for the second round we made a questionnaire and did some statistical tasks on them. Based on the statistical results some of them are accepted and others are rejected.

Keywords: ERP, BSC, ERP project evaluation, IT projects

Procedia PDF Downloads 321

Authors: Cynthia K. Burzell

Abstract:

Aequor's Founder, a Marine and Medical Microbiologist, discovered novel, non-toxic chemicals in the ocean that uniquely remove biofilm in minutes and prevent its formation for days. These chemicals and over 70 synthesized analogs that Aequor developed can replace thousands of toxic biocides used in consumer and industrial products and, as new drug candidates, kill biofilm-forming bacteria and fungi Superbugs -the antimicrobial-resistant (AMR) pathogens for which there is no cure. Cynthia Burzell, PhD., is a Marine and Medical Microbiologist studying natural mechanisms that inhibit biofilm formation on surfaces in contact with water. In 2002, she discovered a new genus and several new species of marine microbes that produce small molecules that remove biofilm in minutes and prevent its formation for days. The molecules include new antimicrobials that can replace thousands of toxic biocides used in consumer and industrial products and can be developed into new drug candidates to kill the biofilm-forming bacteria and fungi -- including the antimicrobial-resistant (AMR) Superbugs for which there is no cure. Today, Aequor has over 70 chemicals that are divided into categories: (1) Novel natural chemicals. Lonza validated that the primary natural chemical removed biofilm in minutes and stated: "Nothing else known can do this at non-toxic doses." (2) Specialty chemicals. 25 of these structural analogs are already approved under the U.S. Environmental Protection Agency (EPA)'s Toxic Substances Control Act, certified as "green" and available for immediate sale. These have been validated for the following agro-industrial verticals: (a) Surface cleaners: The U.S. Department of Agriculture validated that low concentrations of Aequor's formulations provide deep cleaning of inert, nano and organic surfaces and materials; (b) Water treatments: NASA validated that one dose of Aequor's treatment in the International Space Station's water reuse/recycling system lasted 15 months without replenishment. DOE validated that our treatments lower energy consumption by over 10% in buildings and industrial processes. Future validations include pilot projects with the EPA to test efficacy in hospital plumbing systems. (c) Algae cultivation and yeast fermentation: The U.S. Department of Energy (DOE) validated that Aequor's treatment boosted biomass of renewable feedstocks by 40% in half the time -- increasing the profitability of biofuels and biobased co-products. DOE also validated increased yields and crop protection of algae under cultivation in open ponds. A private oil and gas company validated decontamination of oilfield water. (3) New structural analogs. These kill Gram-negative and Gram-positive bacteria and fungi alone, in combinations with each other, and in combination with low doses of existing, ineffective antibiotics (including Penicillin), "potentiating" them to kill AMR pathogens at doses too low to trigger resistance. Both the U.S. National Institutes for Health (NIH) and Department of Defense (DOD) has executed contracts with Aequor to provide the pre-clinical trials needed for these new drug candidates to enter the regulatory approval pipelines. Aequor seeks partners/licensees to commercialize its specialty chemicals and support to evaluate the optimal methods to scale-up of several new structural analogs via activity-guided fractionation and/or biosynthesis in order to initiate the NIH and DOD pre-clinical trials.

Keywords: biofilm, potentiation, prevention, removal

Procedia PDF Downloads 96

Authors: Aly Elshafei, Daniela Romano

Abstract:

With the development of autonomous vehicles, a human-machine interaction (HMI) system is needed for a safe transition of control when a takeover request (TOR) is required. An important part of the HMI system is the ability to monitor the level of situational awareness (SA) of any driver in real-time, in different scenarios, and without any pre-calibration. Presenting state-of-the-art machine learning models used to measure SA is the purpose of this systematic review. Investigating the limitations of each type of sensor, the gaps, and the most suited sensor and computational model that can be used in driving applications. To the author’s best knowledge this is the first literature review identifying online and offline classification methods used to measure SA, explaining which measurements are subject or session-specific, and how many classifications can be done with each classification model. This information can be very useful for researchers measuring SA to identify the most suited model to measure SA for different applications.

Keywords: situational awareness, autonomous driving, gaze metrics, EEG, ECG

Procedia PDF Downloads 114

Authors: James Brown, Stella Kortekaas, Ian Finnie, George Zhang, Christine Devine, Neil Healy

Abstract:

The use of GIS as an analysis tool for engineering decision making is now best practice in the offshore industry. GIS enables multidisciplinary data integration, analysis and visualisation which allows the presentation of large and intricate datasets in a simple map-interface accessible to all project stakeholders. Presenting integrated geoscience and geotechnical data in GIS enables decision makers to be well-informed. This paper is a successful case study of how GIS spatial analysis techniques were applied to help select the most favourable pipeline route. Routing a pipeline through any natural environment has numerous obstacles, whether they be topographical, geological, engineering or financial. Where the pipeline is subjected to external hydrostatic water pressure and is carrying pressurised hydrocarbons, the requirement to safely route the pipeline through hazardous terrain becomes absolutely paramount. This study illustrates how the application of modern, GIS-based pipeline routing techniques enabled the identification of a single most-favourable pipeline route crossing of a challenging seabed terrain. Conventional approaches to pipeline route determination focus on manual avoidance of primary constraints whilst endeavouring to minimise route length. Such an approach is qualitative, subjective and is liable to bias towards the discipline and expertise that is involved in the routing process. For very short routes traversing benign seabed topography in shallow water this approach may be sufficient, but for deepwater geohazardous sites, the need for an automated, multi-criteria, and quantitative approach is essential. This study combined multiple routing constraints using modern least-cost-routing algorithms deployed in GIS, hitherto unachievable with conventional approaches. The least-cost-routing procedure begins with the assignment of geocost across the study area. Geocost is defined as a numerical penalty score representing hazard posed by each routing constraint (e.g. slope angle, rugosity, vulnerability to debris flows) to the pipeline. All geocosted routing constraints are combined to generate a composite geocost map that is used to compute the least geocost route between two defined terminals. The analyses were applied to select the most favourable pipeline route for a potential gas development in deep water. The study area is geologically complex with a series of incised, potentially active, canyons carved into a steep escarpment, with evidence of extensive debris flows. A similar debris flow in the future could cause significant damage to a poorly-placed pipeline. Protruding inter-canyon spurs offer lower-gradient options for ascending an escarpment but the vulnerability of periodic failure of these spurs is not well understood. Close collaboration between geoscientists, pipeline engineers, geotechnical engineers and of course the gas export pipeline operator guided the analyses and assignment of geocosts. Shorter route length, less severe slope angles, and geohazard avoidance were the primary drivers in identifying the most favourable route.

Keywords: geocost, geohazard, pipeline route determination, pipeline route optimisation, spatial analysis

Procedia PDF Downloads 401

Authors: Javkhlan Nyamjav, Simona Fischer, Lauren Garner, Veronica McCracken

Abstract:

Current whole building life cycle assessments (LCA) primarily focus on structural and major architectural elements to measure building embodied carbon. Most of the interior finishes and fixtures are available on digital tools (such as Tally); however, furniture is still left unaccounted for. Due to its repeated refreshments and its complexity, furniture embodied carbon can accumulate over time, becoming comparable to structure and envelope numbers. This paper presents a method to calculate the Global Warming Potential (GWP) of furniture elements in commercial buildings. The calculator uses the quantity takeoff method with GWP averages gathered from environmental product declarations (EPD). The data was collected from EPD databases and furniture manufacturers from North America to Europe. A total of 48 GWP numbers were collected, with 16 GWP coming from alternative EPD. The finalized calculator shows the average GWP of typical commercial furniture and helps the decision-making process to reduce embodied carbon. The calculator was tested on MSR Design projects and showed furniture can account for more than half of the interior embodied carbon. The calculator highlights the importance of adding furniture to the overall conversation. However, the data collection process showed a) acquiring furniture EPD is not straightforward as other building materials; b) there are very limited furniture EPD, which can be explained from many perspectives, including the EPD price; c) the EPD themselves vary in terms of units, LCA scopes, and timeframes, which makes it hard to compare the products. Even though there are current limitations, the emerging focus on interior embodied carbon will create more demand for furniture EPD. It will allow manufacturers to represent all their efforts on reducing embodied carbon. In addition, the study concludes with recommendations on how designers can reduce furniture-embodied carbon through reuse and closed-loop systems.

Keywords: furniture, embodied carbon, calculator, tenant improvement, interior design

Procedia PDF Downloads 211

Authors: D. Hassell, D. De Focatiis

Abstract:

This paper presents work on the application of wiki based coursework for a fourth-year engineering module delivered as part of both a MEng and MSc programme in Chemical Engineering. The module was taught with an equivalent structure simultaneously on two separate campuses, one in the United Kingdom (UK) and one in Malaysia, and the subsequent results were compared. Student feedback was sought via questionnaires, with 45 respondents from the UK and 49 from Malaysia. Results include discussion on; perceived difficulty; student enjoyment and experiences; differences between MEng and MSc students; differences between cohorts on different campuses. The response of students to the use of wiki-based coursework was found to vary based on their experiences and background, with UK students being generally more positive on its application than those in Malaysia.

Keywords: engineering education, student differences, student learning, web based coursework

Procedia PDF Downloads 291

Authors: Zheng Baichuan, Li Shenghui, Li Bingqian, Zhang Ning, Chen Kai

Abstract:

Entity alignment (EA) tasks in knowledge graphs often play a pivotal role in the integration of knowledge graphs, where structural differences often exist between the source and target graphs, such as the presence or absence of attribute information and the types of attribute information (text, timestamps, images, etc.). However, most current research efforts are focused on improving alignment accuracy, often along with an increased reliance on specific structures -a dependency that inevitably diminishes their practical value and causes difficulties when facing knowledge graph alignment tasks with varying structures. Therefore, we propose a universal knowledge graph alignment approach that only utilizes the common basic structures shared by knowledge graphs. We have demonstrated through experiments that our method achieves state-of-the-art performance in fair comparisons.

Keywords: knowledge graph, entity alignment, transformer, deep learning

Procedia PDF Downloads 37

Authors: Purna Prasad Acharya, Madhav Karki, Sunta B. Tamang, Uttam Basnet, Chhatra Katwal

Abstract:

The paper outlines the idea behind achieving sustainable lifestyles based on the spiritual values and teachings of Lord Buddha. This objective is to be achieved by spreading the tenets and teachings of Buddhism throughout the Asia Pacific region and the world from the sacred birth place of Buddha - Lumbini, Nepal. There is an urgent need to advance the relevance of Buddhist philosophy in tackling the triple planetary crisis of climate change, nature’s decline, and pollution. Today, the world is facing an existential crisis due to the above crises, exasperated by hunger, poverty and armed conflict. To address multi-dimensional impacts, the global communities have to adopt simple life styles that respect nature and universal human values. These were the basic teachings of Gautam Buddha. Lumbini, Nepal has the moral obligation to widely disseminate Buddha’s teaching to the world and receive constant feedback and learning to develop human and ecosystem resilience by molding the lifestyles of current and future generations through adaptive learning and simplicity across the geography and nationality based on spirituality and environmental stewardship. By promoting Buddhism, Nepal has developed a pro-nature tourism industry that focuses on both its spiritual and bio-cultural heritage. Nepal is a country rich in ancient wisdom, where sages have sought knowledge, practiced meditation, and followed spiritual paths for thousands of years. It can spread the teachings of Buddha in a way people can search for and adopt ways to live, creating harmony with nature. Using tools of natural sciences and social sciences, the team will package knowledge and share the idea of community well-being within the framework of environmental sustainability, social harmony and universal respect for nature and people in a more holistic manner. This notion takes into account key elements of sustainable development such as food-energy-water-biodiversity interconnections, environmental conservation, ecological integrity, ecosystem health, community resiliency, adaptation capacity, and indigenous culture, knowledge and values. This inclusive concept has garnered a strong network of supporters locally, regionally, and internationally. The key objectives behind this concept are: a) to leverage expertise and passion of a network of global collaborators to advance research, education, and policy outreach in the areas of human sustainability based on lifestyle change using the power of spirituality and Buddha’s teaching, resilient lifestyles, and adaptive living; b) help develop creative short courses for multi-disciplinary teaching in educational institutions worldwide in collaboration with Lumbini Buddha University and other relevant partners in Nepal; c) help build local and regional intellectual and cultural teaching and learning capacity by improving professional collaborations to promote nature based and Buddhist value-based lifestyles by connecting Lumbini to Nepal’s rich nature; d) promote research avenues to provide policy relevant knowledge that is creative, innovative, as well as practical and locally viable; and e) connect local research and outreach work with academic and cultural partners in South Korea so as to open up Lumbini based Buddhist heritage and Nepal’s Karnali River basin’s unique natural landscape to Korean scholars and students to promote sustainable lifestyles leading to human living in harmony with nature.

Keywords: triple planetary crisis, spirituality, sustainable lifestyles, living in harmony with nature, resilience

Procedia PDF Downloads 28

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 122

Authors: Rahma Abed, Sahbi Bahroun, Ezzeddine Zagrouba

Abstract:

Due to the huge amount of data in videos, extracting the relevant frames became a necessity and an essential step prior to performing face recognition. In this context, we propose a method for extracting keyframes from videos based on face quality and deep learning for a face recognition task. This method has two steps. We start by generating face quality scores for each face image based on the use of three face feature extractors, including Gabor, LBP, and HOG. The second step consists in training a Deep Convolutional Neural Network in a supervised manner in order to select the frames that have the best face quality. The obtained results show the effectiveness of the proposed method compared to the methods of the state of the art.

Keywords: keyframe extraction, face quality assessment, face in video recognition, convolution neural network

Procedia PDF Downloads 226

Authors: Stephen Kirkup

Abstract:

This paper discusses the implementation of the boundary element method (BEM) on an Excel spreadsheet and how it can be used in teaching vector calculus and simulation. There are two separate spreadheets, within which Laplace equation is solved by the BEM in two dimensions (LIBEM2) and axisymmetric three dimensions (LBEMA). The main algorithms are implemented in the associated programming language within Excel, Visual Basic for Applications (VBA). The BEM only requires a boundary mesh and hence it is a relatively accessible method. The BEM in the open spreadsheet environment is demonstrated as being useful as an aid to teaching and learning. The application of the BEM implemented on a spreadsheet for educational purposes in introductory vector calculus and simulation is explored. The development of assignment work is discussed, and sample results from student work are given. The spreadsheets were found to be useful tools in developing the students’ understanding of vector calculus and in simulating heat conduction.

Keywords: boundary element method, Laplace’s equation, vector calculus, simulation, education

Procedia PDF Downloads 158

Authors: Carol C. Opara, Olukemi E. Adetuyi-Olu-Francis

Abstract:

The teaching of French language, like every other language, has its numerous challenges. A multilingual community, however, is a linguistic environment housing diverse languages, each with its peculiarity, both pros, and cones. A foreign language will have to strive hard for survival in an environment where various indigenous languages, as well as an established official language, exist. This study examined the challenges and prospects of the teaching of French as a foreign language in a multilingual community. A 22-item questionnaire was used to elicit information from 40 Nigerian Secondary school teachers of French. One of the findings of this study showed that the teachers of the French language are not motivated. Also, the linguistic environment is not favourable for the teaching and learning of French language in Nigeria. One of the recommendations was that training and re-training of teachers of French should be of utmost importance to the Nigerian Federal Ministry of Education.

Keywords: challenges, french as foreign language, multilingual community, teaching

Procedia PDF Downloads 208

Authors: Diane Ghanem, Oscar Covarrubias, Michael Raad, Dawn LaPorte, Babar Shafiq

Abstract:

Introduction: Standardized exams have long been considered a cornerstone in measuring cognitive competency and academic achievement. Their fixed nature and predetermined scoring methods offer a consistent yardstick for gauging intellectual acumen across diverse demographics. Consequently, the performance of artificial intelligence (AI) in this context presents a rich, yet unexplored terrain for quantifying AI's understanding of complex cognitive tasks and simulating human-like problem-solving skills. Publicly available AI language models such as ChatGPT have demonstrated utility in text generation and even problem-solving when provided with clear instructions. Amidst this transformative shift, the aim of this study is to assess ChatGPT’s performance on the orthopaedic surgery in-training examination (OITE). Methods: All 213 OITE 2021 web-based questions were retrieved from the AAOS-ResStudy website. Two independent reviewers copied and pasted the questions and response options into ChatGPT Plus (version 4.0) and recorded the generated answers. All media-containing questions were flagged and carefully examined. Twelve OITE media-containing questions that relied purely on images (clinical pictures, radiographs, MRIs, CT scans) and could not be rationalized from the clinical presentation were excluded. Cohen’s Kappa coefficient was used to examine the agreement of ChatGPT-generated responses between reviewers. Descriptive statistics were used to summarize the performance (% correct) of ChatGPT Plus. The 2021 norm table was used to compare ChatGPT Plus’ performance on the OITE to national orthopaedic surgery residents in that same year. Results: A total of 201 were evaluated by ChatGPT Plus. Excellent agreement was observed between raters for the 201 ChatGPT-generated responses, with a Cohen’s Kappa coefficient of 0.947. 45.8% (92/201) were media-containing questions. ChatGPT had an average overall score of 61.2% (123/201). Its score was 64.2% (70/109) on non-media questions. When compared to the performance of all national orthopaedic surgery residents in 2021, ChatGPT Plus performed at the level of an average PGY3. Discussion: ChatGPT Plus is able to pass the OITE with a satisfactory overall score of 61.2%, ranking at the level of third-year orthopaedic surgery residents. More importantly, it provided logical reasoning and justifications that may help residents grasp evidence-based information and improve their understanding of OITE cases and general orthopaedic principles. With further improvements, AI language models, such as ChatGPT, may become valuable interactive learning tools in resident education, although further studies are still needed to examine their efficacy and impact on long-term learning and OITE/ABOS performance.

Keywords: artificial intelligence, ChatGPT, orthopaedic in-training examination, OITE, orthopedic surgery, standardized testing

Procedia PDF Downloads 81

Authors: A. Kolendo

Abstract:

Pacific Islands are one of the most vulnerable areas to climate change. Sea level rise and accelerating storm surge continuously threaten the communities' habitats on low-lying atolls. With scientific predictions of encroaching tides on their land, the Islanders have been informed about the need for future relocation planning. However, some communities oppose such retreat strategies through the reasoning that comprehends current climatic changes through the lenses of the biblical ark of Noah. This parable states God's promise never to flood the Earth again and never deprive people of their land and habitats. Several interpretations of this parable emerged in Oceania, prompting either climate action or denial. Resistance to relocation planning expressed through Christian thoughts led religion to be perceived as a barrier to dialogue between the Islanders and scientists. Since climate change concerns natural processes, the attitudes towards environmental stewardship prompt the communities' responses to it; some Christian teachings indicate humanity's responsibility over the environment, whereas others ascertain the people's dominion, which prompts resistance and sometimes denial. With church denominations and their various environmental standpoints, competing responses to climate change emerged in Oceania. Before miss-ionization, traditional knowledge had guided the environmental sphere, influencing current Christian teachings. Each atoll characterizes a distinctive manner of traditional knowledge; however, the unique relationship with nature unites all islands. The interconnectedness between the land, sea and people indicates the integrity between the communities and their environments. Such a factor influences the comprehension of Noah's story in the context of climate change that threatens their habitats. Pacific Islanders experience climate change through the slow disappearance of their homelands. However, the Western world perceives it as a global issue that will affect the population in the long-term perspective. Therefore, the Islanders seek to comprehend this global phenomenon in a local context that reads climate change as the Great Deluge. Accordingly, the safety measures that this parable promotes compensate for the danger of climate change. The rainbow covenant gives hope in God's promise never to flood the Earth again. At the same time, Noah's survival relates to the Islanders' current situation. Since these communities have the lowest carbon emissions rate, their contribution to anthropogenic climate change is scarce. Therefore, the lack of environmental sin would contextualize them as contemporary Noah with the ultimate survival of sea level rise. This study aims to defy religion constituting a barrier through secondary data analysis from a risk compensation perspective. Instead, religion is portrayed as a source of knowledge that enables comprehension of the communities' situation. By demonstrating that the Pacific Islanders utilize Noah's story as a vessel for coping with the danger of climate change, the study argues that religion provides safety measures that compensate for the future projections of land's disappearance. The purpose is to build a bridge between religious communities and scientific bodies and ultimately bring an understanding of two diverse perspectives. By addressing the practical challenges of interdisciplinary research with faith-based systems, this study uplifts the voices of communities and portrays their experiences expressed through Christian thoughts.

Keywords: Christianity, climate change, existential threat, Pacific Islands, story of Noah

Procedia PDF Downloads 91

Authors: Maryam Kalkatechi

Abstract:

Architecture students spent most of their qualitative time in studios during their years of study. The studio table’s importance as furniture in the studio is that it elevates the quality of the projects and positively influences the student’s productivity. This paper first describes the aspects considered in designing comprehensive studio table and later details on each aspect. Comprehensive studio tables are meant to transform the studio space to an efficient yet immense place of learning, collaboration, and participation. One aspect of these tables is that the surface transforms to a place of accommodation for design conversations, the other aspect of these tables is the efficient interactive platform of the tools. The discussion factors of the comprehensive studio include; the comprehensive studio setting of workspaces, the arrangement of the comprehensive studio tables, the collaboration aspects in the studio, the studio display and lightings shaped by the tables and lighting of the studio.

Keywords: studio tables, student performance, productivity, hologram, 3D printer

Procedia PDF Downloads 184

Authors: Jabulani Sibanda, Clemence Chikiwa

Abstract:

This paper traces diverse language practices representative of equally diverse conceptions of language. To be dynamic with languaging practices, one needs to appreciate nuanced languaging practices, their challenges, prospects, and opportunities. The paper presents what we envision as three major conceptions of language that give impetus to diverse language practices. It examines theoretical models of the bilingual mental lexicon and how they inform language practices. The paper explores classroom languaging practices that have been promulgated and experimented with. The paper advocates the deployment of multisensory semiotic systems to complement linguistic classroom communication and the acknowledgement of learners’ linguistic and semiotic resources as valid in the learning enterprise. It recommends the enactment of specific clauses on language in education policies and curriculum documents that empower classroom interactants to exercise discretion in languaging practices.

Keywords: languaging, monolingual, multilingual, semiotic and linguistic repertoire

Procedia PDF Downloads 66

Authors: Dana Zelicha

Abstract:

The objective of this paper is to present innovative mindfulness tools specifically designed by OWBA—The Well Being Agency for organisations and corporate wellness programmes. The OWBA Mindfulness Tools (OWBA-MT) consist of practical mindfulness exercises to educate and train employees and business leaders to think, feel, and act more mindfully. Among these cutting-edge interventions are Mindful Meetings, Mindful Decision Making and Unitasking activities, intended to cultivate mindful communication and compassion in the workplace and transform organisational culture. In addition to targeting CEO’s and leaders within large corporations, OWBA-MT is also directed at the needs of specific populations such as entrepreneurs’ resilience and women empowerment. The goals of the OWBA-MT are threefold: to inform, inspire and implement. The first goal is to inform participants about the relationship between workplace stress, distractibility and miscommunication in the framework of mindfulness. The second goal is for the audience to be inspired to share those practices with other members of their organisation. The final objective is to equip participants with the tools to foster a compassionate, mindful and well-balanced work environment. To assess these tools, a 6-week case study was conducted as part of an employee wellness programme for a large international corporation. The OWBA-MT were introduced in a workshop forum once-a-week, with participants practicing these tools both in the office and at home. The workshops occurred 1 day a week (2 hours each), with themes and exercises varying weekly. To reinforce practice at home, participants received reflection forms and guided meditations online. Materials were sent via-email at the same time each day to ensure consistency and participation. To evaluate the effectiveness of the mindfulness intervention, improvements in four categories were measured: listening skills, mindfulness levels, prioritising skills and happiness levels. These factors were assessed using online self-reported questionnaires administered at the start of the intervention, and then again 4-weeks following completion. The measures included the Mindfulness Attention Awareness Scale (MAAS), Listening Skills Inventory (LSI), Time Management Behaviour Scale (TMBS) and a modified version of the Oxford Happiness Questionnaire (OHQ). All four parameters showed significant improvements from the start of the programme to the 4-week follow-up. Participant testimonials exhibited high levels of satisfaction and the overall results indicate that the OWBA-MT intervention substantially impacted the corporation in a positive way. The implications of these results suggest that OWBA-MT can improve employees’ capacities to listen and work well with others, to manage time effectively, and to experience enhanced satisfaction both at work and in life. Although corporate mindfulness programmes have proven to be effective, the challenge remains the low engagement levels at home in between training sessions and to implement the tools beyond the scope of the intervention. OWBA-MT has offered an innovative approach to enforce engagement levels at home by sending daily online materials outside the workshop forum with a personalised response. The limitations also noteworthy to consider for future research include the afterglow effect and lack of generalisability, as this study was conducted on a small and fairly homogenous sample.

Keywords: corporate mindfulness, listening skills, mindful leadership, mindfulness tools, organisational well being

Procedia PDF Downloads 240

Authors: Aisha Liaquat Ali, Ghulam Sarwar Gachal, Muhammad Yusuf Sheikh

Abstract:

The Parrot rose ringed (Psittaculla krameri) commonly known as Tota, belongs to the order ‘Psittaciformes’ and family ‘Psittacidea’. Its sub-species inhabiting Pakistan are Psittaculla borealis. The parrot rose-ringed has been categorized the least concern species, the core aim of the present study is to investigate the ecology and taxonomy of parrot (rose-ringed). Sampling was obtained for the taxonomic identification from various adjoining areas in City Mirpurkhas by non-random method, which was conducted from Feb to June 2017. The different parameters measured with the help of a vernier caliper, foot scale, digital weighing machine. Body parameters were measured via; length of body, length of the wings, length of tail, mass in grams. During present study, a total number of 36 specimens were collected from different localities of City Mirpurkhas (38.2%) were male and (62.7%) were female. Maximum population density of Psittaculla Krameri borealis (52.9%) was collected from Sindh Horticulture Research Station (fruit farm) Mirpurkhas. Minimum no: of Psittaculla krameri borealis (5.5%) collected in urban parks. It was observed that Psittaculla krameri borealis were in dense population during the months of ‘May’ and ‘June’ when the temperature ranged between 20°C and 45°C. A Psittaculla krameri borealis female was found the heaviest in body weight. The species of parrot (rose ringed) captured during study having green plumage, coverts were gray, upper beak, red and lower beak black, shorter tail in female long tail in the male which was similar to the Psittaculla krameri borealis.

Keywords: Mirpurkhas Sindh Pakistan, environmental ecology, parrot, rose-ringed, taxonomy

Procedia PDF Downloads 171

Authors: Chinaza Uleanya, Martin Duma, Bongani Gamede

Abstract:

The research investigated pedagogic malpractices by lecturers in sub-Sahara African universities. The population of the study consisted of undergraduates and lecturers in selected universities in Nigeria and South Africa. Mixed method approach was adopted for data collection. The sample population of the study was 480 undergraduate students and 16 lecturers. Questionnaires with 4 point Likert-scale were administered to 480 respondents while interviews were conducted with 6 lecturers. In addition, the teaching strategies of 10 lecturers were observed. Data analyses indicated that poor work environment demotivates lecturers and makes them involved in pedagogic malpractice which is one of the causes of learning challenges faced by undergraduates. The finding of the study also shows that pedagogic malpractice contributes to the high rate of dropout in sub-Sahara African universities. Based on the results, it was recommended that qualified lecturers be employed and given conducive environments to work.

Keywords: malpractice, pedagogy, pedagogic malpractice, correlates

Procedia PDF Downloads 301

Authors: Yi-Guang Li, Suresh Sampath

Abstract:

Gas turbine performance degrades over time, and the degradation is greatly affected by environmental, ambient, and operating conditions. The engines may degrade slowly under favorable conditions and result in a waste of engine life if a scheduled maintenance scheme is followed. They may also degrade fast and fail before a scheduled overhaul if the conditions are unfavorable, resulting in serious secondary damage, loss of engine availability, and increased maintenance costs. To overcome these problems, gas turbine owners are gradually moving from scheduled maintenance to condition-based maintenance, where condition monitoring is one of the key supporting technologies. This paper presents an integrated adaptive GPA diagnostics and performance monitoring system developed at Cranfield University for gas turbine gas path condition monitoring. It has the capability to predict the performance degradation of major gas path components of gas turbine engines, such as compressors, combustors, and turbines, using gas path measurement data. It is also able to predict engine key performance parameters for condition monitoring, such as turbine entry temperature that cannot be directly measured. The developed technology has been implemented into digital twin computer Software, Pythia, to support the condition monitoring of gas turbine engines. The capabilities of the integrated GPA condition monitoring system are demonstrated in three test cases using a model gas turbine engine similar to the GE aero-derivative LM2500 engine widely used in power generation and marine propulsion. It shows that when the compressor of the model engine degrades, the Adaptive GPA is able to predict the degradation and the changing engine performance accurately using gas path measurements. Such a presented technology and software are generic, can be applied to different types of gas turbine engines, and provide crucial engine health and performance parameters to support condition monitoring and condition-based maintenance.

Keywords: gas turbine, adaptive GPA, performance, diagnostics, condition monitoring

Procedia PDF Downloads 84

Authors: Érick Aragão Ribeiro, George André Pereira Thé, José Marques Soares

Abstract:

Graphical Human Interface, also known as supervision software, are increasingly present in industrial processes supported by Supervisory Control and Data Acquisition (SCADA) systems and so it is evident the need for qualified developers. In order to make engineering students able to produce high quality supervision software, method for the development must be created. In this paper we propose model, based on the international standards ISO/IEC 25010 and ISO/IEC 25040, for the development of graphical human interface. When compared with to other methods through experiments, the model here presented leads to improved quality indexes, therefore help guiding the decisions of programmers. Results show the efficiency of the models and the contribution to student learning. Students assessed the training they have received and considered it satisfactory.

Keywords: software development models, software quality, supervision software, fuzzy logic

Procedia PDF Downloads 369

Authors: Rachel Wachtendonk, John Calambokidis, Kiirsten Flynn

Abstract:

Large marine mammals can serve as an indicator of the overall state of the environment due to their long lifespan and apex position in marine food webs. Reductions in prey, driven by changes in environmental conditions can have resounding impacts on the trophic system as a whole; this can manifest in reduced fat stores that are visible on large whales. Poor health can lead to reduced survivorship and fitness, both of which can be detrimental to a recovering population. A non-invasive technique was used for monitoring blue whale health and for seeing if it changes with ocean conditions. Digital photographs of blue whales taken in the NE Pacific by Cascadia Research and collaborators from 2005-2018 (n=3,545) were scored for overall body condition based on visible vertebrae and body shape on a scale of 0-3 where a score of 0 indicated best body condition and a score of 3 indicated poorest. The data was analyzed to determine if there were patterns in the health of whales across years and whether overall poor health was related to oceanographic conditions and predictors of prey abundance on the California coast. The year was a highly significant factor in body condition (Chi-Square, p<0.001). The proportion of whales showing poor body condition (scores 2 & 3) overall was 33% but by year varied widely from a low of 18% (2008) to a high of 55% (2015). The only two years where >50% of animals had poor body condition were 2015 and 2017 (no other year was above 45%). The 2015 maximum proportion of whales in poor body condition coincide with the marine heat wave that affected the NE Pacific 2014-16 and impacted other whale populations. This indicates that the scoring method was an effective way to evaluate blue whale health and how they respond to a changing ocean.

Keywords: blue whale, body condition, environmental variability, photo-identification

Procedia PDF Downloads 202

Authors: Collins Molua

Abstract:

This paper investigated the effect of Teaching Strategies on Academic Achievement in Secondary Physics Education as a quality assurance process for the teaching and learning of the subject. Teaching strategies investigated were the interactive, independent and dependent strategies. Three null hypotheses were tested at p< 0.05 using one instrument, physics achievement test(PAT).The data were analyzed using analysis of covariance (ANCOVA).Results showed that teaching strategies have significant effect on students achievement; the joint effect of the teaching strategies was also significant on students achievement in Physics. The interactive teaching strategies was recommended for teaching the subject and the students should be exposed to practical, computer literacy to stimulate interest and curiosity to enhance quality.

Keywords: quality, assurance, secondary education, strategies, physics

Procedia PDF Downloads 320

Authors: Vyacheslav Furtak, Merja Roivainen, Olga Mirochnichenko, Majid Laassri, Bella Bidzhieva, Tatiana Zagorodnyaya, Vladimir Chizhikov, Konstantin Chumakov

Abstract:

Molecular epidemiology and environmental surveillance are parts of a rational strategy to control infectious diseases. They have been widely used in the worldwide campaign to eradicate poliomyelitis, which otherwise would be complicated by the inability to rapidly respond to outbreaks and determine sources of the infection. The conventional scheme involves isolation of viruses from patients and the environment, followed by their identification by nucleotide sequences analysis to determine phylogenetic relationships. This is a tedious and time-consuming process that yields definitive results when it may be too late to implement countermeasures. Because of the difficulty of high-throughput full-genome sequencing, most such studies are conducted by sequencing only capsid genes or their parts. Therefore the important information about the contribution of other parts of the genome and inter- and intra-species recombination to viral evolution is not captured. Here we propose a new approach based on the rapid concentration of sewage samples with tangential flow filtration followed by deep sequencing and reconstruction of nucleotide sequences of viruses present in the samples. The entire nucleic acids content of each sample is sequenced, thus preserving in digital format the complete spectrum of viruses. A set of rapid algorithms was developed to separate deep sequence reads into discrete populations corresponding to each virus and assemble them into full-length consensus contigs, as well as to generate a complete profile of sequence heterogeneities in each of them. This provides an effective approach to study molecular epidemiology and evolution of natural viral populations.

Keywords: poliovirus, eradication, environmental surveillance, laboratory diagnosis

Procedia PDF Downloads 276

Authors: Shohnaza Uzokova Beknazarovna

Abstract:

This research study provides a comprehensive exploration of the Theory of Media Activity and its relevance as a conceptual framework for journalism education. The author offers a thorough review of existing literature on media activity theory, emphasizing its potential to enhance the understanding of the evolving media landscape and its implications for journalism practice. Through a combination of theoretical analysis and practical examples, the paper elucidates the ways in which the Theory of Media Activity can inform and enrich journalism education, particularly in relation to the interactive and participatory nature of contemporary media. The author presents a compelling argument for the integration of media activity theory into journalism curricula, emphasizing its capacity to equip students with a nuanced understanding of the reciprocal relationship between media producers and consumers. Furthermore, the paper discusses the implications of technological advancements on media production and consumption, highlighting the need for journalism educators to prepare students to navigate and contribute to the future of journalism in a rapidly changing media environment. Overall, this research paper offers valuable insights into the potential benefits of embracing the Theory of Media Activity as a foundational framework for journalism education. Its thorough analysis and practical implications make it a valuable resource for educators, researchers, and practitioners seeking to enhance journalism pedagogy in response to the dynamic nature of contemporary media.

Keywords: theory of media activity, journalism education, media landscape, media production, media consumption, interactive media, participatory media, technological advancements, media producers, media consumers, journalism practice, contemporary media environment, journalism pedagogy, media theory, media studies

Procedia PDF Downloads 43