Search results for: co-citation networks; keyword co-occurrence network

Authors: Redouane Larbi Boufeniza, Jing-Jia Luo

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This study assesses the suitability of deep learning (DL) for downscaling sea surface temperature (SST) over the Mediterranean Sea in the context of seasonal forecasting. We design a set of experiments that compare different DL configurations and deploy the best-performing architecture to downscale one-month lead forecasts of June–September (JJAS) SST from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0) for the period of 1982–2020. We have also introduced predictors over a larger area to include information about the main large-scale circulations that drive SST over the Mediterranean Sea region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results showed that the convolutional neural network (CNN)-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme SST spatial patterns. Besides, the CNN-based downscaling yields a much more accurate forecast of extreme SST and spell indicators and reduces the significant relevant biases exhibited by the raw model predictions. Moreover, our results show that the CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of the Mediterranean Sea. The results demonstrate the potential usefulness of CNN in downscaling seasonal SST predictions over the Mediterranean Sea, particularly in providing improved forecast products.

Keywords: Mediterranean Sea, sea surface temperature, seasonal forecasting, downscaling, deep learning

Procedia PDF Downloads 72

Authors: Vishal Das, Tianyi Mao, Zhicheng Geng, Carmen Flores, Diego Pelloso, Fang Wang

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Accurate sell-in and sell-out forecasting is a ubiquitous problem in the retail industry. It is an important element of any demand planning activity. As a global food and beverage company, Nestlé has hundreds of products in each geographical location that they operate in. Each product has its sell-in and sell-out time series data, which are forecasted on a weekly and monthly scale for demand and financial planning. To address this challenge, Nestlé Chilein collaboration with Amazon Machine Learning Solutions Labhas developed their in-house solution of using machine learning models for forecasting. Similar products are combined together such that there is one model for each product category. In this way, the models learn from a larger set of data, and there are fewer models to maintain. The solution is scalable to all product categories and is developed to be flexible enough to include any new product or eliminate any existing product in a product category based on requirements. We show how we can use the machine learning development environment on Amazon Web Services (AWS) to explore a set of forecasting models and create business intelligence dashboards that can be used with the existing demand planning tools in Nestlé. We explored recent deep learning networks (DNN), which show promising results for a variety of time series forecasting problems. Specifically, we used a DeepAR autoregressive model that can group similar time series together and provide robust predictions. To further enhance the accuracy of the predictions and include domain-specific knowledge, we designed an ensemble approach using DeepAR and XGBoost regression model. As part of the ensemble approach, we interlinked the sell-out and sell-in information to ensure that a future sell-out influences the current sell-in predictions. Our approach outperforms the benchmark statistical models by more than 50%. The machine learning (ML) pipeline implemented in the cloud is currently being extended for other product categories and is getting adopted by other geomarkets.

Keywords: sell-in and sell-out forecasting, demand planning, DeepAR, retail, ensemble machine learning, time-series

Procedia PDF Downloads 258

Authors: Ananchai Ukaew, Choopong Chauypen

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Design concepts of real-time embedded system can be realized initially by introducing novel design approaches. In this literature, model based design approach and in-the-loop testing were employed early in the conceptual and preliminary phase to formulate design requirements and perform quick real-time verification. The design and analysis methodology includes simulation analysis, model based testing, and in-the-loop testing. The design of conceptual drive-by-wire, or DBW, algorithm for electronic control unit, or ECU, was presented to demonstrate the conceptual design process, analysis, and functionality evaluation. The concepts of DBW ECU function can be implemented in the vehicle system to improve electric vehicle, or EV, conversion drivability. However, within a new development process, conceptual ECU functions and parameters are needed to be evaluated. As a result, the testing system was employed to support conceptual DBW ECU functions evaluation. For the current setup, the system components were consisted of actual DBW ECU hardware, electric vehicle models, and control area network or CAN protocol. The vehicle models and CAN bus interface were both implemented as real-time applications where ECU and CAN protocol functionality were verified according to the design requirements. The proposed system could potentially benefit in performing rapid real-time analysis of design parameters for conceptual system or software algorithm development.

Keywords: drive-by-wire ECU, in-the-loop testing, model-based design, real-time embedded system

Procedia PDF Downloads 344

Authors: Erich Gomes Schaitza, Antônio Francisco Savi, Glaucia Aparecida Prates

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The penetration of renewable energy into the electricity supply in Brazil is high, one of the highest in the World. Centralized hydroelectric generation is the main source of energy, followed by biomass and wind. Surprisingly, mini and micro-generation are negligible, with less than 2,000 connections to the national grid. In 2015, a new regulatory framework was put in place to change this situation. In the agricultural sector, the framework was complemented by the offer of low interest rate loans to in-farm renewable generation. Brazil proposed to more than double its area of planted forests as part of its INDC- Intended Nationally Determined Contributions to the UNFCCC-U.N. Framework Convention on Climate Change (UNFCCC). This is an ambitious target which will be achieved only if forests are attractive to farmers. Therefore, this paper analyses whether planting forests for in-farm energy generation with a with a woodchip gasifier is economically viable for microgeneration under the new framework and at if they could be an economic driver for forest plantation. At first, a static case was analyzed with data from Eucalyptus plantations in five farms. Then, a broader analysis developed with the use of Monte Carlo technique. Planting short rotation forests to generate energy could be a viable alternative and the low interest loans contribute to that. There are some barriers to such systems such as the inexistence of a mature market for small scale equipment and of a reference network of good practices and examples.

Keywords: biomass, distribuited generation, small-scale, Monte Carlo

Procedia PDF Downloads 281

Authors: Etienne Mfoumou, Mo Shamma, Martin Tango, Michael Locke

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Water literacy is essential for addressing emerging water management challenges in southwest Nova Scotia (SWNS), where growing concerns over water scarcity and sustainability have highlighted the need for improved educational frameworks. Current approaches often fail to fully represent the complexity of water systems, focusing narrowly on the water cycle while neglecting critical aspects such as groundwater infiltration and the interconnectedness of surface and subsurface water systems. To address these gaps, this paper proposes a comprehensive framework for water literacy that integrates the physical dimensions of water systems with key aspects of understanding, including processes, energy, scale, and human dependency. Moreover, a suggested tool to enhance this framework is a real-time hydrometric data map supported by a network of water level monitoring devices deployed across the province. These devices, particularly for monitoring dug wells, would provide critical data on groundwater levels and trends, offering stakeholders actionable insights into water availability and sustainability. This real-time data would facilitate deeper understanding and engagement with local water issues, complementing the educational framework and empowering stakeholders to make informed decisions. By integrating this tool, the proposed framework offers a practical, interdisciplinary approach to improving water literacy and promoting sustainable water management in SWNS.

Keywords: water education, water literacy, water management, water systems, Southwest Nova Scotia

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Authors: Sanghamitra Acharya, Suwarna Datar

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Extensive use of digital and smart communication createsprolong expose of unwanted electromagnetic (EM) radiations. This harmful radiation creates not only malfunctioning of nearby electronic gadgets but also severely affects a human being. So, a suitable microwave absorbing material (MAM) becomes a necessary urge in the field of stealth and radar technology. Initially, Aluminum based hexa ferrite was prepared by sol-gel technique and for carbon derived composite was prepared by the simple one port chemical reduction method. Finally, composite films of Poly (Vinylidene) Fluoride (PVDF) are prepared by simple gel casting technique. Present work demands that aluminum-based hexaferrite phase conjugated with graphene in PVDF matrix becomes a suitable candidate both in commercially important X and Ku band. The structural and morphological nature was characterized by X-Ray diffraction (XRD), Field emission-scanning electron microscope (FESEM) and Raman spectra which conforms that 30-40 nm particles are well decorated over graphene sheet. Magnetic force microscopy (MFM) and conducting force microscopy (CFM) study further conforms the magnetic and conducting nature of composite. Finally, shielding effectiveness (SE) of the composite film was studied by using Vector network analyzer (VNA) both in X band and Ku band frequency range and found to be more than 30 dB and 40 dB, respectively. As prepared composite films are excellent microwave absorbers.

Keywords: carbon nanocomposite, microwave absorbing material, electromagnetic shielding, hexaferrite

Procedia PDF Downloads 173

Authors: Yehjune Heo

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Fingerprint Anti-Spoofing approaches have been actively developed and applied in real-world applications. One of the main problems for Fingerprint Anti-Spoofing is not robust to unseen samples, especially in real-world scenarios. A possible solution will be to generate artificial, but realistic fingerprint samples and use them for training in order to achieve good generalization. This paper contains experimental and comparative results with currently popular GAN based methods and uses realistic synthesis of fingerprints in training in order to increase the performance. Among various GAN models, the most popular StyleGAN is used for the experiments. The CNN models were first trained with the dataset that did not contain generated fake images and the accuracy along with the mean average error rate were recorded. Then, the fake generated images (fake images of live fingerprints and fake images of spoof fingerprints) were each combined with the original images (real images of live fingerprints and real images of spoof fingerprints), and various CNN models were trained. The best performances for each CNN model, trained with the dataset of generated fake images and each time the accuracy and the mean average error rate, were recorded. We observe that current GAN based approaches need significant improvements for the Anti-Spoofing performance, although the overall quality of the synthesized fingerprints seems to be reasonable. We include the analysis of this performance degradation, especially with a small number of samples. In addition, we suggest several approaches towards improved generalization with a small number of samples, by focusing on what GAN based approaches should learn and should not learn.

Keywords: anti-spoofing, CNN, fingerprint recognition, GAN

Procedia PDF Downloads 180

Authors: Ali Riza Perçin, Eser Bingül

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Internet technologies have created opportunities with which people share their ideologies, thoughts and products. This virtual world, named social media has given the chance of gathering individual users and people from the world's remote locations and establishing an interaction between them. However, to an increasingly higher degree terrorist organizations today use the internet and most notably social-network media to create the effects they desire through a series of on-line activities. These activities, designed to support their activities, include information collection (intelligence), target selection, propaganda, fundraising and recruitment to name a few. Meanwhile, these have been used as the most important tool for recruitment especially from the different region of the world, especially disenfranchised youth, in the West in order to mobilize support and recruit “foreign fighters.” The recruits have obtained the statue, which is not accessible in their society and have preferred the style of life that is offered by the terrorist organizations instead of their current life. Like other terrorist groups, for a while now the terrorist organization Islamic State (IS) in Iraq and Syria has employed a social-media strategy in order to advance their strategic objectives. At the moment, however, IS seems to be more successful in their on-line activities than other similar organizations. IS uses social media strategically as part of its armed activities and for the sustainability of their military presence in Syria and Iraq. In this context, “Generation Y”, which could exist at the critical position and undertake active role, has been examined. Additionally, the explained characteristics of “Generation Y” have been put forward and the duties of families and society have been stated as well.

Keywords: social media, "generation Y", terrorist organization, islamic state IS

Procedia PDF Downloads 423

Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

Procedia PDF Downloads 147

Authors: Mariana Damova, Stanko Stankov, Emil Stoyanov, Hristo Hristov, Hermand Pessek, Plamen Chernev

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We will present one of the first use cases on the DestinE platform, a joint initiative of the European Commission, European Space Agency and EUMETSAT, providing access to global earth observation, meteorological and statistical data, and emphasize the good practice of intergovernmental agencies acting in concert. Further, we will discuss the importance of space-bound disruptive solutions for improving the balance between the ever-increasing water-related disasters coming from climate change and minimizing their economic and societal impact. The use case focuses on forecasting floods and estimating the impact of flood events on the urban environment and the ecosystems in the affected areas with the purpose of helping municipal decision-makers to analyze and plan resource needs and to forge human-environment relationships by providing farmers with insightful information for improving their agricultural productivity. For the forecast, we will adopt an EO4AI method of our platform ISME-HYDRO, in which we employ a pipeline of neural networks applied to in-situ measurements and satellite data of meteorological factors influencing the hydrological and hydrodynamic status of rivers and dams, such as precipitations, soil moisture, vegetation index, snow cover to model flood events and their span. ISME-HYDRO platform is an e-infrastructure for water resources management based on linked data, extended with further intelligence that generates forecasts with the method described above, throws alerts, formulates queries, provides superior interactivity and drives communication with the users. It provides synchronized visualization of table views, graphviews and interactive maps. It will be federated with the DestinE platform.

Keywords: flood simulation, AI, Earth observation, e-Infrastructure, flood forecasting, flood areas localization, response planning, resource estimation

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Authors: Emilee M. Cruz

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Teachers take part in short-term teaching fellowships abroad, and their preparation before, during, and after the experience is critical to affecting teachers’ feelings of success in the international classroom. A program evaluation of the teacher preparation within TALMA: The Israel Program for Excellence in English (TALMA) full-year teaching fellowship was conducted. A questionnaire was developed that examined professional development, deliberate reflection, and cultural and language immersion offered before, during, and after the short-term experience. The evaluation also surveyed teachers’ feelings of preparedness for the Israeli classroom and any recommendations they had for future teacher preparation within the fellowship program. The review suggests the TALMA program includes integrated professional learning communities between fellows and Israeli co-teachers, more opportunities for immersive Hebrew language learning, a broader professional network with Israelis, and opportunities for guided discussion with the TALMA community continued participation in TALMA events and learning following the full-year fellowship. Similar short-term international programs should consider the findings in the design of their participation preparation programs. The review also offers direction for future program evaluation of short-term participant preparation, including the need for frequent response item updates to match current offerings and evaluation of participant feelings of preparedness before, during, and after the full-year fellowship.

Keywords: educational program evaluation, international teaching, short-term teaching, teacher beliefs, teaching fellowship, teacher preparation

Procedia PDF Downloads 176

Authors: Majid Amidpour, Parisa Karimi, Marzieh Joda

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For the last few years, release of burned undesirable by-products has become a challenging issue in oil industries. Flaring, as one of the main sources of air contamination, involves detrimental and long-lasting effects on human health and is considered a substantial reason for energy losses worldwide. This research involves studying the implications of two main flare gas recovery methods at three oil refineries, all in Iran as the case I, case II, and case III in which the production capacities are increasing respectively. In the proposed methods, flare gases are converted into more valuable products, before combustion by the flare networks. The first approach involves collecting, compressing and converting the flare gas to smokeless fuel which can be used in the fuel gas system of the refineries. The other scenario includes utilizing the flare gas as a feed into liquefied petroleum gas (LPG) production unit already established in the refineries. The processes of these scenarios are simulated, and the capital investment is calculated for each procedure. The cumulative profits of the scenarios are evaluated using Net Present Value method. Furthermore, the sensitivity analysis based on total propane and butane mole fraction is carried out to make a rational comparison for LPG production approach, and the results are illustrated for different mole fractions of propane and butane. As the mole fraction of propane and butane contained in LPG differs in summer and winter seasons, the results corresponding to LPG scenario are demonstrated for each season. The results of the simulations show that cumulative profit in fuel gas production scenario and LPG production rate increase with the capacity of the refineries. Moreover, the investment return time in LPG production method experiences a decline, followed by a rising trend with an increase in C₃ and C₄ content. The minimum value of time return occurs at propane and butane sum concentration values of 0.7, 0.6, and 0.7 in case I, II, and III, respectively. Based on comparison of the time of investment return and cumulative profit, fuel gas production is the superior scenario for three case studies.

Keywords: flare gas reduction, liquefied petroleum gas, fuel gas, net present value method, sensitivity analysis

Procedia PDF Downloads 154

Authors: Dararat Tongdee, Surapong Chirarattananon, Somchai Maneewan, Chantana Punlek

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Recently, the Internet of Things (IoT) is the important technology that connects devices to the network and people can access real-time communication. This technology is used to report, collect, and analyze the big data for achieving a purpose. For a smart building, there are many IoT technologies that enable management and building operators to improve occupant thermal comfort, indoor air quality, and building energy efficiency. In this research, we propose monitoring and controlling performance of a smart dedicated outdoor air system (SDOAS) based on IoT platform. The SDOAS was specifically designed with the desiccant unit and thermoelectric module. The designed system was intended to monitor, notify, and control indoor environmental factors such as temperature, humidity, and carbon dioxide (CO₂) level. The SDOAS was tested under the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE 62.2) and indoor air quality standard. The system will notify the user by Blynk notification when the status of the building is uncomfortable or tolerable limits are reached according to the conditions that were set. The user can then control the system via a Blynk application on a smartphone. The experimental result indicates that the temperature and humidity of indoor fresh air in the comfort zone are approximately 26 degree Celsius and 58% respectively. Furthermore, the CO₂ level was controlled lower than 1000 ppm by indoor air quality standard condition. Therefore, the proposed system can efficiently work and be easy to use for buildings.

Keywords: internet of things, indoor air quality, smart dedicated outdoor air system, thermal comfort

Procedia PDF Downloads 195

Authors: Kawthar F. Albasri, Abdullah M. AlHudaithi, Dana B. AlTurairi, Abdullaziz S. AlQuraini, Adoub Y. AlDerazi, Reem A. Hubail, Haitham A. Jahrami

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Several studies have found that medical students have a significant prevalence of anxiety. The purpose of this review paper is to carefully evaluate the current research on anxiety among medical students in the MENA region and, as a result, estimate the prevalence of these disturbances. Multiple databases, including the CINAHL (Cumulative Index to Nursing and Allied Health Literature), Cochrane Library, Embase, MEDLINE (Medical Literature Analysis and Retrieval System Online), PubMed, PsycINFO (Psychological Information Database), Scopus, Web of Science, UpToDate, ClinicalTrials.gov, WHO Global Health Library, EbscoHost, ProQuest, JAMA Network, and ScienceDirect, were searched. The retrieved article reference lists were rigorously searched and rated for quality. A random effects meta-analysis was performed to compute estimates. The current meta-analysis revealed an alarming estimated pooled prevalence of anxiety (K = 46, N = 27023) of 52.5% [95%CI: 43.3%–61.6%]. A total of 62.0% [95% CI 42.9%; 78.0%] of the students (K = 18, N = 16466) suffered from anxiety during the COVID-19 pandemic, while 52.5% [95% CI 43.3%; 61.6%] had anxiety before COVID-19. Based on the GAD-7 measure, a total of 55.7% [95%CI 30.5%; 78.3%] of the students (K = 10, N = 5830) had anxiety, and a total of 54.7% of the students (K = 18, N = 12154) [95%CI 42.8%; 66.0%] had anxiety using the DASS-21 or 42 measure. Anxiety is a common issue among medical students, making it a genuine problem. Further research should be conducted post-COVD 19, with a focus on anxiety prevention and intervention initiatives for medical students.

Keywords: anxiety, medical students, MENA, meta-analysis, prevalence

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Authors: Pardhasaradhi Gudla, Imanual A.

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The generated electrical power in offshore needs to be transmitted to grid which is located in onshore by using subsea cables. Long subsea cables produce reactive power, which should be compensated in order to limit transmission losses, to optimize the transmission capacity, and to keep the grid voltage within the safe operational limits. Installation cost of wind farm includes the structure design cost and electrical system cost. India has targeted to achieve 175GW of renewable energy capacity by 2022 including offshore wind power generation. Due to sea depth is more in India, the installation cost will be further high when compared to European countries where offshore wind energy is already generating successfully. So innovations are required to reduce the offshore wind power project cost. This paper presents the optimized techniques to reduce the installation cost of offshore wind firm with respect to electrical transmission systems. This technical paper provides the techniques for increasing the current carrying capacity of subsea cable by decreasing the reactive power generation (capacitance effect) of the subsea cable. There are many methods for reactive power compensation in wind power plants so far in execution. The main reason for the need of reactive power compensation is capacitance effect of subsea cable. So if we diminish the cable capacitance of cable then the requirement of the reactive power compensation will be reduced or optimized by avoiding the intermediate substation at midpoint of the transmission network.

Keywords: offshore wind power, optimized techniques, power system, sub sea cable

Procedia PDF Downloads 188

Authors: Sajjad Akbar, Rabia Bashir

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With the growth of the number of users, the Internet usage has evolved. Due to its key design principle, there is an incredible expansion in its size. This tremendous growth of the Internet has brought new applications (mobile video and cloud computing) as well as new user’s requirements i.e. content distribution environment, mobility, ubiquity, security and trust etc. The users are more interested in contents rather than their communicating peer nodes. The current Internet architecture is a host-centric networking approach, which is not suitable for the specific type of applications. With the growing use of multiple interactive applications, the host centric approach is considered to be less efficient as it depends on the physical location, for this, Information Centric Networking (ICN) is considered as the potential future Internet architecture. It is an approach that introduces uniquely named data as a core Internet principle. It uses the receiver oriented approach rather than sender oriented. It introduces the naming base information system at the network layer. Although ICN is considered as future Internet architecture but there are lot of criticism on it which mainly concerns that how ICN will manage the most relevant content. For this Web Content Mining(WCM) approaches can help in appropriate data management of ICN. To address this issue, this paper contributes by (i) discussing multiple ICN approaches (ii) analyzing different Web Content Mining approaches (iii) creating a new Internet architecture by merging ICN and WCM to solve the data management issues of ICN. From ICN, Content-Centric Networking (CCN) is selected for the new architecture, whereas, Agent-based approach from Web Content Mining is selected to find most appropriate data.

Keywords: agent based web content mining, content centric networking, information centric networking

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Authors: Xiang Li, Jian-Qiao Sun

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As the crucial component of city traffic network, intersections have significant impacts on urban traffic performance. Despite of the rapid development in transportation systems, increasing traffic volumes result in severe congestions especially at intersections in urban areas. Effective regulation of vehicle flows at intersections has always been an important issue in the traffic control system. This study presents a multi-objective optimization method at intersections with cellular automata to achieve better traffic performance. Vehicle conflicts and pedestrian interference are considered. Three categories of the traffic performance are studied including transportation efficiency, energy consumption and road safety. The left-turn signal type, signal timing and lane assignment are optimized for different traffic flows. The multi-objective optimization problem is solved with the cell mapping method. The optimization results show the conflicting nature of different traffic performance. The influence of different traffic variables on the intersection performance is investigated. It is observed that the proposed optimization method is effective in regulating the traffic at the intersection to meet multiple objectives. Transportation efficiency can be usually improved by the permissive left-turn signal, which sacrifices safety. Right-turn traffic suffers significantly when the right-turn lanes are shared with the through vehicles. The effect of vehicle flow on the intersection performance is significant. The display pattern of the optimization results can be changed remarkably by the traffic volume variation. Pedestrians have strong interference with the traffic system.

Keywords: cellular automata, intersection, multi-objective optimization, traffic system

Procedia PDF Downloads 578

Authors: Alisan Kayabolen, Dilek Keskin, Ferit Avcu, Andac Aykan, Fatih Zor, Aysen Tezcaner

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Adipose tissue engineering is a promising field for regeneration of soft tissue defects. However, only very thin implants can be used in vivo since vascularization is still a problem for thick implants. Another problem is finding a biocompatible scaffold with good mechanical properties. In this study, the aim is to develop a thick vascularized adipose tissue that will integrate with the host, and perform its in vitro and in vivo characterizations. For this purpose, a hydrogel of decellularized adipose tissue (DAT) and fibroin was produced, and both endothelial cells and adipocytes that were differentiated from adipose derived stem cells were encapsulated in this hydrogel. Mixing DAT with fibroin allowed rapid gel formation by vortexing. It also provided to adjust mechanical strength by changing fibroin to DAT ratio. Based on compression tests, gels of DAT/fibroin ratio with similar mechanical properties to adipose tissue was selected for cell culture experiments. In vitro characterizations showed that DAT is not cytotoxic; on the contrary, it has many natural ECM components which provide biocompatibility and bioactivity. Subcutaneous implantation of hydrogels resulted with no immunogenic reaction or infection. Moreover, localized empty hydrogels gelled successfully around host vessel with required shape. Implantations of cell encapsulated hydrogels and histological analyses are under study. It is expected that endothelial cells inside the hydrogel will form a capillary network and they will bind to the host vessel passing through hydrogel.

Keywords: adipose tissue engineering, decellularization, encapsulation, hydrogel, vascularization

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Authors: Hafiz Muhammad Adeel Sharif, Tian Li, Changping Li

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Recently, the emission of nitrogen-sulphur oxides (NOₓ, SO₂) has become a global issue and causing serious threats to health and the environment. Catalytic reduction of NOx and SOₓ gases into friendly gases is considered one of the best approaches. However, regeneration of the catalyst, higher bond-dissociation energy for NOx, i.e., 150.7 kcal/mol, escape of intermediate gas (N₂O, a greenhouse gas) with treated flue-gas, and limited activity of catalyst remains a great challenge. Here, a cheap, binderless naturally-extracted bass-wood thin carbon electrode (TCE) is presented, which shows excellent catalytic activity towards NOx reduction. The bass-wood carbonization at 900 ℃ followed by thermal activation in the presence of CO2 gas at 750 ℃. The thermal activation resulted in an increase in epoxy groups on the surface of the TCE and enhancement in the surface area as well as the degree of graphitization. The TCE unique 3D strongly inter-connected network through hierarchical micro/meso/macro pores that allow large electrode/electrolyte interface. Owing to these characteristics, the TCE exhibited excellent catalytic efficiency towards NOx (~83.3%) under ambient conditions and enhanced catalytic response under pH and sulphite exposure as well as excellent stability up to 168 hours. Moreover, a temperature-dependent activity trend was found where the highest catalytic activity was achieved at 80 ℃, beyond which the electrolyte became evaporative and resulted in a performance decrease. The designed electrocatalyst showed great potential for effective NOx-reduction, which is highly cost-effective, green, and sustainable.

Keywords: electrocatalyst, NOx-reduction, bass-wood electrode, integrated wet-scrubbing, sustainable

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Authors: Ma Yuzhe, Burra Venkata Durga Kumar

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The computer is a great invention. As people use computers more and more frequently, the demand for PCs is growing, and the performance of computer hardware is also rising to face more complex processing and operation. However, the operating system, which provides the soul for computers, has stopped developing at a stage. In the face of the high price of UNIX (Uniplexed Information and Computering System), batch after batch of personal computer owners can only give up. Disk Operating System is too simple and difficult to bring innovation into play, which is not a good choice. And MacOS is a special operating system for Apple computers, and it can not be widely used on personal computers. In this environment, Linux, based on the UNIX system, was born. Linux combines the advantages of the operating system and is composed of many microkernels, which is relatively powerful in the core architecture. Linux system supports all Internet protocols, so it has very good network functions. Linux supports multiple users. Each user has no influence on their own files. Linux can also multitask and run different programs independently at the same time. Linux is a completely open source operating system. Users can obtain and modify the source code for free. Because of these advantages of Linux, it has also attracted a large number of users and programmers. The Linux system is also constantly upgraded and improved. It has also issued many different versions, which are suitable for community use and commercial use. Linux system has good security because it relies on a file partition system. However, due to the constant updating of vulnerabilities and hazards, the using security of the operating system also needs to be paid more attention to. This article will focus on the analysis and discussion of Linux security issues.

Keywords: Linux, operating system, system management, security

Procedia PDF Downloads 103

Authors: Jung Park, Anca Mazare, Klaus Von Der Mark, Patrik Schmuki

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In clinical application of TiO2 implants on tooth and hip replacement, migration, adhesion and differentiation of neighboring mesenchymal stem cells onto implant surfaces are critical steps for successful bone regeneration. In a recent decade, accumulated attention has been paid on nanoscale electrochemical surface modifications on TiO2 layer for improving bone-TiO2 surface integration. We generated, on titanium surfaces, self-assembled layers of vertically oriented TiO2 nanotubes with defined diameters between 15 and 100 nm and here we show that mesenchymal stem cells finely sense TiO2 nanotubular geometry and quickly decide their cell fate either to differentiation into osteoblasts or to programmed cell death (apoptosis) on TiO2 nanotube layers. These cell fate decisions are critically dependent on nanotube size differences (15-100nm in diameters) of TiO2 nanotubes sensing by integrin clustering. We further demonstrate that nanoscale topography-sensing is feasible not only in mesenchymal stem cells but rather seems as generalized nanoscale microenvironment-cell interaction mechanism in several cell types composing bone tissue network including osteoblasts, osteoclast, endothelial cells and hematopoietic stem cells. Additionally we discuss the synergistic effect of simultaneous stimulation by nanotube-bound growth factor and nanoscale topographic cues on enhanced bone regeneration.

Keywords: TiO2 nanotube, stem cell fate decision, nano-scale microenvironment, bone regeneration

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Authors: Emmanuel Benjamin, Matthias Blum

Abstract:

Purpose – We address smallholder farmers’ restricted possibilities to adopt sustainable technologies which have direct and indirect benefits. Smallholders often face little asset endowment due to small farm size und insecure property rights, therefore experiencing constraints in adopting agricultural innovation. A program involving payments for ecosystem services (PES) benefits poor smallholder farmers in developing countries in many ways and has been suggested as a means of easing smallholder farmers’ financial constraints. PES may also provide additional mainstay which can eventually result in more favorable credit contract terms due to the availability of collateral substitute. Results of this study may help to understand the barriers, motives and incentives for smallholders’ participation in PES and help in designing a strategy to foster participation in beneficial programs. Design/methodology/approach – This paper uses a random utility model and a logistic regression approach to investigate factors that influence agroforestry adoption. We investigate non-monetary factors, such as information spillover, that influence the decision to adopt such conservation strategies. We collected original data from non-government-run agroforestry mitigation programs with PES that have been implemented in the Mount Kenya region. Preliminary Findings – We find that spread of information, existing networks and peer involvement in such programs drive participation. Conversely, participation by smallholders does not seem to be influenced by education, land or asset endowment. Contrary to some existing literature, we found weak evidence for a positive correlation between the adoption of agroforestry with PES and age of smallholder, e.g., one increases with the other, in the Mount Kenyan region. Research implications – Poverty alleviation policies for developing countries should target social capital to increase the adoption rate of modern technologies amongst smallholders.

Keywords: agriculture innovation, agroforestry adoption, smallholders, payment for ecosystem services, Sub-Saharan Africa

Procedia PDF Downloads 371

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 76

Authors: Mohammad Derakhshani, Saeed Reza Allahkarama, Michael Isakhani-Zakaria, Masoud Samadian, Hojjat Sharifi Rasaey

Abstract:

In order to investigate the effects of stray current from DC traction systems (subway) on cathodically protected gas pipelines, the subway and the gas network maps in the city of Tehran were superimposed and a comprehensive map was prepared. 213 intersections and about 100150 meters of parallel sections of gas pipelines were found with respect to the railway right of way which was specified for field measurements. The potential measurements data were logged for one hour in each test point. 24-hour potential monitoring was carried out in selected test points as well. Results showed that dynamic stray current from subway on pipeline potential appears as fluctuations in its static potential that is visible in the diagrams during night periods. These fluctuations can cause the pipeline potential to exit the safe zone and lead to corrosion or overprotection. In this study, a maximum potential shift of 100 mv in the pipe-to-soil potential was considered as a criterion for dynamic stray current effective presence. Results showed that a potential fluctuation range between 100 mV to 3 V exists in measured points on pipelines which exceeds the proposed criterion and needs to be investigated. Corrosion rates influenced by stray currents were calculated using coupons. Results showed that coupon linked to the pipeline in one of the locations at region 1 of the city of Tehran has a corrosion rate of 4.2 mpy (with cathodic protection and under influence of stray currents) which is about 1.5 times more than free corrosion rate of 2.6 mpy.

Keywords: stray current, DC traction, subway, buried Pipelines, cathodic protection list

Procedia PDF Downloads 816

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should evaluate properly their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, neural networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable of offering an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 65

Authors: Arnaldo G. de Oliveira, Jr, Matthieu Tubino

Abstract:

The transesterification process of triglycerides with alcohols that occurs during the biodiesel synthesis causes continuous changes in several physical properties of the reaction mixture, such as refractive index, viscosity and density. Amongst them, density can be an useful parameter to monitor the reaction, in order to predict the composition of the reacting mixture and to verify the conversion of the oil into biodiesel. In this context, a system was constructed in order to continuously determine changes in the density of the reacting mixture containing soybean oil, methanol and sodium methoxide (30 % w/w solution in methanol), stirred at 620 rpm at room temperature (about 27 °C). A polyethylene pipe network connected to a peristaltic pump was used in order to collect the mixture and pump it through a coil fixed on the plate of an analytical balance. The collected mass values were used to trace a curve correlating the mass of the system to the reaction time. The density variation profile versus the time clearly shows three different steps: 1) the dispersion of methanol in oil causes a decrease in the system mass due to the lower alcohol density followed by stabilization; 2) the addition of the catalyst (sodium methoxide) causes a larger decrease in mass compared to the first step (dispersion of methanol in oil) because of the oil conversion into biodiesel; 3) the final stabilization, denoting the end of the reaction. This density variation profile provides information that was used to predict the composition of the mixture over the time and the reaction rate. The precise knowledge of the duration of the synthesis means saving time and resources on a scale production system. This kind of monitoring provides several interesting features such as continuous measurements without collecting aliquots.

Keywords: biodiesel, density measurements, online continuous monitoring, synthesis

Procedia PDF Downloads 571

Authors: Berkas Khaoula, Chaoui Kamel

Abstract:

Polyethylene (PE) pipes are one of the best options for water and gas transmission networks. The main reason for such a choice is its high-quality performance in service conditions over long periods of time. PE pipes are installed in contact with different soils having various chemical compositions with confirmed aggressiveness. As a result, PE pipe surfaces undergo unwanted oxidation reactions. Usually, the polymer mixture is designed to include some additives, such as anti-oxidants, to inhibit or reduce the degradation effects. Some other additives are intended to increase resistance to the ESC phenomenon associated with polymers (ESC: Environmental Stress Cracking). This situation occurs in contact with aggressive external environments following different contaminations of soil, groundwater and transported fluids. In addition, bacterial activity and other physical or chemical media, such as temperature and humidity, can play an enhancing role. These conditions contribute to modifying the PE pipe structure and degrade its properties during exposure. In this work, the effect of distilled water, sodium hypochlorite (bleach), diluted sulfuric acid (H2SO4) and toluene-methanol (TM) mixture are studied when extruded PE samples are exposed to those environments for given periods. The chosen exposure periods are 7, 14 and 28 days at room temperature and in sealed glass containers. Post-exposure observations and ISO impact tests are presented as a function of time and chemical medium. Water effects are observed to be limited in explaining such use in real applications, whereas the changes in TM and acidic media are very significant. For the TM medium, the polymer toughness increased drastically (from 15.95 kJ/m² up to 32.01 kJ/m²), while sulfuric acid showed a steady augmentation over time. This situation may correspond to a hardening phenomenon of PE increasing its brittleness and its ability for structural degradation because of localized oxidation reactions and changes in crystallinity.

Keywords: polyethylene, toluene-methanol mixture, environmental stress cracking, degradation, impact resistance

Procedia PDF Downloads 70

Authors: Raymond Xu, Ashley Hua, Andrew Wang, Yuru Lin

Abstract:

During COVID-19, the depression rate has increased dramatically. Young adults are most vulnerable to the mental health effects of the pandemic. Lower-income families have a higher ratio to be diagnosed with depression than the general population, but less access to clinics. This research aims to achieve early depression detection at low cost, large scale, and high accuracy with an interdisciplinary approach by incorporating clinical practices defined by American Psychiatric Association (APA) as well as multimodal AI framework. The proposed approach detected the nine depression symptoms with Natural Language Processing sentiment analysis and a symptom-based Lexicon uniquely designed for young adults. The experiments were conducted on the multimedia survey results from adolescents and young adults and unbiased Twitter communications. The result was further aggregated with the facial emotional cues analyzed by the Convolutional Neural Network on the multimedia survey videos. Five experiments each conducted on 10k data entries reached consistent results with an average accuracy of 88.31%, higher than the existing natural language analysis models. This approach can reach 300+ million daily active Twitter users and is highly accessible by low-income populations to promote early depression detection to raise awareness in adolescents and young adults and reveal complementary cues to assist clinical depression diagnosis.

Keywords: artificial intelligence, COVID-19, depression detection, psychiatric disorder

Procedia PDF Downloads 128

Authors: Sasha Mullally

Abstract:

This paper investigates the transnational circulation of European Nordic ideas about and programs for manual education and training over the decades spanning the late 19th and early 20th centuries. Based on the unexamined but voluminous correspondence (English-language) of Otto Salomon, an internationally famous education reformer who popularized a form of manual training called "slöjd" (anglicized as "sloyd"), this paper examines it's circulation and translation across global cultures. Salomon, a multilingual promoter of new standardized program for manual training, based his curricula on traditional handcrqafts, particularly Swedish woodworking. He and his followers claimed that the integration of manual training and craft work provided primary and secondary educators with an opportunity to cultivate the mental, but also the physical, and tangentially, the spiritual, health of children. While historians have examined the networks who came together in person to train at his slöjd school for educators in western Sweden, no one has mapped the international community he cultivated over decades of letter writing. Additionally, while the circulation of his ideas in Britain and Germany, as well as the northeastern United States has been placed in a broader narrative of "western" education reform in the Progressive or late Victorian era, no one has examined the correspondence for evidence of the program's wider international appeal beyond Europe and North America. This paper fills this gap by examining the breadth of his reach through active correspondence with educators in Asia (Japan), South America (Brazil), and Africa (South Africa and Zimbabwe). As such, this research presents an opportunity to map the international communities of education reformers active at the turn of the last century, compare and contrast their understandings of and interpretations of "holistic" education, and reveal the ways manual formation was understood to be foundational to the healthy development of children.

Keywords: history of education, history of medicine and psychiatry, child health, child formation, internationalism

Procedia PDF Downloads 101

Authors: Javier Muro, Anja Linstadter, Florian Manner, Lisa Schwarz, Stephan Wollauer, Paul Magdon, Gohar Ghazaryan, Olena Dubovyk

Abstract:

Monitoring the trade-off between biomass production and biodiversity in grasslands is critical to evaluate the effects of management practices across environmental gradients. New generations of remote sensing sensors and machine learning approaches can model grasslands’ characteristics with varying accuracies. However, studies often fail to cover a sufficiently broad range of environmental conditions, and evidence suggests that prediction models might be case specific. In this study, biomass production and biodiversity indices (species richness and Fishers’ α) are modeled in 150 grassland plots for three sites across Germany. These sites represent a North-South gradient and are characterized by distinct soil types, topographic properties, climatic conditions, and management intensities. Predictors used are derived from Sentinel-1 & 2 and a set of topoedaphic variables. The transferability of the models is tested by training and validating at different sites. The performance of feed-forward deep neural networks (DNN) is compared to a random forest algorithm. While biomass predictions across gradients and sites were acceptable (r2 0.5), predictions of biodiversity indices were poor (r2 0.14). DNN showed higher generalization capacity than random forest when predicting biomass across gradients and sites (relative root mean squared error of 0.5 for DNN vs. 0.85 for random forest). DNN also achieved high performance when using the Sentinel-2 surface reflectance data rather than different combinations of spectral indices, Sentinel-1 data, or topoedaphic variables, simplifying dimensionality. This study demonstrates the necessity of training biomass and biodiversity models using a broad range of environmental conditions and ensuring spatial independence to have realistic and transferable models where plot level information can be upscaled to landscape scale.

Keywords: ecosystem services, grassland management, machine learning, remote sensing

Procedia PDF Downloads 214