Search results for: green infrastructure network

Authors: Patrizia Frontera, Anastasia Macario, Simona Crispi, Angela Malara, Pierantonio De Luca, Stefano Trocino

Abstract:

The exploration of solar energy for the photoelectrochemical splitting of water into hydrogen and oxygen has been extensively researched as a means of generating sustainable H₂ fuel. However, despite these efforts, commercialization of this technology has not yet materialized. Presently, the primary impediments to commercialization include low solar-to-hydrogen efficiency (2-3% in PEC with an active area of up to 10-15 cm²), the utilization of costly and critical raw materials (e.g., BiVO₄), and energy losses during the separation of H₂ from O₂ and H₂O vapours in the output stream. The SERGIO partners have identified an advanced approach to fabricate photoelectrode materials, coupled with an appropriate scientific direction to achieve cost-effective solar-driven H₂ production in a tandem photoelectrochemical cell. This project is designed to reach Technology Readiness Level (TRL) 4 by validating the technology in the laboratory using a cell with an active area of up to 10 cm², boasting a solar-to-hydrogen efficiency of 5%, and ensuring acceptable hydrogen purity (99.99%). Our objectives include breakthroughs in cost efficiency, conversion efficiency, and H₂ purity.

Keywords: photoelectrolysis, green hydrogen, photoelectrochemical cell, semiconductors

Procedia PDF Downloads 76

Authors: Nurudeen Ayeni, Kasali Bello, Ovi Abayeh

Abstract:

Study of the effect of dye–resin complexation on the degree of dye absorption were carried out using Procion Blue MX-R to dye cotton fabric in the presence hexamethylol melamine (MR 6) and its phosphate derivative (MPR 4) for resination. The highest degree of dye exhaustion was obtained at 400 C for 1 hour with the resinated fabric showing more affinity for the dye than the ordinary fiber. Improved fastness properties was recorded which show a relatively higher stability of dye–resin–cellulose network formed.

Keywords: cotton fabric, reactive dye, dyeing, resination

Procedia PDF Downloads 413

Authors: Menaahyl Seraj, Gokce Ozdemir

Abstract:

Tourism at destinations is dependent on various resources such as archeology and architecture. The need to preserve those resources is of the utmost importance when long-term tourism development is aimed. Shahi Guzargah (Royal Trail) was subject to a preservation project that is a linear historical passage within the Walled City of Lahore. Even though Lahore with its congested streets, lacks proper infrastructure and economically weak but yet it has the potential of transforming it into a tourist destination. This study highlights the potential hidden in the preservation of cultural resources through proper and concrete planning of living heritage city, and how it improves socio-economic standards of the community and affects tourism. Semi-structured open-ended interview question-forms were used to collect qualitative data from 14 respective stakeholders of the walled city and 10 concerned officials. The results of the study show that the preservation of cultural resources impacts and accelerates positively the development process of a destination. All opinions and gathered information reflect the importance of cultural preservation and its effect on increasing tourism.

Keywords: cultural tourism, cultural resources, destination, preservation

Procedia PDF Downloads 171

Authors: Diego Diaz H., Cristian Larroulet

Abstract:

Although a large earthquake has clear and immediate consequences such as deaths, destruction of infrastructure and displacement (at least temporary) of part of the population, scientific research about the impact of a geological disaster in economic activity is inconclusive, especially when looking beyond the very short term. Estimating the economic impact years after a disaster strike is non-trivial since there is an unavoidable difficulty in attributing the observed effect to the disaster and not to other economic shocks. Case studies are performed that determine the impact of earthquakes in Chile, Japan, and New Zealand at a regional level by applying the synthetic control method, using the natural disaster as treatment. This consisted in constructing a counterfactual from every region in the same country that is not affected (or is slightly affected) by the earthquake. The results show that the economies of Canterbury and Tohoku achieved greater levels of GDP per capita in the years after the disaster than they would have in the absence of the disaster. For the case of Chile, however, the region of Maule experiences a decline in GDP per capita because of the earthquake. All the results are robust according to the placebo tests. Also, the results suggest that national institutional quality improve the growth process after the disaster.

Keywords: earthquake, economic growth, institutional quality, synthetic control

Procedia PDF Downloads 228

Authors: R. Ghasemi, M. R. Rahimi Khoygani

Abstract:

This paper proposes the designing direct adaptive neural controller to apply for a class of a nonlinear pendulum dynamic system. The radial basis function (RBF) neural adaptive controller is robust in presence of external and internal uncertainties. Both the effectiveness of the controller and robustness against disturbances are importance of this paper. The simulation results show the promising performance of the proposed controller.

Keywords: adaptive neural controller, nonlinear dynamical, neural network, RBF, driven pendulum, position control

Procedia PDF Downloads 485

Authors: Sven Ratković

Abstract:

The survey of public attitudes and knowledge was conducted as part of the Dinara back to LIFE project during June and July 2020. The aim of the research was to collect public opinions and knowledge on the topics of the biodiversity of Dinara, perception of tourist potential, sustainable development, and acceptance of the project. The research was conducted using the survey method in the cities of Sinj, Knin, Vrlika, and Trilj, and the municipalities of Hrvace, Otok, Kijevo, and Civljane, where a total of 404 people were surveyed. The respondents perceive the cultural and recreational potential of Dinara and recognize it as a potential for agriculture and tourism. According to respondents, the biological diversity of Dinara is most affected by fires and human activity. When it comes to nature protection, the majority of respondents don’t trust local self-government units and relevant ministries. The obtained results indicate the need for informing and educating the community, and they serve to adjust the project activities and better guide the touristic development of the project area. The examination will be repeated in the last project year (2023).

Keywords: protected area tourism, Dinara Nature Park, dry grasslands, touristic infrastructure

Procedia PDF Downloads 104

Authors: Hooman Rowshanaie

Abstract:

The majority of green plants have 70%-90% water, this amount depend on age of plants, species, tissues of plants and also the environmental conditions that plants growth and development on it. Because of intense plant demanding to achieve the available water for growing and developing, always plants need a water sources and also mechanisms to retention the water and reduction water loss under critical situation and water deficit conditions otherwise the yield of plants would be decreased. Decreasing the yield depend on genotypes, intense of water deficit and also growth stage. Recently the mechanisms and also compound that have major role to water stress adaption of plants would be consideration. Osmotic adjustment is one of the most important mechanisms in terms of this field that many valuable researches focused on it because the majority of organic and inorganic solutes directly or even indirectly have pivotal role in this phenomenon. The contribution of OA to prevent water loss in response to water deficit and resistance to water stress taken to consideration recently and also the organic and inorganic compounds to OA tended has a high rate of significant.

Keywords: water deficit, drought stress, osmotic adjustment, organic compound, inorganic compound, solute

Procedia PDF Downloads 422

Authors: Sascha Kraus, Wolfgang Hora, Philipp Stieg, Thomas Niemand, Ferdinand Thies, Matthias Filser

Abstract:

In most countries, small and medium-sized enterprises (SME) are the backbone of the economy due to their impact on job creation, innovation and wealth creation. Moreover, the ongoing globalization makes it inevitable – even for SME that traditionally focused on their domestic markets – to internationalize their business activities to realize further growth and survive in international markets. Thus, internationalization has become one of the most common growth strategies for SME and has received increasing scholarly attention over the last two decades. One the downside internationalization can be also regarded as the most complex strategy that a firm can undertake. Particularly for family firms, that are often characterized by limited financial capital, a risk-averse nature and limited growth aspirations, it could be argued that family firms are more likely to face greater challenges when taking the pathway to internationalization. Especially the triangulation of family, ownership, and management (so-called ‘familiness’) manifests in a unique behavior and decision-making process which is often characterized by the importance given to noneconomic goals and distinguishes a family firm from other businesses. Taking this into account, the concept of socio-emotional wealth (SEW) has been evolved to describe the behavior of family firms. In order to investigate how different internal and external firm characteristics shape internationalization success of family firms, we drew on a sample consisting of 297 small and medium-sized family firms from Germany, Austria, Switzerland, and Liechtenstein. Thus, we include SEW as essential family firm characteristic and added the two major intra-organizational characteristics, entrepreneurial orientation (EO), absorptive capacity (AC) as well as collaboration intensity (CI) and relational knowledge (RK) as two major external network characteristics. Based on previous research we assume that these characteristics are important to explain internationalization success of family firm SME. Regarding the data analysis, we applied a Fuzzy Set Qualitative Comparative Analysis (fsQCA), an approach that allows identifying configurations of firm characteristics, specifically used to study complex causal relationships where traditional regression techniques reach their limits. Results indicate that several combinations of these family firm characteristics can lead to international success, with no permanently required key characteristic. Instead, there are many roads to walk down for family firms to achieve internationalization success. Consequently, our data states that family owned SME are heterogeneous and internationalization is a complex and dynamic process. Results further show that network related characteristics occur in all sets, thus represent an essential element in the internationalization process of family owned SME. The contribution of our study is twofold, as we investigate different forms of international expansion for family firms and how to improve them. First, we are able to broaden the understanding of the intersection between family firm and SME internationalization with respect to major intra-organizational and network-related variables. Second, from a practical perspective, we offer family firm owners a basis for setting up internal capabilities to achieve international success.

Keywords: entrepreneurial orientation, family firm, fsQCA, internationalization, socio-emotional wealth

Procedia PDF Downloads 244

Authors: Carole Salis, Marie F. Wilson, Fabrizio Murgia, Cristian Lai, Franco Atzori, Giulia M. Orrù

Abstract:

ANDASA is a knowledge management platform for the capitalization of knowledge and cultural assets for the artistic and cultural sectors. It was built based on the priorities expressed by the participating artists. Through mapping artistic activities and specificities, it enables to highlight various aspects of the artistic research and production. Such instrument will contribute to create networks and partnerships, as it enables to evidentiate who does what, in what field, using which methodology. The platform is accessible to network participants and to the general public.

Keywords: cultural promotion, knowledge representation, cultural maping, ICT

Procedia PDF Downloads 430

Authors: Ondrej Slach, Jan Machacek, Jan Zenka, Lucie Hyllova, Petr Rumpel

Abstract:

The aim of the paper is to identify and asses different trajectories of two fashionable urban policies –creative and smart cities– in specific post-socialistic context. Drawing on the case of Czechia, we employ the concept of policy mobility research. More specifically, we employ a discourse analysis in order to identify the so-called 'infrastructure' of both policies (such as principal actors, journals, conferences, events), with the special focus on 'agents of transfer' in a multiscale perspective. The preliminary results indicate faster and more aggressive spatial penetration of smart cities policy compared to creative cities policy in Czechia. Further, it seems that existed translation and implementation of smart cities policy into the national and urban context resulted in deliberated fragmented policy of smart cities in Czechia (pure technocratic view), which might be a threat for the future development of social sustainability, especially in cities that are facing increasing social polarisation. Last but not least, due to the fast spatial penetration of the concept and policies of smart cities, it seems that creative cities policy has almost been crowded out of the Czech urban agenda.

Keywords: policy mobility, smart cities, creative cities, Czechia

Procedia PDF Downloads 175

Authors: Bruno RamaëL, GwenaëL Page, Catherine Knopf-Lenoir, Olivier Baledent, Anne-Virginie Salsac

Abstract:

No routine technique currently exists for clinicians to measure the mechanical properties of vascular walls non-invasively. Most of the data available in the literature come from traction or dilatation tests conducted ex vivo on native blood vessels. The objective of the study is to develop a non-invasive characterization technique based on Magnetic Resonance Imaging (MRI) measurements of the deformation of vascular walls under pulsating blood flow conditions. The goal is to determine the mechanical properties of the vessels by inverse analysis, coupling imaging measurements and numerical simulations of the fluid-structure interactions. The hyperelastic properties are identified using Solidworks and Ansys workbench (ANSYS Inc.) solving an optimization technique. The vessel of interest targeted in the study is the common carotid artery. In vivo MRI measurements of the vessel anatomy and inlet velocity profiles was acquired along the facial vascular network on a cohort of 30 healthy volunteers: - The time-evolution of the blood vessel contours and, thus, of the cross-section surface area was measured by 3D imaging angiography sequences of phase-contrast MRI. - The blood flow velocity was measured using a 2D CINE MRI phase contrast (PC-MRI) method. Reference arterial pressure waveforms were simultaneously measured in the brachial artery using a sphygmomanometer. The three-dimensional (3D) geometry of the arterial network was reconstructed by first creating an STL file from the raw MRI data using the open source imaging software ITK-SNAP. The resulting geometry was then transformed with Solidworks into volumes that are compatible with Ansys softwares. Tetrahedral meshes of the wall and fluid domains were built using the ANSYS Meshing software, with a near-wall mesh refinement method in the case of the fluid domain to improve the accuracy of the fluid flow calculations. Ansys Structural was used for the numerical simulation of the vessel deformation and Ansys CFX for the simulation of the blood flow. The fluid structure interaction simulations showed that the systolic and diastolic blood pressures of the common carotid artery could be taken as reference pressures to identify the mechanical properties of the different arteries of the network. The coefficients of the hyperelastic law were identified using Ansys Design model for the common carotid. Under large deformations, a stiffness of 800 kPa is measured, which is of the same order of magnitude as the Young modulus of collagen fibers. Areas of maximum deformations were highlighted near bifurcations. This study is a first step towards patient-specific characterization of the mechanical properties of the facial vessels. The method is currently applied on patients suffering from facial vascular malformations and on patients scheduled for facial reconstruction. Information on the blood flow velocity as well as on the vessel anatomy and deformability will be key to improve surgical planning in the case of such vascular pathologies.

Keywords: identification, mechanical properties, arterial walls, MRI measurements, numerical simulations

Procedia PDF Downloads 322

Authors: Joy Cao, Min Zhou

Abstract:

Purpose: Acute Type A aortic dissection is a well-known cause of extremely high mortality rate. A highly accurate and cost-effective non-invasive predictor is critically needed so that the patient can be treated at earlier stage. Although various CFD approaches have been tried to establish some prediction frameworks, they are sensitive to uncertainty in both image segmentation and boundary conditions. Tedious pre-processing and demanding calibration procedures requirement further compound the issue, thus hampering their clinical applicability. Using the latest physics informed deep learning methods to establish an accurate and cost-effective predictor framework are amongst the main goals for a better Type A aortic dissection treatment. Methods: Via training a novel physics-informed deep residual network, with non-invasive 4D MRI displacement vectors as inputs, the trained model can cost-effectively calculate all these biomarkers: aortic blood pressure, WSS, and OSI, which are used to predict potential type A aortic dissection to avoid the high mortality events down the road. Results: The proposed deep learning method has been successfully trained and tested with both synthetic 3D aneurysm dataset and a clinical dataset in the aortic dissection context using Google colab environment. In both cases, the model has generated aortic blood pressure, WSS, and OSI results matching the expected patient’s health status. Conclusion: The proposed novel physics-informed deep residual network shows great potential to create a cost-effective, non-invasive predictor framework. Additional physics-based de-noising algorithm will be added to make the model more robust to clinical data noises. Further studies will be conducted in collaboration with big institutions such as Cleveland Clinic with more clinical samples to further improve the model’s clinical applicability.

Keywords: type-a aortic dissection, deep residual networks, blood flow modeling, data-driven modeling, non-invasive diagnostics, deep learning, artificial intelligence.

Procedia PDF Downloads 94

Authors: Joel M. De La Rosa R.

Abstract:

In this paper, the results obtained when monitoring the horizontal deformations of the soil mass are detailed, during each of the construction stages of several vertical shafts located in the soft soils of the Valley of Mexico, by means of the flotation method. From the analysis of these results, the magnitude and percentage relationship with respect to the diameter and depth of excavation of the horizontal deformations that occurred during the monitoring period is established. Based on the horizontal deformation monitoring system and the information provided by the supervisor's site log, the construction stages that have the greatest impact on deformations are established. Additionally, an analysis of the deformations is carried out, which takes into account the resistance and deformability characteristics of the excavated soils, as well as the prevailing hydraulic conditions. This work will allow construction engineers and institutions in charge of infrastructure works in the Valley of Mexico to establish permissible ranges for horizontal deformations that can occur in very soft and saturated soils, during the different construction stages; improving response protocols to potentially dangerous behaviors.

Keywords: vertical shaft, flotation method, very soft clays, construction supervision

Procedia PDF Downloads 192

Authors: Tarun Jain, Lovnish Gupta, Soumen Basu

Abstract:

Organic pollutants like phenols and organic dyes present in industrial waste water are posing a hazardous threat to aquatic ecosystem. Several measures have been adopted for the neutralization and photodecomposition of these harmful organic moieties, among these semiconductor photocatalysis has been provided a major thrust after the discovery of Honda-Fujishema effect. Present study demonstrates the adsorption of TiO2- P25 in nano size (~36 nm) on cement balls for effective photodegradation of Alizarin and penta chlorophenol (PCP) under UV light illumination. Triton-X was used as a stabilizer for effective adsorption of TiO2 on cement balls (TCB) followed by calcination at ~300oC for 4 h. The TCB’s were dispersed randomly in a self designed reactor for phototcatalytic performance as shown in scheme 1. The change in concentration of alizarin and PCP was observed under UV-Vis spectroscopy, PCP was detoxified within 40 min while alizarin photodecomposed within 15 min of UV light irradiation. Taking into consideration the go green slogan and future prospective this technique can be also utilized under visible light and on mass scale because this is an effective tool for environmental remediation and waste water treatment.

Keywords: organic pollutants, TiO2 cement balls, photodegradation, UV light irradiation

Procedia PDF Downloads 267

Authors: Jo Kuys, Kirsten Day

Abstract:

Improving urban transportation systems requires an emphasis on users’ end-to-end journey experience, from the moment the user steps out of their home to when they arrive at their destination. In considering such end-to-end experiences, human centred design (HCD) must be integrated from the very beginning to generate viable outcomes for the public. An HCD approach will encourage innovative outcomes while acknowledging all factors that need to be understood along the journey. We provide evidence to show that when designing for public transportation, it is not just about the physical manifestation of a particular outcome; moreover, it’s about the context and human behaviours that need to be considered throughout the design process. Humans and their behavioural factors are vitally important to successful implementation of sustainable public transport systems. Through an in-depth literature review of HCD approaches for urban transportation systems, we provide a base to exploit the benefits and highlight the importance of including HCD in public transportation projects for greater patronage, resulting in more sustainable cities. An HCD approach is critical to all public transportation projects to understand different levels of transportation design, from the setting of transport policy to implementation to infrastructure, vehicle, and interface design.

Keywords: human centred design, public transportation, urban planning, user experience

Procedia PDF Downloads 192

Authors: Wanqing You, Kai Qian, Ying Qian

Abstract:

The concepts of Cloud Computing provide users with infrastructure, platform and software as service, which make those services more accessible for people via Internet. To better analysis the performance of Cloud Computing provisioning policies as well as resources allocation strategies, a toolkit named CloudSim proposed. With CloudSim, the Cloud Computing environment can be easily constructed by modelling and simulating cloud computing components, such as datacenter, host, and virtual machine. A good scheduling strategy is the key to achieve the load balancing among different machines as well as to improve the utilization of basic resources. Recently, the existing scheduling algorithms may work well in some presumptive cases in a single machine; however they are unable to make the best decision for the unforeseen future. In real world scenario, there would be numbers of tasks as well as several virtual machines working in parallel. Based on the concepts of multi-queue, this paper presents a new scheduling algorithm to schedule tasks with CloudSim by taking into account several parameters, the machines’ capacity, the priority of tasks and the history log.

Keywords: hierarchical queue, load balancing, CloudSim, information technology

Procedia PDF Downloads 428

Authors: Flavio Contrera, Paulo Cesar Gregorio

Abstract:

Recent political developments and government control actions in the face of the COVID-19 pandemic draw attention to the contrast between the duties of government and the demands of democratic representation. Elected by mobilizing far-right issues, Trump and Bolsonaro moved away from the WHO guidelines but had to accommodate demands on the health and on the social protection system on the one hand and demands from the economic sector on the other. This study used the MARPOR Project method to assess the impact of the COVID-19 pandemic on the issue and ideological congruence between the electoral and governmental arena in both the Trump and Bolsonaro Administrations. Findings reveal issue congruence between arenas in "National Way of Life: Positive", "Law and Order," and "Technology and Infrastructure" for Donald Trump, and "Welfare State Expansion" for Bolsonaro. Ideological estimation results show that Trump and Bolsonaro positioned to the right in their presidential elections, initially moved to the center-right. However, welfare policies actions at high frequency during the COVID-19 pandemic moved the ideological estimations of both governments to the center-left, despite their denial rhetoric.

Keywords: congruence, COVID-19, Donald Trump, Jair Bolsonaro

Procedia PDF Downloads 235

Authors: Xizhen Zhou, Yanjie Ji

Abstract:

The charging behavior of drivers provides a reference for the planning and management of charging facilities. Based on the real trajectory data of electric vehicles, this study explored the influence of the surrounding environments of the parking spot on charging decisions. The built environment, the condition of vehicles, and the nearest charging station were all considered. And the mixed binary logit model was used to capture the impact of unobserved heterogeneity. The results show that the number of fast chargers in the charging station, parking price, dwell time, and shopping services all significantly impact the charging decision, while the leisure services, scenic spots, and mileage since the last charging are opposite. Besides, factors related to unobserved heterogeneity include the number of fast chargers, parking and charging prices, residential areas, etc. The interaction effects of random parameters further illustrate the complexity of charging choice behavior. The results provide insights for planning and managing charging facilities.

Keywords: charging decision, trajectory, electric vehicle, infrastructure, mixed logit

Procedia PDF Downloads 75

Authors: Arjun Kumar Rath, Titus Dhanasingh

Abstract:

Embedded product development requires software to test hardware functionality during development and finding issues during manufacturing in larger quantities. As the components are getting integrated, the devices are tested for their full functionality using advanced software tools. Benchmarking tools are used to measure and compare the performance of product features. At present, these tests are based on a variety of methods involving varying hardware and software platforms. Typically, these tests are custom built for every product and remain unusable for other variants. A majority of the tests goes undocumented, not updated, unusable when the product is released. To bridge this gap, a solution accelerator in the form of a framework can address these issues for running all these tests from one place, using an off-the-shelf tests library in a continuous integration environment. There are many open-source test frameworks or tools (fuego. LAVA, AutoTest, KernelCI, etc.) designed for testing embedded system devices, with each one having several unique good features, but one single tool and framework may not satisfy all of the testing needs for embedded systems, thus an extensible framework with the multitude of tools. Embedded product testing includes board bring-up testing, test during manufacturing, firmware testing, application testing, and assembly testing. Traditional test methods include developing test libraries and support components for every new hardware platform that belongs to the same domain with identical hardware architecture. This approach will have drawbacks like non-reusability where platform-specific libraries cannot be reused, need to maintain source infrastructure for individual hardware platforms, and most importantly, time is taken to re-develop test cases for new hardware platforms. These limitations create challenges like environment set up for testing, scalability, and maintenance. A desirable strategy is certainly one that is focused on maximizing reusability, continuous integration, and leveraging artifacts across the complete development cycle during phases of testing and across family of products. To get over the stated challenges with the conventional method and offers benefits of embedded testing, an embedded test framework (ETF), a solution accelerator, is designed, which can be deployed in embedded system-related products with minimal customizations and maintenance to accelerate the hardware testing. Embedded test framework supports testing different hardwares including microprocessor and microcontroller. It offers benefits such as (1) Time-to-Market: Accelerates board brings up time with prepacked test suites supporting all necessary peripherals which can speed up the design and development stage(board bring up, manufacturing and device driver) (2) Reusability-framework components isolated from the platform-specific HW initialization and configuration makes the adaptability of test cases across various platform quick and simple (3) Effective build and test infrastructure with multiple test interface options and preintegrated with FUEGO framework (4) Continuos integration - pre-integrated with Jenkins which enabled continuous testing and automated software update feature. Applying the embedded test framework accelerator throughout the design and development phase enables to development of the well-tested systems before functional verification and improves time to market to a large extent.

Keywords: board diagnostics software, embedded system, hardware testing, test frameworks

Procedia PDF Downloads 152

Authors: Hen Friman

Abstract:

Various categories of aqueous solutions are discharged within residential, institutional, commercial, and industrial structures. To safeguard public health and preserve the environment, it is imperative to subject wastewater to treatment processes that eliminate pathogens (such as bacteria and viruses), nutrients (such as nitrogen and phosphorus), and other compounds. Failure to address untreated sewage accumulation can result in an array of adverse consequences. Israel exemplifies a special case in wastewater management. Appropriate wastewater treatment significantly benefits sectors such as agriculture, tourism, horticulture, and industry. Nevertheless, untreated sewage in settlements lacking proper sewage collection or transportation networks remains an ongoing and substantial threat. Notably, the process of wastewater treatment entails substantial energy consumption. Consequently, this study explores the integration of solar energy as a renewable power source within the wastewater treatment framework. By incorporating renewable energy sources into the process, costs can be minimized, and decentralized facilities can be established even in areas lacking adequate infrastructure for traditional treatment methods.

Keywords: renewable energy, solar energy, innovative, wastewater treatment

Procedia PDF Downloads 114

Authors: Irene Yi

Abstract:

Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Computational linguistics is a growing field dealing with such issues of data collection for technological development. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Computational analysis on such linguistic data is used to find patterns of misogyny. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: computational analysis, gendered grammar, misogynistic language, neural networks

Procedia PDF Downloads 126

Authors: Luojie Tang

Abstract:

The article takes the "7.20 Heavy Rainstorm in Zhengzhou" as a starting point, collects relevant disaster data, reproduces the entire process of the disaster, and identifies the main problems exposed by the city in responding to super-standard rain and flood disasters. Based on the review of resilience theory, the article proposes a shift in thinking about the response to super-standard rain and flood disasters from the perspective of resilience, clarifies the differences in the emphasis on resilience at different stages of disasters, and preliminarily constructs a response system for super-standard rain and flood disasters based on the guidance of resilience theory. Finally, combined with the highlighted problems in the 7.20 Heavy Rainstorm in Zhengzhou, the article proposes targeted response strategies from three perspectives: institutional management, technological support, and infrastructure, under the perspective of resilience.

Keywords: resilient city, exceedance-based stormwater management, disaster risk reduction, megalopolis

Procedia PDF Downloads 123

Authors: E. Azizi Asiyabar

Abstract:

The phenomenon of migration to larger cities has brought about a range of consequences, including increased travel demand and the necessity for smooth traffic flow to expedite transportation. Regrettably, insufficient urban transportation infrastructure has given rise to various issues, including air pollution, heightened fuel consumption, and wasted time. To address traffic-related problems and the economic, social, and environmental challenges that ensue, a well-equipped, efficient, fast, cost-effective, and high-capacity transportation system is imperative, with a focus on reliability. This study undertakes a comprehensive examination of rail transportation systems and subsequently compares their advantages and limitations. The findings of this investigation reveal that hybrid monorails exhibit lower maintenance requirements and associated costs when compared to other types of monorails, standard trains, and urban light rail systems. Given their favorable attributes in terms of pollution reduction, increased transportation speed, and enhanced quality of service, hybrid monorails emerge as a highly recommended and suitable option.

Keywords: comparing, most efficient, selecting, urban transportation

Procedia PDF Downloads 88

Authors: Forouzan Salehi Fergeni

Abstract:

Converting the movement intents of a person into commands for action employing brain signals like electroencephalogram signals is a brain-computer interface (BCI) system. When left or right-hand motions are imagined, different patterns of brain activity appear, which can be employed as BCI signals for control. To make better the brain-computer interface (BCI) structures, effective and accurate techniques for increasing the classifying precision of motor imagery (MI) based on electroencephalography (EEG) are greatly needed. Subject dependency and non-stationary are two features of EEG signals. So, EEG signals must be effectively processed before being used in BCI applications. In the present study, after applying an 8 to 30 band-pass filter, a car spatial filter is rendered for the purpose of denoising, and then, a method of analysis of variance is used to select more appropriate and informative channels from a category of a large number of different channels. After ordering channels based on their efficiencies, a sequential forward channel selection is employed to choose just a few reliable ones. Features from two domains of time and wavelet are extracted and shortlisted with the help of a statistical technique, namely the t-test. Finally, the selected features are classified with different machine learning and neural network classifiers being k-nearest neighbor, Probabilistic neural network, support-vector-machine, Extreme learning machine, decision tree, Multi-layer perceptron, and linear discriminant analysis with the purpose of comparing their performance in this application. Utilizing a ten-fold cross-validation approach, tests are performed on a motor imagery dataset found in the BCI competition III. Outcomes demonstrated that the SVM classifier got the greatest classification precision of 97% when compared to the other available approaches. The entire investigative findings confirm that the suggested framework is reliable and computationally effective for the construction of BCI systems and surpasses the existing methods.

Keywords: brain-computer interface, channel selection, motor imagery, support-vector-machine

Procedia PDF Downloads 55

Authors: Sunil Chandra, Himanshu Rawat, Vikas Gusain, Triparna Barman

Abstract:

Forest fires are a recurrent phenomenon in the Himalayan region owing to the presence of vulnerable forest types, topographical gradients, climatic weather conditions, and anthropogenic pressure. The present study focuses on the identification of forest fire-affected areas in a small part of the West Himalayan region using a differential normalized burnt ratio method and spectral unmixing methods. The study area has a rugged terrain with the presence of sub-tropical pine forest, montane temperate forest, and sub-alpine forest and scrub. The major reason for fires in this region is anthropogenic in nature, with the practice of human-induced fires for getting fresh leaves, scaring wild animals to protect agricultural crops, grazing practices within reserved forests, and igniting fires for cooking and other reasons. The fires caused by the above reasons affect a large area on the ground, necessitating its precise estimation for further management and policy making. In the present study, two approaches have been used for carrying out a burnt area analysis. The first approach followed for burnt area analysis uses a differenced normalized burnt ratio (dNBR) index approach that uses the burnt ratio values generated using the Short-Wave Infrared (SWIR) band and Near Infrared (NIR) bands of the Sentinel-2 image. The results of the dNBR have been compared with the outputs of the spectral mixing methods. It has been found that the dNBR is able to create good results in fire-affected areas having homogenous forest stratum and with slope degree <5 degrees. However, in a rugged terrain where the landscape is largely influenced by the topographical variations, vegetation types, tree density, the results may be largely influenced by the effects of topography, complexity in tree composition, fuel load composition, and soil moisture. Hence, such variations in the factors influencing burnt area assessment may not be effectively carried out using a dNBR approach which is commonly followed for burnt area assessment over a large area. Hence, another approach that has been attempted in the present study utilizes a spectral mixing method where the individual pixel is tested before assigning an information class to it. The method uses a neural network approach utilizing Sentinel-2 bands. The training and testing data are generated from the Sentinel-2 data and the national field inventory, which is further used for generating outputs using ML tools. The analysis of the results indicates that the fire-affected regions and their severity can be better estimated using spectral unmixing methods, which have the capability to resolve the noise in the data and can classify the individual pixel to the precise burnt/unburnt class.

Keywords: categorical data, log linear modeling, neural network, shifting cultivation

Procedia PDF Downloads 57

Authors: Akeel A. Shah, Tong Zhang

Abstract:

Computational approaches to learning the properties of materials are commonplace, motivated by the need to screen or design materials for a given application, e.g., semiconductors and energy storage. Experimental approaches can be both time consuming and costly. Unfortunately, computational approaches such as ab-initio electronic structure calculations and classical or ab-initio molecular dynamics are themselves can be too slow for the rapid evaluation of materials, often involving thousands to hundreds of thousands of candidates. Machine learning assisted approaches have been developed to overcome the time limitations of purely physics-based approaches. These approaches, on the other hand, require large volumes of data for training (hundreds of thousands on many standard data sets such as QM7b). This means that they are limited by how quickly such a large data set of physics-based simulations can be established. At high fidelity, such as configuration interaction, composite methods such as G4, and coupled cluster theory, gathering such a large data set can become infeasible, which can compromise the accuracy of the predictions - many applications require high accuracy, for example band structures and energy levels in semiconductor materials and the energetics of charge transfer in energy storage materials. In order to circumvent this problem, multi-fidelity approaches can be adopted, for example the Δ-ML method, which learns a high-fidelity output from a low-fidelity result such as Hartree-Fock or density functional theory (DFT). The general strategy is to learn a map between the low and high fidelity outputs, so that the high-fidelity output is obtained a simple sum of the physics-based low-fidelity and correction, Although this requires a low-fidelity calculation, it typically requires far fewer high-fidelity results to learn the correction map, and furthermore, the low-fidelity result, such as Hartree-Fock or semi-empirical ZINDO, is typically quick to obtain, For high-fidelity outputs the result can be an order of magnitude or more in speed up. In this work, a new multi-fidelity approach is developed, based on a graph convolutional network (GCN) combined with semi-supervised learning. The GCN allows for the material or molecule to be represented as a graph, which is known to improve accuracy, for example SchNet and MEGNET. The graph incorporates information regarding the numbers of, types and properties of atoms; the types of bonds; and bond angles. They key to the accuracy in multi-fidelity methods, however, is the incorporation of low-fidelity output to learn the high-fidelity equivalent, in this case by learning their difference. Semi-supervised learning is employed to allow for different numbers of low and high-fidelity training points, by using an additional GCN-based low-fidelity map to predict high fidelity outputs. It is shown on 4 different data sets that a significant (at least one order of magnitude) increase in accuracy is obtained, using one to two orders of magnitude fewer low and high fidelity training points. One of the data sets is developed in this work, pertaining to 1000 simulations of quinone molecules (up to 24 atoms) at 5 different levels of fidelity, furnishing the energy, dipole moment and HOMO/LUMO.

Keywords: .materials screening, computational materials, machine learning, multi-fidelity, graph convolutional network, semi-supervised learning

Procedia PDF Downloads 46

Authors: Ilham Ihoume, Rachid Tadili, Nora Arbaoui

Abstract:

One of the most crucial ways to combat food insecurity is to minimize crop losses; food drying is one of the most organic, efficient, low-cost, and energy-saving food preservation methods. In this regard, we undertake in this study an experimental evaluation and analysis of the thermal performance of different natural convection drying systems: a solar greenhouse dryer, an indirect solar dryer with a single compartment, and a solar dryer with two compartments. These systems have been implemented at the Solar Energy and Environment Laboratory of Mohammed V University (Morocco). The objective of this work is to study the feasibility of converting a solar greenhouse into a solar dryer for use during the summer. On the other hand, to study the thermal performances of this greenhouse dryer by comparing it with other solar dryers. The experimental study showed that the drying of verbena leaves took 6 hours in the indirect dryer 1, 3 hours in the indirect dryer, and 2 and 4 hours in the greenhouse dryer, but the amortization period of the solar greenhouse dryer is lower than the other two solar dryers. The results of this study provide key information on the implementation and performance of these systems for drying food of great global interest.

Keywords: indirect solar dryer, solar energy, agricultural greenhouse, green energy

Procedia PDF Downloads 98

Authors: D. Pranav

Abstract:

Pesticides are a class of Biocide, whose use in agriculture has led to a momentous increase in the yield of crops, fruits and vegetables all over the word and its effective use has also been the pillars of success for the Green Revolution. However, the incessant use of pesticides has now reached alarming levels. In 2007 alone, the world used an estimated 2.4 million tons of pesticides. Despite its tremendous benefits for agriculture, pesticide has been one of the major reasons for degradation of the natural environment and undesirable effects on human beings. It has not only caused damage to human health, but has also threatened the survival of few birds and animal species. In India, the sale and usage of banned pesticide, increased usage of pesticides and its inadequate labeling has caused Bio magnification, which is causing deleterious effects on child development, resulting in stunted mental and physical growth. This paper aims to bring to shed light on major loopholes in the current pesticide regulations such as the Insecticide Act of 1968. It further discusses loopholes in the yet to be tabled Pesticides Management Bill of 2008. It discusses and arrives at potential amendments to the laws and regulations concerning pesticides; that cannot only be applied to the Indian subcontinent but other developing countries as well.

Keywords: pesticides, India, human health, environment, regulations, reform

Procedia PDF Downloads 327

Authors: Amaro Olímpio Pereira Junior, Silvio Carlos Anibal de Almeida, Matheus Dias da Rocha

Abstract:

Hydrogen plays an important role in transitioning to a low-carbon economy as an alternative to fossil fuels. However, to become competitive, hydrogen must overcome technical and economic barriers: production costs, storage, transportation, and large-scale production from renewable sources. Due to its low cost, steam reforming of natural gas is the most used route for hydrogen generation. However, besides not reducing the dependence on fossil fuels, the process presents the inconvenience of GHG emissions. This work evaluates the economic feasibility and emissions of different hydrogen production routes from methane, which can be obtained from biogas, a renewable fuel. Three routes for hydrogen production were compared: steam reforming, catalytic pyrolysis, and plasma pyrolysis. The results analyzed CO₂ emissions and hydrogen production costs. Steam reforming presented hydrogen production costs ranging from R$ 20.08 to R$ 22.70/kgH₂ and pyrolysis from R$ 34.18 to R$ 36.74/kgH₂. However, considering the commercialization of carbon black, a byproduct of pyrolysis, the hydrogen production cost can be reduced from R$ 25.26 to R$ 27.72/kgH₂. Regarding emissions, values for steam reforming range from 1.39 to 6.75 kg CO₂/kgH₂, considering CCS technologies, and those for pyrolysis range from 0.18 to 1.19 kg CO₂/kgH₂.

Keywords: hydrogen, pyrolysis, plasma reforming, methane, decarbonization

Procedia PDF Downloads 6

Authors: Omar Alelweet

Abstract:

Carbon emissions are quantified in terms of carbon dioxide equivalents, generated through a specific activity or accumulated throughout the life stages of a product or service. Given the growing concern about climate change and the role of carbon dioxide emissions in global warming, this initiative aims to create awareness and understanding of the impact of human activities and identify potential areas for improvement regarding the management of the carbon footprint on campus. Given that trees play a vital role in reducing carbon emissions by absorbing CO₂ during the photosynthesis process, this paper evaluated the contribution of each tree to reducing those emissions. Collecting data over an extended period of time is essential to monitoring carbon dioxide levels. This will help capture changes at different times and identify any patterns or trends in the data. By linking the data to specific activities, events, or environmental factors, it is possible to identify sources of emissions and areas where carbon dioxide levels are rising. Analyzing the collected data can provide valuable insights into ways to reduce emissions and mitigate the impact of climate change.

Keywords: sustainability, green building, environmental impact, CO₂

Procedia PDF Downloads 77