Search results for: innovation network

Authors: Shiva Kumar, G. S. Vijay, Srinivas Pai P., Shrinivasa Rao B. R.

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In the present study RBF neural networks were used for predicting the performance and emission parameters of a biodiesel engine. Engine experiments were carried out in a 4 stroke diesel engine using blends of diesel and Honge methyl ester as the fuel. Performance parameters like BTE, BSEC, Tech and emissions from the engine were measured. These experimental results were used for ANN modeling. RBF center initialization was done by random selection and by using Clustered techniques. Network was trained by using fixed and varying widths for the RBF units. It was observed that RBF results were having a good agreement with the experimental results. Networks trained by using clustering technique gave better results than using random selection of centers in terms of reduced MRE and increased prediction accuracy. The average MRE for the performance parameters was 3.25% with the prediction accuracy of 98% and for emissions it was 10.4% with a prediction accuracy of 80%.

Keywords: radial basis function networks, emissions, performance parameters, fuzzy c means

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Authors: Georgios Boustras, Cleo Varianou Mikellidou, Christos Argyropoulos

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Summer 2021 was marked by a number of prolific fires in the EU (Greece, Cyprus, France) as well as outside the EU (USA, Turkey, Israel). This series of dramatic events have stretched national civil protection systems and first responders in particular. Despite the introduction of National, Regional and International frameworks (e.g. rescEU), a number of challenges have arisen, not only related to climate change. RESPOND-A (funded by the European Commission by Horizon 2020, Contract Number 883371) introduces a unique five-tier project architectural structure for best associating modern telecommunications technology with novel practices for First Responders of saving lives, while safeguarding themselves, more effectively and efficiently. The introduced architecture includes Perception, Network, Processing, Comprehension, and User Interface layers, which can be flexibly elaborated to support multiple levels and types of customization, so, the intended technologies and practices can adapt to any European Environment Agency (EEA)-type disaster scenario. During the preparation of the RESPOND-A proposal, some of our First Responder Partners expressed the need for an information management system that could boost existing emergency response tools, while some others envisioned a complete end-to-end network management system that would offer high Situational Awareness, Early Warning and Risk Mitigation capabilities. The intuition behind these needs and visions sits on the long-term experience of these Responders, as well, their smoldering worry that the evolving threat of climate change and the consequences of industrial accidents will become more frequent and severe. Three large-scale pilot studies are planned in order to illustrate the capabilities of the RESPOND-A system. The first pilot study will focus on the deployment and operation of all available technologies for continuous communications, enhanced Situational Awareness and improved health and safety conditions for First Responders, according to a big fire scenario in a Wildland Urban Interface zone (WUI). An important issue will be examined during the second pilot study. Unobstructed communication in the form of the flow of information is severely affected during a crisis; the flow of information between the wider public, from the first responders to the public and vice versa. Call centers are flooded with requests and communication is compromised or it breaks down on many occasions, which affects in turn – the effort to build a common operations picture for all firstr esponders. At the same time the information that reaches from the public to the operational centers is scarce, especially in the aftermath of an incident. Understandably traffic if disrupted leaves no other way to observe but only via aerial means, in order to perform rapid area surveys. Results and work in progress will be presented in detail and challenges in relation to civil protection will be discussed.

Keywords: first responders, safety, civil protection, new technologies

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Authors: Syed Mahbubuz Zaman, A. B. M. Abrar Haque, Mehedi Hassan Nayeem, Misbah Uddin Sagor

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Everyday e-mails are being used by millions of people as an effective form of communication over the Internet. Although e-mails allow high-speed communication, there is a constant threat known as spam. Spam e-mail is often called junk e-mails which are unsolicited and sent in bulk. These unsolicited emails cause security concerns among internet users because they are being exposed to inappropriate content. There is no guaranteed way to stop spammers who use static filters as they are bypassed very easily. In this paper, a smart system is proposed that will be using neural networks to approach spam in a different way, and meanwhile, this will also detect the most relevant features that will help to design the spam filter. Also, a comparison of different parameters for different neural network models has been shown to determine which model works best within suitable parameters.

Keywords: long short-term memory, bidirectional long short-term memory, gated recurrent unit, natural language processing, natural language processing

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Authors: Laura M. F. Bertens

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There is a long tradition of visually representing the art historical canon, in schematic overviews and diagrams. This is indicative of the desire for scientific, ‘objective’ knowledge of the kind (seemingly) produced in the natural sciences. These diagrams will, however, always retain an element of subjectivity and the modelling methods colour our perception of the represented information. In recent decades visualisations of art historical data, such as hand-drawn diagrams in textbooks, have been extended to include digital, computational tools. These tools significantly increase modelling strength and functionality. As such, they might be used to deconstruct and amend the very problem caused by traditional visualisations of the canon. In this paper, the use of digital tools for modelling the art historical canon is studied, in order to draw attention to the artificial nature of the static models that art historians are presented with in textbooks and lectures, as well as to explore the potential of digital, dynamic tools in creating new models. To study the way diagrams of the canon mediate the represented information, two modelling methods have been used on two case studies of existing diagrams. The tree diagram Stammbaum der neudeutschen Kunst (1823) by Ferdinand Olivier has been translated to a social network using the program Visone, and the famous flow chart Cubism and Abstract Art (1936) by Alfred Barr has been translated to an ontological model using Protégé Ontology Editor. The implications of the modelling decisions have been analysed in an art historical context. The aim of this project has been twofold. On the one hand the translation process makes explicit the design choices in the original diagrams, which reflect hidden assumptions about the Western canon. Ways of organizing data (for instance ordering art according to artist) have come to feel natural and neutral and implicit biases and the historically uneven distribution of power have resulted in underrepresentation of groups of artists. Over the last decades, scholars from fields such as Feminist Studies, Postcolonial Studies and Gender Studies have considered this problem and tried to remedy it. The translation presented here adds to this deconstruction by defamiliarizing the traditional models and analysing the process of reconstructing new models, step by step, taking into account theoretical critiques of the canon, such as the feminist perspective discussed by Griselda Pollock, amongst others. On the other hand, the project has served as a pilot study for the use of digital modelling tools in creating dynamic visualisations of the canon for education and museum purposes. Dynamic computer models introduce functionalities that allow new ways of ordering and visualising the artworks in the canon. As such, they could form a powerful tool in the training of new art historians, introducing a broader and more diverse view on the traditional canon. Although modelling will always imply a simplification and therefore a distortion of reality, new modelling techniques can help us get a better sense of the limitations of earlier models and can provide new perspectives on already established knowledge.

Keywords: canon, ontological modelling, Protege Ontology Editor, social network modelling, Visone

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Authors: Nadia Sid, Alan Taylor, Marion Bourebrab

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The construction sector is on the critical path to decarbonise the European economy by 2050. In order to achieve this objective it must enable reducing its CO2 emission by 90% and its energy consumption by as much as 50%. For this reason, a new class of low environmental impact construction materials named “eco-material” are becoming increasingly important in the struggle against climate change. A European funded collaborative project ISOBIO coordinated by TWI is aimed at taking a radical approach to the use of bio-based aggregates to create novel construction materials that are usable in high volume in using traditional methods, as well as developing markets such as exterior insulation of existing house stocks. The approach taken for this project is to use finely chopped material protected from bio-degradation through the use of functionalized silica nanoparticles. TWI is exploring the development of novel inorganic-organic hybrid nano-materials, to be applied as a surface treatment onto bio-based aggregates. These nanoparticles are synthesized by sol-gel processing and then functionalised with silanes to impart multifunctionality e.g. hydrophobicity, fire resistance and chemical bonding between the silica nanoparticles and the bio-based aggregates. This talk will illustrate the approach taken by TWI to design the functionalized silica nanoparticles by using a material-by-design approach. The formulation and synthesize process will be presented together with the challenges addressed by those hybrid nano-materials. The results obtained with regards to the water repellence and fire resistance will be displayed together with preliminary public results of the ISOBIO project. (This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641927).

Keywords: bio-sourced material, composite material, durable insulation panel, water repellent material

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Authors: M. S. Gaafar, S. Y. Marzouk, I. S. Mahmoud, S. Al-Zobaidi

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Makishima and Mackenzie model was used to simulation of acoustic properties (longitudinal and shear ultrasonic wave velocities, elastic moduli theoretically for many tellurite and borate glasses. The model was proposed mainly depending on the values of the experimentally measured density, which are obtained before. In this search work, we are trying to obtain the values of densities of amorphous glasses (as the density depends on the geometry of the network structure of these glasses). In addition, the problem of simulating the slope of linear regression between the experimentally determined bulk modulus and the product of packing density and experimental Young's modulus, were solved in this search work. The results showed good agreement between the experimentally measured values of densities and both ultrasonic wave velocities, and those theoretically determined.

Keywords: glasses, ultrasonic wave velocities, elastic modulus, Makishima & Mackenzie Model

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Authors: Ferinar Moaidi, Mahdi Moaidi

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Environmental issues and the ever-increasing in demand of electrical energy make it necessary to have distributed generation (DG) resources in the power system. In this research, in order to realize the goals of reducing losses and improving the voltage profile in a microgrid, the allocation and sizing of DGs have been used. The proposed Genetic Algorithm (GA) is described from the array of artificial intelligence methods for solving the problem. The algorithm is implemented on the IEEE 33 buses network. This study is presented in two scenarios, primarily to illustrate the effect of location and determination of DGs has been done to reduce losses and improve the voltage profile. On the other hand, decisions made with the one-level assumptions of load are not universally accepted for all levels of load. Therefore, in this study, load modelling is performed and the results are presented for multi-levels load state.

Keywords: distributed generation, genetic algorithm, microgrid, load modelling, loss reduction, voltage improvement

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Authors: Julia DeVoy, Olivia Berlin

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The United States exports over 1.6 billion pounds of post-consumer textiles every year, primarily to countries in the Global South. These textiles make their way to landfills and open-air dumps where they decompose, contaminating water systems and releasing harmful greenhouse gases. Through this inequitable system of waste disposal, countries with less political and economic power are coerced into accepting the environmental and health consequences of over-consumption in the Global North. Thus, the global trade of post-consumer textile waste represents a serious issue of environmental justice and a public health hazard. Our research located, characterizes, and quantifies the environmental and human health risks that occur when post-consumer textiles are left to decompose in landfills and open-air dumps in the Global South. In our work, we make use of United Nations International Trade Statistics data to map the global distribution of post-consumer textiles exported from the United States. Next, we present our landfill simulating reactor designed to measure toxicity of leachate resulting from the decomposition of textiles in developing countries and to quantify the related greenhouse gas emissions. This design makes use of low-cost and sustainable materials to promote frugal innovation and make landfill reactors more accessible. Finally, we describe how the data generated from these tools can be leveraged to inform individual consumer behaviors, local policies around textile waste disposal, and global advocacy efforts to mitigate the environmental harms caused by textile waste.

Keywords: sustainability, textile design, public health, built environment

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Authors: Rajaian Hoonejani Mohammad, Eshraghi Pegah, Zomorodian Zahra Sadat, Tahsildoost Mohammad

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Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

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Authors: Ramon Hernán, Maria Teresa Corzo

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This study examines the impact and contribution of the main journals in the discipline of behavioral finance to determine the state of the art of the discipline and the growth lines and concepts studied to date. This is a unique and novel study given that a review of the discipline has not been carried out through the keywords of the articles that allows visualizing through this component of the research, which are the main topics of discussion and the relationships that arise between the concepts discussed. To carry out this study, 3,876 articles have been taken as a reference, which includes 15,859 keywords from the main journals responsible for the growth of the discipline.; Journal of Behavioral Finance, Review of Behavioral Finance, Journal of Behavioral and Experimental Economics, Journal of Behavioral and Experimental Economics and Review of Behavioral Finance. The results indicate which are the topics most covered in the discipline throughout the period from 2000 to 2020, how these concepts have been dealt with on a recurring basis along with others throughout the aforementioned period and how the different concepts have been grouped based on the keywords established by the authors for the classification of their articles with a network diagram to complete the analysis.

Keywords: behavioral finance, keywords, co-words, top journals, data visualization

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Authors: Amal M. Alrayes, Hayat M. Ali

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Since the beginning of distance education with the rapid evolution of technology, the social network plays a vital role in the educational process to enforce the interaction been the learners and teachers. There are many Web 2.0 technologies, services and tools designed for educational purposes. This research aims to investigate instructors’ acceptance towards web-based learning systems in higher educational institutes in Kingdom of Bahrain. Questionnaire is used to investigate the instructors’ usage of Web 2.0 and the factors affecting their acceptance. The results confirm that instructors had high accessibility to such technologies. However, patterns of use were complex. Whilst most expressed interest in using online technologies to support learning activities, learners seemed cautious about other values associated with web-based system, such as the shared construction of knowledge in a public format. The research concludes that there are main factors that affect instructors’ adoption which are security, performance expectation, perceived benefits, subjective norm, and perceived usefulness.

Keywords: Web 2.0, higher education, acceptance, students' perception

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Authors: M. Soubadra Devy, Savitha Swamy, Chethana V. Casiker

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Sustainable smart cities are emerging as an important concept in response to the exponential rise in the world’s urbanizing population. While earlier, only technical, economic and governance based solutions were considered, more and more layers are being added in recent times. With the prefix of 'sustainability', solutions which help in judicious use of resources without negatively impacting the environment have become critical. We present a case study of Bangalore city which has transformed from being a garden city and pensioners' paradise to being an IT city with a huge, young population from different regions and diverse cultural backgrounds. This has had a big impact on the green spaces in the city and the biodiversity that they support, as well as on farming/gardening practices. Edible landscapes comprising farms lands, home gardens and neighbourhood parks (NPs henceforth) were examined. The land prices of areas having NPs were higher than those that did not indicate an appreciation of their aesthetic value. NPs were part of old and new residential areas largely managed by the municipality. They comprised manicured gardens which were similar in vegetation structure and composition. Results showed that NPs that occurred in higher density supported reasonable levels of biodiversity. In situations where NPs occurred in lower density, the presence of a larger green space such as a heritage park or botanical garden enhanced the biodiversity of these parks. In contrast, farm lands and home gardens which were common within the city are being lost at an unprecedented scale to developmental projects. However, there is also the emergence of a 'neo-culture' of home-gardening that promotes 'locovory' or consumption of locally grown food as a means to a sustainable living and reduced carbon footprint. This movement overcomes the space constraint by using vertical and terrace gardening techniques. Food that is grown within cities comprises of vegetables and fruits which are largely pollinator dependent. This goes hand in hand with our landscape-level study that has shown that cities support pollinator diversity. Maintaining and improving these man-made ecosystems requires analysing the functioning and characteristics of the existing structures of governance. Social network analysis tool was applied to NPs to examine relationships, between actors and ties. The management structures around NPs, gaps, and means to strengthen the networks from the current state to a near-ideal state were identified for enhanced services. Learnings from NPs were used to build a hypothetical governance structure and functioning of integrated governance of NPs and edible landscapes to enhance ecosystem services such as biodiversity support, food production, and aesthetic value. They also contribute to the sustainability axis of smart cities.

Keywords: biodiversity support, ecosystem services, edible green spaces, neighbourhood parks, sustainable smart city

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Authors: Samira Khalil, Adib Saad, Malek Ali

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The aim of this research is to determine fish species belonging to the order Pleuronectiforme fish found in Syrian marine water confirm or deny the continuity of the previously registered species, and record the unregistered species that appeared during this research for the first time. The research was carried out in the Laboratory of Marine Sciences, Faculty of Agriculture (Tishreen University); fish samples were collected periodically (bi-monthly) from fishermen in landing areas along the Syrian coast caught from depths (3m to 700m), using various mediums. An appropriate hand is available to fishermen on the Syrian coast (cliff bottom, fixed nets, enclosure nets, shelf nest, and manual disposal network; 451 individuals were captured and studied during the research period. During this study, it was found that the Syrian water includes 15 species, including one species recorded for the first time. On the eastern coast of the Mediterranean, it is Pegusa impar.

Keywords: pleuronectiformes, Syrian coast, flatfish, mediterranean

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Authors: Adriano Araujo Carvalho, Arturo Alatrista Corrales

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This paper intends to show how electrical energy consumption and production data analysis were used to find opportunities to save energy at Taboada wastewater treatment plant in Callao, Peru. In order to access the data, it was used independent data networks for both electrical and process instruments, which were taken to analyze under an ISO 50001 energy audit, which considered, thus, Energy Performance Indexes for each process and a step-by-step guide presented in this text. Due to the use of aforementioned methodology and data mining techniques applied on information gathered through electronic multimeters (conveniently placed on substation switchboards connected to a cloud network), it was possible to identify thoroughly the performance of each process and thus, evidence saving opportunities which were previously hidden before. The data analysis brought both costs and energy reduction, allowing the plant to save significant resources and to be certified under ISO 50001.

Keywords: energy and production data analysis, energy management, ISO 50001, wastewater treatment plant energy analysis

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Authors: Debora P. Arce, Flavia J. Krsticevic, Marco R. Bertolaccini, Joaquín Ezpeleta, Estela M. Valle, Sergio D. Ponce, Elizabeth Tapia

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Small Heat Shock Proteins (sHSPs) are low molecular weight chaperones that play an important role during stress response and development in all living organisms. Fruit maturation and oxidative stress can induce sHSP synthesis both in Arabidopsis and tomato plants. RNA-Seq technology is becoming widely used in various transcriptomics studies; however, analyzing and interpreting the RNA-Seq data face serious challenges. In the present work, we de novo assembled the Solanum lycopersicum transcriptome for three different maturation stages (mature green, breaker and red ripe). Differential gene expression analysis was carried out during tomato fruit development. We identified 12 sHSPs differentially expressed that might be involved in breaker and red ripe fruit maturation. Interestingly, these sHSPs have different subcellular localization and suggest a complex regulation of the fruit maturation network process.

Keywords: sHSPs, maturation, tomato, RNA-Seq, assembly

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Authors: Ikechukwu Ogeze Ukeje, Chinyere Ori Elom, Chukwudum Collins Umoke

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This study explores the challenges in the integration of Artificial Intelligence (AI) and Information and Communication Technology (ICT) practices in enhancing governance and sustainable solution modeling in higher education, focusing on Alex Ekwueme Federal University Ndufu-Alike (AE-FUNAI), Nigeria. In the context of a developing country like Nigeria, leveraging AI and ICT tools presents a unique opportunity to improve teaching, learning, administrative processes, and governance. The research aims to evaluate how AI and ICT technologies can contribute to sustainable educational practices, enhance decision-making processes, and improve engagement among key stakeholders: students, lecturers, and administrative staff. Students are involved to provide insights into their interactions with AI and ICT tools, particularly in learning and participation in governance. Lecturers’ perspectives will offer a view into how these technologies influence teaching, research, and curriculum development. Administrative staff will provide a crucial understanding of how AI and ICT tools can streamline operations, support data-driven governance, and enhance institutional efficiency. This study will use a mixed-method approach to collect both qualitative and quantitative data. The finding of this study is geared towards shaping the future of education in Nigeria and beyond by developing an Inclusive AI-governance Integration Framework (I-AIGiF) for enhanced performance in the system. Examining the roles of these stakeholder groups, this research could guide the development of policies for more effective AI and ICT integration, leading to sustainable educational innovation and governance.

Keywords: university administration, AI, higher education governance, education sustainability, ICT challenges

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Authors: Adekunle K. O, Adeniyi A. E, Kolawole E

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One of the most dramatic trends in the communications market in recent years has been the growth of prepaid services. Today, prepaid no longer constitutes the low-revenue, basic-service segment. It is driven by a high margin, value-add service customers who view it as a convenient way of retaining control over their usage and communication spending while expecting high service levels. To service providers, prepaid services offer the advantage of reducing bad accounts while allowing them to predict usage and plan network resources. Yet, the real-time demands of prepaid services require a scalable, real-time platform to manage customers through their entire life cycle. It delivers integrated real-time rating, voucher management, recharge management, customer care and service provisioning for the generation of new prepaid services. It carries high scalability that can handle millions of prepaid customers in real-time through their entire life cycle.

Keywords: prepaid billing, voucher management, customers, automated, security

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Authors: Mahdi Bazarganigilani

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Aortic enlargement, also known as an aortic aneurysm, can occur when the walls of the aorta become weak. This disease can become deadly if overlooked and undiagnosed. In this paper, a computer-aided diagnosis (CAD) system was introduced to accurately diagnose aortic enlargement from chest x-ray images. An enhanced convolutional neural network (CNN) was employed and then trained by transfer learning by using three different main areas from the original images. The areas included the left lung, heart, and right lung. The accuracy of the system was then evaluated on 1001 samples by using 4-fold cross-validation. A promising accuracy of 90% was achieved in terms of the F-measure indicator. The results showed using different areas from the original image in the training phase of CNN could increase the accuracy of predictions. This encouraged the author to evaluate this method on a larger dataset and even on different CAD systems for further enhancement of this methodology.

Keywords: computer-aided diagnosis systems, aortic enlargement, chest X-ray, image processing, convolutional neural networks

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Authors: Toushik Maiti, Saibal Chatterjee, Kamaljyoti Gogoi, Arijit Basuray

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Eastern Regional(ER) Grid and North Eastern Regional (NER) Grid are two major grids of Eastern Part of India. Both of the grid consists of voltage level 765kV, 400 kV, 220 kV and numerous buses at lower voltage range. Eastern Regional Grid and North Eastern Regional Grid are not only connected among themselves but are also connected to various other grids of India. ER and NER Grid having various HVDC lines or back to back systems which form the total network. The studied system comprises of 340 buses of different voltage levels and transmission lines running over a length of 32089 km. The validation of load flow has been done using IEEE STANDARD 30 bus system. The power flow simulation analysis has been performed after synchronizing both the Eastern Grid and North-Eastern Regional Grid of India using Power System Simulators for Engineering (PSS®E) Important inferences has been drawn from the study.

Keywords: HVDC, load flow, PSS®E, unsymmetrical and symmetrical faults

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Authors: Mohamed R. Mhereeg

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The paper discusses the design of a .NET Windows Service based agent system called MACS (Multi-Agent Classification System). MACS is a system aims to accurately classify spread-sheet developers competency over a network. It is designed to automatically and autonomously monitor spread-sheet users and gather their development activities based on the utilization of the software Multi-Agent Technology (MAS). This is accomplished in such a way that makes management capable to efficiently allow for precise tailor training activities for future spread-sheet development. The monitoring agents of MACS are intended to be distributed over the WWW in order to satisfy the monitoring and classification of the multiple developer aspect. The Prometheus methodology is used for the design of the agents of MACS. Prometheus has been used to undertake this phase of the system design because it is developed specifically for specifying and designing agent-oriented systems. Additionally, Prometheus specifies also the communication needed between the agents in order to coordinate to achieve their delegated tasks.

Keywords: classification, design, MACS, MAS, prometheus

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Authors: Saba Rahimi, Ozan Oktay, Javier Alvarez-Valle, Sujeeth Bharadwaj

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Successful application of deep learning in medical image analysis necessitates unprecedented amounts of labeled training data. Unlike conventional 2D applications, radiological images can be three-dimensional (e.g., CT, MRI), consisting of many instances within each image. The problem is exacerbated when expert annotations are required for effective pixel-wise labeling, which incurs exorbitant labeling effort and cost. Active learning is an established research domain that aims to reduce labeling workload by prioritizing a subset of informative unlabeled examples to annotate. Our contribution is a cost-effective approach for U-Net 3D models that uses Monte Carlo sampling to analyze pixel-wise uncertainty. Experiments on the AAPM 2017 lung CT segmentation challenge dataset show that our proposed framework can achieve promising segmentation results by using only 42% of the training data.

Keywords: image segmentation, active learning, convolutional neural network, 3D U-Net

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Authors: Kun Xue, Lvlan Miu, Jiarui Li

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Jetting structures are widely found in particle rings or shells dispersed by the central explosion. In contrast, some explosive dispersal of particles only results in a dispersed cloud without distinctive structures. Employing the coupling method of the compressible computational fluid mechanics and discrete element method (CCFD-DEM), we reveal the underlying physics governing the formation of the jetting structure, which is related to the competition between the shock compaction and gas infiltration, two major processes during the shock interaction with the granular media. If the shock compaction exceeds the gas infiltration, the discernable jetting structures are expected, precipitated by the agglomerates of fast-moving particles induced by the heterogenous network of force chains. Otherwise, particles are uniformly accelerated by the interstitial flows, and no distinguishable jetting structures are formed. We proceed to devise the phase map of the jetting formation in the space defined by two dimensionless parameters which characterize the timescales of the shock compaction and the gas infiltration, respectively.

Keywords: compressible multiphase flows, DEM, granular jetting, pattern formation

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Authors: Samiah Alammari, Nassim Ammour

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When providing a massive number of tasks successively to a deep learning process, a good performance of the model requires preserving the previous tasks data to retrain the model for each upcoming classification. Otherwise, the model performs poorly due to the catastrophic forgetting phenomenon. To overcome this shortcoming, we developed a successful continual learning deep model for remote sensing hyperspectral image regions classification. The proposed neural network architecture encapsulates two trainable subnetworks. The first module adapts its weights by minimizing the discrimination error between the land-cover classes during the new task learning, and the second module tries to learn how to replicate the data of the previous tasks by discovering the latent data structure of the new task dataset. We conduct experiments on HSI dataset Indian Pines. The results confirm the capability of the proposed method.

Keywords: continual learning, data reconstruction, remote sensing, hyperspectral image segmentation

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Authors: Ourn Vimoil, Kallaya Suntornvongsagul

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Cambodia is considered as one of the most disaster prone countries in South East Asia, and most of natural disasters are related to floods. Cambodia, a developing country, faces significant impacts from floods, such as environmental, social, and economic losses. Using data accessed from focus group discussions and field surveys with villagers in Ba Baong commune, prey Veng province, Cambodia, the research would like to examine roles of social networks in raising community’s coping capacity with floods. The findings indicate that social capital play crucial roles in three stages of floods, namely preparedness, response, and recovery to overcome the crisis. People shared their information and resources, and extent their assistances to one another in order to adapt to floods. The study contribute to policy makers, national and international agencies working on this issue to pay attention on social networks as one factors to accelerate flood coping capacity at community level.

Keywords: social network, community, coping capacity, flood, Cambodia

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Authors: Umesh Kumar Singh, Abhishek Raghuvanshi, Suyash Kumar Singh

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As IoT networks gain popularity, they are more susceptible to security breaches. As a result, it is crucial to analyze the IoT platform as a whole from the standpoint of core security concepts. The Internet of Things relies heavily on wireless networks, which are well-known for being susceptible to a wide variety of attacks. This article provides an analysis of many techniques that may be used to identify vulnerabilities in the software and hardware associated with the Internet of Things (IoT). In the current investigation, an experimental setup is built with the assistance of server computers, client PCs, Internet of Things development boards, sensors, and cloud subscriptions. Through the use of network host scanning methods and vulnerability scanning tools, raw data relating to IoT-based applications and devices may be collected. Shodan is a tool that is used for scanning, and it is also used for effective vulnerability discovery in IoT devices as well as penetration testing. This article presents an efficient mitigation plan for encountering vulnerabilities in the Internet of Things.

Keywords: internet of things, security, privacy, vulnerability identification, mitigation plan

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Authors: A. Melka, N. Slater, A. Mualem, Y. Louzoun

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Networks are often presented as containing a “core” and a “periphery.” The existence of a core suggests that some vertices are central and form the skeleton of the network, to which all other vertices are connected. An alternative view of graphs is through communities. Multiple measures have been proposed for dense communities in graphs, the most classical being k-cliques, k-cores, and k-plexes, all presenting groups of tightly connected vertices. We here show that the edge number thresholds for such communities to emerge and for their percolation into a single dense connectivity component are very close, in all networks studied. These percolating cliques produce a natural core and periphery structure. This result is generic and is tested in configuration models and in real-world networks. This is also true for k-cores and k-plexes. Thus, the emergence of this connectedness among communities leading to a core is not dependent on some specific mechanism but a direct result of the natural percolation of dense communities.

Keywords: cliques, core structure, percolation, phase transition

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Authors: Alberto Alcalá-Alvarez, Pablo Padilla-Longoria

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The present paper describes a framework for constructing mathematical networks encoding relevant musical information from a music score for structural analysis. These graphs englobe statistical information about music elements such as notes, chords, rhythms, intervals, etc., and the relations among them, and so become helpful in visualizing and understanding important stylistic features of a music fragment. In order to build such networks, musical data is parsed out of a digital symbolic music file. This data undergoes different analytical procedures from Graph Theory, such as measuring the centrality of nodes, community detection, and entropy calculation. The resulting networks reflect important structural characteristics of the fragment in question: predominant elements, connectivity between them, and complexity of the information contained in it. Music pieces in different styles are analyzed, and the results are contrasted with the traditional analysis outcome in order to show the consistency and potential utility of this method for music analysis.

Keywords: computational musicology, mathematical music modelling, music analysis, style classification

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Authors: M. Rojo, M. Blanco, T. Lacruz, S. Solano, L. Beltran, M. Graell, A. R. Sepulveda.

Abstract:

There is an association between an early accumulation of Stressful Life Events (SLE) during childhood and various physical and psychological health complications. However, there are only a few studies on this topic in children and adolescents with overweight or obesity. The general aim of the study was to evaluate the accumulation and type of SLE in 200 children from 8 to 12 years old and analyze the relationship with their emotional well-being and weight status (obesity, overweight and normal weight). The children and their families completed an interview. The evaluated variables that are included in this study are sociodemographic measures, medical/psychological history, anthropometric measures (BMI, z-BMI), and psychological variables (children's clinical interview K-SADS-PL(Schedule for Affective Disorders and Schizophrenia for School-Age Children Present and Lifetime Version) and battery of questionnaires). Results: Children with overweight and obesity accumulate more stressful events from an early age and have a significantly higher percentage of psychiatric diagnoses, compared to their peers with normal weight. Presenting a child psychiatric disorder is related to greater z-BMI and the total number of SLE (p < 0.001). A higher z-BMI is also related to a greater number of stressful events during childhood. There is also a positive and significant relationship between the total number of SLE and worse emotional well-being (higher levels of anxious and depressive symptoms and low self-esteem of children) (p < 0.01). Conclusion: Children with overweight and obesity grow up in a family, school, and social context where more stressors are accumulated. This is also directly associated with worse emotional well-being. It is necessary to implement multidisciplinary prevention and intervention strategies in different changes (school, family, and health). This study is included in a project funded by the Ministry of Innovation and Science (PSI2011-23127).

Keywords: childhood obesity, emotional well-being, psychopathology, stressful life events

Procedia PDF Downloads 125

Authors: Wei Quan, Liu Chao, Mohammed N. Afsar

Abstract:

The dielectric properties and ionic conductivity of novel "ceramic state" polymer electrolytes for high capacity lithium battery are characterized by radio-frequency and Microwave methods in two broad frequency ranges from 50 Hz to 20 KHz and 4 GHz to 40 GHz. This innovative solid polymer electrolyte which is highly ionic conductive (10-3 S/cm at room temperature) from -40 oC to +150 oC and can be used in any battery application. Such polymer exhibits properties more like a ceramic rather than polymer. The various applied measurement methods produced accurate dielectric results for comprehensive analysis of electrochemical properties and ion transportation mechanism of this newly invented polymer electrolyte. Two techniques and instruments employing air gap measurement by capacitance bridge and inwave guide measurement by vector network analyzer are applied to measure the complex dielectric spectra. The complex dielectric spectra are used to determine the complex alternating current electrical conductivity and thus the ionic conductivity.

Keywords: polymer electrolyte, dielectric permittivity, lithium battery, ionic relaxation, microwave measurement

Procedia PDF Downloads 476

Authors: Bushra

Abstract:

This paper explores the historical significance, ecological benefits, and contemporary applications of natural dyes in textile dyeing, aiming to provide a comprehensive overview of their potential to contribute to a sustainable fashion industry while minimizing ecological footprints. This research explores the potential of natural dyes as a sustainable alternative to synthetic dyes in the textile industry, examining their historical context, sources, and environmental benefits. Natural dyes come from plants, animals, and minerals, including roots, leaves, bark, fruits, flowers, insects, and metal salts, used as mordants to fix dyes to fabrics. Natural dyeing involves extraction, mordanting, and dyeing techniques. Optimizing these processes can enhance the performance of natural dyes, making them viable for contemporary textile applications based on experimental research. Natural dyes offer eco-friendly benefits like biodegradability, non-toxicity, and resource renewables, reducing pollution, promoting biodiversity, and reducing reliance on petrochemicals. Natural dyes offer benefits but face challenges in color consistency, scalability, and performance, requiring industrial production to meet modern consumer standards for durability and colorfastness. Contemporary initiatives in the textile industry include fashion brands like Eileen Fisher and Patagonia incorporating natural dyes, artisans like India Flint's Botanical Alchemy promoting traditional dyeing techniques, and research projects like the European Union's Horizon 2020 program. Natural dyes offer a sustainable textile industry solution, reducing environmental impact and promoting harmony with nature. Research and innovation are paving the way for widespread adoption, transforming textile dyeing.

Keywords: historical significance, textile industry, natural dyes, sustainability

Procedia PDF Downloads 47