Search results for: Artificial Neural Networks (ANN)

Authors: M. Almutairi, S. Hadjiloucas

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The harmonic distortion of voltage is important in relation to power quality due to the interaction between the large diffusion of non-linear and time-varying single-phase and three-phase loads with power supply systems. However, harmonic distortion levels can be reduced by improving the design of polluting loads or by applying arrangements and adding filters. The application of passive filters is an effective solution that can be used to achieve harmonic mitigation mainly because filters offer high efficiency, simplicity, and are economical. Additionally, possible different frequency response characteristics can work to achieve certain required harmonic filtering targets. With these ideas in mind, the objective of this paper is to determine what size single tuned passive filters work in distribution networks best, in order to economically limit violations caused at a given point of common coupling (PCC). This article suggests that a single tuned passive filter could be employed in typical industrial power systems. Furthermore, constrained optimization can be used to find the optimal sizing of the passive filter in order to reduce both harmonic voltage and harmonic currents in the power system to an acceptable level, and, thus, improve the load power factor. The optimization technique works to minimize voltage total harmonic distortions (VTHD) and current total harmonic distortions (ITHD), where maintaining a given power factor at a specified range is desired. According to the IEEE Standard 519, both indices are viewed as constraints for the optimal passive filter design problem. The performance of this technique will be discussed using numerical examples taken from previous publications.

Keywords: harmonics, passive filter, power factor, power quality

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Authors: Anirudh Lahiri

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Neuromorphic computing, inspired by the intricate operations of biological neural networks, offers a revolutionary approach to overcoming the limitations of traditional computing architectures. This research proposes the integration of spintronics with neuromorphic systems, aiming to enhance computational performance, scalability, and energy efficiency. Traditional computing systems, based on the Von Neumann architecture, struggle with scalability and efficiency due to the segregation of memory and processing functions. In contrast, the human brain exemplifies high efficiency and adaptability, processing vast amounts of information with minimal energy consumption. This project explores the use of spintronics, which utilizes the electron's spin rather than its charge, to create more energy-efficient computing systems. Spintronic devices, such as magnetic tunnel junctions (MTJs) manipulated through spin-transfer torque (STT) and spin-orbit torque (SOT), offer a promising pathway to reducing power consumption and enhancing the speed of data processing. The integration of these devices within a neuromorphic framework aims to replicate the efficiency and adaptability of biological systems. The research is structured into three phases: an exhaustive literature review to build a theoretical foundation, laboratory experiments to test and optimize the theoretical models, and iterative refinements based on experimental results to finalize the system. The initial phase focuses on understanding the current state of neuromorphic and spintronic technologies. The second phase involves practical experimentation with spintronic devices and the development of neuromorphic systems that mimic synaptic plasticity and other biological processes. The final phase focuses on refining the systems based on feedback from the testing phase and preparing the findings for publication. The expected contributions of this research are twofold. Firstly, it aims to significantly reduce the energy consumption of computational systems while maintaining or increasing processing speed, addressing a critical need in the field of computing. Secondly, it seeks to enhance the learning capabilities of neuromorphic systems, allowing them to adapt more dynamically to changing environmental inputs, thus better mimicking the human brain's functionality. The integration of spintronics with neuromorphic computing could revolutionize how computational systems are designed, making them more efficient, faster, and more adaptable. This research aligns with the ongoing pursuit of energy-efficient and scalable computing solutions, marking a significant step forward in the field of computational technology.

Keywords: material science, biological engineering, mechanical engineering, neuromorphic computing, spintronics, energy efficiency, computational scalability, synaptic plasticity.

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Authors: Mohammed Mainoma, Abubakar Tijanni, Mohammed Aliyu

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Globalization has brought a structural change in industry. It is the breaking of artificial boundaries and given way to new product, new service, new market, and new technology among others. It leads to the realization that men entrepreneurs’ alone cannot meet the demand of the teeming population. Therefore there is a need for the participation, involvement, and engagement of females in the production and distribution of goods and services. This will enhance growth and development of a nation. It is in line with the above that this paper attempt to discuss meaning of women entrepreneurs, roles, types, problems, and prospects. Also, on the basis of conclusion the paper recommended that entrepreneurship education should be introduced in all Tertiary Institutions in Nigeria.

Keywords: women, entrepreneurs, issues, challenges

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Authors: Shreya Poddar

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Legal drafting and revising are recognized as highly demanding tasks for legal professionals. This paper introduces an approach to automate and refine these processes through the use of advanced Artificial Intelligence (AI). The method employs Large Language Models (LLMs), with a specific focus on 'Chain of Thoughts' (CoT) and knowledge injection via prompt engineering. This approach differs from conventional methods that depend on comprehensive training or fine-tuning of models with extensive legal knowledge bases, which are often expensive and time-consuming. The proposed method incorporates knowledge injection directly into prompts, thereby enabling the AI to generate more accurate and contextually appropriate legal texts. This approach substantially decreases the necessity for thorough model training while preserving high accuracy and relevance in drafting. Additionally, the concept of guardrails is introduced. These are predefined parameters or rules established within the AI system to ensure that the generated content adheres to legal standards and ethical guidelines. The practical implications of this method for legal work are considerable. It has the potential to markedly lessen the time lawyers allocate to document drafting and revision, freeing them to concentrate on more intricate and strategic facets of legal work. Furthermore, this method makes high-quality legal drafting more accessible, possibly reducing costs and expanding the availability of legal services. This paper will elucidate the methodology, providing specific examples and case studies to demonstrate the effectiveness of 'Chain of Thoughts' and knowledge injection in legal drafting. The potential challenges and limitations of this approach will also be discussed, along with future prospects and enhancements that could further advance legal work. The impact of this research on the legal industry is substantial. The adoption of AI-driven methods by legal professionals can lead to enhanced efficiency, precision, and consistency in legal drafting, thereby altering the landscape of legal work. This research adds to the expanding field of AI in law, introducing a method that could significantly alter the nature of legal drafting and practice.

Keywords: AI-driven legal drafting, legal automation, futureoflegalwork, largelanguagemodels

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Authors: Tori Reddy Dodla, Laura Ann Jones

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The objective of this study was to propose strategies to improve the efficiency of Knowledge Management Systems (KMS). This study highlights best practices from various industries to create an overall summary of Knowledge Management (KM) and efficiency in organizational performance. Results indicated eleven best practices for maximizing the efficiency of organizational KMS that can be divided into four categories: Designing the KMS, Identifying Case Studies, Implementing the KMS, and Promoting adoption and usage. Our findings can be used as a foundation for scholars to conduct further research on KMS efficiency.

Keywords: artificial intelligence, knowledge management efficiency, knowledge management systems, organizational performance

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Authors: Jianwei Qian, Rob Law

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With the expansion of globalization and the increment of market competition, the festival, especially the traditional one, has demonstrated its vitality under the new context. As a new tourist attraction, festivals play a critically important role in promoting the tourism economy, because the organization of a festival can engage more tourists, generate more revenues and win a wider media concern. However, in the current stage of China, traditional festivals as a way to disseminate national culture are undergoing the challenge of foreign festivals and the related culture. Different from those special events created solely for developing economy, traditional festivals have their own culture and connotation. Therefore, it is necessary to conduct a study on not only protecting the tradition, but promoting its development as well. This study conducts a comparative study of the development of China’s Valentine’s Day and Western Valentine’s Day under the Chinese context and centers on newspaper reports in China from 2000 to 2016. Based on the literature, two main research focuses can be established: one is concerned about the festival’s impact and the other is about tourists’ motivation to engage in a festival. Newspaper reports serve as the research discourse and can help cover the two focal points. With the assistance of content mining techniques, semantic networks for both Days are constructed separately to help depict the status quo of these two festivals in China. Based on the networks, two models are established to show the key component system of traditional festivals in the hope of perfecting the positive role festival tourism plays in the promotion of economy and culture. According to the semantic networks, newspaper reports on both festivals have similarities and differences. The difference is mainly reflected in its cultural connotation, because westerners and Chinese may show their love in different ways. Nevertheless, they share more common points in terms of economy, tourism, and society. They also have a similar living environment and stakeholders. Thus, they can be promoted together to revitalize some traditions in China. Three strategies are proposed to realize the aforementioned aim. Firstly, localize international festivals to suit the Chinese context to make it function better. Secondly, facilitate the internationalization process of traditional Chinese festivals to receive more recognition worldwide. Finally, allow traditional festivals to compete with foreign ones to help them learn from each other and elucidate the development of other festivals. It is believed that if all these can be realized, not only the traditional Chinese festivals can obtain a more promising future, but foreign ones are the same as well. Accordingly, the paper can contribute to the theoretical construction of festival images by the presentation of the semantic network. Meanwhile, the identified features and issues of festivals from two different cultures can enlighten the organization and marketing of festivals as a vital tourism activity. In the long run, the study can enhance the festival as a key attraction to keep the sustainable development of both the economy and the society.

Keywords: Chinese context, comparative study, festival tourism, semantic network analysis, valentine’s day

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Authors: Thomai Voulgari, Vasilis Vasilopoulos, Antonis Skamnakis

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The aim of this study is to investigate the effect of the European election campaigns (May 23-26, 2019) on Twitter achieving with artificial intelligence tools such as troll factories and automated inauthentic accounts. Our research focuses on the last European Parliamentary elections that took place between 23 and 26 May 2019 specifically in Italy, Greece, Germany and France. It is difficult to estimate how many Twitter users are actually bots (Echeverría, 2017). Detection for fake accounts is becoming even more complicated as AI bots are made more advanced. A political bot can be programmed to post comments on a Twitter account for a political candidate, target journalists with manipulated content or engage with politicians and artificially increase their impact and popularity. We analyze variables related to 1) the scope of activity of automated bots accounts and 2) degree of coherence and 3) degree of interaction taking into account different factors, such as the type of content of Twitter messages and their intentions, as well as the spreading to the general public. For this purpose, we collected large volumes of Twitter accounts of party leaders and MEP candidates between 10th of May and 26th of July based on content analysis of tweets based on hashtags while using an innovative network analysis tool known as MediaWatch.io (https://mediawatch.io/). According to our findings, one of the highest percentage (64.6%) of automated “bot” accounts during 2019 European election campaigns was in Greece. In general terms, political bots aim to proliferation of misinformation on social media. Targeting voters is a way that it can be achieved contribute to social media manipulation. We found that political parties and individual politicians create and promote purposeful content on Twitter using algorithmic tools. Based on this analysis, online political advertising play an important role to the process of spreading misinformation during elections campaigns. Overall, inauthentic accounts and social media algorithms are being used to manipulate political behavior and public opinion.

Keywords: artificial intelligence tools, human-bot interactions, political manipulation, social networking, troll factories

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Authors: Isomar Lima da Silva, Alcilene Batalha Pontes, Aristeu Jonatas Leite de Oliveira, Roberto Maia Augusto

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Context: Mechanical ventilators are medical devices that help provide oxygen and ventilation to patients with respiratory difficulties. This equipment consists of a manual breathing unit that can be operated by a doctor or nurse and a mechanical ventilator that controls the airflow and pressure in the patient's respiratory system. This type of ventilator is commonly used in emergencies and intensive care units where it is necessary to provide breathing support to critically ill or injured patients. Objective: In this context, this work aims to develop a reliable and low-cost mechanical ventilator to meet the demand of hospitals in treating people affected by Covid-19 and other severe respiratory diseases, offering a chance of treatment as an alternative to mechanical ventilators currently available in the market. Method: The project presents the development of a low-cost auxiliary ventilator with a controlled ventilatory system assisted by integrated hardware and firmware for respiratory cycle control in non-invasive mechanical ventilation treatments using a manual artificial respiration unit. The hardware includes pressure sensors capable of identifying positive expiratory pressure, peak inspiratory flow, and injected air volume. The embedded system controls the data sent by the sensors. It ensures efficient patient breathing through the operation of the sensors, microcontroller, and actuator, providing patient data information to the healthcare professional (system operator) through the graphical interface and enabling clinical parameter adjustments as needed. Results: The test data of the developed mechanical ventilator presented satisfactory results in terms of performance and reliability, showing that the equipment developed can be a viable alternative to commercial mechanical ventilators currently available, offering a low-cost solution to meet the increasing demand for respiratory support equipment.

Keywords: mechanical fans, breathing, medical equipment, COVID-19, intensive care units

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Authors: Paria Tajabor, Azin Azarbani

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The purpose of this research study is exploring two kind of semantic repositories with regards to various factors to find the best approaches that an artificial manager can use to produce ontology in a system based on their interaction, association and research. To this end, as the best way to evaluate each system and comparing with others is analysis, several benchmarking over these two repositories were examined. These two semantic repositories: OWLIM and AllegroGraph will be the main core of this study. The general objective of this study is to be able to create an efficient and cost-effective manner reports which is required to support decision making in any large enterprise.

Keywords: OWLIM, allegrograph, RDF, reasoning, semantic repository, semantic-web, SPARQL, ontology, query

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Authors: Simone Cinquemani, Lorenzo Mantovani, Aleksander Dabek

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New technologies in agriculture are opening new challenges and new opportunities. Among these, certainly, robotics, vision, and artificial intelligence are the ones that will make a significant leap, compared to traditional agricultural techniques, possible. In particular, the indoor farming sector will be the one that will benefit the most from these solutions. Vertical farming is a new field of research where mechanical engineering can bring knowledge and know-how to transform a highly labor-based business into a fully autonomous system. The aim of the research is to develop a multi-purpose, modular, and perfectly integrated platform for crop production in indoor vertical farming. Activities will be based both on hardware development such as automatic tools to perform different activities on soil and plants, as well as research to introduce an extensive use of monitoring techniques based on machine learning algorithms. This paper presents the preliminary results of a research project of a vertical farm living lab designed to (i) develop and test vertical farming cultivation practices, (ii) introduce a very high degree of mechanization and automation that makes all processes replicable, fully measurable, standardized and automated, (iii) develop a coordinated control and management environment for autonomous multiplatform or tele-operated robots in environments with the aim of carrying out complex tasks in the presence of environmental and cultivation constraints, (iv) integrate AI-based algorithms as decision support system to improve quality production. The coordinated management of multiplatform systems still presents innumerable challenges that require a strongly multidisciplinary approach right from the design, development, and implementation phases. The methodology is based on (i) the development of models capable of describing the dynamics of the various platforms and their interactions, (ii) the integrated design of mechatronic systems able to respond to the needs of the context and to exploit the strength characteristics highlighted by the models, (iii) implementation and experimental tests performed to test the real effectiveness of the systems created, evaluate any weaknesses so as to proceed with a targeted development. To these aims, a fully automated laboratory for growing plants in vertical farming has been developed and tested. The living lab makes extensive use of sensors to determine the overall state of the structure, crops, and systems used. The possibility of having specific measurements for each element involved in the cultivation process makes it possible to evaluate the effects of each variable of interest and allows for the creation of a robust model of the system as a whole. The automation of the laboratory is completed with the use of robots to carry out all the necessary operations, from sowing to handling to harvesting. These systems work synergistically thanks to the knowledge of detailed models developed based on the information collected, which allows for deepening the knowledge of these types of crops and guarantees the possibility of tracing every action performed on each single plant. To this end, artificial intelligence algorithms have been developed to allow synergistic operation of all systems.

Keywords: automation, vertical farming, robot, artificial intelligence, vision, control

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Authors: V. R. Kakulte, K. N. Gaikwad

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The emergence pattern of different types of cercariae form three snail hosts Melania tuberculata, Lymnea auricularia Viviparous bengalensis has been studied in detail. In natural emerging method the snails (2 to 3 at a time) were kept in separate test tube. This was constant source of living cercariae naturally emerging from the snails. The sunlight and artificial light play an important positive role in stimulating the emergence of cercariae has been observed. The effect of light and dark on the emission pattern of cercariae has been studied.

Keywords: cercariae, snail host, emergence pattern, gastropoda

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Authors: Behrooz Daneshmand

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Software Defined Networking (SDN) is designed to meet the future needs of 5G mobile networks. The SDN architecture offers a new solution that involves separating the control plane from the data plane, which is usually paired together. Network functions traditionally performed on specific hardware can now be abstracted and virtualized on any device, and a centralized software-based administration approach is based on a central controller, facilitating the development of modern applications and services. These plan standards clear the way for a more adaptable, speedier, and more energetic network beneath computer program control compared with a conventional network. We accept SDN gives modern inquire about openings to security, and it can significantly affect network security research in numerous diverse ways. Subsequently, the SDN architecture engages systems to effectively screen activity and analyze threats to facilitate security approach modification and security benefit insertion. The segregation of the data planes and control and, be that as it may, opens security challenges, such as man-in-the-middle attacks (MIMA), denial of service (DoS) attacks, and immersion attacks. In this paper, we analyze security threats to each layer of SDN - application layer - southbound interfaces/northbound interfaces - controller layer and data layer. From a security point of see, the components that make up the SDN architecture have a few vulnerabilities, which may be abused by aggressors to perform noxious activities and hence influence the network and its administrations. Software-defined network assaults are shockingly a reality these days. In a nutshell, this paper highlights architectural weaknesses and develops attack vectors at each layer, which leads to conclusions about further progress in identifying the consequences of attacks and proposing mitigation strategies.

Keywords: software-defined networking, security, SDN, 5G/IMT-2020

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Authors: Hilal Turkoglu Sasmazel, Ozan Ozkan, Seda Surucu

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Tissue engineering is the field of treating defects caused by injuries, trauma or acute/chronic diseases by using artificial scaffolds that mimic the extracellular matrix (ECM), the natural biological support for the tissues and cells within the body. The main aspects of a successful artificial scaffold are (i) large surface area in order to provide multiple anchorage points for cells to attach, (ii) suitable porosity in order to achieve 3 dimensional growth of the cells within the scaffold as well as proper transport of nutrition, biosignals and waste and (iii) physical, chemical and biological compatibility of the material in order to obtain viability throughout the healing process. By hybrid scaffolds where two or more different materials were combined with advanced fabrication techniques into complex structures, it is possible to combine the advantages of individual materials into one single structure while eliminating the disadvantages of each. Adding this to the complex structure provided by advanced fabrication techniques enables obtaining the desired aspects of a successful artificial tissue scaffold. In this study, fibroblast compatibility of poly(ε-caprolactone) (PCL)/chitosan core-shell electrospun hybrid scaffolds with proper mechanical, chemical and physical properties successfully developed in our previous study was investigated. Standard 7-day cell culture was carried out with L929 fibroblast cell line. The viability of the cells cultured with the scaffolds was monitored with 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) viability assay for every 48 h starting with 24 h after the initial seeding. In this assay, blank commercial tissue culture polystyrene (TCPS) Petri dishes, single electrospun PCL and single electrospun chitosan mats were used as control in order to compare and contrast the performance of the hybrid scaffolds. The adhesion, proliferation, spread and growth of the cells on/within the scaffolds were observed visually on the 3rd and the 7th days of the culture period with confocal laser scanning microscopy (CSLM) and scanning electron microscopy (SEM). The viability assay showed that the hybrid scaffolds caused no toxicity for fibroblast cells and provided a steady increase in cell viability, effectively doubling the cell density for every 48 h for the course of 7 days, as compared to TCPS, single electrospun PCL or chitosan mats. The cell viability on the hybrid scaffold was ~2 fold better compared to TCPS because of its 3D ECM-like structure compared to 2D flat surface of commercially cell compatible TCPS, and the performance was ~2 fold and ~10 fold better compared to single PCL and single chitosan mats, respectively, even though both fabricated similarly with electrospinning as non-woven fibrous structures, because single PCL and chitosan mats were either too hydrophobic or too hydrophilic to maintain cell attachment points. The viability results were verified with visual images obtained with CSLM and SEM, in which cells found to achieve characteristic spindle-like fibroblast shape and spread on the surface as well within the pores successfully at high densities.

Keywords: chitosan, core-shell, fibroblast, electrospinning, PCL

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Authors: Ding He, Ping Hu

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This article presents a “networking approach” as an alternative to the “layering model” in the issue of the historic urban landscape [HUL], based on research conducted in the historic city of Jingdezhen, the center of the porcelain industry in China. This study points out that the existing HUL concept, which can be traced back to the fundamental conceptual divisions set forth by western science, tends to analyze the various elements of urban heritage (composed of hybrid natural-cultural elements) by layers and ignore the nuanced connections and interweaving structure of various elements. Instead, the networking analysis approach can respond to the challenges of complex heritage networks and to the difficulties that are often faced when modern schemes of looking and thinking of landscape in the Eurocentric heritage model encounters local knowledge of Chinese settlement. The fieldwork in this paper examines the local language regarding place names and everyday uses of urban spaces, thereby highlighting heritage systems grounded in local life and indigenous knowledge. In the context of Chinese “Fengshui”, this paper demonstrates the local knowledge of nature and local intelligence of settlement location and design. This paper suggests that industrial elements (kilns, molding rooms, piers, etc.) and spiritual elements (temples for ceramic saints or water gods) are located in their intimate natural networks. Furthermore, the functional, spiritual, and natural elements are perceived as a whole and evolve as an interactive system. This paper proposes a local and cognitive approach in heritage, which was initially developed in European Landscape Convention and historic landscape characterization projects, and yet seeks a more tentative and nuanced model based on urban ethnography in a Chinese city.

Keywords: Chinese city, historic urban landscape, heritage conservation, network

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Authors: Adam Gushgari

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The SARS-CoV-2 pandemic has catalyzed the rapid adoption of wastewater-based epidemiology (WBE) methodologies both domestically and internationally. To support the rapid scale-up of pandemic-response wastewater surveillance systems, multiple federal agencies (i.e. US CDC), non-government organizations (i.e. Water Environment Federation), and private charities (i.e. Bill and Melinda Gates Foundation) have funded over $220 million USD supporting development and expanding equitable access of surveillance methods. Funds were primarily distributed directly to municipalities under the CARES Act (90.6%), followed by academic projects (7.6%), and initiatives developed by private companies (1.8%). In addition to federal funding for wastewater monitoring primarily conducted at wastewater treatment plants, state/local governments and private companies have leveraged wastewater sampling to obtain health and lifestyle data on student, prison inmate, and employee populations. We explore the viable paths for expansion of the WBE m1ethodology across a variety of analytical methods; the development of WBE-specific samplers and real-time wastewater sensors; and their application to various governments and private sector industries. Considerable investment in, and public acceptance of WBE suggests the methodology will be applied to other future notifiable diseases and health risks. Early research suggests that WBE methods can be applied to a host of additional “biological insults” including communicable diseases and pathogens, such as influenza, Cryptosporidium, Giardia, mycotoxin exposure, hepatitis, dengue, West Nile, Zika, and yellow fever. Interest in chemical insults is also likely, providing community health and lifestyle data on narcotics consumption, use of pharmaceutical and personal care products (PPCP), PFAS and hazardous chemical exposure, and microplastic exposure. Successful application of WBE to monitor analytes correlated with carcinogen exposure, community stress prevalence, and dietary indicators has also been shown. Additionally, technology developments of in situ wastewater sensors, WBE-specific wastewater samplers, and integration of artificial intelligence will drastically change the landscape of WBE through the development of “smart sewer” networks. The rapid expansion of the WBE field is creating significant business opportunities for professionals across the scientific, engineering, and technology industries ultimately focused on community health improvement.

Keywords: wastewater surveillance, wastewater-based epidemiology, smart cities, public health, pandemic management, substance abuse

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Authors: Suneet Jagdev

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Rapid urbanization has resulted in informal settlements on the periphery of nearly all big cities in the developing world due to lack of affordable housing options in the city. Residents of these communities have to travel long distances to get to work or search for jobs in these cities, and women, children and elderly people are excluded from urban opportunities. Affordable and safe public transport facilities can help them expand their possibilities. The aim of this research is to identify social capital as another important element of livable cities that can be protected and nurtured through transit-oriented development, as a tool to provide real resources that can help these transit-oriented communities become self-sustainable. Social capital has been referred to the collective value of all social networks and the inclinations that arise from these networks to do things for each other. It is one of the key component responsible to build and maintain democracy. Public spaces, pedestrian amenities and social equity are the other essential part of Transit Oriented Development models that will be analyzed in this research. The data has been collected through the analysis of several case studies, the urban design strategies implemented and their impact on the perception and on the community´s experience, and, finally, how these focused on the social capital. Case studies have been evaluated on several metrics, namely ecological, financial, energy consumption, etc. A questionnaire and other tools were designed to collect data to analyze the research objective and reflect the dimension of social capital. The results of the questionnaire indicated that almost all the participants have a positive attitude towards this dimensions of building a social capital with the aid of transit-oriented development. Statistical data of the identified key motivators against against demographic characteristics have been generated based on the case studies used for the paper. The findings suggested that there is a direct relation between urbanization, transit-oriented developments, and social capital.

Keywords: better opportunities, low-income settlements, social capital, social inclusion, transit oriented development

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Authors: Andrea Hernandez Hurtado

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Research on social networks (SNs) -- the interactions individuals share with others has shed important light in helping to explain differential use of variable linguistic forms, both in L1s and L2s. Nevertheless, the acquisition of nonstandard L2 Spanish in the Region of Murcia, Spain, and how learners interact with other speakers while sojourning there have received little attention. Murcian Spanish (MuSp) was widely influenced by Panocho, a divergent evolution of Hispanic Latin, and differs from the more standard Peninsular Spanish (StSp) in phonology, morphology, and lexicon. For instance, speakers from this area will most likely palatalize diminutive endings, producing animalico [̩a.ni.ma.ˈli.ko] instead of animalito [̩a.ni.ma.ˈli.to] ‘little animal’. Because L1 speakers of the area produce and prefer salient regional lexicon and morphology (particularly the palatalized diminutive -ico) in their speech, the current research focuses on how international residents in the Region of Murcia use Spanish: (1) whether or not they acquire (perceptively and/or productively) any of the salient regional features of MuSp, and (2) how their SNs explain such acquisition. This study triangulates across three tasks -recognition, production, and preference- addressing both lexicon and morphology, with each task specifically created for the investigation of MuSp features. Among other variables, the effects of L1, residence, and identity are considered. As an ongoing dissertation research, data are currently being gathered through an online questionnaire. So far, 7 participants from multiple nationalities have completed the survey, although a minimum of 25 are expected to be included in the coming months. Preliminary results revealed that MuSp lexicon and morphology were successfully recognized by participants (p<.001). In terms of regional lexicon production (10.0%) and preference (47.5%), although participants showed higher percentages of StSp, results showed that international residents become aware of stigmatized lexicon and may incorporate it into their language use. Similarly, palatalized diminutives (production 14.2%, preference 19.0%) were present in their responses. The Social Network Analysis provided information about participants’ relationships with their interactants, as well as among them. Results indicated that, generally, when residents were more immersed in the culture (i.e., had more Murcian alters) they produced and preferred more regional features. This project contributes to the knowledge of language variation acquisition in L2 speakers, focusing on a stigmatized Spanish dialect and exploring how stigmatized varieties may affect L2 development. Results will show how L2 Spanish speakers’ language is affected by their stay in Murcia. This, in turn, will shed light on the role of SNs in language acquisition, the acquisition of understudied and marginalized varieties, and the role of immersion on language acquisition. As the first systematic account on the acquisition of L2 Spanish lexicon and morphology in the Region of Murcia, it lays important groundwork for further research on the connection between SNs and the acquisition of regional variants, applicable to Murcia and beyond.

Keywords: international residents, L2 Spanish, lexicon, morphology, nonstandard language acquisition, social networks

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Authors: Kyosuke Kamada, Christoph Kapeller, Michael Jordan, Mostafa Mohammadpour, Christy Li, Christoph Guger

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Cortico-cortical evoked potentials (CCEP) refer to responses generated by cortical electrical stimulation at distant brain sites. These responses provide insights into the functional networks associated with language or motor functions, and in the context of epilepsy, they can reveal pathological networks. Locating the origin and spread of seizures within the cortex is crucial for pre-surgical planning. This process can be enhanced by employing cortical stimulation at the seizure onset zone (SOZ), leading to the generation of CCEPs in remote brain regions that may be targeted for disconnection. In the case of a 24-year-old male patient suffering from intractable epilepsy, corpus callosotomy was performed as part of the treatment. DTI-MRI imaging, conducted using a 3T MRI scanner for fiber tracking, along with CCEP, is used as part of an assessment for surgical planning. Stimulation of the SOZ, with alternating monophasic pulses of 300µs duration and 15mA current intensity, resulted in CCEPs on the contralateral frontal cortex, reaching a peak amplitude of 206µV with a latency of 31ms, specifically in the left pars triangularis. The related fiber tracts were identified with a two-tensor unscented Kalman filter (UKF) technique, showing transversal fibers through the corpus callosum. The CCEPs were monitored through the progress of the surgery. Notably, the SOZ-associated CCEPs exhibited a reduction following the resection of the anterior portion of the corpus callosum, reaching the identified connecting fibers. This intervention demonstrated a potential strategy for mitigating the impact of intractable epilepsy through targeted disconnection of identified cortical regions.

Keywords: CCEP, SOZ, Corpus callosotomy, DTI

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Authors: Ursula Das, Manavjit Singh Dhindsa, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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Wildland fires, also known as forest fires or wildfires, are exhibiting an alarming surge in frequency in recent times, further adding to its perennial global concern. Forest fires often lead to devastating consequences ranging from loss of healthy forest foliage and wildlife to substantial economic losses and the tragic loss of human lives. Despite the existence of substantial literature on the detection of active forest fires, numerous potential research avenues in forest fire management, such as preventative measures and ancillary effects of forest fires, remain largely underexplored. This paper undertakes a systematic review of these underexplored areas in forest fire research, meticulously categorizing them into distinct phases, namely pre-fire, during-fire, and post-fire stages. The pre-fire phase encompasses the assessment of fire risk, analysis of fuel properties, and other activities aimed at preventing or reducing the risk of forest fires. The during-fire phase includes activities aimed at reducing the impact of active forest fires, such as the detection and localization of active fires, optimization of wildfire suppression methods, and prediction of the behavior of active fires. The post-fire phase involves analyzing the impact of forest fires on various aspects, such as the extent of damage in forest areas, post-fire regeneration of forests, impact on wildlife, economic losses, and health impacts from byproducts produced during burning. A comprehensive understanding of the three stages is imperative for effective forest fire management and mitigation of the impact of forest fires on both ecological systems and human well-being. Artificial intelligence and machine learning (AI/ML) methods have garnered much attention in the cyber-physical systems domain in recent times leading to their adoption in decision-making in diverse applications including disaster management. This paper explores the current state of AI/ML applications for managing the activities in the aforementioned phases of forest fire. While conventional machine learning and deep learning methods have been extensively explored for the prevention, detection, and management of forest fires, a systematic classification of these methods into distinct AI research domains is conspicuously absent. This paper gives a comprehensive overview of the state of forest fire research across more recent and prominent AI/ML disciplines, including big data, classical machine learning, computer vision, explainable AI, generative AI, natural language processing, optimization algorithms, and time series forecasting. By providing a detailed overview of the potential areas of research and identifying the diverse ways AI/ML can be employed in forest fire research, this paper aims to serve as a roadmap for future investigations in this domain.

Keywords: artificial intelligence, computer vision, deep learning, during-fire activities, forest fire management, machine learning, pre-fire activities, post-fire activities

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Authors: Kara J. Coffman-Rea, Karen E. Samonds

Abstract:

Microplastic pollution is an urgent global threat to our planet and human health. Microplastic particles have been detected within our food, water, and atmosphere, and found within the human stool, placenta, and lung tissue. However, most spectrometric microplastic detection methods require chemical digestion which can alter or destroy microplastic particles and makes it impossible to acquire information about their in-situ distribution. MALDI TOF MS (Matrix-assisted laser desorption ionization-time of flight mass spectrometry) is an analytical method using a soft ionization technique that can be used for polymer analysis. This method provides a valuable opportunity to both acquire information regarding the in-situ distribution of microplastics and also minimizes the destructive element of chemical digestion. In addition, MALDI TOF MS allows for expanded analysis of the microplastics including detection of specific additives that may be present within them. MALDI TOF MS is particularly sensitive to sample preparation and has not yet been used to analyze environmental microplastics within their specific location (e.g., biological tissues, sediment, water). In this study, microplastics were created using polyethylene gloves, polystyrene micro-foam, and polyethylene terephthalate cable sleeving. Plastics were frozen using liquid nitrogen and ground to obtain small fragments. An artificial tissue was created using a cellulose sponge as scaffolding coated with a MaxGel Extracellular Matrix to simulate human lung tissue. Optimal preparation techniques (e.g., matrix, cationization reagent, solvent, mixing ratio, laser intensity) were first established for each specific polymer type. The artificial tissue sample was subsequently spiked with microplastics, and specific polymers were detected using MALDI-TOF-MS. This study presents a novel method for the detection of environmental polyethylene, polyethylene terephthalate, and polystyrene microplastics within a complex sample. Results of this study provide an effective method that can be used in future microplastics research and can aid in determining the potential threats to environmental and human health that they pose.

Keywords: environmental plastic pollution, MALDI-TOF MS, microplastics, polymer identification

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Authors: R. Poonkuzhali, M. Y. Sanavullah, M. R. Gurupriya

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Opportunistic routing is used, where the network has the features like dynamic topology changes and intermittent network connectivity. In Delay Tolerant network or Disruption tolerant network opportunistic forwarding technique is widely used. The key idea of opportunistic routing is selecting forwarding nodes to forward data and coordination among these nodes to avoid duplicate transmissions. This paper gives the analysis of pros and cons of various opportunistic routing techniques used in MANET.

Keywords: ETX, opportunistic routing, PSR, throughput

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Authors: Driss Kettani

Abstract:

ICT4D actors are naturally attempted to include AI in the range of enabling technologies and tools that could support and boost the Development process, and to refer to these as AI4D. But, doing so, assumes that AI complies with the very specific features of ICT4D context, including, among others, affordability, relevance, openness, and ownership. Clearly, none of these is fulfilled, and the enthusiastic posture that AI4D is a natural part of ICT4D is not grounded and, to certain extent, does not serve the purpose of Technology for Development at all. In the context of Development, it is important to emphasize and prioritize ICT4D, in the national digital transformation strategies, instead of borrowing "trendy" waves of the IT Industry that are motivated by business considerations, with no specific care/consideration to Development.

Keywords: AI, ICT4D, technology for development, position paper

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Authors: Snehal G. Teli, R. J. Shelke

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CNN and MultiUNet models are the framework for the proposed method for enhancing and reconstructing underwater images. Multiscale merging of features and regeneration are both performed by the MultiUNet. CNN collects relevant features. Extensive tests on benchmark datasets show that the proposed strategy performs better than the latest methods. As a result of this work, underwater images can be represented and interpreted in a number of underwater applications with greater clarity. This strategy will advance underwater exploration and marine research by enhancing real-time underwater image processing systems, underwater robotic vision, and underwater surveillance.

Keywords: convolutional neural network, image enhancement, machine learning, multiunet, underwater images

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Authors: Ali Hamza

Abstract:

Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

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Authors: Dulani Jayasuriya

Abstract:

This study outlines the developmental steps of a chatbot for administrative purposes of a large undergraduate course. The chatbot is able to handle student queries about administrative details, including assessment deadlines, course documentation, how to navigate the course, group formation, etc. The development window screenshots are that of a free account on the Snatchbot platform such that this can be adopted by the wider public. While only one connection to an answer based on possible keywords is shown here, one needs to develop multiple connections leading to different answers based on different keywords for the actual chatbot to function. The overall flow of the chatbot showing connections between different interactions is depicted at the end.

Keywords: chatbots, education, technology, snatch bot, artificial intelligence

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Authors: Reham Banwair, Lana Alshehri, Sara Hadrawi

Abstract:

The purpose of this project is to identify user satisfaction with the AI functions on Snapchat, in order to generate improvement proposals that allow its development within the app. To achieve this, a qualitative analysis was carried out through interviews to people who usually use the application, revealing their satisfaction or dissatisfaction with the usefulness of the AI. In addition, the background of the company and its introduction in these algorithms were analyzed. Furthermore, the characteristics of the three main functions of AI were explained: identify songs, solve mathematical problems, and recognize plants. As a result, it was obtained that 50% still do not know the characteristics of AI, 50% still believe song recognition is not always correct, 41.7% believe that math problems are usually accurate and 91.7% believes the plant detection tool is working properly.

Keywords: artificial intelligence, scanning, Snapchat, machine learning

Procedia PDF Downloads 125

Authors: Saradha Priya

Abstract:

Developing virtual human is very important to meet the challenges occurred in many applications where human find difficult or risky to perform the task. A robot is a machine that can perform a task automatically or with guidance. Robotics is generally a combination of artificial intelligence and physical machines (motors). Computational intelligence involves the programmed instructions. This project proposes a robotic vehicle that has a camera, PIR sensor and text command based movement. It is specially designed to perform surveillance and other few tasks in the most efficient way. Serial communication has been occurred between a remote Base Station, GUI Application, and PC.

Keywords: Zigbee, camera, pirsensor, wireless transmission, DC motor

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Authors: Yara Bajnaid, Malak Zaidan, Eman Dakkak

Abstract:

This research explores the use of Artificial Intelligence in Duolingo, a popular mobile application for language learning. Duolingo's success hinges on its gamified approach and adaptive learning system, both heavily reliant on AI functionalities. The research also analyzes user feedback regarding Duolingo's AI functionalities. While a significant majority (70%) consider Duolingo a reliable tool for language learning, there's room for improvement. Overall, AI plays a vital role in personalizing the learning journey and delivering interactive exercises. However, continuous improvement based on user feedback can further enhance the effectiveness of Duolingo's AI functionalities.

Keywords: AI, Duolingo, language learning, application

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Authors: M. R. Ahmad, I. Musirin, M. M. Othman, N. A. Rahmat

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Plug-in Hybrid-Electric Vehicle (PHEV) has gained immense popularity in recent years. PHEV offers numerous advantages compared to the conventional internal-combustion engine (ICE) vehicle. Millions of PHEVs are estimated to be on the road in the USA by 2020. Uncoordinated PHEV charging is believed to cause severe impacts to the power grid; i.e. feeders, lines and transformers overload and voltage drop. Nevertheless, improper PHEV data model used in such studies may cause the findings of their works is in appropriated. Although smart charging is more attractive to researchers in recent years, its implementation is not yet attainable on the street due to its requirement for physical infrastructure readiness and technology advancement. As the first step, it is finest to study the impact of charging PHEV based on real vehicle travel data from National Household Travel Survey (NHTS) and at present charging rate. Due to the lack of charging station on the street at the moment, charging PHEV at home is the best option and has been considered in this work. This paper proposed a technique that comprehensively presents the impact of charging PHEV on power system networks considering huge numbers of PHEV samples with its traveling data pattern. Vehicles Charging Load Profile (VCLP) is developed and implemented in IEEE 30-bus test system that represents a portion of American Electric Power System (Midwestern US). Normalization technique is used to correspond to real time loads at all buses. Results from the study indicated that charging PHEV using opportunity charging will have significant impacts on power system networks, especially whereas bigger battery capacity (kWh) is used as well as for higher penetration level.

Keywords: plug-in hybrid electric vehicle, transportation electrification, impact of charging PHEV, electricity demand profile, load profile

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Authors: Sarah J. C. Slabbert

Abstract:

The uptake of research as part of the diffusion or adoption of innovation by practitioners, whether individuals or organisations, has been a popular topic in agricultural development studies for many decades. In the classical, linear model of innovation theory, the innovation originates from an expert source such as a state-supported research organisation or academic institution. The changing context of agriculture led to the development of the agricultural innovation systems model, which recognizes innovation as a complex interaction between individuals and organisations, which include private industry and collective action organisations. In terms of this model, an innovation can be developed and adopted without any input or intervention from a state or parastatal research organisation. This evolution in the diffusion of agricultural innovation has put forward new challenges for state or parastatal research organisations, which have to demonstrate the impact of their research to the legislature or a regulatory authority: Unless the organisation and the research it produces cross the knowledge paths of the intended audience, there will be no awareness, no uptake and certainly no impact. It is therefore critical for such a research organisation to base its communication strategy on a thorough understanding of the knowledge needs, information sources and knowledge networks of the intended target audience. In 2016, the South African Water Research Commission (WRC) commissioned a study to investigate the knowledge needs, information sources and knowledge networks of Water User Associations and commercial irrigators with the aim of improving uptake of its research on efficient water use in irrigation. The first phase of the study comprised face-to-face interviews with the CEOs and Board Chairs of four Water User Associations along the Orange River in South Africa, and 36 commercial irrigation farmers from the same four irrigation schemes. Intermediaries who act as knowledge conduits to the Water User Associations and the irrigators were identified and 20 of them were subsequently interviewed telephonically. The study found that irrigators interact regularly with grower organisations such as SATI (South African Table Grape Industry) and SAPPA (South African Pecan Nut Association) and that they perceive these organisations as credible, trustworthy and reliable, within their limitations. State and parastatal research institutions, on the other hand, are associated with a range of negative attributes. As a result, the awareness of, and interest in, the WRC and its research on water use efficiency in irrigated agriculture are low. The findings suggest that a communication strategy that involves collaboration with these grower organisations would empower the WRC to participate much more efficiently and with greater impact in agricultural innovation networks. The paper will elaborate on the findings and discuss partnering frameworks and opportunities to manage perceptions and uptake.

Keywords: agricultural innovation systems, communication strategy, diffusion of innovation, irrigated agriculture, knowledge paths, research organisations, target audiences, water use efficiency

Procedia PDF Downloads 105