Search results for: intelligent gathering

Authors: Luc Beal

Abstract:

Introduction. Among the stakeholders involved in the tourist decision-making process, the accommodation provider has the potential to play a crucial role in raising awareness, disseminating information, and thus influencing the tourists’ choice of transportation. Since the early days of tourism, the accommodation provider has consistently served as the primary point of contact with the destination, and consequently, as the primary source of information for visitors. By offering accommodation and hospitality, the accommodation provider has evolved into a trusted third party, functioning as an 'ambassador' capable of recommending the finest attractions and activities available at the destination. In contemporary times, when tourists plan their trips, they make a series of consecutive decisions, with the most important decision being to lock-in the accommodation reservation for the earliest days, so as to secure a safe arrival. Consequently, tourists place their trust in the accommodation provider not only for lodging but also for recommendations regarding restaurants, activities, and more. Thus, the latter has the opportunity to inform and influence tourists well in advance of their arrival, particularly during the booking phase, namely when it comes to selecting their mode of transportation. The pressing need to reduce greenhouse gas emissions within the tourism sector presents an opportunity to underscore the influence that accommodation providers have historically exerted on tourist decision-making . Methodology A participatory research, currently ongoing in south-western France, in collaboration with a nationwide hotel group and several destination management organizations, aims at examining the factors that determine the ability of accommodation providers to influence tourist transportation choices. Additionally, the research seeks to identify the conditions that motivate accommodation providers to assume a proactive role, such as fostering customer loyalty, reduced distribution costs, and financial compensation mechanisms. A panel of hotels participated in a series of focus group sessions with tourists, with the objective of modeling the decision-making process of tourists regarding their choice of transportation mode and to identify and quantify the types and levels of incentives liable to encourage environmentally responsible choices. Individual interviews were also conducted with hotel staff, including receptionists and guest relations officers, to develop a framework for interactions with tourists during crucial decision-making moments related to transportation choices. The primary finding of this research indicates that financial incentives significantly outweigh symbolic incentives in motivating tourists to opt for eco-friendly modes of transportation. Another noteworthy result underscores the crucial impact of organizational conditions governing interactions with tourists both before and during their stay. These conditions greatly influence the ability to raise awareness at key decision-making moments and the possibility of gathering data about the chosen transportation mode during the stay. In conclusion, this research has led to the formulation of practical recommendations for accommodation providers and Destination Marketing Organizations (DMOs). These recommendations pertain to communication protocols with tourists, the collection of evidences confirming chosen transportation modes, and the implementation of necessary incentives. Through these measures, accommodation provider can assume a central role in guiding tourists towards making responsible choices in terms of transportation.

Keywords: accommodation provider, trusted third party, environmentally-friendly transportation, green house gas, tourist decision-making process

Procedia PDF Downloads 58

Authors: Betül Gelengül Eki̇mci̇, Semra Günay Aktaş

Abstract:

Festive, habitual activities celebrated on the specified date by a local community, are conducive to recognition of the region. The main function of festive events is to help gathering people via an annual celebration to create an atmosphere of understanding and the opportunity to participate in the joy of life. At the same time, festive events may serve as special occasions on which immigrants return home to celebrate with their family and community, reaffirming their identity and link to the community’s traditions. Festivals also support the local economy by bringing in different visitors to the region. The tradition of “Beet Brewing-Molasses Production,” which is held in İdrisyayla Village is an intangible cultural heritage with customs, traditions, and rituals carrying impacts of cuisine culture of Rumelian immigrants in the Ottoman. After the harvest of the beet plant in the autumn season of the year, Beet Brewing Molasses syrup is made by traditional production methods with co-op of the local community. Festive occurring brewing paste made process provided transmission of knowledge and experience to the young generations. Making molasses, which is a laborious process, is accompanied by folk games such as "sayacı," which is vital element of the festive performed in İdrisyayla. Performance provides enjoyable time and supporting motivation. Like other forms of intangible cultural heritage, “Beet Brewing-Molasses Festive in İdrasyayla is threatened by rapid urbanisation, young generation migration, industrialisation and environmental change. The festive events are threatened with gradual disappearance due to changes communities undergo in modern societies because it depends on the broad participation of practitioners. Ensuring the continuity of festive events often requires the mobilization of large numbers of individuals and the social, political and legal institutions and mechanisms of society. In 2015, Intangible cultural heritage research project with the title of "İdrisyayla Molasses Process" managed by the Eskişehir Governorship, City Directorate of Culture and Tourism and Anadolu University, project members took part in the festival organization to promote sustainability, making it visible, to encourage the broadest public participation possible, to ensure public awareness on the cultural importance. To preserve the originality of and encourage participation in the festive İdrisyayla, local associations, researchers and institutions created foundation and supports festive events, such as "sayacı" folk game, which is vital element of the festive performed in İdrisyayla. Practitioners find new opportunity to market İdrisyayla Molasses production. Publicity program through the press and exhibition made it possible to stress the cultural importance of the festive in İdrisyayla Village. The research reported here used a survey analysis to evaluate an affect of the festive after the spirit of the 2015 Festive in İdrisyayla Village. Particular attention was paid to the importance of the cultural aspects of the festival. Based on a survey of more than a hundred festival attendees, several recommendations are made to festival planners. Results indicate that the variety of festive activities and products offered for sale very important to attendees. The local participants care product sales rather than cultural heritage.

Keywords: agritourism, cultural tourism, festival, sustainable cultural heritage

Procedia PDF Downloads 221

Authors: Reza Karimpour

Abstract:

Ports play a decisive role in the EU's external and internal trade, as about 74% of imports and exports and 37% of exchanges go through ports. Although ports, especially Deep Sea Shipping (DSS) ports, are integral nodes within multimodal logistic flows, Short Sea Shipping (SSS) and inland waterways are not so well integrated. The automated vessels and supply chain optimisations for sustainable shortsea shipping (MOSES) project aims to enhance the short sea shipping component of the European supply chain by addressing the vulnerabilities and strains related to the operation of large containerships. The MOSES concept can be shortly described as a large containership (mother-vessel) approaching a DSS port (or a large container terminal). Upon her arrival, a combined intelligent mega-system consisting of the MOSES Autonomous tugboat swarm for manoeuvring and the MOSES adapted AutoMoor system. Then, container handling processes are ready to start moving containers to their destination via hinterland connections (trucks and/or rail) or to be shipped to destinations near small ports (on the mainland or island). For the first case, containers are stored in a dedicated port area (Storage area), waiting to be moved via trucks and/or rail. For the second case, containers are stacked by existing port equipment near-dedicated berths of the DSS port. They then are loaded on the MOSES Innovative Feeder Vessel, equipped with the MOSES Robotic Container-Handling System that provides (semi-) autonomous (un) feeding of the feeder. The Robotic Container-Handling System is remotely monitored through a Shore Control Centre. When the MOSES innovative Feeder vessel approaches the small port, where her docking is achieved without tugboats, she automatically unloads the containers using the Robotic Container-Handling System on the quay or directly on trucks. As a result, ports with minimal or no available infrastructure may be effectively integrated with the container supply chain. Then, the MOSES innovative feeder vessel continues her voyage to the next small port, or she returns to the DSS port. MOSES exploitation activity mainly aims to exploit research outcomes beyond the project, facilitate utilisation of the pilot results by others, and continue the pilot service after the project ends. By the mid-lifetime of the project, the exploitation plan introduces the reader to the MOSES project and its key exploitable results. It provides a plan for delivering the MOSES innovations to the market as part of the overall exploitation plan.

Keywords: automated vessels, exploitation, shortsea shipping, supply chain

Procedia PDF Downloads 110

Authors: Arzu Karahan, Loriano Ballarin, Baruch Rinkevich

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Marine invertebrates, the highly diverse phyla of multicellular organisms, represent phenomena that are either not found or highly restricted in the vertebrates. These include phenomena like budding, fission, a fusion of ramets, and high regeneration power, such as the ability to create whole new organisms from either tiny parental fragment, many of which are controlled by totipotent, pluripotent, and multipotent stem cells. Thus, there is very much that can be learned from these organisms on the practical and evolutionary levels, further resembling Darwin's words, “It is not the strongest of the species that survives, nor the most intelligent, but the one most responsive to change”. The ‘stem cell’ notion highlights a cell that has the ability to continuously divide and differentiate into various progenitors and daughter cells. In vertebrates, adult stem cells are rare cells defined as lineage-restricted (multipotent at best) with tissue or organ-specific activities that are located in defined niches and further regulate the machinery of homeostasis, repair, and regeneration. They are usually categorized by their morphology, tissue of origin, plasticity, and potency. The above description not always holds when comparing the vertebrates with marine invertebrates’ stem cells that display wider ranges of plasticity and diversity at the taxonomic and the cellular levels. While marine/aquatic invertebrates stem cells (MISC) have recently raised more scientific interest, the know-how is still behind the attraction they deserve. MISC, not only are highly potent but, in many cases, are abundant (e.g., 1/3 of the entire animal cells), do not locate in permanent niches, participates in delayed-aging and whole-body regeneration phenomena, the knowledge of which can be clinically relevant. Moreover, they have massive hidden potential for the discovery of new bioactive molecules that can be used for human health (antitumor, antimicrobial) and biotechnology. The MARISTEM COST action (Stem Cells of Marine/Aquatic Invertebrates: From Basic Research to Innovative Applications) aims to connect the European fragmented MISC community. Under this scientific umbrella, the action conceptualizes the idea for adult stem cells that do not share many properties with the vertebrates’ stem cells, organizes meetings, summer schools, and workshops, stimulating young researchers, supplying technical and adviser support via short-term scientific studies, making new bridges between the MISC community and biomedical disciplines.

Keywords: aquatic/marine invertebrates, adult stem cell, regeneration, cell cultures, bioactive molecules

Procedia PDF Downloads 169

Authors: Petra Hofmann, Dana Koniel, Jussi Luukkanen, Walter Nieminen, Lea Hannola, Ilkka Donoghue

Abstract:

The Covid-19 pandemic forced people to isolate and become physically less connected. The pandemic has not only reshaped people’s behaviours and needs but also accelerated digital transformation (DT). DT of cities has become an imperative with the outlook of converting them into smart cities in the future. Embedding digital infrastructure and smart city initiatives as part of normal design, construction, and operation of cities provides a unique opportunity to improve the connection between people. The Internet of Things (IoT) is an emerging technology and one of the drivers in DT. It has disrupted many industries by introducing different services and business models, and IoT solutions are being applied in multiple fields, including smart cities. As IoT and data are fundamentally linked together, IoT solutions can only create value if the data generated by the IoT devices is analysed properly. Extracting relevant conclusions and actionable insights by using established techniques, data analytics contributes significantly to the growth and success of IoT applications and investments. Companies must grasp DT and be prepared to redesign their offerings and business models to remain competitive in today’s marketplace. As there are many IoT solutions available today, the amount of data is tremendous. The challenge for companies is to understand what solutions to focus on and how to prioritise and which data to differentiate from the competition. This paper explains how IoT and data analytics can impact competitive advantage and how companies should approach IoT and data analytics to translate them into concrete offerings and solutions in the smart city context. The study was carried out as a qualitative, literature-based research. A case study is provided to validate the preservation of company’s competitive advantage through smart city solutions. The results of the research contribution provide insights into the different factors and considerations related to creating competitive advantage through IoT and data analytics deployment in the smart city context. Furthermore, this paper proposes a framework that merges the factors and considerations with examples of offerings and solutions in smart cities. The data collected through IoT devices, and the intelligent use of it, can create competitive advantage to companies operating in smart city business. Companies should take into consideration the five forces of competition that shape industries and pay attention to the technological, organisational, and external contexts which define factors for consideration of competitive advantages in the field of IoT and data analytics. Companies that can utilise these key assets in their businesses will most likely conquer the markets and have a strong foothold in the smart city business.

Keywords: data analytics, smart cities, competitive advantage, internet of things

Procedia PDF Downloads 133

Authors: Tingwei Shu, Dong Zhou, Chengjun Guo

Abstract:

Semantic communication is an emerging form of communication that realize intelligent communication by extracting semantic information of data at the source and transmitting it, and recovering the data at the receiving end. It can effectively solve the problem of data transmission under the situation of large data volume, low SNR and restricted bandwidth. With the development of Deep Learning, semantic communication further matures and is gradually applied in the fields of the Internet of Things, Uumanned Air Vehicle cluster communication, remote sensing scenarios, etc. We propose an improved semantic communication system for the situation where the data volume is huge and the spectrum resources are limited during the transmission of remote sensing images. At the transmitting, we need to extract the semantic information of remote sensing images, but there are some problems. The traditional semantic communication system based on Convolutional Neural Network cannot take into account the global semantic information and local semantic information of the image, which results in less-than-ideal image recovery at the receiving end. Therefore, we adopt the improved vision-Transformer-based structure as the semantic encoder instead of the mainstream one using CNN to extract the image semantic features. In this paper, we first perform pre-processing operations on remote sensing images to improve the resolution of the images in order to obtain images with more semantic information. We use wavelet transform to decompose the image into high-frequency and low-frequency components, perform bilinear interpolation on the high-frequency components and bicubic interpolation on the low-frequency components, and finally perform wavelet inverse transform to obtain the preprocessed image. We adopt the improved Vision-Transformer structure as the semantic coder to extract and transmit the semantic information of remote sensing images. The Vision-Transformer structure can better train the huge data volume and extract better image semantic features, and adopt the multi-layer self-attention mechanism to better capture the correlation between semantic features and reduce redundant features. Secondly, to improve the coding efficiency, we reduce the quadratic complexity of the self-attentive mechanism itself to linear so as to improve the image data processing speed of the model. We conducted experimental simulations on the RSOD dataset and compared the designed system with a semantic communication system based on CNN and image coding methods such as BGP and JPEG to verify that the method can effectively alleviate the problem of excessive data volume and improve the performance of image data communication.

Keywords: semantic communication, transformer, wavelet transform, data processing

Procedia PDF Downloads 78

Authors: Taylan Kabbani, Ekrem Duman

Abstract:

The design of adaptive systems that take advantage of financial markets while reducing the risk can bring more stagnant wealth into the global market. However, most efforts made to generate successful deals in trading financial assets rely on Supervised Learning (SL), which suffered from various limitations. Deep Reinforcement Learning (DRL) offers to solve these drawbacks of SL approaches by combining the financial assets price "prediction" step and the "allocation" step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. In this paper, a continuous action space approach is adopted to give the trading agent the ability to gradually adjust the portfolio's positions with each time step (dynamically re-allocate investments), resulting in better agent-environment interaction and faster convergence of the learning process. In addition, the approach supports the managing of a portfolio with several assets instead of a single one. This work represents a novel DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem, or what is referred to as The Agent Environment as Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. More specifically, we design an environment that simulates the real-world trading process by augmenting the state representation with ten different technical indicators and sentiment analysis of news articles for each stock. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which can learn policies in high-dimensional and continuous action spaces like those typically found in the stock market environment. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of deep reinforcement learning in financial markets over other types of machine learning such as supervised learning and proves its credibility and advantages of strategic decision-making.

Keywords: the stock market, deep reinforcement learning, MDP, twin delayed deep deterministic policy gradient, sentiment analysis, technical indicators, autonomous agent

Procedia PDF Downloads 178

Authors: Petra Hofmann, Dana Koniel, Jussi Luukkanen, Walter Nieminen, Lea Hannola, Ilkka Donoghue

Abstract:

The Covid-19 pandemic forced people to isolate and become physically less connected. The pandemic hasnot only reshaped people’s behaviours and needs but also accelerated digital transformation (DT). DT of cities has become an imperative with the outlook of converting them into smart cities in the future. Embedding digital infrastructure and smart city initiatives as part of the normal design, construction, and operation of cities provides a unique opportunity to improve connection between people. Internet of Things (IoT) is an emerging technology and one of the drivers in DT. It has disrupted many industries by introducing different services and business models, and IoT solutions are being applied in multiple fields, including smart cities. As IoT and data are fundamentally linked together, IoT solutions can only create value if the data generated by the IoT devices is analysed properly. Extracting relevant conclusions and actionable insights by using established techniques, data analytics contributes significantly to the growth and success of IoT applications and investments. Companies must grasp DT and be prepared to redesign their offerings and business models to remain competitive in today’s marketplace. As there are many IoT solutions available today, the amount of data is tremendous. The challenge for companies is to understand what solutions to focus on and how to prioritise and which data to differentiate from the competition. This paper explains how IoT and data analytics can impact competitive advantage and how companies should approach IoT and data analytics to translate them into concrete offerings and solutions in the smart city context. The study was carried out as a qualitative, literature-based research. A case study is provided to validate the preservation of company’s competitive advantage through smart city solutions. The results of the researchcontribution provide insights into the different factors and considerations related to creating competitive advantage through IoT and data analytics deployment in the smart city context. Furthermore, this paper proposes a framework that merges the factors and considerations with examples of offerings and solutions in smart cities. The data collected through IoT devices, and the intelligent use of it, can create a competitive advantage to companies operating in smart city business. Companies should take into consideration the five forces of competition that shape industries and pay attention to the technological, organisational, and external contexts which define factors for consideration of competitive advantages in the field of IoT and data analytics. Companies that can utilise these key assets in their businesses will most likely conquer the markets and have a strong foothold in the smart city business.

Keywords: internet of things, data analytics, smart cities, competitive advantage

Procedia PDF Downloads 94

Authors: Lauren B. Birney

Abstract:

The project utilizes the Billion Oyster Project (BOP-CCERS) place-based “restoration through education” model to promote computational thinking in NYC high school teachers and their students. Key learning standards such as Next Generation Science Standards and the NYC CS4All Equity and Excellence initiative are used to develop a computer science curriculum that connects students to their Harbor through hands-on activities based on BOP field science and educational programming. Project curriculum development is grounded in BOP-CCERS restoration science activities and data collection, which are enacted by students and educators at two Restoration Science STEM Hubs or conveyed through virtual materials. New York City Public School teachers with relevant experience are recruited as consultants to provide curriculum assessment and design feedback. The completed curriculum units are then conveyed to NYC high school teachers through professional learning events held at the Pace University campus and led by BOP educators. In addition, Pace University educators execute the Summer STEM Institute, an intensive two-week computational thinking camp centered on applying data analysis tools and methods to BOP-CCERS data. Both qualitative and quantitative analyses were performed throughout the five-year study. STEM+C – Community Based Restoration STEM Hubs. STEM Hubs are active scientific restoration sites capable of hosting school and community groups of all grade levels and professional scientists and researchers conducting long-term restoration ecology research. The STEM Hubs program has grown to include 14 STEM Hubs across all five boroughs of New York City and focuses on bringing in-field monitoring experience as well as coastal classroom experience to students. Restoration Science STEM Hubs activities resulted in: the recruitment of 11 public schools, 6 community groups, 12 teachers, and over 120 students receiving exposure to BOP activities. Field science protocols were designed exclusively around the use of the Oyster Restoration Station (ORS), a small-scale in situ experimental platforms which are suspended from a dock or pier. The ORS is intended to be used and “owned” by an individual school, teacher, class, or group of students, whereas the STEM Hub is explicitly designed as a collaborative space for large-scale community-driven restoration work and in-situ experiments. The ORS is also an essential tool in gathering Harbor data from disparate locations and instilling ownership of the research process amongst students. As such, it will continue to be used in that way. New and previously participating students will continue to deploy and monitor their own ORS, uploading data to the digital platform and conducting analysis of their own harbor-wide datasets. Programming the STEM Hub will necessitate establishing working relationships between schools and local research institutions. NYHF will provide introductions and the facilitation of initial workshops in school classrooms. However, once a particular STEM Hub has been established as a space for collaboration, each partner group, school, university, or CBO will schedule its own events at the site using the digital platform’s scheduling and registration tool. Monitoring of research collaborations will be accomplished through the platform’s research publication tool and has thus far provided valuable information on the projects’ trajectory, strategic plan, and pathway.

Keywords: environmental science, citizen science, STEM, technology

Procedia PDF Downloads 96

Authors: Yihenew Simegniew Birhan, Hsieh Chih Tsai

Abstract:

The recent advancements in synthetic chemistry and nanotechnology fostered the development of different nanocarriers for enhanced intracellular delivery of pharmaceutical agents to tumor cells. Polymeric micelles (PMs), characterized by small size, appreciable drug loading capacity (DLC), better accumulation in tumor tissue via enhanced permeability and retention (EPR) effect, and the ability to avoid detection and subsequent clearance by the mononuclear phagocyte (MNP) system, are convenient to improve the poor solubility, slow absorption and non-selective biodistribution of payloads embedded in their hydrophobic cores and hence, enhance the therapeutic efficacy of chemotherapeutic agents. Recently, redox-responsive polymeric micelles have gained significant attention for the delivery and controlled release of anticancer drugs in tumor cells. In this study, we synthesized redox-responsive diselenide bond containing amphiphilic polymer, Bi(mPEG-PLGA)-Se₂ from mPEG-PLGA, and 3,3'-diselanediyldipropanoic acid (DSeDPA) using DCC/DMAP as coupling agents. The successful synthesis of the copolymers was verified by different spectroscopic techniques. Above the critical micelle concentration, the amphiphilic copolymer, Bi(mPEG-PLGA)-Se₂, self-assembled into stable micelles. The DLS data indicated that the hydrodynamic diameter of the micelles (123.9 ± 0.85 nm) was suitable for extravasation into the tumor cells through the EPR effect. The drug loading content (DLC) and encapsulation efficiency (EE) of DOX-loaded micelles were found to be 6.61 wt% and 54.9%, respectively. The DOX-loaded micelles showed initial burst release accompanied by sustained release trend where 73.94% and 69.54% of encapsulated DOX was released upon treatment with 6mM GSH and 0.1% H₂O₂, respectively. The biocompatible nature of Bi(mPEG-PLGA)-Se₂ copolymer was confirmed by the cell viability study. In addition, the DOX-loaded micelles exhibited significant inhibition against HeLa cells (44.46%), at a maximum dose of 7.5 µg/mL. The fluorescent microscope images of HeLa cells treated with 3 µg/mL (equivalent DOX concentration) revealed efficient internalization and accumulation of DOX-loaded Bi(mPEG-PLGA)-Se₂ micelles in the cytosol of cancer cells. In conclusion, the intelligent, biocompatible, and the redox stimuli-responsive behavior of Bi(mPEG-PLGA)-Se₂ copolymer marked the potential applications of diselenide-linked mPEG-PLGA micelles for the delivery and on-demand release of chemotherapeutic agents in cancer cells.

Keywords: anticancer drug delivery, diselenide bond, polymeric micelles, redox-responsive

Procedia PDF Downloads 110

Authors: Mujahid Muhammad, Paul Kearney, Adel Aneiba

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The ability for vehicles to communicate with other vehicles (V2V), the physical (V2I) and network (V2N) infrastructures, pedestrians (V2P), etc. – collectively known as V2X (Vehicle to Everything) – will enable a broad and growing set of applications and services within the intelligent transport domain for improving road safety, alleviate traffic congestion and support autonomous driving. The telecommunication research and industry communities and standardization bodies (notably 3GPP) has finally approved in Release 14, cellular communications connectivity to support V2X communication (known as LTE – V2X). LTE – V2X system will combine simultaneous connectivity across existing LTE network infrastructures via LTE-Uu interface and direct device-to-device (D2D) communications. In order for V2X services to function effectively, a robust security mechanism is needed to ensure legal and safe interaction among authenticated V2X entities in the LTE-based V2X architecture. The characteristics of vehicular networks, and the nature of most V2X applications, which involve human safety makes it significant to protect V2X messages from attacks that can result in catastrophically wrong decisions/actions include ones affecting road safety. Attack vectors include impersonation attacks, modification, masquerading, replay, MiM attacks, and Sybil attacks. In this paper, we focus our attention on LTE-based V2X security and access control mechanisms. The current LTE-A security framework provides its own access authentication scheme, the AKA protocol for mutual authentication and other essential cryptographic operations between UEs and the network. V2N systems can leverage this protocol to achieve mutual authentication between vehicles and the mobile core network. However, this protocol experiences technical challenges, such as high signaling overhead, lack of synchronization, handover delay and potential control plane signaling overloads, as well as privacy preservation issues, which cannot satisfy the adequate security requirements for majority of LTE-based V2X services. This paper examines these challenges and points to possible ways by which they can be addressed. One possible solution, is the implementation of the distributed peer-to-peer LTE security mechanism based on the Bitcoin/Namecoin framework, to allow for security operations with minimal overhead cost, which is desirable for V2X services. The proposed architecture can ensure fast, secure and robust V2X services under LTE network while meeting V2X security requirements.

Keywords: authentication, long term evolution, security, vehicle-to-everything

Procedia PDF Downloads 167

Authors: Ayesha Selwyn, Annapoorni Chandrashekar, Kumar Ashwarya, Nishant Baghel

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Project-based learning (PBL) activities are typically implemented in technology-enabled schools by highly trained teachers. In rural India, students have limited access to technology and quality education. Implementing typical PBL activities is challenging. This study details how Pratham Education Foundation’s Hybrid Learning model was used to implement two PBL activities related to music in 900 remote Indian villages with 46,000 students aged 10-14. The activities were completed by 69% of groups that submitted a total of 15,000 videos (completed projects). Pratham’s H-Learning model reaches 100,000 students aged 3-14 in 900 Indian villages. The community-driven model engages students in 20,000 self-organized groups outside of school. The students are guided by 6,000 youth volunteers and 100 facilitators. The students partake in learning activities across subjects with the support of community stakeholders and offline digital content on shared Android tablets. A training and implementation toolkit for PBL activities is designed by subject experts. This toolkit is essential in ensuring efficient implementation of activities as facilitators aren’t highly skilled and have limited access to training resources. The toolkit details the activity at three levels of student engagement - enrollment, participation, and completion. The subject experts train project leaders and facilitators who train youth volunteers. Volunteers need to be trained on how to execute the activity and guide students. The training is focused on building the volunteers’ capacity to enable students to solve problems, rather than developing the volunteers’ subject-related knowledge. This structure ensures that continuous intervention of subject matter experts isn’t required, and the onus of judging creativity skills is put on community members. 46,000 students in the H-Learning program were engaged in two PBL activities related to Music from April-June 2019. For one activity, students had to conduct a “musical survey” in their village by designing a survey and shooting and editing a video. This activity aimed to develop students’ information retrieval, data gathering, teamwork, communication, project management, and creativity skills. It also aimed to identify talent and document local folk music. The second activity, “Pratham Idol”, was a singing competition. Students participated in performing, producing, and editing videos. This activity aimed to develop students’ teamwork and creative skills and give students a creative outlet. Students showcased their completed projects at village fairs wherein a panel of community members evaluated the videos. The shortlisted videos from all villages were further evaluated by experts who identified students and adults to participate in advanced music workshops. The H-Learning framework enables students in low resource settings to engage in PBL and develop relevant skills by leveraging community support and using video creation as a tool. In rural India, students do not have access to high-quality education or infrastructure. Therefore designing activities that can be implemented by community members after limited training is essential. The subject experts have minimal intervention once the activity is initiated, which significantly reduces the cost of implementation and allows the activity to be implemented at a massive scale.

Keywords: community supported learning, project-based learning, self-organized learning, education technology

Procedia PDF Downloads 186

Authors: Sara A. Ben Lashihar

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The heritage building information modelling (HBIM) of the historical masonry buildings has expanded lately to meet the urgent needs for conservation and structural analysis. The masonry structures are unique features for ancient building architectures worldwide that have special cultural, spiritual, and historical significance. However, there is a research gap regarding the reliability of the HBIM modeling process of these structures. The HBIM modeling process of the masonry structures faces significant challenges due to the inherent complexity and uniqueness of their structural systems. Most of these processes are based on tracing the point clouds and rarely follow documents, archival records, or direct observation. The results of these techniques are highly abstracted models where the accuracy does not exceed LOD 200. The masonry assemblages, especially curved elements such as arches, vaults, and domes, are generally modeled with standard BIM components or in-place models, and the brick textures are graphically input. Hence, future investigation is necessary to establish a methodology to generate automatically parametric masonry components. These components are developed algorithmically according to mathematical and geometric accuracy and the validity of the survey data. The main aim of this paper is to provide a comprehensive review of the state of the art of the existing researches and papers that have been conducted on the HBIM modeling of the masonry structural elements and the latest approaches to achieve parametric models that have both the visual fidelity and high geometric accuracy. The paper reviewed more than 800 articles, proceedings papers, and book chapters focused on "HBIM and Masonry" keywords from 2017 to 2021. The studies were downloaded from well-known, trusted bibliographic databases such as Web of Science, Scopus, Dimensions, and Lens. As a starting point, a scientometric analysis was carried out using VOSViewer software. This software extracts the main keywords in these studies to retrieve the relevant works. It also calculates the strength of the relationships between these keywords. Subsequently, an in-depth qualitative review followed the studies with the highest frequency of occurrence and the strongest links with the topic, according to the VOSViewer's results. The qualitative review focused on the latest approaches and the future suggestions proposed in these researches. The findings of this paper can serve as a valuable reference for researchers, and BIM specialists, to make more accurate and reliable HBIM models for historic masonry buildings.

Keywords: HBIM, masonry, structure, modeling, automatic, approach, parametric

Procedia PDF Downloads 165

Authors: Ali Mashayekh, Mahdiye Khorasani, Thomas weyh

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The demand for battery electric vehicles (BEV) is steadily increasing, and it can be assumed that electric mobility will dominate the market for individual transportation in the future. Regarding BEVs, the focus of state-of-the-art research and development is on vehicle batteries since their properties primarily determine vehicles' characteristic parameters, such as price, driving range, charging time, and lifetime. State-of-the-art battery packs consist of invariable configurations of battery cells, connected in series and parallel. A promising alternative is battery systems based on multilevel inverters, which can alter the configuration of the battery cells during operation via semiconductor switches. The main benefit of such topologies is that a three-phase AC voltage can be directly generated from the battery pack, and no separate power inverters are required. Therefore, modular battery systems based on different multilevel inverter topologies and reconfigurable battery systems are currently under investigation. Another advantage of the multilevel concept is that the possibility to reconfigure the battery pack allows battery cells with different states of charge (SoC) to be connected in parallel, and thus low-loss balancing can take place between such cells. In contrast, in conventional battery systems, parallel connected (hard-wired) battery cells are discharged via bleeder resistors to keep the individual SoCs of the parallel battery strands balanced, ultimately reducing the vehicle range. Different multilevel inverter topologies and reconfigurable batteries have been described in the available literature that makes the before-mentioned advantages possible. However, what has not yet been described is how an intelligent operating algorithm needs to look like to keep the SoCs of the individual battery strands of a modular battery system with integrated power electronics balanced. Therefore, this paper suggests an SoC balancing approach for Battery Modular Multilevel Management (BM3) converter systems, which can be similarly used for reconfigurable battery systems or other multilevel inverter topologies with parallel connectivity. The here suggested approach attempts to simultaneously utilize all converter modules (bypassing individual modules should be avoided) because the parallel connection of adjacent modules reduces the phase-strand's battery impedance. Furthermore, the presented approach tries to reduce the number of switching events when changing the switching state combination. Thereby, the ohmic battery losses and switching losses are kept as low as possible. Since no power is dissipated in any designated bleeder resistors and no designated active balancing circuitry is required, the suggested approach can be categorized as a proactive balancing approach. To verify the algorithm's validity, simulations are used.

Keywords: battery management system, BEV, battery modular multilevel management (BM3), SoC balancing

Procedia PDF Downloads 120

Authors: Abdul Basit Kiani

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Web development, the art of creating and maintaining websites, has witnessed remarkable advancements. The aim is to provide an overview of some of the cutting-edge developments in the field. Firstly, the rise of responsive web design has revolutionized user experiences across devices. With the increasing prevalence of smartphones and tablets, web developers have adapted to ensure seamless browsing experiences, regardless of screen size. This progress has greatly enhanced accessibility and usability, catering to the diverse needs of users worldwide. Additionally, the evolution of web frameworks and libraries has significantly streamlined the development process. Tools such as React, Angular, and Vue.js have empowered developers to build dynamic and interactive web applications with ease. These frameworks not only enhance efficiency but also bolster scalability, allowing for the creation of complex and feature-rich web solutions. Furthermore, the emergence of progressive web applications (PWAs) has bridged the gap between native mobile apps and web development. PWAs leverage modern web technologies to deliver app-like experiences, including offline functionality, push notifications, and seamless installation. This innovation has transformed the way users interact with websites, blurring the boundaries between traditional web and mobile applications. Moreover, the integration of artificial intelligence (AI) and machine learning (ML) has opened new horizons in web development. Chatbots, intelligent recommendation systems, and personalization algorithms have become integral components of modern websites. These AI-powered features enhance user engagement, provide personalized experiences, and streamline customer support processes, revolutionizing the way businesses interact with their audiences. Lastly, the emphasis on web security and privacy has been a pivotal area of progress. With the increasing incidents of cyber threats, web developers have implemented robust security measures to safeguard user data and ensure secure transactions. Innovations such as HTTPS protocol, two-factor authentication, and advanced encryption techniques have bolstered the overall security of web applications, fostering trust and confidence among users. Hence, recent progress in web development has propelled the industry forward, enabling developers to craft innovative and immersive digital experiences. From responsive design to AI integration and enhanced security, the landscape of web development continues to evolve, promising a future filled with endless possibilities.

Keywords: progressive web applications (PWAs), web security, machine learning (ML), web frameworks, advancement responsive web design

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Authors: Jonathan Turner, Julie Doyle, Laura O’Philbin, Dympna O’Sullivan

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People living with dementia should be at the centre of decision-making regarding goals for daily living. These goals include basic activities (dressing, hygiene, and mobility), advanced activities (finances, transportation, and shopping), and meaningful activities that promote well-being (pastimes and intellectual pursuits). However, there is limited involvement of people living with dementia in the design of technology to support their goals. A project is described that is co-designing intelligent computer-based support for, and with, people affected by dementia and their carers. The technology will support self-management, empower participation in shared decision-making with carers and help people living with dementia remain healthy and independent in their homes for longer. It includes information from the patient’s care plan, which documents medications, contacts, and the patient's wishes on end-of-life care. Importantly for this work, the plan can outline activities that should be maintained or worked towards, such as exercise or social contact. The authors discuss how to integrate care goal information from such a care plan with data collected from passive sensors in the patient’s home in order to deliver individualized planning and interventions for persons with dementia. A number of scientific challenges are addressed: First, to co-design with dementia patients and their carers computerized support for shared decision-making about their care while allowing the patient to share the care plan. Second, to develop a new and open monitoring framework with which to configure sensor technologies to collect data about whether goals and actions specified for a person in their care plan are being achieved. This is developed top-down by associating care quality types and metrics elicited from the co-design activities with types of data that can be collected within the home, from passive and active sensors, and from the patient’s feedback collected through a simple co-designed interface. These activities and data will be mapped to appropriate sensors and technological infrastructure with which to collect the data. Third, the application of machine learning models to analyze data collected via the sensing devices in order to investigate whether and to what extent activities outlined via the care plan are being achieved. The models will capture longitudinal data to track disease progression over time; as the disease progresses and captured data show that activities outlined in the care plan are not being achieved, the care plan may recommend alternative activities. Disease progression may also require care changes, and a data-driven approach can capture changes in a condition more quickly and allow care plans to evolve and be updated.

Keywords: care goals, decision-making, dementia, self-care, sensors

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Authors: Shengnan Chen, Shuhua Wang

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Successful production of hydrocarbon from unconventional tight oil reserves has changed the energy landscape in North America. The oil contained within these reservoirs typically will not flow to the wellbore at economic rates without assistance from advanced horizontal well and multi-stage hydraulic fracturing. Efficient and economic development of these reserves is a priority of society, government, and industry, especially under the current low oil prices. Meanwhile, society needs technological and process innovations to enhance oil recovery while concurrently reducing environmental impacts. Recently, big data analysis and artificial intelligence become very popular, developing data-driven insights for better designs and decisions in various engineering disciplines. However, the application of data mining in petroleum engineering is still in its infancy. The objective of this research aims to apply intelligent data analysis and data-driven models to exploit unconventional oil reserves both efficiently and economically. More specifically, a comprehensive database including the reservoir geological data, reservoir geophysical data, well completion data and production data for thousands of wells is firstly established to discover the valuable insights and knowledge related to tight oil reserves development. Several data analysis methods are introduced to analysis such a huge dataset. For example, K-means clustering is used to partition all observations into clusters; principle component analysis is applied to emphasize the variation and bring out strong patterns in the dataset, making the big data easy to explore and visualize; exploratory factor analysis (EFA) is used to identify the complex interrelationships between well completion data and well production data. Different data mining techniques, such as artificial neural network, fuzzy logic, and machine learning technique are then summarized, and appropriate ones are selected to analyze the database based on the prediction accuracy, model robustness, and reproducibility. Advanced knowledge and patterned are finally recognized and integrated into a modified self-adaptive differential evolution optimization workflow to enhance the oil recovery and maximize the net present value (NPV) of the unconventional oil resources. This research will advance the knowledge in the development of unconventional oil reserves and bridge the gap between the big data and performance optimizations in these formations. The newly developed data-driven optimization workflow is a powerful approach to guide field operation, which leads to better designs, higher oil recovery and economic return of future wells in the unconventional oil reserves.

Keywords: big data, artificial intelligence, enhance oil recovery, unconventional oil reserves

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Authors: Edward Shizha, Edward Makwarimba

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Newcomer and refugee youth feel excluded in the education system in Canada, and the formal education environment does not fully cater for their learning needs. The objective of this study was to build knowledge and understanding of the educational needs and experiences of these youth in Canada and how available afterschool programs can most effectively support their learning needs and academic outcomes. The Employment and Social Development Canada (ESDC), which funded this research, enables and empowers students to advance their educational experience through targeted investments in services that are delivered by youth-serving organizations outside the formal education system through afterschool initiatives. A literature review and a provincial/territorial internet scan were conducted to determine the availability of services and programs that serve the educational needs and academic outcomes of newcomer youth in 10 provinces and 3 territories in Canada. The goal was to identify intersectional factors (e.g., gender, sexuality, culture, social class, race, etc.) that influence educational outcomes of newcomer/refugee students and to recommend ways the ESDC could complement settlement services to enhance students’ educational success. First, data was collected through a literature search of various databases, including PubMed, Web of Science, Scopus, Google docs, ACADEMIA, and grey literature, including government documents, to inform our analysis. Second, a provincial/territorial internet scan was conducted using a template that was created by ESDC staff with the input of the researchers. The objective of the web-search scan was to identify afterschool programs, projects, and initiatives offered to newcomer/refugee youth by service provider organizations. The method for the scan included both qualitative and quantitative data gathering. Both the literature review and the provincial/territorial scan revealed that there are gender disparities in educational outcomes of newcomer and refugee youth. High school completion rates by gender show that boys are at higher risk of not graduating than girls and that girls are more likely than boys to have at least a high school diploma and more likely to proceed to postsecondary education. Findings from literature reveal that afterschool programs are required for refugee youth who experience mental health challenges and miss out on significant periods of schooling, which affect attendance, participation, and graduation from high school. However, some refugee youth use their resilience and ambition to succeed in their educational outcomes. Another finding showed that some immigrant/refugee students, through ethnic organizations and familial affiliation, maintain aspects of their cultural values, parental expectations and ambitious expectations for their own careers to succeed in both high school and postsecondary education. The study found a significant combination of afterschool programs that include academic support, scholarships, bursaries, homework support, career readiness, internships, mentorship, tutoring, non-clinical counselling, mental health and social well-being support, language skills, volunteering opportunities, community connections, peer networking, culturally relevant services etc. These programs assist newcomer youth to develop self-confidence and prepare for academic success and future career development. The study concluded that advantages of afterschool programs are greatest for youth at risk for poor educational outcomes, such as Latino and Black youth, including 2SLGBTQI+ immigrant youth.

Keywords: afterschool programs, educational outcomes, newcomer youth, refugee youth, youth-serving organizations

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Authors: Victor Kang, Sirene Bellahnid, Amy Al-Simaani

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Efficient documentation and completion of clerical tasks are pillars of efficient patient-centered care in acute settings such as the emergency department (ED). Medical scribes aid physicians with documentation, navigation of electronic health records, results gathering, and communication coordination with other healthcare teams. However, the use of medical scribes is not widespread, with some hospitals even continuing to discontinue their programs. One reason for this could be the lack of studies that have outlined concrete improvements in efficiency and patient and provider satisfaction in emergency departments before and after incorporating scribes. Methods: We conducted a review of the literature concerning the implementation of a medical scribe program and emergency department performance. For this review, a narrative synthesis accompanied by textual commentaries was chosen to present the selected papers. PubMed was searched exclusively. Initially, no date limits were set, but seeing as the electronic medical record was officially implemented in Canada in 2013, studies published after this date were preferred as they provided insight into the interplay between its implementation and scribes on quality improvement. Results: Throughput, efficiency, and cost-effectiveness were the most commonly used parameters in evaluating scribes in the Emergency Department. Important throughput metrics, specifically door-to-doctor and disposition time, were significantly decreased in emergency departments that utilized scribes. Of note, this was shown to be the case in community hospitals, where the burden of documentation and clerical tasks would fall directly upon the attending physician. Academic centers differ in that they rely heavily on residents and students; so the implementation of scribes has been shown to have limited effect on these metrics. However, unique to academic centers was the provider’s perception of incrased time for teaching was unique to academic centers. Consequently, providers express increased work satisfaction in relation to time spent with patients and in teaching. Patients, on the other hand, did not demonstrate a decrease in satisfaction in regards to the care that was provided, but there was no significant increase observed either. Of the studies we reviewed, one of the biggest limitations was the lack of significance in the data. While many individual studies reported that medical scribes in emergency rooms improved relative value units, patient satisfaction, provider satisfaction, and increased number of patients seen, there was no statistically significant improvement in the above criteria when compiled in a systematic review. There is also a clear publication bias; very few studies with negative results were published. To prove significance, data from more emergency rooms with scribe programs would need to be compiled which also includes emergency rooms who did not report noticeable benefits. Furthermore, most data sets focused only on scribes in academic centers. Conclusion: Ultimately, the literature suggests that while emergency room physicians who have access to medical scribes report higher satisfaction due to lower clerical burdens and can see more patients per shift, there is still variability in terms of patient and provider satisfaction. Whether or not this variability exists due to differences in training (in-house trainees versus contractors), population profile (adult versus pediatric), setting (academic versus community), or which shifts scribe work cannot be determined based on the studies that exist. Ultimately, more scribe programs need to be evaluated to determine whether these variables affect outcomes and prove whether scribes significantly improve emergency room efficiency.

Keywords: emergency medicine, medical scribe, scribe, documentation

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Authors: Charles Mazhazhate

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This study interrogates and analyses the intricate kin- and- kith network patterns of corruption and mismanagement of resources prevalent in public sector supply chains bedeviling the developing economies of Sub-Saharan Africa with particular reference to Zimbabwe. This is forcing governments to resort to harsh fiscal policies that see their citizens paying high taxes against a backdrop of incomes below the poverty datum line, and this negatively affects their quality of life. The corporate world is also affected by the various tax-regime instituted. Mismanagement of resources and corrupt practices are rampant in state-owned enterprises to the extent that institutional policies, procedures, and practices are often flouted for the benefit of a clique of individuals. This interwoven in kith and kin blood human relations in organizations where appointments to critical positions are based on ascribed status. People no longer place value in their systems to make them work thereby violating corporate governance principles. Greediness and ‘unholy friendship connections’ are instrumental in fueling the employment of people who know each other from their discrete backgrounds. Such employments or socio-metric unions are meant to protect those at the top by giving them intelligent information through spying on what other subordinates are doing inside and outside the organization. This practice has led to the underperforming of organizations as those employees with connections and their upper echelons favorites connive to abuse resources for their own benefit. Even if culprits are known, no draconian measures are employed as a deterrence measure. Public value along public sector supply chains is lost. The study used a descriptive case study research design on fifty organizations in Zimbabwe mainly state-owned enterprises. Both qualitative and quantitative instrumentations were used. Both Snowball and random sampling techniques were used. The study found out that in all the fifty SOEs, there were employees in key positions related to top management, with tentacles feeding into the law enforcement agents, judiciary, security systems, and the executive. Such employees in public seem not to know each other with but would be involved in dirty scams and then share the proceeds with top people behind the scenes. The study also established that the same employees do not have the necessary competencies, qualifications, abilities, and capabilities to be in those positions. This culture is now strong that it is difficult to bust. The study recommends recruitment of all employees through an independent employment bureau to ensure strategic fit.

Keywords: corruption, state owned enterprises, strategic fit, public sector supply chains, efficiency

Procedia PDF Downloads 160

Authors: Aparajita Bhattacharya, Mousumi Dutta, Zakir Husain, Dionne Sequeira, Asima Mukhopadhyay

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Background: Tata Medical Centre (TMC), Kolkata is a major cancer referral centre in Eastern India and neighbouring countries providing state of the art facilities; however, it is a non-profit organisation with patients requiring to pay at subsidised rates. Although a system for social assessment and applying for governmental/ non-governmental (NGO) funds is in place, access is challenging. Amongst gynaecological cancers (GC), ovarian cancer (OC) is associated with the highest treatment cost; majority of which is required at the beginning when complex surgery is performed and funding arrangements cannot be made in time. We therefore appointed a dedicated Medical Social Worker (MSW) in 2016, supported by NGO for GC patients in order to assist patients/family members to access/avail these funds more readily and assist in economic evaluation for both direct and opportunity costs. Objectives: To reflect on our experience and challenges in collecting data on economic evaluation of cancer patients and compare success rates in achieving fund mobilization after introduction of MSW. Methods: A Retrospective survey. Patients with OC and their relatives were seen by the MSW during the initial outpatients department visit and followed though till discharge from the hospital and during follow-up visits. Assistance was provided in preparing the essential documents/paperwork/contacts for the funding agencies including both governmental (Chief-Minister/Prime-Minister/President) and NGO sources. In addition, a detailed questionnaire was filled up for economic assessment of direct/opportunity costs during the entire treatment and 12 months follow up period which forms a part of the study called HEPTROC (Health economic evaluation of primary treatment for ovarian cancer) developed in collaboration with economics departments of Universities. Results: In 2015, 102 patients were operated for OC; only 16 patients (15.68 %) had availed funding of a total sum of INR 1640000 through the hospital system for social assessment. Following challenges were faced by majority of the relatives: 1. Gathering important documents/proper contact details for governmental funding bodies and difficulty in following up the current status 3. Late arrival of funds. In contrast in 2016, 104 OC patients underwent surgery; the direct cost of treatment was significantly higher (median, INR 300000- 400000) compared to other GCs (n=274). 98/104 (94.23%) OC patients could be helped to apply for funds and 90/104(86.56%) patients received funding amounting to a total of INR 10897000. There has been a tenfold increase in funds mobilized in 2016 after the introduction of dedicated MSW in GC. So far, in 2017 (till June), 46/54(85.18%) OC patients applied for funds and 37/54(68.51%) patients have received funding. In a qualitative survey, all patients appreciated the role of the MSW who subsequently became the key worker for patient follow up and the chief portal for patient reported outcome monitoring. Data collection quality for the HEPTROC study was improved when questionnaires were administered by the MSW compared to researchers. Conclusion: Introduction of cancer specific MSW can expedite the availability of funds required for cancer patients and it can positively impact on patient satisfaction and outcome reporting. The economic assessment will influence fund allocation and decision for policymaking in ovarian cancer. Acknowledgement: Jivdaya Foundation Dallas, Texas.

Keywords: economic evaluation, funding, medical social worker, ovarian cancer

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Authors: Stefan Papastefanou

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Artificial intelligence (AI) is an interdisciplinary field of computer science with the aim of creating intelligent machine behavior. Early approaches to AI have been configured to operate in very constrained environments where the behavior of the AI system was previously determined by formal rules. Knowledge was presented as a set of rules that allowed the AI system to determine the results for specific problems; as a structure of if-else rules that could be traversed to find a solution to a particular problem or question. However, such rule-based systems typically have not been able to generalize beyond the knowledge provided. All over the world and especially in IT-heavy industries such as the United States, the European Union, Singapore, and China, machine learning has developed to be an immense asset, and its applications are becoming more and more significant. It has to be examined how such products of machine learning models can and should be protected by IP law and for the purpose of this paper patent law specifically, since it is the IP law regime closest to technical inventions and computing methods in technical applications. Genetic breeding models are currently less popular than recursive neural network method and deep learning, but this approach can be more easily described by referring to the evolution of natural organisms, and with increasing computational power; the genetic breeding method as a subset of the evolutionary algorithms models is expected to be regaining popularity. The research method focuses on patentability (according to the world’s most significant patent law regimes such as China, Singapore, the European Union, and the United States) of AI inventions and machine learning. Questions of the technical nature of the problem to be solved, the inventive step as such, and the question of the state of the art and the associated obviousness of the solution arise in the current patenting processes. Most importantly, and the key focus of this paper is the problem of patenting inventions that themselves are developed through machine learning. The inventor of a patent application must be a natural person or a group of persons according to the current legal situation in most patent law regimes. In order to be considered an 'inventor', a person must actually have developed part of the inventive concept. The mere application of machine learning or an AI algorithm to a particular problem should not be construed as the algorithm that contributes to a part of the inventive concept. However, when machine learning or the AI algorithm has contributed to a part of the inventive concept, there is currently a lack of clarity regarding the ownership of artificially created inventions. Since not only all European patent law regimes but also the Chinese and Singaporean patent law approaches include identical terms, this paper ultimately offers a comparative analysis of the most relevant patent law regimes.

Keywords: algorithms, inventor, genetic breeding models, machine learning, patentability

Procedia PDF Downloads 108

Authors: Daniel Herrera Gonzalez, Maria Luisa Montas

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There are some factors affecting consumer decision-making regarding the use of the front of package labels in order to find benefits to the well-being of the human being. Currently, there are several labels that help influence or change the purchase decision for food products. These labels communicate the impact that food has on human health; therefore, consumers are more critical and intelligent when buying and consuming food products. The research explores the association between front-of-pack labeling and food choice; the association between label content and purchasing decisions is complex and influenced by different factors, including the packaging itself. The main objective of this study was to examine the perception of health labels and nutritional declarations and their influence on buying decisions in the non-alcoholic beverages sector. This comparative study of two developing countries will show how consumers take nutritional labels into account when deciding to buy certain foods. This research applied a quantitative methodology with correlational scope. This study has a correlational approach in order to analyze the degree of association between variables. Likewise, the confirmatory factor analysis (CFA) method and structural equation modeling (SEM) as a powerful multivariate technique was used as statistical technique to find the relationships between observable and unobservable variables. The main findings of this research were the obtaining of three large groups and their perception and effects on nutritional and wellness labels. The first group is characterized by taking an attitude of high interest on the issue of the imposition of the nutritional information label on products and would agree that all products should be packaged given its importance to preventing illnesses in the consumer. Likewise, they almost always care about the brand, the size, the list of ingredients, and nutritional information of the food, and also the effect of these on health. The second group stands out for presenting some interest in the importance of the label on products as a purchase decision, in addition to almost always taking into account the characteristics of size, money, components, etc. of the products to decide on their consumption and almost always They are never interested in the effect of these products on their health or nutrition, and in group 3, it differs from the others by being more neutral regarding the issue of nutritional information labels, and being less interested in the purchase decision and characteristics of the product and also on the influence of these on health and nutrition. This new knowledge is essential for different companies that manufacture and market food products because they will have information to adapt or anticipate the new laws of developing countries as well as the new needs of health-conscious consumers when they buy food products.

Keywords: healthy labels, consumer behavior, nutritional information, healthy products

Procedia PDF Downloads 107

Authors: Chales Mumbere Musavandalo, Lucie B. Mugherwa, Gloire Kayitoghera Mulondi, Naanson Bweya, Muyisa Musongora, Francis Lelo Nzuzi

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The landscape of the Okapi Wildlife Reserve in the Democratic Republic of Congo harbors a large community of Mbuti indigenous peoples, often described as the guardians of nature. Living in and off the forest has long been a sustainable strategy for preserving natural resources. This strategy, seen as a form of eco-responsible citizenship, draws upon ethnobotanical knowledge passed down through generations. However, these indigenous communities are facing socio-cultural upheaval, which impacts their traditional way of life. This study aims to assess the relationship between the Mbuti indigenous people’s way of life and the preservation of the Okapi Wildlife Reserve. The study was conducted under the assumption that, despite socio-cultural upheavals, the forest and its resources remain central to the Mbuti way of life. The study was conducted in six encampments, three of which were located inside the forest and two in the anthropized zone. The methodological approach initially involved group interviews in six Mbuti encampments. The objective of these interviews was to determine how these people perceive the various services provided by the forest and the resources obtained from this habitat. The technique of using pebbles was adopted to adapt the exercise of weighting services and resources to the understanding of these people. Subsequently, the study carried out ethnobotanical surveys to identify the wood resources frequently used by these communities. This survey was completed in third position by a transect inventory of 1000 m length and 25 m width in order to enhance the understanding of the abundance of these resources around the camps. Two transects were installed in each camp to carry out this inventory. Traditionally, the Mbuti communities sustain their livelihood through hunting, fishing, gathering for self-consumption, and basketry. The Manniophyton fulvum-based net remains the main hunting tool. The primary forest and the swamp are two habitats from which these peoples derive the majority of their resources. However, with the arrival of the Bantu people, who introduced agriculture based on cocoa production, the Mbuti communities started providing services to the Bantu in the form of labor and field guarding. This cultural symbiosis between Mbute and Bantu has also led to non-traditional practices, such as the use of hunting rifles instead of nets and fishing nets instead of creels. The socio-economic and ecological environment in which Mbuti communities live is changing rapidly, including the resources they depend on. By incorporating the time factor into their perception of ecosystem services, only their future (p-value = 0, 0,121), the provision of wood for energy (p-value = 0,1976), and construction (p-value = 0,2548) would be closely associated with the forest in their future. For other services, such as food supply, medicine, and hunting, adaptation to Bantu customs is conceivable. Additionally, the abundance of wood used by the Mbuti people has been high around encampments located in intact forests and low in those in anthropized areas. The traditional way of life of the Mbuti communities is influenced by the cultural symbiosis, reflected in their habits and the availability of resources. The land tenure security of Mbuti areas is crucial to preserve their tradition and forest biodiversity. Conservation efforts in the Okapi Wildlife Reserve must consider this cultural dynamism and promote positive values for the flagship species. The oversight of subsistence hunting is imperative to curtail the transition of these communities to poaching.

Keywords: traditional life, conservation, Indigenous people, cultural symbiosis, forest

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Authors: Begum Koru, Isik Aybay, Demet Celik Ulusoy

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The discipline of law is quite complex and has its own terminology. Apart from written legal rules, there is also living law, which refers to legal practice. Basic legal rules aim at the happiness of individuals in social life and have different characteristics in different branches such as public or private law. On the other hand, law is a national phenomenon. The law of one nation and the legal system applied on the territory of another nation may be completely different. People who are experts in a particular field of law in one country may have insufficient expertise in the law of another country. Today, in addition to the local nature of law, international and even supranational law rules are applied in order to protect basic human values and ensure the protection of human rights around the world. Systems that offer algorithmic solutions to legal problems using artificial intelligence (AI) tools will perhaps serve to produce very meaningful results in terms of human rights. However, algorithms to be used should not be developed by only computer experts, but also need the contribution of people who are familiar with law, values, judicial decisions, and even the social and political culture of the society to which it will provide solutions. Otherwise, even if the algorithm works perfectly, it may not be compatible with the values of the society in which it is applied. The latest developments involving the use of AI techniques in legal systems indicate that artificial law will emerge as a new field in the discipline of law. More AI systems are already being applied in the field of law, with examples such as predicting judicial decisions, text summarization, decision support systems, and classification of documents. Algorithms for legal systems employing AI tools, especially in the field of prediction of judicial decisions and decision support systems, have the capacity to create automatic decisions instead of judges. When the judge is removed from this equation, artificial intelligence-made law created by an intelligent algorithm on its own emerges, whether the domain is national or international law. In this work, the aim is to make a general analysis of this new topic. Such an analysis needs both a literature survey and a perspective from computer experts' and lawyers' point of view. In some societies, the use of prediction or decision support systems may be useful to integrate international human rights safeguards. In this case, artificial law can serve to produce more comprehensive and human rights-protective results than written or living law. In non-democratic countries, it may even be thought that direct decisions and artificial intelligence-made law would be more protective instead of a decision "support" system. Since the values of law are directed towards "human happiness or well-being", it requires that the AI algorithms should always be capable of serving this purpose and based on the rule of law, the principle of justice and equality, and the protection of human rights.

Keywords: AI and law, artificial law, protection of human rights, AI tools for legal systems

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Authors: Nasser S. Shebka

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Fuzzy logic is used in complex adaptive systems where classical tools of representing knowledge are unproductive. Nevertheless, the incorporation of fuzzy logic, as it’s the case with all artificial intelligence tools, raised some inconsistencies and limitations in dealing with increased complexity systems and rules that apply to real-life situations and hinders the ability of the inference process of such systems, but it also faces some inconsistencies between inferences generated fuzzy rules of complex or imprecise knowledge-based systems. The use of fuzzy logic enhanced the capability of knowledge representation in such applications that requires fuzzy representation of truth values or similar multi-value constant parameters derived from multi-valued logic, which set the basis for the three t-norms and their based connectives which are actually continuous functions and any other continuous t-norm can be described as an ordinal sum of these three basic ones. However, some of the attempts to solve this dilemma were an alteration to fuzzy logic by means of non-monotonic logic, which is used to deal with the defeasible inference of expert systems reasoning, for example, to allow for inference retraction upon additional data. However, even the introduction of non-monotonic fuzzy reasoning faces a major issue of conflict resolution for which many principles were introduced, such as; the specificity principle and the weakest link principle. The aim of our work is to improve the logical representation and functional modelling of AI systems by presenting a method of resolving existing and potential rule conflicts by representing temporal modalities within defeasible inference rule-based systems. Our paper investigates the possibility of resolving fuzzy rules conflict in a non-monotonic fuzzy reasoning-based system by introducing temporal modalities and Kripke's general weak modal logic operators in order to expand its knowledge representation capabilities by means of flexibility in classifying newly generated rules, and hence, resolving potential conflicts between these fuzzy rules. We were able to address the aforementioned problem of our investigation by restructuring the inference process of the fuzzy rule-based system. This is achieved by using time-branching temporal logic in combination with restricted first-order logic quantifiers, as well as propositional logic to represent classical temporal modality operators. The resulting findings not only enhance the flexibility of complex rule-base systems inference process but contributes to the fundamental methods of building rule bases in such a manner that will allow for a wider range of applicable real-life situations derived from a quantitative and qualitative knowledge representational perspective.

Keywords: fuzzy rule-based systems, fuzzy tense inference, intelligent systems, temporal modalities

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

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Authors: Elizabeth Parrott, Harry Pointon, Frederic Bezombes, Heather Panter

Abstract:

Unmanned aerial vehicles (UAV’s) are making a profound effect within policing, forensic and fire service procedures worldwide. These intelligent devices have already proven useful in photographing and recording large-scale outdoor and indoor sites using orthomosaic and three-dimensional (3D) modelling techniques, for the purpose of capturing and recording sites during and post-incident. UAV’s are becoming an established tool as they are extending the reach of the photographer and offering new perspectives without the expense and restrictions of deploying full-scale aircraft. 3D reconstruction quality is directly linked to the resolution of captured images; therefore, close proximity flights are required for more detailed models. As technology advances deployment of UAVs in confined spaces is becoming more common. With this in mind, this study investigates the effects of UAV operation within active crimes scenes with regard to the dispersal of particulate evidence. To date, there has been little consideration given to the potential effects of using UAV’s within active crime scenes aside from a legislation point of view. Although potentially the technology can reduce the likelihood of contamination by replacing some of the roles of investigating practitioners. There is the risk of evidence dispersal caused by the effect of the strong airflow beneath the UAV, from the downwash of the propellers. The initial results of this study are therefore presented to determine the height of least effect at which to fly, and the commercial propeller type to choose to generate the smallest amount of disturbance from the dataset tested. In this study, a range of commercially available 4-inch propellers were chosen as a starting point due to the common availability and their small size makes them well suited for operation within confined spaces. To perform the testing, a rig was configured to support a single motor and propeller powered with a standalone mains power supply and controlled via a microcontroller. This was to mimic a complete throttle cycle and control the device to ensure repeatability. By removing the variances of battery packs and complex UAV structures to allow for a more robust setup. Therefore, the only changing factors were the propeller and operating height. The results were calculated via computer vision analysis of the recorded dispersal of the sample particles placed below the arm-mounted propeller. The aim of this initial study is to give practitioners an insight into the technology to use when operating within confined spaces as well as recognizing some of the issues caused by UAV’s within active crime scenes.

Keywords: dispersal, evidence, propeller, UAV

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Authors: A. Degale Desta, Tamirat Kebamo

Abstract:

Background: Recent advances in computer vision and pattern recognition have significantly improved activity recognition through video analysis, particularly with the application of Deep Convolutional Neural Networks (CNNs). One-stage detectors now enable efficient video-based recognition by simultaneously predicting object categories and locations. Such advancements are highly relevant in educational settings where CCTV surveillance could automatically monitor academic activities, enhancing security and classroom management. However, current datasets and recognition systems lack the specific focus on campus environments necessary for practical application in these settings.Objective: This study aims to address this gap by developing a dataset and testing an automated activity recognition system specifically tailored for educational campuses. The EthioCAD dataset was created to capture various classroom activities and teacher-student interactions, facilitating reliable recognition of academic activities using deep learning models. Method: EthioCAD, a novel video-based dataset, was created with a design science research approach to encompass teacher-student interactions across three domains and 18 distinct classroom activities. Using the Roboflow AI framework, the data was processed, with 4.224 KB of frames and 33.485 MB of images managed for frame extraction, labeling, and organization. The Ultralytics YOLOv8 model was then implemented within Google Colab to evaluate the dataset’s effectiveness, achieving high mean Average Precision (mAP) scores. Results: The YOLOv8 model demonstrated robust activity recognition within campus-like settings, achieving an mAP50 of 90.2% and an mAP50-95 of 78.6%. These results highlight the potential of EthioCAD, combined with YOLOv8, to provide reliable detection and classification of classroom activities, supporting automated surveillance needs on educational campuses. Discussion: The high performance of YOLOv8 on the EthioCAD dataset suggests that automated activity recognition for surveillance is feasible within educational environments. This system addresses current limitations in campus-specific data and tools, offering a tailored solution for academic monitoring that could enhance the effectiveness of CCTV systems in these settings. Conclusion: The EthioCAD dataset, alongside the YOLOv8 model, provides a promising framework for automated campus activity recognition. This approach lays the groundwork for future advancements in CCTV-based educational surveillance systems, enabling more refined and reliable monitoring of classroom activities.

Keywords: deep CNN, EthioCAD, deep learning, YOLOv8, activity recognition

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Authors: Kedar J. Mahadeshwar

Abstract:

Concept introduction: This paper talks about how an innovative cloud based analytics enabled solution that could address a major industry challenge that is approaching all of us globally faster than what one would think. The world of supply chain for drugs and devices is changing today at a rapid speed. In the US, the Drug Supply Chain Security Act (DSCSA) is a new law for Tracing, Verification and Serialization phasing in starting Jan 1, 2015 for manufacturers, repackagers, wholesalers and pharmacies / clinics. Similarly we are seeing pressures building up in Europe, China and many countries that would require an absolute traceability of every drug and device end to end. Companies (both manufacturers and distributors) can use this opportunity not only to be compliant but to differentiate themselves over competition. And moreover a country such as UAE can be the leader in coming up with a global solution that brings innovation in this industry. Problem definition and timing: The problem of counterfeit drug market, recognized by FDA, causes billions of dollars loss every year. Even in UAE, the concerns over prevalence of counterfeit drugs, which enter through ports such as Dubai remains a big concern, as per UAE pharma and healthcare report, Q1 2015. Distribution of drugs and devices involves multiple processes and systems that do not talk to each other. Consumer confidence is at risk due to this lack of traceability and any leading provider is at risk of losing its reputation. Globally there is an increasing pressure by government and regulatory bodies to trace serial numbers and lot numbers of every drug and medical devices throughout a supply chain. Though many of large corporations use some form of ERP (enterprise resource planning) software, it is far from having a capability to trace a lot and serial number beyond the enterprise and making this information easily available real time. Solution: The solution here talks about a service provider that allows all subscribers to take advantage of this service. The solution allows a service provider regardless of its physical location, to host this cloud based traceability and analytics solution of millions of distribution transactions that capture lots of each drug and device. The solution platform will capture a movement of every medical device and drug end to end from its manufacturer to a hospital or a doctor through a series of distributor or retail network. The platform also provides advanced analytics solution to do some intelligent reporting online. Why Dubai? Opportunity exists with huge investment done in Dubai healthcare city also with using technology and infrastructure to attract more FDI to provide such a service. UAE and countries similar will be facing this pressure from regulators globally in near future. But more interestingly, Dubai can attract such innovators/companies to run and host such a cloud based solution and become a hub of such traceability globally.

Keywords: cloud, pharmaceutical, supply chain, tracking

Procedia PDF Downloads 526