Search results for: brand-oriented capabilities

Authors: Yan Zhang

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Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

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Authors: Ruolan Zhang, Ryo Imai, Naoko Senda, Tomoyuki Sakai

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Enzymes have attracted increasing attentions in industrial manufacturing for their applicability in catalyzing complex chemical reactions under mild conditions. Directed evolution has become a powerful approach to optimize enzymes and exploit their full potentials under the circumstance of insufficient structure-function knowledge. With the incorporation of cell-free synthetic biotechnology, rapid enzyme synthesis can be realized because no cloning procedure such as transfection is needed. Its open environment also enables direct enzyme measurement. These properties of cell-free biotechnology lead to excellent throughput of enzymes generation. However, the capabilities of current screening methods have limitations. Fluorescence-based assay needs applicable fluorescent label, and the reliability of acquired enzymatic activity is influenced by fluorescent label’s binding affinity and photostability. To acquire the natural activity of an enzyme, another method is to combine pre-screening step and high-performance liquid chromatography (HPLC) measurement. But its throughput is limited by necessary time investment. Hundreds of variants are selected from libraries, and their enzymatic activities are then identified one by one by HPLC. The turn-around-time is 30 minutes for one sample by HPLC, which limits the acquirable enzyme improvement within reasonable time. To achieve the real high-throughput enzyme screening, i.e., obtain reliable enzyme improvement within reasonable time, a widely applicable high-throughput measurement of enzymatic reactions is highly demanded. Here, a high-throughput screening method using broadband coherent anti-Stokes Raman spectroscopy (CARS) was proposed. CARS is one of coherent Raman spectroscopy, which can identify label-free chemical components specifically from their inherent molecular vibration. These characteristic vibrational signals are generated from different vibrational modes of chemical bonds. With the broadband CARS, chemicals in one sample can be identified from their signals in one broadband CARS spectrum. Moreover, it can magnify the signal levels to several orders of magnitude greater than spontaneous Raman systems, and therefore has the potential to evaluate chemical's concentration rapidly. As a demonstration of screening with CARS, alcohol dehydrogenase, which converts ethanol and nicotinamide adenine dinucleotide oxidized form (NAD+) to acetaldehyde and nicotinamide adenine dinucleotide reduced form (NADH), was used. The signal of NADH at 1660 cm⁻¹, which is generated from nicotinamide in NADH, was utilized to measure the concentration of it. The evaluation time for CARS signal of NADH was determined to be as short as 0.33 seconds while having a system sensitivity of 2.5 mM. The time course of alcohol dehydrogenase reaction was successfully measured from increasing signal intensity of NADH. This measurement result of CARS was consistent with the result of a conventional method, UV-Vis. CARS is expected to have application in high-throughput enzyme screening and realize more reliable enzyme improvement within reasonable time.

Keywords: Coherent Anti-Stokes Raman Spectroscopy, CARS, directed evolution, enzyme screening, Raman spectroscopy

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Authors: Seonaid Hume, Gordon Dobie, Graeme West

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Detecting and localizing radioactive sources is a necessity for safe and secure decommissioning of nuclear facilities. An important aspect for the management of the sort-and-segregation process is establishing the spatial distributions and quantities of the waste radionuclides, their type, corresponding activity, and ultimately classification for disposal. The data received from surveys directly informs decommissioning plans, on-site incident management strategies, the approach needed for a new cell, as well as protecting the workforce and the public. Manual classification of nuclear waste from a nuclear cell is time-consuming, expensive, and requires significant expertise to make the classification judgment call. Also, in-cell decommissioning is still in its relative infancy, and few techniques are well-developed. As with any repetitive and routine tasks, there is the opportunity to improve the task of classifying nuclear waste using autonomous systems. Hence, this paper proposes a new framework for the automatic classification of nuclear waste. This framework consists of five main stages; 3D spatial mapping and object detection, object classification, radiological mapping, source localisation based on gathered evidence and finally, waste classification. The first stage of the framework, 3D visual mapping, involves object detection from point cloud data. A review of related applications in other industries is provided, and recommendations for approaches for waste classification are made. Object detection focusses initially on cylindrical objects since pipework is significant in nuclear cells and indeed any industrial site. The approach can be extended to other commonly occurring primitives such as spheres and cubes. This is in preparation of stage two, characterizing the point cloud data and estimating the dimensions, material, degradation, and mass of the objects detected in order to feature match them to an inventory of possible items found in that nuclear cell. Many items in nuclear cells are one-offs, have limited or poor drawings available, or have been modified since installation, and have complex interiors, which often and inadvertently pose difficulties when accessing certain zones and identifying waste remotely. Hence, this may require expert input to feature match objects. The third stage, radiological mapping, is similar in order to facilitate the characterization of the nuclear cell in terms of radiation fields, including the type of radiation, activity, and location within the nuclear cell. The fourth stage of the framework takes the visual map for stage 1, the object characterization from stage 2, and radiation map from stage 3 and fuses them together, providing a more detailed scene of the nuclear cell by identifying the location of radioactive materials in three dimensions. The last stage involves combining the evidence from the fused data sets to reveal the classification of the waste in Bq/kg, thus enabling better decision making and monitoring for in-cell decommissioning. The presentation of the framework is supported by representative case study data drawn from an application in decommissioning from a UK nuclear facility. This framework utilises recent advancements of the detection and mapping capabilities of complex radiation fields in three dimensions to make the process of classifying nuclear waste faster, more reliable, cost-effective and safer.

Keywords: nuclear decommissioning, radiation detection, object detection, waste classification

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Authors: Sanaz Moayer, Fang Huang, Scott Gardner

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In the highly leveraged business world of today, an organisation’s success depends on how it can manage and organize its traditional and intangible assets. In the knowledge-based economy, knowledge as a valuable asset gives enduring capability to firms competing in rapidly shifting global markets. It can be argued that ability to create unique knowledge assets by configuring ICT and human capabilities, will be a defining factor for international competitive advantage in the mid-21st century. The concept of KM is recognized in the strategy literature, and increasingly by senior decision-makers (particularly in large firms which can achieve scalable benefits), as an important vehicle for stimulating innovation and organisational performance in the knowledge economy. This thinking has been evident in professional services and other knowledge intensive industries for over a decade. It highlights the importance of social capital and the value of the intellectual capital embedded in social and professional networks, complementing the traditional focus on creation of intellectual property assets. Despite the growing interest in KM within professional services there has been limited discussion in relation to multinational resource based industries such as mining and petroleum where the focus has been principally on global portfolio optimization with economies of scale, process efficiencies and cost reduction. The Australian minerals and metals mining industry, although traditionally viewed as capital intensive, employs a significant number of knowledge workers notably- engineers, geologists, highly skilled technicians, legal, finance, accounting, ICT and contracts specialists working in projects or functions, representing potential knowledge silos within the organisation. This silo effect arguably inhibits knowledge sharing and retention by disaggregating corporate memory, with increased operational and project continuity risk. It also may limit the potential for process, product, and service innovation. In this paper the strategic application of knowledge management incorporating contemporary ICT platforms and data mining practices is explored as an important enabler for knowledge discovery, reduction of risk, and retention of corporate knowledge in resource based industries. With reference to the relevant strategy, management, and information systems literature, this paper highlights possible connections (currently undergoing empirical testing), between an Strategic Knowledge Management (SKM) framework incorporating supportive Data Mining (DM) practices and competitive advantage for multinational firms operating within the Australian resource sector. We also propose based on a review of the relevant literature that more effective management of soft and hard systems knowledge is crucial for major Australian firms in all sectors seeking to improve organisational performance through the human and technological capability captured in organisational networks.

Keywords: competitive advantage, data mining, mining organisation, strategic knowledge management

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Authors: Roberta Mugellesi Dow

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As the climate crisis becomes unignorable, it is imperative that new services are developed addressing not only the needs of customers but also taking into account its impact on the environment. The Telecommunication and Integrated Application (TIA) Directorate of ESA is supporting the green transition with particular attention to the sustainable mobility.“Accelerating the shift to sustainable and smart mobility” is at the core of the European Green Deal strategy, which seeks a 90% reduction in related emissions by 2050 . Transforming the way that people and goods move is essential to increasing mobility while decreasing environmental impact, and transport must be considered holistically to produce a shared vision of green intermodal mobility. The use of space technologies, integrated with terrestrial technologies, is an enabler of smarter traffic management and increased transport efficiency for automated and connected multimodal mobility. Satellite connectivity, including future 5G networks, and digital technologies such as Digital Twin, AI, Machine Learning, and cloud-based applications are key enablers of sustainable mobility.SatCom is essential to ensure that connectivity is ubiquitously available, even in remote and rural areas, or in case of a failure, by the convergence of terrestrial and SatCom connectivity networks, This is especially crucial when there are risks of network failures or cyber-attacks targeting terrestrial communication. SatCom ensures communication network robustness and resilience. The combination of terrestrial and satellite communication networks is making possible intelligent and ubiquitous V2X systems and PNT services with significantly enhanced reliability and security, hyper-fast wireless access, as well as much seamless communication coverage. SatNav is essential in providing accurate tracking and tracing capabilities for automated vehicles and in guiding them to target locations. SatNav can also enable location-based services like car sharing applications, parking assistance, and fare payment. In addition to GNSS receivers, wireless connections, radar, lidar, and other installed sensors can enable automated vehicles to monitor surroundings, to ‘talk to each other’ and with infrastructure in real-time, and to respond to changes instantaneously. SatEO can be used to provide the maps required by the traffic management, as well as evaluate the conditions on the ground, assess changes and provide key data for monitoring and forecasting air pollution and other important parameters. Earth Observation derived data are used to provide meteorological information such as wind speed and direction, humidity, and others that must be considered into models contributing to traffic management services. The paper will provide examples of services and applications that have been developed aiming to identify innovative solutions and new business models that are allowed by new digital technologies engaging space and non space ecosystem together to deliver value and providing innovative, greener solutions in the mobility sector. Examples include Connected Autonomous Vehicles, electric vehicles, green logistics, and others. For the technologies relevant are the hybrid satcom and 5G providing ubiquitous coverage, IoT integration with non space technologies, as well as navigation, PNT technology, and other space data.

Keywords: sustainability, connectivity, mobility, satellites

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Authors: Leander Van Neste, Kirk Wojno

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The innate immune system reacts to foreign insult in several unique ways, one of which is phagocytosis of perceived threats such as cancer, bacteria, and viruses. The goal of this study was to look for evidence of phagocytosed RNA from tumor cells in circulating monocytes. While all monocytes possess phagocytic capabilities, the non-classical CD14+/FCGR3A+ monocytes and the intermediate CD14++/FCGR3A+ monocytes most actively remove threatening ‘external’ cellular materials. Purified CD14-positive monocyte samples from fourteen patients recently diagnosed with clinically localized prostate cancer (PCa) were investigated by single-cell RNA sequencing using the 10X Genomics protocol followed by paired-end sequencing on Illumina’s NovaSeq. Similarly, samples were processed and used as controls, i.e., one patient underwent biopsy but was found not to harbor prostate cancer (benign), three young, healthy men, and three men previously diagnosed with prostate cancer that recently underwent (curative) radical prostatectomy (post-RP). Sequencing data were mapped using 10X Genomics’ CellRanger software and viable cells were subsequently identified using CellBender, removing technical artifacts such as doublets and non-cellular RNA. Next, data analysis was performed in R, using the Seurat package. Because the main goal was to identify differences between PCa patients and ‘control’ patients, rather than exploring differences between individual subjects, the individual Seurat objects of all 21 patients were merged into one Seurat object per Seurat’s recommendation. Finally, the single-cell dataset was normalized as a whole prior to further analysis. Cell identity was assessed using the SingleR and cell dex packages. The Monaco Immune Data was selected as the reference dataset, consisting of bulk RNA-seq data of sorted human immune cells. The Monaco classification was supplemented with normalized PCa data obtained from The Cancer Genome Atlas (TCGA), which consists of bulk RNA sequencing data from 499 prostate tumor tissues (including 1 metastatic) and 52 (adjacent) normal prostate tissues. SingleR was subsequently run on the combined immune cell and PCa datasets. As expected, the vast majority of cells were labeled as having a monocytic origin (~90%), with the most noticeable difference being the larger number of intermediate monocytes in the PCa patients (13.6% versus 7.1%; p<.001). In men harboring PCa, 0.60% of all purified monocytes were classified as harboring PCa signals when the TCGA data were included. This was 3-fold, 7.5-fold, and 4-fold higher compared to post-RP, benign, and young men, respectively (all p<.001). In addition, with 7.91%, the number of unclassified cells, i.e., cells with pruned labels due to high uncertainty of the assigned label, was also highest in men with PCa, compared to 3.51%, 2.67%, and 5.51% of cells in post-RP, benign, and young men, respectively (all p<.001). It can be postulated that actively phagocytosing cells are hardest to classify due to their dual immune cell and foreign cell nature. Hence, the higher number of unclassified cells and intermediate monocytes in PCa patients might reflect higher phagocytic activity due to tumor burden. This also illustrates that small numbers (~1%) of circulating peripheral blood monocytes that have interacted with tumor cells might still possess detectable phagocytosed tumor RNA.

Keywords: circulating monocytes, phagocytic cells, prostate cancer, tumor immune response

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Authors: Rohit T. P., Sahil Athrij, Sasi Gopalan

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Currently, the content entertainment industry is dominated by mobile devices. As the trends slowly shift towards Augmented/Virtual Reality applications the computational demands on these devices are increasing exponentially and we are already reaching the limits of hardware optimizations. This paper proposes a software solution to this problem. By leveraging the capabilities of cloud computing we can offload the work from mobile devices to dedicated rendering servers that are way more powerful. But this introduces the problem of latency. This paper introduces a protocol that can achieve high-performance low latency Augmented/Virtual Reality experience. There are two parts to the protocol, 1) In-flight compression The main cause of latency in the system is the time required to transmit the camera frame from client to server. The round trip time is directly proportional to the amount of data transmitted. This can therefore be reduced by compressing the frames before sending. Using some standard compression algorithms like JPEG can result in minor size reduction only. Since the images to be compressed are consecutive camera frames there won't be a lot of changes between two consecutive images. So inter-frame compression is preferred. Inter-frame compression can be implemented efficiently using WebGL but the implementation of WebGL limits the precision of floating point numbers to 16bit in most devices. This can introduce noise to the image due to rounding errors, which will add up eventually. This can be solved using an improved interframe compression algorithm. The algorithm detects changes between frames and reuses unchanged pixels from the previous frame. This eliminates the need for floating point subtraction thereby cutting down on noise. The change detection is also improved drastically by taking the weighted average difference of pixels instead of the absolute difference. The kernel weights for this comparison can be fine-tuned to match the type of image to be compressed. 2) Dynamic Load distribution Conventional cloud computing architectures work by offloading as much work as possible to the servers, but this approach can cause a hit on bandwidth and server costs. The most optimal solution is obtained when the device utilizes 100% of its resources and the rest is done by the server. The protocol balances the load between the server and the client by doing a fraction of the computing on the device depending on the power of the device and network conditions. The protocol will be responsible for dynamically partitioning the tasks. Special flags will be used to communicate the workload fraction between the client and the server and will be updated in a constant interval of time ( or frames ). The whole of the protocol is designed so that it can be client agnostic. Flags are available to the client for resetting the frame, indicating latency, switching mode, etc. The server can react to client-side changes on the fly and adapt accordingly by switching to different pipelines. The server is designed to effectively spread the load and thereby scale horizontally. This is achieved by isolating client connections into different processes.

Keywords: 2D kernelling, augmented reality, cloud computing, dynamic load distribution, immersive experience, mobile computing, motion tracking, protocols, real-time systems, web-based augmented reality application

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Authors: Carolina Cuéllar, Paz González

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Research has shown that school leadership has a significant indirect effect on students’ achievements. In Chile, evidence has also revealed that this impact is stronger in vulnerable schools. With the aim of strengthening school leadership, public policy has taken up the challenge of enhancing capabilities of novice principals through the implementation of induction programs, which include a mentoring component, entrusting the task of delivering these programs to universities. The importance of using mentoring or coaching models in the preparation of novice school leaders has been emphasized in the international literature. Thus, it can be affirmed that building leadership capacity through partnership is crucial to facilitate cognitive and affective support required in the initial phase of the principal career, gain role clarification and socialization in context, stimulate reflective leadership practice, among others. In Chile, mentoring is a recent phenomenon in the field of school leadership and it is even more new in the preparation of new principals who work in public schools. This study, funded by the Chilean Ministry of Education, sought to explore the challenges and lessons arising from the design and implementation of mentoring processes which are part of the induction programs, according to the perception of the different actors involved: ministerial agents, university coordinators, mentors and novice principals. The investigation used a qualitative design, based on a study of three cases (three induction programs). The sources of information were 46 semi-structured interviews, applied in two moments (at the beginning and end of mentoring). Content analysis technique was employed. Data focused on the uniqueness of each case and the commonalities within the cases. Five main challenges and lessons emerged in the design and implementation of mentoring within the induction programs for new principals from Chilean public schools. They comprised the need of (i) developing a shared conceptual framework on mentoring among the institutions and actors involved, which helps align the expectations for the mentoring component within the induction programs, along with assisting in establishing a theory of action of mentoring that is relevant to the public school context; (ii) recognizing trough actions and decisions at different levels that the role of a mentor differs from the role of a principal, which challenge the idea that an effective principal will always be an effective mentor; iii) improving mentors’ selection and preparation processes trough the definition of common guiding criteria to ensure that a mentor takes responsibility for developing critical judgment of novice principals, which implies not limiting the mentor’s actions to assist in the compliance of prescriptive practices and standards; (iv) generating common evaluative models with goals, instruments and indicators consistent with the characteristics of mentoring processes, which helps to assess expected results and impact; and (v) including the design of a mentoring structure as an outcome of the induction programs, which helps sustain mentoring within schools as a collective professional development practice. Results showcased interwoven elements that entail continuous negotiations at different levels. Taking action will contribute to policy efforts aimed at professionalizing the leadership role in public schools.

Keywords: induction programs, mentoring, novice principals, school leadership preparation

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Authors: Alfredo Fortunato, Carmelofrancesco Origlia, Sara Laurita, Rossella Nicoletti

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In the post-pandemic recovery phase of tourism, the role of a Destination Management Organization (DMO) as a coordinated management system of all the elements that make up a destination (attractions, access, marketing, human resources, brand, pricing, etc.) is also becoming relevant for local territories. The objective of a DMO is to maximize the visitor's perception of value and quality while ensuring the competitiveness and sustainability of the destination, as well as the long-term preservation of its natural and cultural assets, and to catalyze benefits for the local economy and residents. In carrying out the multiple functions to which it is called, the DMO can leverage a collective intelligence that comes from the ability to pool information, explicit and tacit knowledge, and relationships of the various stakeholders: policymakers, public managers and officials, entrepreneurs in the tourism supply chain, researchers, data journalists, schools, associations and committees, citizens, etc. The DMO potentially has at its disposal large volumes of data and many of them at low cost, that need to be properly processed to produce value. Based on these assumptions, the paper presents a conceptual framework for building an information system to support the DMO in the intelligent management of a tourist destination tested in an area of southern Italy. The approach adopted is data-informed and consists of four phases: (1) formulation of the knowledge problem (analysis of policy documents and industry reports; focus groups and co-design with stakeholders; definition of information needs and key questions); (2) research and metadatation of relevant sources (reconnaissance of official sources, administrative archives and internal DMO sources); (3) gap analysis and identification of unconventional information sources (evaluation of traditional sources with respect to the level of consistency with information needs, the freshness of information and granularity of data; enrichment of the information base by identifying and studying web sources such as Wikipedia, Google Trends, Booking.com, Tripadvisor, websites of accommodation facilities and online newspapers); (4) definition of the set of indicators and construction of the information base (specific definition of indicators and procedures for data acquisition, transformation, and analysis). The framework derived consists of 6 thematic areas (accommodation supply, cultural heritage, flows, value, sustainability, and enabling factors), each of which is divided into three domains that gather a specific information need to be represented by a scheme of questions to be answered through the analysis of available indicators. The framework is characterized by a high degree of flexibility in the European context, given that it can be customized for each destination by adapting the part related to internal sources. Application to the case study led to the creation of a decision support system that allows: •integration of data from heterogeneous sources, including through the execution of automated web crawling procedures for data ingestion of social and web information; •reading and interpretation of data and metadata through guided navigation paths in the key of digital story-telling; •implementation of complex analysis capabilities through the use of data mining algorithms such as for the prediction of tourist flows.

Keywords: collective intelligence, data framework, destination management, smart tourism

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Authors: Yuguang Gao, Mingtao Deng

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The integration of independently operating hospitals into a unified healthcare service system has become a strategic imperative in the pursuit of hospitals’ high-quality development. Central to the concept of group governance over such transformation, exemplified by a collaborative medical alliance, is the delineation of shared value, vision, and goals. Given the inherent disparity in capabilities among hospitals within the alliance, particularly in the treatment of different diseases characterized by Disease Related Groups (DRG) in terms of effectiveness, efficiency and resource utilization, this study aims to address the centralized decision-making of intra-hospital patient referral within the medical alliance to enhance the overall production and quality of service provided. We first introduce the notion of production utility, where a higher production utility for a hospital implies better performance in treating patients diagnosed with that specific DRG group of diseases. Then, a Discrete-Event Simulation (DES) framework is established for patient referral among hospitals, where patient flow modeling incorporates a queueing system with fixed capacities for each hospital. The simulation study begins with a two-member alliance. The pivotal strategy examined is a "whether-to-refer" decision triggered when the bed usage rate surpasses a predefined threshold for either hospital. Then, the decision encompasses referring patients to the other hospital based on DRG groups’ production utility differentials as well as bed availability. The objective is to maximize the total production utility of the alliance while minimizing patients’ average length of stay and turnover rate. Thus the parameter under scrutiny is the bed usage rate threshold, influencing the efficacy of the referral strategy. Extending the study to a three-member alliance, which could readily be generalized to multi-member alliances, we maintain the core setup while introducing an additional “which-to-refer" decision that involves referring patients with specific DRG groups to the member hospital according to their respective production utility rankings. The overarching goal remains consistent, for which the bed usage rate threshold is once again a focal point for analysis. For the two-member alliance scenario, our simulation results indicate that the optimal bed usage rate threshold hinges on the discrepancy in the number of beds between member hospitals, the distribution of DRG groups among incoming patients, and variations in production utilities across hospitals. Transitioning to the three-member alliance, we observe similar dependencies on these parameters. Additionally, it becomes evident that an imbalanced distribution of DRG diagnoses and further disparity in production utilities among member hospitals may lead to an increase in the turnover rate. In general, it was found that the intra-hospital referral mechanism enhances the overall production utility of the medical alliance compared to individual hospitals without partnership. Patients’ average length of stay is also reduced, showcasing the positive impact of the collaborative approach. However, the turnover rate exhibits variability based on parameter setups, particularly when patients are redirected within the alliance. In conclusion, the re-structuring of diagnostic disease groups within the medical alliance proves instrumental in improving overall healthcare service outcomes, providing a compelling rationale for the government's promotion of patient referrals within collaborative medical alliances.

Keywords: collaborative medical alliance, disease related group, patient referral, simulation

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Authors: Rebekah Granger Ellis, Richard B. Speaker, Pat Austin

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This study used two psychological scales to examine the level of social and emotional intelligence and moral judgment of over 500 gifted and talented high school students in various academic and creative arts programs in a large metropolitan area in the southeastern United States. For decades, numerous models and programs purporting to encourage socio-affective characteristics of adolescent development have been explored in curriculum theory and design. Socio-affective merges social, emotional, and moral domains. It encompasses interpersonal relations and social behaviors; development and regulation of emotions; personal and gender identity construction; empathy development; moral development, thinking, and judgment. Examining development in these socio-affective domains can provide insight into why some gifted and talented adolescents are not successful in adulthood despite advanced IQ scores. Particularly whether nonintellectual characteristics of gifted and talented individuals, such as emotional, social and moral capabilities, are as advanced as their intellectual abilities and how these are related to each other. Unique characteristics distinguish gifted and talented individuals; these may appear as strengths, but there is the potential for problems to accompany them. Although many thrive in their school environments, some gifted students struggle rather than flourish. In the socio-affective domain, these adolescents face special intrapersonal, interpersonal, and environmental problems. Gifted individuals’ cognitive, psychological, and emotional development occurs asynchronously, in multidimensional layers at different rates and unevenly across ability levels. Therefore, it is important to examine the long-term effects of participation in various gifted and talented programs on the socio-affective development of gifted and talented adolescents. This quasi-experimental longitudinal study examined students in several gifted and talented education programs (creative arts school, urban charter schools, and suburban public schools) for (1) socio-affective development level and (2) whether a particular gifted and talented program encourages developmental growth. The following research questions guided the study: (1) How do academically and artistically talented gifted 10th and 11th grade students perform on psychometric scales of social and emotional intelligence and moral judgment? Do they differ from their age or grade normative sample? Are their gender differences among gifted students? (2) Does school environment impact 10th and 11th grade gifted and talented students’ socio-affective development? Do gifted adolescents who participate in a particular school gifted program differ in their developmental profiles of social and emotional intelligence and moral judgment? Students’ performances on psychometric instruments were compared over time and by type of program. Participants took pre-, mid-, and post-tests over the course of an academic school year with Defining Issues Test (DIT-2) assessing moral judgment and BarOn EQ-I: YV assessing social and emotional intelligence. Based on these assessments, quantitative differences in growth on psychological scales (individual and school) were examined. Change scores between schools were also compared. If a school showed change, artifacts (culture, curricula, instructional methodology) provided insight as to environmental qualities that produced this difference.

Keywords: gifted and talented education, moral development, socio-affective development, socio-affective education

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Authors: Tracey Woods

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In the ever-evolving landscape of academia, the traditional role of the librarian has undergone a remarkable transformation. Once considered as custodians of books and gatekeepers of information, librarians have the potential to take on the vital role of facilitators of cross and inter-disciplinary projects. This shift is driven by the growing recognition of the value of interdisciplinary collaboration in addressing complex research questions in pursuit of novel solutions to real-world problems. This paper shall explore the potential of the academic librarian’s role in facilitating innovative, multi-disciplinary projects, both recognising and validating the vital role that the librarian plays in a somewhat underplayed profession. Academic libraries support teaching, the strengthening of knowledge discourse, and, potentially, the development of innovative practices. As the role of the library gradually morphs from a quiet repository of books to a community-based information hub, a potential opportunity arises. The academic librarian’s role is to build knowledge across a wide span of topics, from the advancement of AI to subject-specific information, and, whilst librarians are generally not offered the research opportunities and funding that the traditional academic disciplines enjoy, they are often invited to help build research in support of the academic. This identifies that one of the primary skills of any 21st-century librarian must be the ability to collaborate and facilitate multi-disciplinary projects. In universities seeking to develop research diversity and academic performance, there is an increasing awareness of the need for collaboration between faculties to enable novel directions and advancements. This idea has been documented and discussed by several researchers; however, there is not a great deal of literature available from recent studies. Having a team based in the library that is adept at creating effective collaborative partnerships is valuable for any academic institution. This paper outlines the development of such a project, initiated within and around an identified library-specific need: the replication of fragile special collections for object-based learning. The research was developed as a multi-disciplinary project involving the faculties of engineering (digital twins lab), architecture, design, and education. Centred around methods for developing a fragile archive into a series of tactile objects furthers knowledge and understanding in both the role of the library as a facilitator of projects, chairing and supporting, alongside contributing to the research process and innovating ideas through the bank of knowledge found amongst the staff and their liaising capabilities. This paper shall present the method of project development from the initiation of ideas to the development of prototypes and dissemination of the objects to teaching departments for analysis. The exact replication of artefacts is also balanced with the adaptation and evolutionary speculations initiated by the design team when adapted as a teaching studio method. The dynamic response required from the library to generate and facilitate these multi-disciplinary projects highlights the information expertise and liaison skills that the librarian possesses. As academia embraces this evolution, the potential for groundbreaking discoveries and innovative solutions across disciplines becomes increasingly attainable.

Keywords: Liaison librarian, multi-disciplinary collaborations, library innovations, librarian stakeholders

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Authors: Sagar Saroha, Sawan S. Sinha, Sunil Lakshmipathy

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Reynolds averaged Navier-Stokes (RANS) model is the popular computational tool for prediction of turbulent flows. Being computationally less expensive as compared to direct numerical simulation (DNS), RANS has received wide acceptance in industry and research community as well. However, for high Reynolds number flows, the traditional RANS approach based on the Boussinesq hypothesis is incapacitated to capture all the essential flow characteristics, and thus, its performance is restricted in high Reynolds number flows of practical interest. RANS performance turns out to be inadequate in regimes like flow over curved surfaces, flows with rapid changes in the mean strain rate, duct flows involving secondary streamlines and three-dimensional separated flows. In the recent decade, partially averaged Navier-Stokes (PANS) methodology has gained acceptability among seamless bridging methods of turbulence- placed between DNS and RANS. PANS methodology, being a scale resolving bridging method, is inherently more suitable than RANS for simulating turbulent flows. The superior ability of PANS method has been demonstrated for some cases like swirling flows, high-speed mixing environment, and high Reynolds number turbulent flows. In our work, we intend to evaluate PANS in case of separated turbulent flows past bluff bodies -which is of broad aerodynamic research and industrial application. PANS equations, being derived from base RANS, continue to inherit the inadequacies from the parent RANS model based on linear eddy-viscosity model (LEVM) closure. To enhance PANS’ capabilities for simulating separated flows, the shortcomings of the LEVM closure need to be addressed. Inabilities of the LEVMs have inspired the development of non-linear eddy viscosity models (NLEVM). To explore the potential improvement in PANS performance, in our study we evaluate the PANS behavior in conjugation with NLEVM. Our work can be categorized into three significant steps: (i) Extraction of PANS version of NLEVM from RANS model, (ii) testing the model in the homogeneous turbulence environment and (iii) application and evaluation of the model in the canonical case of separated non-homogeneous flow field (flow past prismatic bodies and bodies of revolution at high Reynolds number). PANS version of NLEVM shall be derived and implemented in OpenFOAM -an open source solver. Homogeneous flows evaluation will comprise the study of the influence of the PANS’ filter-width control parameter on the turbulent stresses; the homogeneous analysis performed over typical velocity fields and asymptotic analysis of Reynolds stress tensor. Non-homogeneous flow case will include the study of mean integrated quantities and various instantaneous flow field features including wake structures. Performance of PANS + NLEVM shall be compared against the LEVM based PANS and LEVM based RANS. This assessment will contribute to significant improvement of the predictive ability of the computational fluid dynamics (CFD) tools in massively separated turbulent flows past bluff bodies.

Keywords: bridging methods of turbulence, high Re-CFD, non-linear PANS, separated turbulent flows

Procedia PDF Downloads 144

Authors: Yaroslav Pavlov

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In the modern world, ensuring quality has become increasingly important in various fields of human activity. One universal approach to quality management, proven effective in the food industry, is the HACCP (Hazard Analysis and Critical Control Points) concept. Based on principles of preventing potential hazards to consumers at all stages of production, from raw materials to the final product, HACCP offers a systematic approach to identifying, assessing risks, and managing critical control points (CCPs). Initially used primarily for food production, it was later effectively adapted to the food service sector. Implementing HACCP provides organizations with a reliable foundation for improving food safety, covering all links in the food chain from producer to consumer, making it an integral part of modern quality management systems. The main principles of HACCP—hazard identification, CCP determination, effective monitoring procedures, corrective actions, regular checks, and documentation—are universal and can be adapted to other areas. The adaptation of the HACCP concept is relevant for continuing professional education (CPE) with certain reservations. Specifically, it is reasonable to abandon the term ‘hazards’ as deviations in CCPs do not pose dangers, unlike in food production. However, the approach through CCP analysis and the use of HACCP's main principles for educational services are promising. This is primarily because it allows for identifying key CCPs based on the value creation model of a specific educational organization and consequently focusing efforts on specific CCPs to manage the quality of educational services. This methodology can be called the Analysis of Critical Points in Educational Services (ACPES). ACPES offers a similar approach to managing the quality of educational services, focusing on preventing and eliminating potential risks that could negatively impact the educational process, learners' achievement of set educational goals, and ultimately lead to students rejecting the organization's educational services. ACPES adapts proven HACCP principles to educational services, enhancing quality management effectiveness and student satisfaction. ACPES includes identifying potential problems at all stages of the educational process, from initial interest to graduation and career development. In ACPES, the term "hazards" is replaced with "problematic areas," reflecting the specific nature of the educational environment. Special attention is paid to determining CCPs—stages where corrective measures can most effectively prevent or minimize the risk of failing educational goals. The ACPES principles align with HACCP's principles, adjusted for the specificities of CPE. The method of the learner's journey map (variation of Customer Journey Map, CJM) can be used to overcome the complexity of formalizing the production chain in educational services. CJM provides a comprehensive understanding of the learner's experience at each stage, facilitating targeted and effective quality management. Thus, integrating the learner's journey map into ACPES represents a significant extension of the methodology's capabilities, ensuring a comprehensive understanding of the educational process and forming an effective quality management system focused on meeting learners' needs and expectations.

Keywords: quality management, continuing professional education, customer journey map, HACCP

Procedia PDF Downloads 34

Authors: Vladislav Grigorovitch, Marina Grigorovitch, David Pearlmutter, Erez Gal

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The concept of “electric island” is involved with achieving the balance between the self-power generation ability of each educational institution and energy consumption demand. Photo-Voltaic (PV) solar system installed on the roofs of educational buildings is a common way to absorb the available solar energy and generate electricity for self-consumption and even for returning to the grid. The main objective of this research is to develop and implement an “electric island” monitoring infrastructure for promoting energy efficiency in educational buildings. A microscale monitoring methodology will be developed to provide a platform to estimate energy consumption performance classified by rooms and subspaces rather than the more common macroscale monitoring of the whole building. The monitoring platform will be established on the experimental sites, enabling an estimation and further analysis of the variety of environmental and physical conditions. For each building, separate measurement configurations will be applied taking into account the specific requirements, restrictions, location and infrastructure issues. The direct results of the measurements will be analyzed to provide deeper understanding of the impact of environmental conditions and sustainability construction standards, not only on the energy demand of public building, but also on the energy consumption habits of the children that study in those schools and the educational and administrative staff that is responsible for providing the thermal comfort conditions and healthy studying atmosphere for the children. A monitoring methodology being developed in this research is providing online access to real-time data of Interferential Therapy (IFTs) from any mobile phone or computer by simply browsing the dedicated website, providing powerful tools for policy makers for better decision making while developing PV production infrastructure to achieve “electric islands” in educational buildings. A detailed measurement configuration was technically designed based on the specific conditions and restriction of each of the pilot buildings. A monitoring and analysis methodology includes a large variety of environmental parameters inside and outside the schools to investigate the impact of environmental conditions both on the energy performance of the school and educational abilities of the children. Indoor measurements are mandatory to acquire the energy consumption data, temperature, humidity, carbon dioxide and other air quality conditions in different parts of the building. In addition to that, we aim to study the awareness of the users to the energy consideration and thus the impact on their energy consumption habits. The monitoring of outdoor conditions is vital for proper design of the off-grid energy supply system and validation of its sufficient capacity. The suggested outcomes of this research include: 1. both experimental sites are designed to have PV production and storage capabilities; 2. Developing an online information feedback platform. The platform will provide consumer dedicated information to academic researchers, municipality officials and educational staff and students; 3. Designing an environmental work path for educational staff regarding optimal conditions and efficient hours for operating air conditioning, natural ventilation, closing of blinds, etc.

Keywords: sustainability, electric island, IOT, smart building

Procedia PDF Downloads 179

Authors: Richard McGregor, Carmen Reaiche, Stephen Boyle

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Cyber threats are a relatively recent phenomenon and offer cyber insurers a dynamic and intelligent peril. As individuals en mass become increasingly digitally dependent, Personal Cyber Insurance (PCI) offers an attractive option to mitigate cyber risk at a personal level. This abstract proposes a literature review that conceptualises a framework for siting Personal Cyber Insurance (PCI) within the context of cyberspace. The lack of empirical research within this domain demonstrates an immediate need to define the scope of PCI to allow cyber insurers to understand personal cyber risk threats and vectors, customer awareness, capabilities, and their associated needs. Additionally, this will allow cyber insurers to conceptualise appropriate frameworks allowing effective management and distribution of PCI products and services within a landscape often in-congruent with risk attributes commonly associated with traditional personal line insurance products. Cyberspace has provided significant improvement to the quality of social connectivity and productivity during past decades and allowed enormous capability uplift of information sharing and communication between people and communities. Conversely, personal digital dependency furnish ample opportunities for adverse cyber events such as data breaches and cyber-attacksthus introducing a continuous and insidious threat of omnipresent cyber risk–particularly since the advent of the COVID-19 pandemic and wide-spread adoption of ‘work-from-home’ practices. Recognition of escalating inter-dependencies, vulnerabilities and inadequate personal cyber behaviours have prompted efforts by businesses and individuals alike to investigate strategies and tactics to mitigate cyber risk – of which cyber insurance is a viable, cost-effective option. It is argued that, ceteris parabus, the nature of cyberspace intrinsically provides characteristic peculiarities that pose significant and bespoke challenges to cyber insurers, often in-congruent with risk attributes commonly associated with traditional personal line insurance products. These challenges include (inter alia) a paucity of historical claim/loss data for underwriting and pricing purposes, interdependencies of cyber architecture promoting high correlation of cyber risk, difficulties in evaluating cyber risk, intangibility of risk assets (such as data, reputation), lack of standardisation across the industry, high and undetermined tail risks, and moral hazard among others. This study proposes a thematic overview of the literature deemed necessary to conceptualise the challenges to issuing personal cyber coverage. There is an evident absence of empirical research appertaining to PCI and the design of operational business models for this business domain, especially qualitative initiatives that (1) attempt to define the scope of the peril, (2) secure an understanding of the needs of both cyber insurer and customer, and (3) to identify elements pivotal to effective management and profitable distribution of PCI - leading to an argument proposed by the author that postulates that the traditional general insurance customer journey and business model are ill-suited for the lineaments of cyberspace. The findings of the review confirm significant gaps in contemporary research within the domain of personal cyber insurance.

Keywords: cyberspace, personal cyber risk, personal cyber insurance, customer journey, business model

Procedia PDF Downloads 101

Authors: Biva Samadder, Samita Manna

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India is a melting pot of races, tribes, castes and communities. The population of India can be roughly branched into the huge majority of “Civilized” Indians of the Plains and the minority of Tribal population of the hill area and the forest who constituting almost 16 percent of total population of India. The Kirat community composed of four ethnic tribes: Limbu, Lepcha, Dhimal, and Rai. These Kirat people were found to be rich in indigenous knowledge, skill and practices especially for the use on medicinal plants and livelihood purposes. The “Mundhum" is the oral scripture or the “Bible of the Limbus” which serves as the canon of the codes of the Limbu socialization, their moral values and the very orientation of their lifestyle. From birth till death the Limbus are disciplined in the life with full of religious rituals, traditions and culture governed by community norms with a rich legacy of indigenous knowledge and traditional practices. The present study has been conducted using both secondary as well as primary data by applying social methodology consisting of the social survey, questionnaire, interviews and observations in the Kalimpong Block-I of Darjeeling District of west Bengal of India, which is a heterogeneous zone in terms of its ethnic composition and where the Limbus are pre-dominantly concentrated. Due to their close contact with other caste and communities Limbus are now adjusted with the changing situation by borrowing some cultural traits from the other communities and changes that have taken place in their cultural practices, religious beliefs, economic aspects, languages and in social roles and relationships which is bringing the change in their material culture. Limbu language is placed in the Tibeto- Burman Language category. But due to the political and cultural domination of educationally sound and numerically dominant Bengali race, the different communities in this area forced to come under the one umbrella of the Nepali or Gorkhali nation (nation-people). Their respective identities had to be submerged in order to constitute as a strong force to resist Nepali domination and ensure their common survival. As Nepali is a lingua-franca of the area knowing and speaking Nepali language helps them in procuring economic and occupational facilities. Ironically, present day younger generation does not feel comfortable speaking in their own Limbu tongue. The traditional knowledge about medicinal plants, healing, and health culture is found to be wear away due to the lack of interest of young generation. Not only poverty, along with exclusion due to policies they are in the phase of extinction, but their capabilities are ignored and not documented and preserved especially in the case of Limbus who having a great cultural heritage of an oral tradition. Attempts have been made to discuss the persistence and changes in socioeconomic pattern of life in relation to the social structure, material culture, cultural practices, social relationships, indigenous technology, ethos and their values and belief system.

Keywords: changing social relationship, cultural transition, identity, indigenous knowledge, language

Procedia PDF Downloads 171

Authors: Pablo Oteiza, Ricardo Alvarez, Mehrdad Pirnia, Fuat Can

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To effectively combat climate change, many countries around the world have committed to a decarbonisation of their electricity, along with promoting a large-scale integration of renewable energy sources (RES). While this trend represents a unique opportunity to effectively combat climate change, achieving a sound and cost-efficient energy transition towards low-carbon power systems poses significant challenges for the multi-year Transmission Network Expansion Planning (TNEP) problem. The objective of the multi-year TNEP is to determine the necessary network infrastructure to supply the projected demand in a cost-efficient way, considering the evolution of the new generation mix, including the integration of RES. The rapid integration of large-scale RES increases the variability and uncertainty in the power system operation, which in turn increases short-term flexibility requirements. To meet these requirements, flexible generating technologies such as energy storage systems must be considered within the TNEP as well, along with proper models for capturing the operational challenges of future power systems. As a consequence, TNEP formulations are becoming more complex and difficult to solve, especially for its application in realistic-sized power system models. To meet these challenges, there is an increasing need for developing efficient algorithms capable of solving the TNEP problem with reasonable computational time and resources. In this regard, a promising research area is the use of artificial intelligence (AI) techniques for solving large-scale mixed-integer optimization problems, such as the TNEP. In particular, the use of AI along with mathematical optimization strategies based on decomposition has shown great potential. In this context, this paper presents an efficient algorithm for solving the multi-year TNEP problem. The algorithm combines AI techniques with Column Generation, a traditional decomposition-based mathematical optimization method. One of the challenges of using Column Generation for solving the TNEP problem is that the subproblems are of mixed-integer nature, and therefore solving them requires significant amounts of time and resources. Hence, in this proposal we solve a linearly relaxed version of the subproblems, and trained a binary classifier that determines the value of the binary variables, based on the results obtained from the linearized version. A key feature of the proposal is that we integrate the binary classifier into the optimization algorithm in such a way that the optimality of the solution can be guaranteed. The results of a study case based on the HRP 38-bus test system shows that the binary classifier has an accuracy above 97% for estimating the value of the binary variables. Since the linearly relaxed version of the subproblems can be solved with significantly less time than the integer programming counterpart, the integration of the binary classifier into the Column Generation algorithm allowed us to reduce the computational time required for solving the problem by 50%. The final version of this paper will contain a detailed description of the proposed algorithm, the AI-based binary classifier technique and its integration into the CG algorithm. To demonstrate the capabilities of the proposal, we evaluate the algorithm in case studies with different scenarios, as well as in other power system models.

Keywords: integer optimization, machine learning, mathematical decomposition, transmission planning

Procedia PDF Downloads 83

Authors: Ashleigh Williams

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Micro- and nanoplastics’ (MNLPs) omnipresence and ecological accumulation is evident when surveying recent environmental impact studies. For example, in 2014 it was estimated that at least 52.3 trillion plastic microparticles are floating at sea, and scientists have even found plastics present remote Arctic ice and snow (5,6). Plastics have even found their way into precipitation, with more than 1000 tons of microplastic rain precipitating onto the Western United States in 2020. Even more recent studies evaluating the chemical safety of reusable plastic bottles found that hundreds of chemicals leached into the control liquid in the bottle (ddH2O, ph = 7) during a 24-hour time period. A consequence of the increased abundance in plastic waste in the air, land, and water every year is the bioaccumulation of MNLPs in ecosystems and trophic niches of the animal food chain, which could potentially cause increased direct and indirect exposure of humans to MNLPs via inhalation, ingestion, and dermal contact. Though the detrimental, toxic effects of MNLPs have been established in marine biota, much less is known about the potentially hazardous health effects of chronic MNLP ingestion in humans. Recent data indicate that long-term exposure to MNLPs could cause possible inflammatory and dysbiotic effects. However, toxicity seems to be largely dose-, as well as size-dependent. In addition, the transcytotic uptake of MNLPs through the intestinal epithelia in humans remain relatively unknown. To this point, the goal of the current study was to investigate the mechanisms of micro- and nanoplastic uptake and transcytosis of Polystyrene (PE) in human stem-cell derived, physiologically relevant in vitro intestinal model systems, and to compare the relative effect of particle size (30 nm, 100 nm, 500 nm and 1 µm), and concentration (0 µg/mL, 250 µg/mL, 500 µg/mL, 1000 µg/mL) on polystyrene MNLP uptake, transcytosis and intestinal epithelial model integrity. Observational and quantitative data obtained from confocal microscopy, immunostaining, transepithelial electrical resistance (TEER) measurements, cryosectioning, and ELISA cytokine assays of the proinflammatory cytokines Interleukin-6 and Interleukin-8 were used to evaluate the localization and transcytosis of polystyrene MNPs and its impact on epithelial integrity in human-derived intestinal in vitro model systems. The effect of Microfold (M) cell induction on polystyrene micro- and nanoparticle (MNP) uptake, transcytosis, and potential inflammation was also assessed and compared to samples grown under standard conditions. Microfold (M) cells, link the human intestinal system to the immune system and are the primary cells in the epithelium responsible for sampling and transporting foreign matter of interest from the lumen of the gut to underlying immune cells. Given the uptake capabilities of Microfold cells to interact both specifically and nonspecific to abiotic and biotic materials, it was expected that M- cell induced in vitro samples would have increased binding, localization, and potentially transcytosis of Polystyrene MNLPs across the epithelial barrier. Experimental results of this study would not only help in the evaluation of the plastic toxicity, but would allow for more detailed modeling of gut inflammation and the intestinal immune system.

Keywords: nanoplastics, enteroids, intestinal barrier, tissue engineering, microfold (M) cells

Procedia PDF Downloads 84

Authors: Zakia Hbellaq

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The Mediterranean region is considered a hotbed for climate change. Morocco is a semi-arid Mediterranean country facing water shortages and poor water quality. Its limited water resources limit the activities of various economic sectors. Most of Morocco's territory is in arid and desert areas. The potential water resources are estimated at 22 billion m3, which is equivalent to about 700 m3/inhabitant/year, and Morocco is in a state of structural water stress. Strictly speaking, the Kingdom of Morocco is one of the “very riskiest” countries, according to the World Resources Institute (WRI), which oversees the calculation of water stress risk in 167 countries. The surprising results of the Institute (WRI) rank Morocco as one of the riskiest countries in terms of water scarcity, ranking 3.89 out of 5, thus occupying the 23rd place out of a total of 167 countries, which indicates that the demand for water exceeds the available resources. Agriculture with a score of 3.89 is most affected by water stress from irrigation and places a heavy burden on the water table. Irrigation is an unavoidable technical need and has undeniable economic and social benefits given the available resources and climatic conditions. Irrigation, and therefore the agricultural sector, currently uses 86% of its water resources, while industry uses 5.5%. Although its development has undeniable economic and social benefits, it also contributes to the overfishing of most groundwater resources and the surprising decline in levels and deterioration of water quality in some aquifers. In this context, REUSE is one of the proposed solutions to reduce the water footprint of the agricultural sector and alleviate the shortage of water resources. Indeed, wastewater reuse, also known as REUSE (reuse of treated wastewater), is a step forward not only for the circular economy but also for the future, especially in the context of climate change. In particular, water reuse provides an alternative to existing water supplies and can be used to improve water security, sustainability, and resilience. However, given the introduction of organic trace pollutants or, organic micro-pollutants, the absorption of emerging contaminants, and decreasing salinity, it is possible to tackle innovative capabilities to overcome these problems and ensure food and health safety. To this end, attention will be paid to the adoption of an integrated and attractive approach, based on the reinforcement and optimization of the treatments proposed for the elimination of the organic load with particular attention to the elimination of emerging pollutants, to achieve this goal. , membrane bioreactors (MBR) as stand-alone technologies are not able to meet the requirements of WHO guidelines. They will be combined with heterogeneous Fenton processes using persulfate or hydrogen peroxide oxidants. Similarly, adsorption and filtration are applied as tertiary treatment In addition, the evaluation of crop performance in terms of yield, productivity, quality, and safety, through the optimization of Trichoderma sp strains that will be used to increase crop resistance to abiotic stresses, as well as the use of modern omics tools such as transcriptomic analysis using RNA sequencing and methylation to identify adaptive traits and associated genetic diversity that is tolerant/resistant/resilient to biotic and abiotic stresses. Hence, ensuring this approach will undoubtedly alleviate water scarcity and, likewise, increase the negative and harmful impact of wastewater irrigation on the condition of crops and the health of their consumers.

Keywords: water scarcity, food security, irrigation, agricultural water footprint, reuse, emerging contaminants

Procedia PDF Downloads 157

Authors: Chandra Shekhar Prasad, Ludek Pesek Prasad

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In the present day fan blade of aero engine, turboprop propellers, gas turbine or steam turbine low-pressure blades are getting bigger, lighter and thus, become more flexible. Therefore, flutter, forced blade response and vibration related failure of the high aspect ratio blade are of main concern for the designers, thus need to be address properly in order to achieve successful component design. At the preliminary design stage large number of design iteration is need to achieve the utter free safe design. Most of the numerical method used for aeroelastic analysis is based on field-based methods such as finite difference method, finite element method, finite volume method or coupled. These numerical schemes are used to solve the coupled fluid Flow-Structural equation based on full Naiver-Stokes (NS) along with structural mechanics’ equations. These type of schemes provides very accurate results if modeled properly, however, they are computationally very expensive and need large computing recourse along with good personal expertise. Therefore, it is not the first choice for aeroelastic analysis during preliminary design phase. A reduced order aeroelastic model (ROAM) with acceptable accuracy and fast execution is more demanded at this stage. Similar ROAM are being used by other researchers for aeroelastic and force response analysis of turbomachinery. In the present paper new medium fidelity ROAM is successfully developed and implemented in numerical tool to simulated the aeroelastic stability phenomena in turbomachinery and well as flexible wings. In the present, a hybrid flow solver based on 3D viscous-inviscid coupled 3D panel method (PM) and 3d discrete vortex particle method (DVM) is developed, viscous parameters are estimated using boundary layer(BL) approach. This method can simulate flow separation and is a good compromise between accuracy and speed compared to CFD. In the second phase of the research work, the flow solver (PM) will be coupled with ROM non-linear beam element method (BEM) based FEM structural solver (with multibody capabilities) to perform the complete aeroelastic simulation of a steam turbine bladed disk, propellers, fan blades, aircraft wing etc. The partitioned based coupling approach is used for fluid-structure interaction (FSI). The numerical results are compared with experimental data for different test cases and for the blade cascade test case, experimental data is obtained from in-house lab experiments at IT CAS. Furthermore, the results from the new aeroelastic model will be compared with classical CFD-CSD based aeroelastic models. The proposed methodology for the aeroelastic stability analysis of gas turbine or steam turbine blades, or propellers or fan blades will provide researchers and engineers a fast, cost-effective and efficient tool for aeroelastic (classical flutter) analysis for different design at preliminary design stage where large numbers of design iteration are required in short time frame.

Keywords: aeroelasticity, beam element method (BEM), discrete vortex particle method (DVM), classical flutter, fluid-structure interaction (FSI), panel method, reduce order aeroelastic model (ROAM), turbomachinery, viscous-inviscid coupling

Procedia PDF Downloads 264

Authors: Saif Al Ghafri, Thomas Hughes, Armand Karimi, Kumarini Seneviratne, Jordan Oakley, Michael Johns, Eric F. May

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Knowledge of the thermophysical properties of complex mixtures at extreme conditions of pressure and temperature have always been essential to the Liquefied Natural Gas (LNG) industry’s evolution because of the tremendous technical challenges present at all stages in the supply chain from production to liquefaction to transport. Each stage is designed using predictions of the mixture’s properties, such as density, viscosity, surface tension, heat capacity and phase behaviour as a function of temperature, pressure, and composition. Unfortunately, currently available models lead to equipment over-designs of 15% or more. To achieve better designs that work more effectively and/or over a wider range of conditions, new fundamental property data are essential, both to resolve discrepancies in our current predictive capabilities and to extend them to the higher-pressure conditions characteristic of many new gas fields. Furthermore, innovative experimental techniques are required to measure different thermophysical properties at high pressures and over a wide range of temperatures, including near the mixture’s critical points where gas and liquid become indistinguishable and most existing predictive fluid property models used breakdown. In this work, we present a wide range of experimental measurements made for different binary and ternary mixtures relevant to LNG processing, with a particular focus on viscosity, surface tension, heat capacity, bubble-points and density. For this purpose, customized and specialized apparatus were designed and validated over the temperature range (200 to 423) K at pressures to 35 MPa. The mixtures studied were (CH4 + C3H8), (CH4 + C3H8 + CO2) and (CH4 + C3H8 + C7H16); in the last of these the heptane contents was up to 10 mol %. Viscosity was measured using a vibrating wire apparatus, while mixture densities were obtained by means of a high-pressure magnetic-suspension densimeter and an isochoric cell apparatus; the latter was also used to determine bubble-points. Surface tensions were measured using the capillary rise method in a visual cell, which also enabled the location of the mixture critical point to be determined from observations of critical opalescence. Mixture heat capacities were measured using a customised high-pressure differential scanning calorimeter (DSC). The combined standard relative uncertainties were less than 0.3% for density, 2% for viscosity, 3% for heat capacity and 3 % for surface tension. The extensive experimental data gathered in this work were compared with a variety of different advanced engineering models frequently used for predicting thermophysical properties of mixtures relevant to LNG processing. In many cases the discrepancies between the predictions of different engineering models for these mixtures was large, and the high quality data allowed erroneous but often widely-used models to be identified. The data enable the development of new or improved models, to be implemented in process simulation software, so that the fluid properties needed for equipment and process design can be predicted reliably. This in turn will enable reduced capital and operational expenditure by the LNG industry. The current work also aided the community of scientists working to advance theoretical descriptions of fluid properties by allowing to identify deficiencies in theoretical descriptions and calculations.

Keywords: LNG, thermophysical, viscosity, density, surface tension, heat capacity, bubble points, models

Procedia PDF Downloads 273

Authors: Festus Okotie

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Nigeria is the giant of Africa with great and diverse transport potentials yet to be fully tapped into and explored.it is the most populated nation in Africa with nearly 200 million people, the sixth largest oil producer overall and largest oil producer in Africa with proven oil and gas reserves of 37 billion barrels and 192 trillion cubic feet, over 300 square kilometers of arable land and significant deposits of largely untapped minerals. A world bank indicator which measures trading across border ranked Nigeria at 183 out of 185 countries in 2017 and although different governments in the past made efforts through different interventions such as 2007 ports reforms led by Ngozi Okonjo-Iweala, a former minister of Finance and world bank managing director also attempted to resolve some of the challenges such as infrastructure shortcomings, policy and regulatory inconsistencies, overlapping functions and duplicated roles among the different MDA’S. It is one of the fundamental structures smart nations and cities are using to improve the living conditions of its citizens and achieving sustainability. Examples of some of its benefits includes tracking high pedestrian areas, traffic patterns, railway stations, planning and scheduling bus times, it also enhances interoperability, creates alerts of transport situation and has swift capacity to share information among the different platforms and transport modes. It also offers a comprehensive approach to risk management, putting emergency procedures and response capabilities in place, identifying dangers, including vandalism or violence, fare evasion, and medical emergencies. The Nigerian transport system is urgently in need of modern infrastructures such as ITS. Smart city transport technology helps cities to function productively, while improving services for businesses and lives of is citizens. This technology has the ability to improve travel across traditional modes of transport, such as cars and buses, with immediate benefits for city dwellers and also helps in managing transport systems such as dangerous weather conditions, heavy traffic, and unsafe speeds which can result in accidents and loss of lives. Intelligent transportation systems help in traffic control such as permitting traffic lights to react to changing traffic patterns, instead of working on a fixed schedule in traffic. Intelligent transportation systems is very important in Nigeria’s transportation sector and so would require trained personnel to drive its efficiency to greater height because the purpose of introducing it is to add value and at the same time reduce motor vehicle miles and traffic congestion which is a major challenge around Tin can island and Apapa Port, a major transportation hub in Nigeria. The need for the federal government, state governments, houses of assembly to organise a national transportation workshop to begin the process of addressing the challenges in our nation’s transport sector is highly expedient and so bills that will facilitate the implementation of policies to promote intelligent transportation systems needs to be sponsored because of its potentials to create thousands of jobs for our citizens, provide farmers with better access to cities and a better living condition for Nigerians.

Keywords: intelligent, transport, system, Nigeria

Procedia PDF Downloads 115

Authors: Marco Frieslaar, Bing Chu, Eric Rogers

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Stroke is a devastating illness that is the second biggest cause of death in the world (after heart disease). Where it does not kill, it leaves survivors with debilitating sensory and physical impairments that not only seriously harm their quality of life, but also cause a high incidence of severe depression. It is widely accepted that early intervention is essential for recovery, but current rehabilitation techniques largely favor hospital-based therapies which have restricted access, expensive and specialist equipment and tend to side-step the emotional challenges. In addition, there is insufficient funding available to provide the long-term assistance that is required. As a consequence, recovery rates are poor. The relatively unexplored solution is to develop therapies that can be harnessed in the home and are formulated from technologies that already exist in everyday life. This would empower individuals to take control of their own improvement and provide choice in terms of when and where they feel best able to undertake their own healing. This research seeks to identify how effective post-stroke, rehabilitation therapy can be applied to upper limb mobility, within the physical context of a home rather than a hospital. This is being achieved through the design and construction of an automation scheme, based on iterative learning control and the Riener muscle model, that has the ability to adapt to the user and react to their level of fatigue and provide tangible physical recovery. It utilizes a SMART Phone and laptop to construct an iterative learning control (ILC) system, that monitors upper arm movement in three dimensions, as a series of exercises are undertaken. The equipment generates functional electrical stimulation to assist in muscle activation and thus improve directional accuracy. In addition, it monitors speed, accuracy, areas of motion weakness and similar parameters to create a performance index that can be compared over time and extrapolated to establish an independent and objective assessment scheme, plus an approximate estimation of predicted final outcome. To further extend its assessment capabilities, nerve conduction velocity readings are taken by the software, between the shoulder and hand muscles. This is utilized to measure the speed of response of neuron signal transfer along the arm and over time, an online indication of regeneration levels can be obtained. This will prove whether or not sufficient training intensity is being achieved even before perceivable movement dexterity is observed. The device also provides the option to connect to other users, via the internet, so that the patient can avoid feelings of isolation and can undertake movement exercises together with others in a similar position. This should create benefits not only for the encouragement of rehabilitation participation, but also an emotional support network potential. It is intended that this approach will extend the availability of stroke recovery options, enable ease of access at a low cost, reduce susceptibility to depression and through these endeavors, enhance the overall recovery success rate.

Keywords: home-based therapy, iterative learning control, Riener muscle model, SMART phone, stroke rehabilitation

Procedia PDF Downloads 264

Authors: Satam Alotibi, Osama A. Hussein, Aziz H. Al-Shaibani, Nawaf A. Al-Aqeel, Abdellah Kaiba, Fatehia S. Alhakami, Mohammed Alyami, Talal F. Qahtan

Abstract:

The demand for sustainable and efficient energy conversion using solar energy has grown rapidly in recent years. In this pursuit, solar-to-chemical conversion has emerged as a promising approach, with oxygen vacancies-rich tungsten trioxide (WO₃) playing a crucial role. This study presents a method for synthesizing oxygen vacancies-rich WO3, resulting in a significant enhancement of its photocatalytic activity, representing a significant step towards sustainable energy solutions. Experimental results underscore the importance of oxygen vacancies in modifying the properties of WO₃. These vacancies introduce additional energy states within the material, leading to a reduction in the bandgap, increased light absorption, and acting as electron traps, thereby reducing emissions. Our focus lies in developing oxygen vacancies-rich WO₃, which demonstrates unparalleled potential for improved photocatalytic applications. The effectiveness of oxygen vacancies-rich WO₃ in solar-to-chemical conversion was showcased through rigorous assessments of its photocatalytic degradation performance. Sunlight irradiation was employed to evaluate the material's effectiveness in degrading organic pollutants in wastewater. The results unequivocally demonstrate the superior photocatalytic performance of oxygen vacancies-rich WO₃ compared to conventional WO₃ nanomaterials, establishing its efficacy in sustainable and efficient energy conversion. Furthermore, the synthesized material is utilized to fabricate films, which are subsequently employed in immobilized WO₃ and oxygen vacancies-rich WO₃ reactors for water purification under natural sunlight irradiation. This application offers a sustainable and efficient solution for water treatment, harnessing solar energy for effective decontamination. In addition to investigating the photocatalytic capabilities, we extensively analyze the structural and chemical properties of the synthesized material. The synthesis process involves in situ thermal reduction of WO₃ nano-powder in a nitrogen environment, meticulously monitored using thermogravimetric analysis (TGA) to ensure precise control over the synthesis of oxygen vacancies-rich WO₃. Comprehensive characterization techniques such as UV-Vis spectroscopy, X-ray photoelectron spectroscopy (XPS), FTIR, Raman spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and selected area electron diffraction (SAED) provide deep insights into the material's optical properties, chemical composition, elemental states, structure, surface properties, and crystalline structure. This study represents a significant advancement in sustainable energy conversion through solar-to-chemical processes and water purification. By harnessing the unique properties of oxygen vacancies-rich WO₃, we not only enhance our understanding of energy conversion mechanisms but also pave the way for the development of highly efficient and environmentally friendly photocatalytic materials. The application of this material in water purification demonstrates its versatility and potential to address critical environmental challenges. These findings bring us closer to a sustainable energy future and cleaner water resources, laying a solid foundation for a more sustainable planet.

Keywords: sustainable energy conversion, solar-to-chemical conversion, oxygen vacancies-rich tungsten trioxide (WO₃), photocatalytic activity enhancement, water purification

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Authors: Amber Dhoot, Tarush Gupta, Andrea Gurr, William Jenkins, Sandro Pietrunti, Alexis Tang

Abstract:

Background: The COVID-19 pandemic has driven pharmacovigilance towards a new paradigm. Nowadays, more people than ever before are recognising and reporting adverse reactions from medications, treatments, and vaccines. In the modern era, with over 3.8 billion users, social media has become the most accessible medium for people to voice their opinions and so provides an opportunity to engage with more patient-centric and accessible pharmacovigilance. However, the pharmaceutical industry has been slow to incorporate social media into its modern pharmacovigilance strategy. This project aims to make social media a more effective tool in pharmacovigilance, and so reduce drug costs, improve drug safety and improve patient outcomes. This will be achieved by firstly uncovering and categorising the barriers facing the widespread adoption of social media in pharmacovigilance. Following this, the potential opportunities of social media will be explored. We will then propose realistic, practical recommendations to make social media a more effective tool for pharmacovigilance. Methodology: A comprehensive systematic literature review was conducted to produce a categorised summary of these barriers. This was followed by conducting 11 semi-structured interviews with pharmacovigilance experts to confirm the literature review findings whilst also exploring the unpublished and real-life challenges faced by those in the pharmaceutical industry. Finally, a survey of the general public (n = 112) ascertained public knowledge, perception, and opinion regarding the use of their social media data for pharmacovigilance purposes. This project stands out by offering perspectives from the public and pharmaceutical industry that fill the research gaps identified in the literature review. Results: Our results gave rise to several key analysis points. Firstly, inadequacies of current Natural Language Processing algorithms hinder effective pharmacovigilance data extraction from social media, and where data extraction is possible, there are significant questions over its quality. Social media also contains a variety of biases towards common drugs, mild adverse drug reactions, and the younger generation. Additionally, outdated regulations for social media pharmacovigilance do not align with new, modern General Data Protection Regulations (GDPR), creating ethical ambiguity about data privacy and level of access. This leads to an underlying mindset of avoidance within the pharmaceutical industry, as firms are disincentivised by the legal, financial, and reputational risks associated with breaking ambiguous regulations. Conclusion: Our project uncovered several barriers that prevent effective pharmacovigilance on social media. As such, social media should be used to complement traditional sources of pharmacovigilance rather than as a sole source of pharmacovigilance data. However, this project adds further value by proposing five practical recommendations that improve the effectiveness of social media pharmacovigilance. These include: prioritising health-orientated social media; improving technical capabilities through investment and strategic partnerships; setting clear regulatory guidelines using multi-stakeholder processes; creating an adverse drug reaction reporting interface inbuilt into social media platforms; and, finally, developing educational campaigns to raise awareness of the use of social media in pharmacovigilance. Implementation of these recommendations would speed up the efficient, ethical, and systematic adoption of social media in pharmacovigilance.

Keywords: adverse drug reaction, drug safety, pharmacovigilance, social media

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Authors: Ding Liangxiao

Abstract:

The technological advancements in Micro-Electro-Mechanical Systems (MEMS) have significantly contributed to the development of new, flexible capacitive pressure sensors,which are pivotal in transforming wearable and medical device technologies. This study employs the sophisticated simulation tools available in COMSOL Multiphysics® to develop and analyze a MEMS-based sensor with a tri-layered design. This sensor comprises top and bottom electrodes made from gold (Au), noted for their excellent conductivity, a middle dielectric layer made from a composite of Silver Nanowires (AgNWs) embedded in Thermoplastic Polyurethane (TPU), and a flexible, durable substrate of Polydimethylsiloxane (PDMS). This research was directed towards understanding how changes in the physical characteristics of the AgNWs/TPU dielectric layer—specifically, its thickness and surface area—impact the sensor's operational efficacy. We assessed several key electrical properties: capacitance, electric potential, and membrane displacement under varied pressure conditions. These investigations are crucial for enhancing the sensor's sensitivity and ensuring its adaptability across diverse applications, including health monitoring systems and dynamic user interface technologies. To ensure the reliability of our simulations, we applied the Effective Medium Theory to calculate the dielectric constant of the AgNWs/TPU composite accurately. This approach is essential for predicting how the composite material will perform under different environmental and operational stresses, thus facilitating the optimization of the sensor design for enhanced performance and longevity. Moreover, we explored the potential benefits of innovative three-dimensional structures for the dielectric layer compared to traditional flat designs. Our hypothesis was that 3D configurations might improve the stress distribution and optimize the electrical field interactions within the sensor, thereby boosting its sensitivity and accuracy. Our simulation protocol includes comprehensive performance testing under simulated environmental conditions, such as temperature fluctuations and mechanical pressures, which mirror the actual operational conditions. These tests are crucial for assessing the sensor's robustness and its ability to function reliably over extended periods, ensuring high reliability and accuracy in complex real-world environments. In our current research, although a full dynamic simulation analysis of the three-dimensional structures has not yet been conducted, preliminary explorations through three-dimensional modeling have indicated the potential for mechanical and electrical performance improvements over traditional planar designs. These initial observations emphasize the potential advantages and importance of incorporating advanced three-dimensional modeling techniques in the development of Micro-Electro-Mechanical Systems (MEMS)sensors, offering new directions for the design and functional optimization of future sensors. Overall, this study not only highlights the powerful capabilities of COMSOL Multiphysics® for modeling sophisticated electronic devices but also underscores the potential of innovative MEMS technology in advancing the development of more effective, reliable, and adaptable sensor solutions for a broad spectrum of technological applications.

Keywords: MEMS, flexible sensors, COMSOL Multiphysics, AgNWs/TPU, PDMS, 3D modeling, sensor durability

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Authors: Lam Pham, Peter Hubber, Russell Tytler

Abstract:

Students’ conceptual understandings of chemistry concepts/phenomena involve capability to coordinate across the three levels of Johnston’s triangle model. This triplet model is based on reasoning about chemical phenomena across macro, sub-micro and symbolic levels. In chemistry education, there is a need for further examining inquiry-based approaches that enhance students’ conceptual learning and problem solving skills. This research adopted a directed inquiry pedagogy based on students constructing and coordinating representations, to investigate senior school students’ capabilities to flexibly move across Johnston’ levels when learning dilution and molar concentration concepts. The participants comprise 50 grade 11 and 20 grade 10 students and 4 chemistry teachers who were selected from 4 secondary schools located in metropolitan Melbourne, Victoria. This research into classroom practices used ethnographic methodology, involved teachers working collaboratively with the research team to develop representational activities and lesson sequences in the instruction of a unit on solution chemistry. The representational activities included challenges (Representational Challenges-RCs) that used ‘representational tools’ to assist students to move across Johnson’s three levels for dilution phenomena. In this report, the ‘representational tool’ called ‘cross and portion’ model was developed and used in teaching and learning the molar concentration concept. Students’ conceptual understanding and problem solving skills when learning with this model are analysed through group case studies of year 10 and 11 chemistry students. In learning dilution concepts, students in both group case studies actively conducted a practical experiment, used their own language and visualisation skills to represent dilution phenomena at macroscopic level (RC1). At the sub-microscopic level, students generated and negotiated representations of the chemical interactions between solute and solvent underpinning the dilution process. At the symbolic level, students demonstrated their understandings about dilution concepts by drawing chemical structures and performing mathematical calculations. When learning molar concentration with a ‘cross and portion’ model (RC2), students coordinated across visual and symbolic representational forms and Johnson’s levels to construct representations. The analysis showed that in RC1, Year 10 students needed more ‘scaffolding’ in inducing to representations to explicit the form and function of sub-microscopic representations. In RC2, Year 11 students showed clarity in using visual representations (drawings) to link to mathematics to solve representational challenges about molar concentration. In contrast, year 10 students struggled to get match up the two systems, symbolic system of mole per litre (‘cross and portion’) and visual representation (drawing). These conceptual problems do not lie in the students’ mathematical calculation capability but rather in students’ capability to align visual representations with the symbolic mathematical formulations. This research also found that students in both group case studies were able to coordinate representations when probed about the use of ‘cross and portion’ model (in RC2) to demonstrate molar concentration of diluted solutions (in RC1). Students mostly succeeded in constructing ‘cross and portion’ models to represent the reduction of molar concentration of the concentration gradients. In conclusion, this research demonstrated how the strategic introduction and coordination of chemical representations across modes and across the macro, sub-micro and symbolic levels, supported student reasoning and problem solving in chemistry.

Keywords: cross and portion, dilution, Johnston's triangle, molar concentration, representations

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Authors: Salvatore Luongo, Carlo Luongo

Abstract:

This paper discusses the implementation solutions to reduce the computational load for the Traffic Situational Awareness with Alerts (TSAA) application, based on Automatic Dependent Surveillance-Broadcast (ADS-B) technology. In 2008, there were 23 total mid-air collisions involving general aviation fixed-wing aircraft, 6 of which were fatal leading to 21 fatalities. These collisions occurred during visual meteorological conditions, indicating the limitations of the see-and-avoid concept for mid-air collision avoidance as defined in the Federal Aviation Administration’s (FAA). The commercial aviation aircraft are already equipped with collision avoidance system called TCAS, which is based on classic transponder technology. This system dramatically reduced the number of mid-air collisions involving air transport aircraft. In general aviation, the same reduction in mid-air collisions has not occurred, so this reduction is the main objective of the TSAA application. The major difference between the original conflict detection application and the TSAA application is that the conflict detection is focused on preventing loss of separation in en-route environments. Instead TSAA is devoted to reducing the probability of mid-air collision in all phases of flight. The TSAA application increases the flight crew traffic situation awareness providing alerts of traffic that are detected in conflict with ownship in support of the see-and-avoid responsibility. The relevant effort has been spent in the design process and the code generation in order to maximize the efficiency and performances in terms of computational load and memory consumption reduction. The TSAA architecture is divided into two high-level systems: the “Threats database” and the “Conflict detector”. The first one receives the traffic data from ADS-B device and provides the memorization of the target’s data history. Conflict detector module estimates ownship and targets trajectories in order to perform the detection of possible future loss of separation between ownship and each target. Finally, the alerts are verified by additional conflict verification logic, in order to prevent possible undesirable behaviors of the alert flag. In order to reduce the computational load, a pre-check evaluation module is used. This pre-check is only a computational optimization, so the performances of the conflict detector system are not modified in terms of number of alerts detected. The pre-check module uses analytical trajectories propagation for both target and ownship. This allows major accuracy and avoids the step-by-step propagation, which requests major computational load. Furthermore, the pre-check permits to exclude the target that is certainly not a threat, using an analytical and efficient geometrical approach, in order to decrease the computational load for the following modules. This software improvement is not suggested by FAA documents, and so it is the main innovation of this work. The efficiency and efficacy of this enhancement are verified using fast-time and real-time simulations and by the execution on a real device in several FAA scenarios. The final implementation also permits the FAA software certification in compliance with DO-178B standard. The computational load reduction allows the installation of TSAA application also on devices with multiple applications and/or low capacity in terms of available memory and computational capabilities

Keywords: traffic situation awareness, general aviation, aircraft conflict detection, computational load reduction, implementation solutions, software certification

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Authors: K. O'Malley

Abstract:

Background: The emergence and rapid evolution of large language models (LLMs) (i.e., models of generative artificial intelligence, or AI) has been unprecedented. ChatGPT is one of the most widely used LLM platforms. Using natural language processing technology, it generates customized responses to user prompts, enabling it to mimic human conversation. Responses are generated using predictive modeling of vast internet text and data swathes and are further refined and reinforced through user feedback. The popularity of LLMs is increasing, with a growing number of students utilizing these platforms for study and revision purposes. Notwithstanding its many novel applications, LLM technology is inherently susceptible to bias and error. This poses a significant challenge in the educational setting, where academic integrity may be undermined. This study aims to evaluate the performance of the latest iteration of ChatGPT (ChatGPT4.0) in standardized state medical licensing examinations. Methods: A considered search strategy was used to interrogate the PubMed electronic database. The keywords ‘ChatGPT’ AND ‘medical education’ OR ‘medical school’ OR ‘medical licensing exam’ were used to identify relevant literature. The search included all peer-reviewed literature published in the past five years. The search was limited to publications in the English language only. Eligibility was ascertained based on the study title and abstract and confirmed by consulting the full-text document. Data was extracted into a Microsoft Excel document for analysis. Results: The search yielded 345 publications that were screened. 225 original articles were identified, of which 11 met the pre-determined criteria for inclusion in a narrative synthesis. These studies included performance assessments in national medical licensing examinations from the United States, United Kingdom, Saudi Arabia, Poland, Taiwan, Japan and Germany. ChatGPT 4.0 achieved scores ranging from 67.1 to 88.6 percent. The mean score across all studies was 82.49 percent (SD= 5.95). In all studies, ChatGPT exceeded the threshold for a passing grade in the corresponding exam. Conclusion: The capabilities of ChatGPT in standardized academic assessment in medicine are robust. While this technology can potentially revolutionize higher education, it also presents several challenges with which educators have not had to contend before. The overall strong performance of ChatGPT, as outlined above, may lend itself to unfair use (such as the plagiarism of deliverable coursework) and pose unforeseen ethical challenges (arising from algorithmic bias). Conversely, it highlights potential pitfalls if users assume LLM-generated content to be entirely accurate. In the aforementioned studies, ChatGPT exhibits a margin of error between 11.4 and 32.9 percent, which resonates strongly with concerns regarding the quality and veracity of LLM-generated content. It is imperative to highlight these limitations, particularly to students in the early stages of their education who are less likely to possess the requisite insight or knowledge to recognize errors, inaccuracies or false information. Educators must inform themselves of these emerging challenges to effectively address them and mitigate potential disruption in academic fora.

Keywords: artificial intelligence, ChatGPT, generative ai, large language models, licensing exam, medical education, medicine, university

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