Data Sharing in RoPax Ports: Challenges and Opportunities Iancu, Bogdan; Morariu, Andrei-Raoul; Chen, Yiran ...
2023 33rd Conference of Open Innovations Association (FRUCT),
2023-May-24, Volume:
33, Issue:
1
Conference Proceeding, Journal Article
Peer reviewed
Open access
Aiming to engage additional sectors of the market, numerous companies adopt data-intensive solutions for their customers. Data ecosystems have emerged across a variety of industrial sectors. By ...design, they create value exploiting data exchange among participating organizations. Establishing such an ecosystem in the maritime sector is accompanied by various impediments. However, exploiting maritime data to the fullest and optimizing the supply chains might become critical for future generations. We investigate, in this article, data sharing in the maritime domain, focusing on data ecosystems in RoPax ports. These ports accommodate maritime vessels which allow the transport of vehicles, passengers and goods. The choice of maritime domain and of RoPax ports specifically is rooted in their crucial role in global trade, transportation, and environmental sustainability. To understand data sharing in this context, we conducted an exploratory case study based on a Finnish RoPaX port. This study was based on a qualitative case-study approach for data collection and analysis. We identified the main challenges and practical implications of data sharing, along with their benefits in the context of RoPax ports. In addition, we provide a conceptual diagram for data sharing for future maritime data ecosystems.
MLOps (Machine Learning Operations) is an engineering approach to streamline the development, deployment and maintenance of machine learning (ML) solutions in an operational environment. Managing the ...ML life-cycle at scale poses a variety of challenges which MLOps addresses, from the inter-dependency of various systems and their interoperability to the deployment of scalable pipelines. The maritime industry is no exception to this. This sector encounters distinct challenges in implementing machine learning operations, such as predicting the weather, optimizing shipping routes, and detecting anomalies in vessel behaviour. These requirements are addressed by creating specialized ML models tailored to the maritime domain. However, developing and deploying these models can be challenging due to the complexity of the maritime environment and the need for real-time decision-making. This study uses a systematic mapping analysis to evaluate and index existing literature on frameworks and practices for MLOps solutions that would be suitable for maritime applications. The discussion section addresses recommendations for applying MLOps to the maritime domain, difficulties with implementation and possible solutions, security, privacy, and already-implemented use cases, as well as future perspectives.
Practical quantum computing (QC) is still in its in-fancy and problems considered are usually fairly small, especially in quantum machine learning when compared to its classical counterpart. Image ...processing applications in particular require models that are able to handle a large amount of features, and while classical approaches can easily tackle this, it is a major challenge and a cause for harsh restrictions in contemporary QC. In this paper, we apply a hybrid quantum machine learning approach to a practically relevant problem with real world-data. That is, we apply hybrid quantum transfer learning to an image processing task in the field of medical image processing. More specifically, we classify large CT-scans of the lung into COVID-19, CAP, or Normal. We discuss quantum image embedding as well as hybrid quantum machine learning and evaluate several approaches to quantum transfer learning with various quantum circuits and embedding techniques.
Practical quantum computing (QC) is still in its infancy and problems considered are usually fairly small, especially in quantum machine learning when compared to its classical counterpart. Image ...processing applications in particular require models that are able to handle a large amount of features, and while classical approaches can easily tackle this, it is a major challenge and a cause for harsh restrictions in contemporary QC. In this paper, we apply a hybrid quantum machine learning approach to a practically relevant problem with real world-data. That is, we apply hybrid quantum transfer learning to an image processing task in the field of medical image processing. More specifically, we classify large CT-scans of the lung into COVID-19, CAP, or Normal. We discuss quantum image embedding as well as hybrid quantum machine learning and evaluate several approaches to quantum transfer learning with various quantum circuits and embedding techniques.
Since transcription factor Forkhead Box P3 (FoxP3) was identified as a specific regulatory T cell (Treg) marker, researchers have scrutinized its value as a potential novel therapeutic target or a ...prognostic factor in various types of cancer with inconsistent results. The present analysis was performed to assess the influence of Treg FoxP3 expression on the prognosis of primary melanoma and to evaluate the correlations with various clinicopathological prognostic factors. We analyzed all eligible patients with stage pT3 primary malignant melanomas treated in a tertiary cancer center. Immunohistochemical staining for Treg FoxP3 expression was performed on retrospectively identified paraffin blocks and subsequently correlated with the outcomes of the patients. A total of 81% of the patients presented a positive Treg FoxP3 expression, being correlated with a higher risk of lymph node metastasis, tumor relapse, and death. Moreover, positive expression was statistically associated with a shorter OS. The tumor relapse rate was estimated at 36.7%. A positive expression of Treg FoxP3 and lymph node metastasis were associated with a higher risk of death based on multivariate analysis. Treg FoxP3 expression may be used as an independent prognostic factor in patients with malignant melanoma to evaluate tumor progression and survival.
: The aim of this systematic review was to assess the efficiency of using allografts for sinus lift.
: This systematic review was written under the Preferred Reporting Items for Systematic Reviews ...and Meta-Analyses (PRISMA) guidelines and recommendation of the Cochrane Handbook for Systematic Reviews of Interventions. Three electronic databases were screened until October 2023. The risk of bias was assessed according to the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guidelines. Statistical analysis was performed for median bone volume and implant survival rate.
: From 321 articles retrieved, 7 articles were included in this review. A comparison between freeze-dried bone allograft (FDBA) and deproteinized bovine bone (DBB) for mean bone volume indicated a weighted mean difference (WMD) of -0.17 -0.69, 0.36 (95% confidence interval (CI)),
= 0.53. For implant survival rate, a comparison was made between FDBA and autogenous bone indicating a risk ratio (RR) of 1.00 0.96, 1.05 (95% CI),
= 1.00.
: The available evidence suggested that allograft bone can be used in sinus lift procedures. The results obtained are insufficient to compare with other types of bone graft, requiring a longer follow-up time. Future clinical trials are needed in order to evaluate the advantages of using allograft bone.
Basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC) are the most frequently occurring non-melanocytic skin cancers. The objective of our study is to present the pathophysiology of ...BCC and cSCC and its direct relationship with the histopathological diagnostics and the differential diagnostics of these types of cancer, based on the morphological characteristics, immunohistochemical profile, and genetic alterations. The qualitative study was based on emphasizing the morphological characteristics and immunohistochemistry profiles of BCC and cSCC and the differential diagnostics based on the tissue samples from the Clinical Pathology Department of Mures Clinical County Hospital between 2020 and 2022. We analyzed the histopathological appearances and immunohistochemical profiles of BCC and cSCC in comparison with those of Bowen disease, keratoacanthoma, hyperkeratotic squamous papilloma, metatypical carcinoma, pilomatricoma, trichoblastoma, Merkel cell carcinoma, pleomorphic dermal sarcoma (PDS), and melanoma. Our study showed the importance of the correct histopathological diagnosis, which has a direct impact on the appropriate treatment and outcome for each patient. The study highlighted the histopathological and morphological characteristics of NMSCs and the precursor lesions in HE and the immunohistochemical profile for lesions that may make the differential diagnosis difficult to establish.
Mobile technology and artificial intelligence are opening new avenues for improving public health, particularly in the field of dermatology. This work presents the concept of a mobile application ...designed to assist in detecting potentially cancerous moles, with the aim of promoting early detection of skin cancer and reducing the burden on healthcare systems. Skin cancer is a growing public health issue worldwide, and in Romania, despite a relatively low incidence of melanoma, there are some of the highest mortality rates associated with this disease. This paradox highlights the need for effective methods for early diagnosis and rapid intervention. The proposed research investigation uses a Convolutional Neural Network (CNN) to classify images of moles based on their risk for skin cancer. Users can capture a photograph of the suspect mole, which the application then processes using a specialized CNN model. The model is trained with labeled datasets by expert dermatologists, using the HAM10000 dataset, which contains over 10,000 dermoscopic images of pigmented lesions. The application provides a preliminary classification into seven categories, indicating whether the mole is benign or malignant, with a target accuracy of at least 93%, aligning with other similar studies. This initiative can promote awareness and early detection of skin cancer, offering a preliminary screening tool that is easy for the use of general public.
