This is a single portfolio entry
Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Aenean commodo ligula eget dolor. Aenean massa. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Donec quam felis, ultricies nec, pellentesque eu, pretium quis, sem. Nulla consequat massa quis enim.
Donec pede justo, fringilla vel, aliquet nec, vulputate eget, arcu. In enim justo, rhoncus ut, imperdiet a, venenatis vitae, justo. Nullam dictum felis eu pede mollis pretium. Integer tincidunt. Cras dapibus. Vivamus elementum semper nisi. Aenean vulputate eleifend tellus. Aeneaellus.
Recent Entries
C122115030226
The Diamond field from onshore in Gabon was brought into production by Perenco in 2000, but its development has mainly focused on deep light oil reservoirs. This study reassesses the potential of the shallow Senonian and Turonian reservoirs, which have remained underexploited due to technical challenges, including the presence of heavy oil (18-22 API) and significant water production. The results of structural modelling and petrophysical analysis (CMR) estimate a total oil in place (STOIIP) volume of 48 Mmstbo for the base case, rising to 718 Mmstbo in the high scenario. The analysis identifies 12 potential productive intervals, including six (6) in the light oil zone and six (6) in the heavy oil zone. Although the current recovery factor for the EM1 & LC2 zone is only 5.5%, as illustrated by the RK-3 well’s historical production of 500,000 barrels in July 2010, the study shows that recovery rates of up to 40% are possible. Confirmation of these volumes, representing possible contingent resources of 287 Mmstb, now requires a dedicated appraisal phase to reduce structural uncertainties.
B121015020126
Aiming at energy IoT applications for demand-side automation of electricity usage in residential and commercial buildings, this paper presents systems and methodologies that advance the research objectives. We developed and implemented an intelligent switch system that provides real-time energy feedback, automatic control, and optimisation to monitor the system’s energy performance metrics. Based on 58 households and a six-month field study, the system achieved an average saving of 24.7%, with a maximum saving of 37.2%. We consider the challenges of ubiquitous deployment, interoperability, security, and system cost. Further optimisations can be made toward energy efficiency, such as dynamic load balancing, machine-learning-based predictive models for SLA requirements , and adaptive scheduling algorithms. This paper demonstrates the feasibility of IoT for regulating household energy use through analyses of a prototype and a dataset. The prototype enables households to achieve approximately 412 watt-hours of annual energy savings, thereby illustrating the potential of energy management and the feasibility of the proposed system.
B120815020126
The increasing prevalence of cyber threats across Internet of Medical Things (IoMT) ecosystems poses critical challenges for safeguarding patient safety and data integrity, necessitating a dynamic, resilient intrusion detection system (IDS). In this work, we present a comprehensive machine learning framework for classifying cyberattacks in IoMT settings using biometric and network traffic data from the publicly available WUSTL-EHMS-2020 dataset. We conduct a unique comparative analysis using three paradigms: a Graph Neural Network (GNN) model to capture structural dependencies; a Transformer deep learning model to capture contextual relationships; and a lightweight baseline classifier, Logistic Regression. We undertook extensive data preparation, including label encoding, normalisation, and stratified sampling to maintain class balance. The Transformer achieved the highest overall classification accuracy in the IoMT ecosystem (93.5%), outperforming both GNN (88.7%) and Logistic Regression (92.8%) across all evaluation metrics. Our research demonstrates the superior ability of attention-based models to identify complex threat patterns in heterogeneous IoMT data. Our study provides a reproducible benchmarking framework and lays the groundwork for future efforts related to hybrid modelling, explainable AI, and federated learning to improve the cybersecurity of Smart Healthcare Systems.
A120315011225
Sentiment analysis of short text has posed a significant challenge in natural language processing, particularly for context rich and low-resource languages such as Vietnamese. User generated texts are usually brief; therefore, they do not explicitly express their sentiments. Consequently, traditional models struggle to process those reviews. This paper introduces a new approach that leverages the strengths of large language models to address the gap in context scarcity. The method works primarily in two ways: a) by feeding in structured metadata, such as restaurant name and location, directly into the model input, and b) using large language models to automatically generate likely contextual sentences so that short reviews become long informative statements. Results from comprehensive experiments carried out on a newly assembled Vietnamese food review dataset show improved sentiment analysis output based on this kind of context enrichment, beating several strong baselines, including the state of-the-art monolingual PhoBERT model, particularly when it came to resolving semantic vagueness typical of ultra-short word reviews or even short reviews with implicit subjects. This work offers a strong, flexible approach to addressing the problem of missing context in low-resource languages. This will bring value to both the commercial world and academic study.
