Northern Ireland Office

Abstract This chapter showcases over 20 examples of ocean literacy activities developed during the Ocean Incubator Network Living Laboratory and beyond (the Foraminifera Box contains more than six activities that can be downloaded freely). The authors of these activities include students, scientists, researchers, educators, linguists, legal scholars, and experts in accessibility. Each activity has been scrutinized through the educational and pedagogical lens of Marcelle Dabbah to ensure its efficacy and relevance. Both teachers and learners can find inspiration in these activities, which are available for download and can be tailored to meet specific learning objectives and classroom needs. Each activity is structured according to an outline developed by Laura Vita, and includes details such as the contributors, activity name, target audience, focus of the activity, learning objectives, and guidance or instructions.

On November 30, 2022, OpenAI, a prominent US company with headquarters in San Francisco, released a publicly available version of a chatbot called ChatGPT that transformed the world of Conversational Artificial Intelligence (AI) and ignited what has come to be known as “The Conversational AI Arms Race.” Within just five days of its launch, ChatGPT had acquired a million users, and within two months, it was estimated to have 100 million active users. In February 2023, Microsoft, having invested heavily in OpenAI, launched a version of its Bing search engine powered by the technology behind ChatGPT. Google responded in March 2023 by releasing its own AI-powered chatbot called Bard. Others followed, including Anthropic, funded initially by Google and subsequently by Amazon, with a chatbot called Claude, as well as major Chinese tech firms, such as Baidu and Alibaba.

In Chapter 5 , we introduced a new discipline called prompt engineering, which is rapidly evolving and becoming more defined as time goes by. We have already learned about basic elements of prompts such as various prompt patterns and use cases, as well as useful prompt techniques that might help conversation designers to be more productive in creating conversational interfaces.

As a preliminary exploration, based on the historical order data of the battery-swap stations, this paper predicts the order quantity of the stations in the future period, which can be used as a reference for the battery scheduling of battery-swap stations. Compared with the existing studies, we establish the network of battery-swap stations based on the real large-scale order data, and build the graph model combined with multihead attention to predict. The challenges in this paper include similarity analysis, building the adjacency matrix of battery-swap stations, and the model for distributed stations fine-grained order volume prediction. To address the above challenges, we build the GAT model to accurately predict the order quantity of batteryswap stations. Our work consists of two parts. First, we construct three features and calculate the similarity among the features based on the spatio-temporal correlation data of battery-swap stations. The similarity will be used to construct the association matrix of stations and fuse it with the distance adjacency matrix to construct the topological network of battery-swap stations. Second, we build a graph neural network model and input the network structure of battery-swap stations and historical order data for prediction. In order to capture the correlation features of data, we introduce the multi-attention mechanism, which uses attention to capture the features of different graph nodes, and finally achieves better prediction effect through multi-connection. Our model performs well in hourly order prediction, with an average MAE of about 1.07. We believe that this work can provide reference and ideas for enterprises in order balance, battery scheduling and other aspects.

The British Society of Gastroenterology has recommended the Edinburgh Dysphagia Score (EDS) to risk-stratify dysphagia referrals during the endoscopy COVID recovery phase. External validation of the diagnostic accuracy of EDS and exploration of potential changes to improve its diagnostic performance. A prospective multicentre study of consecutive patients referred with dysphagia on an urgent suspected upper gastrointestinal (UGI) cancer pathway between May 2020 and February 2021. The sensitivity and negative predictive value (NPV) of EDS were calculated. Variables associated with UGI cancer were identified by forward stepwise logistic regression and a modified Cancer Dysphagia Score (CDS) developed. 1301 patients were included from 19 endoscopy providers; 43% male; median age 62 (IQR 51-73) years. 91 (7%) UGI cancers were diagnosed, including 80 oesophageal, 10 gastric and one duodenal cancer. An EDS ≥3.5 had a sensitivity of 96.7 (95% CI 90.7-99.3)% and an NPV of 99.3 (97.8-99.8)%. Age, male sex, progressive dysphagia and unintentional weight loss >3kg were positively associated and acid reflux and localisation to the neck were negatively associated with UGI cancer. Dysphagia duration <6months utilised in EDS was replaced with progressive dysphagia in CDS. CDS ≥5.5 had a sensitivity of 97.8 (92.3-99.7)% and NPV of 99.5 (98.1-99.9)%. Area under receiver operating curve was 0.83 for CDS, compared to 0.81 for EDS. In a national cohort, the EDS has high sensitivity and NPV as a triage tool for UGI cancer. The CDS offers even higher diagnostic accuracy. The EDS or CDS should be incorporated into the urgent suspected UGI cancer pathway.

Rowhammer is a destructive software-induced DRAM fault, which an attacker can leverage to break system security. Both individual customers and enterprise users (e.g., cloud providers) might refrain from using a computing system if it is vulnerable to rowhammer vulnerability. In this paper, we provide the first end-to-end tool, coined BitMine, that systematically assesses a DRAM chip’s vulnerability to rowhammer bit flips. BitMine is an extension of DRAMDig. As DRAM address mappings are proprietary techniques and critical in inducing rowhammer bit flips, DRAMDig, our prior work, leverages domain knowledge to efficiently and deterministically reverse-engineer DRAM address mappings on Intel machines. By incorporating DRAMDig, BitMine configures three key parameters, i.e., hammer methods, hammer patterns, data patterns, on the effectiveness of finding rowhammer bit flips. BitMine by default implements 13 hammer methods, 4 hammer patterns and 16 data patterns and is extensible to support more. We evaluate DRAMDig and BitMine against multiple machine models that combine different DRAM chips and Intel microarchitectures. Our experiment results show that DRAMDig efficiently uncovers a deterministic DRAM address mapping for each machine model, and every implemented parameter in BitMine has its distinct effectiveness in triggering bit flips for different machine models.

Abstract This study analyses seismic data to investigate the kinematic indicators within the mass transport deposits (MTDs) of the Donegal Barra Fan complex in the Rockall Trough, along the NW European continental margin. Five episodes of mega-scale MTDs (DBF-01, -02, -03, -04 and -05) are identified. DBF-01 is the largest MTD in the NW British continental margin, comprising 1907 km 3 of sediments. Fold-and-thrusts were identified within the MTDs where they attain maximum thickness of c. 300–380 ms TWT, but not at the toe region. This indicates that local erosion and deceleration caused bulking up of the MTD volume, but the MTD was not fully arrested due to the high mobility of the mass flow. MTD thickness distribution and thrust fault orientations indicate source areas and flow direction of MTD. The MTDs show a compensational stacking pattern with earlier deposits influencing the position and flow direction of succeeding slides, suggesting that glaciogenic debris flows are sensitive to topographic variability. We propose that increased sediment input associated with at least five expansions of the British–Irish Ice Sheet to the shelf edge led to the development of these MTDs and that the youngest of them, DBF-05, corresponds to the Last Glacial Maximum.

As there are more and more electric vehicles in the automotive market, the braking systems are facing some new challenges. There are many important functions in electric vehicles that impact the brake behavior, one of the key and unique features is the application of high-intensity regenerative braking. To study the challenges, a brake durability test for electric vehicles was specially designed and conducted in the famous Huangshan region of China. The back to back comparison testing was set up to understand the impact of different regen-brake level.In this paper, the Huangshan test that is popular in Chinese OEM for brake endurance and NVH will be initially introduced, then the tested electric vehicle and its features will be described, as well as the experimental settings. Later, in the analysis of test data, many changes of braking characteristics between vehicles with and without regenerative braking, including braking energy consumption, brake pressure, brake deceleration and brake temperature etc., would be revealed. These changes have generated a large impact on the brake NVH behavior and brake wear performance, related findings were demonstrated with comparison results and typical examples of the test. Finally, from an electric vehicle OEM point of view, the valuable experience and challenges of electric vehicle brake system will be summarized, and it is believed that the results will be very interesting and helpful for the brake community. The future work will be proposed as well, to improve the rig test procedure and Huangshan road test procedure for electric vehicles.