Predicting shoreline changes using deep learning techniques with Bayesian optimisation

Accurate prediction of shoreline change is vital for effective coastal planning and management, especially under increasing climate variabilities. This study explores the applicability of deep learning (DL) techniques, particularly Long Short-Term Memory (LSTM) and Convolutional Neural Network-LSTM (CNN-LSTM) models, for shoreline forecasting at monthly to inter-annual timescales, under two modelling approaches—direct input (DI) and autoregressive (AR). All models demonstrated the ability to reproduce temporal shoreline variability, while the autoregressive DL models were performing better.Further, a noise impact assessment revealed that seasonal decomposition and noise filtering significantly enhanced the model performance. In particular, the models using 52-week data decomposition and residual noise reduction improved the model performance. The reduction of data noises also resulted in narrower ensemble prediction envelopes, indicating that ensemble candidate models behave with low diversity. The temporal data resolution analysis showed that lower data resolutions reduce the predictive performance of the model and at least fortnightly data are required to satisfactorily capture the trend of variability of the shoreline position at this beach.The use of ensemble predictions, derived from a selected subset of model trials based on their collective performance, proved beneficial by capturing diverse temporal behaviours, thereby offering a quasi-probabilistic forecast with minimal computational cost. Overall, the study underscores the potential of DL models, particularly with autoregressive architectures, for reliable and transferable shoreline change prediction. It also emphasizes the importance of data quality, resolution, and preprocessing in improving model robustness, laying the groundwork for future research into use of DL in multi-scale shoreline predictions

High temperature interlaminar tensile strength of a SiCf/SiC ceramic matrix composite determined through diametrical compression testing up to 1200°C

The diametrical compression test method was used in this study to determine the high temperature interlaminar tensile strength of a SiCf/SiC Ceramic Matrix Composite. Two disk geometries are employed (Φ4.5 mm and Φ9 mm) with tests performed up to 1200°C, building upon previous room temperature investigations conducted by the authors [1]. For all tests, disks failed parallel to the loading axis spanning between the upper and lower contact points, ensuring repeatability and reliability even at high temperatures. Digital image correlation was applied to selected tests to measure the full-field strain and observe damage progression to ultimate failure. Weibull distribution was implemented to determine the characteristic strength and distribution, to understand the influence of specimen volume and high temperature oxidation. High temperature results were revealed to have a higher characteristic strength and Weibull modulus owing to the associated oxidation mechanisms, whether the formation of silica rich regions or degradation of the interphase

Promoting global collaboration to improve bioaerosol exposure assessment and understanding of associated health impacts: outcomes from a series of workshops

We are surrounded, in both indoor and outdoor environments, by air containing particles of biological origin (bioaerosols). We constantly inhale them, and, depending upon their size, they deposit in different parts of our airways. Despite their ubiquitous nature and our constant exposure, bioaerosol diversity and composition of the environment are not well characterized, and we understand little about which bioaerosols we are exposed to and how this impacts our health, either positively or negatively. Indoor/Outdoor Bioaerosols Interface and Relationships Network (BioAirNet), a Clean Air Programme-funded network, has recognized the need for the bioaerosol community to reflect on the current challenges facing bioaerosol exposure assessment and the determination of the associated cellular/molecular responses driving specific health outcomes. A series of online workshops for the bioaerosol community were hosted by BioAirNet in September 2022, which aimed to bring together global expertise to discuss the current challenges impeding improved assessment of bioaerosol exposure and understanding of the downstream cellular and molecular mechanisms driving health outcomes by discussing these challenges; considering where we need to be, where we are now and how we get there. Professional facilitation was key to their success, enabling the multidisciplinary bioaerosol community to explore and address these challenges within a focused and productive environment to prioritize themes and agree on action plans for continued momentum following the workshops. These themes were as follows: (1) conceptual model; (2) stakeholder mapping; (3) knowledge transfer; (4) writing project and (5) conference-type event, collectively covering research, knowledge mobilization and networking activities. A subsequent in-person follow-up workshop was held in November 2023. It provided an opportunity to share progress on the five themes, critique what had already been done and act as a launch-pad to progress the actions further. Delegates also had the opportunity to share ongoing or upcoming work, particularly projects requiring input from others, to encourage collaborative working and sharing expertise. The use of facilitated workshops is a valuable tool for all scientific communities to collectively explore and successfully address key issues within their field

Optimizing In Vitro Skin Permeation Studies to Obtain Meaningful Data in Topical and Transdermal Drug Delivery

Drug delivery through the skin provides several advantages over other administration routes, including the avoidance of first-pass metabolism and gastrointestinal side effects, prolonged drug release, and significant improvement in patient compliance. It is imperative to study the in vitro behavior of drugs and formulations before proceeding to in vivo evaluations. As the ethical guidelines for scientific research evolve, there is an increasing emphasis on adopting alternative methods to reduce animal use. An in vitro permeation study (IVPT) estimates the rate and extent of drug permeation from a topical or transdermal delivery, determining its availability at the skin layers or into the systemic circulation. Vertical Franz diffusion cells are commonly employed for IVPT studies to evaluate the permeation of drugs across skin or other biorelevant membranes. This comprehensive review provides a clear understanding of the importance of optimizing in vitro experimental conditions to obtain reliable and reproducible data. We discuss various in vitro skin models, including excised human and animal skins, human skin equivalents (HSEs), synthetic membranes, and 3D-printed skin models. Additionally, a broad overview of setting up in vitro diffusion cells is provided. Emphasis is given on donor phase design, receptor medium selection, the importance of solubility and stability studies, sampling techniques, and analysis methods. Meticulous design and optimization of in vitro permeation experiments are crucial for generating reproducible data, which are essential for predicting the dermatokinetics of drugs and formulations

Secure Identity Management System in Unmanned Aerial Vehicles Network

In recent years, rapid advancements in digital transformation and communication technologies have led to the widespread adoption of autonomous systems, particularly Unmanned Aerial Vehicles (UAVs), in societal and industrial applications. The integration of smart cities, the Internet of Things (IoT), and 5G technologies has enabled UAVs to be utilized effectively in more complex and dynamic tasks. For instance, during the COVID-19 pandemic, UAVs played critical roles in maintaining social distancing, delivering medical supplies, and managing crowds. Such contemporary applications have once again highlighted the importance and potential of UAV networks. The flexibility and versatility offered by UAVs facilitate the development of innovative solutions across a wide spectrum—from agriculture to logistics, disaster management to security. Specifically, swarm UAV systems surpass the limitations of individual vehicles, providing advantages such as real-time data collection, large-scale monitoring, and the parallel execution of complex tasks.However, the effective and secure operation of such systems depends on the reliability and efficiency of intra-network communication and identity management protocols. In today's cyber-physical systems, security threats and cyber-attacks are becoming increasingly sophisticated. UAV networks are not exempt from these threats; risks such as identity spoofing, data manipulation, and Denial-of-Service (DoS) attacks endanger the success and security of operations. Addressing these security vulnerabilities is of vital importance, especially in sensitive areas like the protection of critical infrastructures, border security, and emergency interventions. This thesis aims to enhance the operational efficiency and security of UAV networks by developing a lightweight and dynamic identity management protocol alongside a consensus mechanism specifically optimized for UAV networks. The proposed identity management protocol employs symmetric cryptography and hash functions, featuring low computational and communication overhead while adapting to dynamic network topologies. The protocol is resilient against common security threats such as identity spoofing, replay attacks, and man-in-the-middle attacks.Furthermore, leveraging the advantages of blockchain technology, a fast and efficient consensus mechanism suitable for UAV networks has been designed. Instead of energy-intensive and high-latency methods like traditional Proof of Work (PoW), an adapted version of the Practical Byzantine Fault Tolerance (PBFT) algorithm and a Fuzzy C-Means Clustering algorithm (FCMCA) are utilized to reduce latency and computational costs. This mechanism enables secure and effective data sharing and decision-making processes among UAVs. Simulations and performance analyses have demonstrated that the proposed solutions provide lower latency and reduced resource consumption compared to existing methods, while exhibiting high resilience against security threats. These findings contribute significantly to the safer, more efficient, and scalable use of UAV networks in real-world applications. The study aims to establish a solid foundation for the evolution and sustainability of UAV networks and serves as a valuable reference for future technological developments and applications

A Survey of the Current UK Physician Associate Educator Workforce and Recommendations for Courses and Provider Organizations

In the United Kingdom there are 37 physician associate (PA) training programs with limited knowledge of the educators involved, their training, and specific needs. An online questionnaire was sent to PA educators in all UK training programs requesting information on academic title and responsibilities, clinical and nonclinical background, education and qualifications before becoming a PA educator, formal and informal training received in the role, and insights into career progression. The questionnaire highlighted 5 specific areas that should be specific recommendations for UK training programs to support PA educators, alongside existing guidance. These centered on academic and leadership development, clinical engagement, student support, and pedagogical research. We believe that implementing these recommendations across training programs will improve the opportunities for all those delivering PA education and consequently improve the offering to the students undertaking PA studies programmes. [Abstract copyright: Copyright © 2024 PA Education Association.