Silver Saplings Adventures for Older People: a qualitative review and evaluation of intended positive interventional change and wellbeing effects for participants of the programme.

The Silver Saplings Adventures (SSA) programme is a nature-based wellbeing intervention targeting older people, located in Moray, Aberdeenshire and areas of the Highlands. It consists of monthly day trips to outdoor settings, providing opportunities for participants to engage within communities, connect with others, as well as learn about and from their local natural environment. The SSA programme has been purposively designed in this way to promote individual wellbeing, social cohesion and feelings of inclusivity. This report presents a focused, independent evaluation of the SSA programme, based on a sample of 17 participants who engaged with the 12-month programme during its third (and penultimate) cycle, spanning January – December 2023. Evaluating the SSA programme was undertaken to optimise the development and continuation of the programme beyond its current funding, and to inform the transferability of this to other similar programmes and contexts. The primary objective of this evaluation was to assess the SSA programme to ascertain any wellbeing effects on participants, how these effects manifested and the broader ramifications, and to identify what specific aspects of the programme might be responsible for generating these reported effects

A comparative study of novelty detection models for zero day intrusion detection in industrial Internet of Things.

The detection of zero-day attacks in the IoT network is a challenging task due to unknown security vulnerabilities. Also, the unavailability of the data makes it difficult to train a machine learning (ML) model about new vulnerabilities. The existing supervised ML-based Intrusion Detection Systems (IDS) are trained to detect only known attacks. On the contrary, the unsupervised ML-based IDSs show a high false-positive rate. In this paper, we experimented on three novelty detection algorithms named One-Class SVM (OCSVM), Local Outlier Factor (LOF), and Isolation Forest (IF), which follow the one-vs-all strategy for zero-day-intrusion detection for IoT datasets. UNSW-NB15 and IoTID20 datasets are considered for the experiment. Experimental results show that OCSVM outperformed the other two models for zero-day intrusion or unseen anomaly detection in IoT domain

The state of the art in hydrogen storage.

The global renewable energy mix is set to change even further with the increasing demand for hydrogen. The production levels are dramatically increasing, and it is becoming prevalent that the storage of hydrogen gas is much more complex than natural gas. There are many different hydrogen storage options being investigated, trialled, and used within the energy industry. On land storage of hydrogen use compressed pressure vessels for gas, cryogenic storage for liquid hydrogen and the blending of hydrogen into natural gas to be stored in current pipeline systems. Underground storage options are found in depleted hydrocarbon reservoirs, deep aquifers, and salt caverns. The storage of hydrogen gas presents numerous challenges and opportunities as discussed within this paper, such as design and manufacturing, hydrogen embrittlement and behaviour, structural integrity, standards and regulation, safety of high-pressure storage, subsea storage and circular economy prospects in structural design. Numerous types of vessel compositions have been explored for the most suitable materials combination in pressure vessel designs, with type IV being the most widely used. However, there are many aspects of the vessel design that have areas for improvement to store hydrogen at a higher efficiency. There are also many opportunities for further developments in hydrogen storage, such as subsea storage and circular economy incorporated designs

"Doing" is never enough, if "being" is neglected: exploring midwives' perspectives on the influence of an emotional intelligence education programme: a qualitative study. [Article]

The role of the midwife is emotionally demanding, with many midwives experiencing high levels of stress and burnout, and a great number considering leaving the profession. This has serious implications for the delivery of high-quality, safe maternity care. One of the major factors leading to job dissatisfaction is the conflict between midwives' aspiration of truly 'being' with the woman and the institutional expectations of the role, which focuses on the 'doing' aspects of the job. 'Being' present to a woman's psychological needs, whilst meeting the institutional demands, requires high levels of emotional intelligence (EI) in the midwife. Therefore, enhancing midwives' EI could be beneficial. An EI programme was made available to midwives with the intention to promote their emotional intelligence and enable them to utilise relaxation techniques for those in their care. The aim of this study was to explore midwives' perspectives on the influence of the EI education programme on their emotional wellbeing and experiences of practice. The study took a descriptive qualitative approach. Thirteen midwives participated in focus group interviews. The data were analysed using thematic analysis. The overarching theme of 'The Ripple Effect' included three themes of 'Me and my relationships', 'A different approach to practice', and 'Confidence and empowerment'. The programme was seen to create a positive ripple effect, influencing midwives personally, their approach to practice, and feelings of confidence in their role. The study concluded that EI education can reduce emotional stress in midwives, enhance their empathy and feelings of confidence, and thereby improve the quality of care that they provide

Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things.

Embedded systems, including the Internet of things (IoT), play a crucial role in the functioning of critical infrastructure. However, these devices face significant challenges such as memory footprint, technical challenges, privacy concerns, performance trade-offs and vulnerability to cyber-attacks. One approach to address these concerns is minimising computational overhead and adopting lightweight intrusion detection techniques. In this study, we propose a highly efficient model called optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in IoT environments. The proposed OCFSDA model incorporates feature selection, data compression, pruning, and deparameterization. We deployed the model on a Raspberry Pi4 using the TFLite interpreter by leveraging optimisation and inferencing with semi-supervised learning. Using the MQTT-IoT-IDS2020 and CIC-IDS2017 datasets, our experimental results demonstrate a remarkable reduction in the computation cost in terms of time and memory use. Notably, the model achieved an overall average accuracies of 99% and 97%, along with comparable performance on other important metrics such as precision, recall, and F1-score. Moreover, the model accomplished the classification tasks within 0.30 and 0.12 s using only 2KB of memory

Comparative effect size distributions in strength and conditioning and implications for future research: a meta-analysis.

Controlled experimental designs are frequently used in strength and conditioning (S&C) to determine which interventions are most effective. The purpose of this large meta-analysis was to quantify the distribution of comparative effect sizes in S&C to determine likely magnitudes and inform future research regarding sample sizes and inference methods. Baseline and follow-up data were extracted from a large database of studies comparing at least two active S&C interventions. Pairwise comparative standardised mean difference effect sizes were calculated and categorised according to the outcome domain measured. Hierarchical Bayesian meta-analyses and meta-regressions were used to model overall comparative effect size distributions and correlations, respectively. The direction of comparative effect sizes within a study were assigned arbitrarily (e.g. A vs. B, or B vs. A), with bootstrapping performed to ensure effect size distributions were symmetric and centred on zero. The middle 25, 50, and 75% of distributions were used to define small, medium, and large thresholds, respectively. A total of 3874 pairwise effect sizes were obtained from 417 studies comprising 958 active interventions. Threshold values were estimated as: small = 0.14 [95%CrI: 0.12 to 0.15]; medium: = 0.29 [95%CrI: 0.28 to 0.30]; and large = 0.51 [95%CrI: 0.50 to 0.53]. No differences were identified in the threshold values across different outcome domains. Correlations ranged widely (0.06 ≤ r ≤0.36), but were larger when outcomes within the same outcome domain were considered. The finding that comparative effect sizes in S&C are typically below 0.30 and can be moderately correlated has important implications for future research. Sample sizes should be substantively increased to appropriately power controlled trials with pre-post intervention data. Alpha adjustment approaches used to control for multiple testing should account for correlations between outcomes and not assume independence

Advancing AI with green practices and adaptable solutions for the future. [Article summary]

Despite AI's achievements, how can its limitations be addressed to reduce computational costs, enhance transparency and pioneer eco-friendly practices

Transdisciplinary and arts-centred approaches to stewardship and sustainability of urban nature.

This paper explores case studies of how artists working with scientists and land managers affiliated with the Urban Field Station Collaborative Arts Program (UFS Arts) are fostering new relations of care with urban nature and thereby informing landscape decisions. The 'wicked' problems related to sustainability demand novel, holistic approaches to transformation that engage multiple ways of knowing. We present 4 examples from UFS Arts by triangulating data across programmatic documentation, evaluation, and ethnographic materials from 2016-present. Matthew López-Jensen's Tree Love and Nikki Lindt’s Underground Sound Project sensitise us to the capacities of trees and forests through image and sound. Mary Mattingly’s Swale is a floating food forest that enacts new forms of community stewardship. The exhibition Who Takes Care of New York? maps the stories and practices of civic environmental groups. Three themes in these works suggest opportunities for transformation throughout the knowledge production cycle: posing novel questions, engaging multiple methodologies, and communicating ideas with the public. Through these transdisciplinary works, we learn things we could not have learned via traditional disciplinary or interdisciplinary work and assert that stewardship offers a pathway towards sustainability transforming management practices and landscape decisions by reshaping our relationships to community and the land

The climate‐induced changes in the life history of the common cuttlefish in the English Channel.

The population of common cuttlefish Sepia officinalis in the English Channel recently developed two life cycles: annual (spawning 1 y.o.) and biennial (spawning 2 y.o.) instead of the biennial strategy known before, associated with increasing environmental temperatures in recent decades because of climate changes. Both groups differ in the size of mature animals (110–196 mm mantle length vs. 140–262 mm) and the number of chambers in the cuttlebone (60–97 in annual vs. 93–152 in biennial). The annual group represented some 15%–20% of the population, and the proportion of early spawners increased during the reproductive period, from 3%–5% in February/March to 50%–70% in June/July. Among spawning cuttlefish males predominated as ~2:1. Such environmentally driven changes in historical ecology as exemplified by the cuttlefish might be a critical link in the adaptation of the cephalopod life cycles to changing ecosystems

Detection-driven exposure-correction network for nighttime drone-view object detection.

Drone-view object detection (DroneDet) models typically suffer a significant performance drop when applied to nighttime scenes. Existing solutions attempt to employ an exposure-adjustment module to reveal objects hidden in dark regions before detection. However, most exposure-adjustment models are only optimized for human perception, where the exposure-adjusted images may not necessarily enhance recognition. To tackle this issue, we propose a novel Detection-driven Exposure-Correction network for nighttime DroneDet, called DEDet. The DEDet conducts adaptive, non-linear adjustment of pixel values in a spatially fine-grained manner to generate DroneDet-friendly images. Specifically, we develop a Fine-grained Parameter Predictor (FPP) to estimate pixel-wise parameter maps of the image filters. These filters, along with the estimated parameters, are used to adjust pixel values of the low-light image based on non-uniform illuminations in drone-captured images. In order to learn the non-linear transformation from the original nighttime images to their DroneDet-friendly counterparts, we propose a Progressive Filtering module that applies recursive filters to iteratively refine the exposed image. Furthermore, to evaluate the performance of the proposed DEDet, we have built a dataset NightDrone to address the scarcity of the datasets specifically tailored for this purpose. Extensive experiments conducted on four nighttime datasets show that DEDet achieves a superior accuracy compared with the state-of-the-art methods. Furthermore, ablation studies and visualizations demonstrate the validity and interpretability of our approach. Our NightDrone dataset can be downloaded from https://github.com/yuexiemail/NightDrone-Dataset