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Flower‐visitor and pollen‐load data provide complementary insight into species and individual network roles
Get PDFMost animal pollination results from plant–insect interactions, but how we perceive these interactions may differ with the sampling method adopted. The two most common methods are observations of visits by pollinators to plants and observations of pollen loads carried by insects. Each method could favour the detection of different species and interactions, and pollen load observations typically reveal more interactions per individual insect than visit observations. Moreover, while observations concern plant and insect individuals, networks are frequently analysed at the level of species. Although networks constructed using visitation and pollen‐load data have occasionally been compared in relatively specialised, bee‐dominated systems, it is not known how sampling methodology will affect our perception of how species (and individuals within species) interact in a more generalist system. Here we use a Diptera‐dominated high‐Arctic plant–insect community to explore how sampling approach shapes several measures of species' interactions (focusing on specialisation), and what we can learn about how the interactions of individuals relate to those of species. We found that species degrees, interaction strengths, and species motif roles were significantly correlated across the two method‐specific versions of the network. However, absolute differences in degrees and motif roles were greater than could be explained by the greater number of interactions per individual provided by the pollen‐load data. Thus, despite the correlations between species roles in networks built using visitation and pollen‐load data, we infer that these two perspectives yield fundamentally different summaries of the ways species fit into their communities. Further, individuals' roles generally predicted the species' overall role, but high variability among individuals means that species' roles cannot be used to predict those of particular individuals. These findings emphasize the importance of adopting a dual perspective on bipartite networks, as based on the different information inherent in insect visits and pollen loads
‘Remember that time?’: introducing retrospective collaborative autoethnography
No full textAutoethnography is a well-established methodological approach in research, including tourism studies. Despite the frequent application of autoethnography in research, some scholars have raised challenges in employing autoethnography such as individual bias, selective memory, change of participants’ attitudes over time, logistical challenges, conducting synchronous research, and influence by dominant researchers within the group. This Research Letter proposes Retrospective Collaborative Autoethnography (RCA) as a method of qualitative inquiry situated within autoethnography, collaborative autoethnography, and retrospective autoethnography, which mitigate/reduce these challenges
A collaborative adaptation game for promoting climate action: Minions of Disruptions™
Get PDFWith the onset of climate change, adaptive action must occur at all scales, including locally, placing increasing responsibility on the public. Effective communication strategies are essential, and adaptation games have shown potential in fostering social learning and bridging the knowledge–action gap. However, few research efforts so far give voice to participants that engage with collaborative games in organisational and community settings. This paper presents a novel approach to studying designer–participant interactions in adaptation games, diverging from traditional learning-focused frameworks. Specifically, it examines Minions of Disruptions™ (MoD), a collaborative tabletop board game, through the lens of how participant perception aligns with the game's design intentions as described by the game designers and facilitators. Through focus group interviews with designers and facilitators, 10 core design intentions were identified and compared with responses from post-game surveys of participants from 2019–2022. Key insights reveal that collaboration and team building are highly effective frames for climate adaptation. However, some design elements, such as time pressure, can hinder discussion, suggesting a need to balance objectives. The method adopted manages to avoid traditional expert-to-public analysis structures and places emphasis on the importance of iterative design based on participant insights. This approach provides valuable guidance for future adaptation game designs, demonstrating that games can effectively engage diverse groups and support local adaptation efforts by creating a sense of belonging and collective purpose
A binary particle swarm optimization-based pruning approach for environmentally sustainable and robust CNNs
Get PDFDeep Convolutional Neural Networks (CNNs), continue to demonstrate remarkable performance across various tasks. However, their computational demands and energy consumption present significant drawbacks, restricting their practical deployment and contributing to a substantial carbon footprint. This paper addresses this challenge by proposing a novel method named Binary Particle Swarm Optimization Layer Pruner (BPSO-LPruner), aimed at achieving substantial computational reduction and mitigating environmental impact during CNN inference. BPSO-LPruner utilizes a constrained Binary Particle Swarm Optimization for CNN layer pruning, integrating a masked-bit strategy and a new population initialization strategy to enhance search performance. We illustrate the effectiveness of our method in reducing model computational costs and carbon footprint emissions while improving performance across multiple models (VGG16, VGG19, DenseNet-40, ResNet18, ResNet20, ResNet34, ResNet44, ResNet56, ResNet110, ResNet50, and MobileNetv2) and diverse datasets (CIFAR-10, CIFAR-100, Tiny- ImageNet, COVID-19 X-ray dataset). Promising results underscore the performance of the proposed method. Additionally, we demonstrate that layer pruning yields benefits beyond enhanced computational performance. Our experimentation reveals that BPSO-LPruner enhances the model’s reliability and robustness by effectively addressing variations in input data, inherent ambiguity in model parameters, and adversarial images
A Case Study on the Community ART Group Model of Care: Does It work for People Living with HIV and Healthcare Service Providers in Lesotho
Get PDFThe Community ART Group (CAG) model is a community-led model implemented to support people living with HIV to address barriers to HIV treatment continuity which remain a challenge in Lesotho. This study sought to explore the perspectives of people living with HIV and that of the healthcare service providers, regarding the CAG model in selected health facilities in Lesotho. An explorative descriptive qualitative study was conducted among purposively selected 20 people living with HIV and 8 healthcare service providers at 3 healthcare facilities. Qualitative data were collected through face-to-face in-depth interviews using semi-structured interview guides. All interviews were audio-recorded and transcribed verbatim. Thematic analysis was used following an inductive approach and sub-themes and themes were developed. The CAG model was relevant and acceptable to most of the respondents. They felt that it provided support to people living with HIV, promoted good adherence to treatment, improved treatment access, reduced transport costs, saved time, and reduced stigma. Good retention, favorable clinical outcomes and decongestion of health facilities were identified as key achievements linked to the CAG model. Age, proximity to the health facilities, readiness to disclose positive HIV status, availability of a variety of differentiated service delivery models, family support, and the level of trust emerged as factors affecting the acceptability of the model. Conflicts arising among members of the groups compromised service delivery quality and insufficient resources emerged as challenges. The results confirmed that the Community ART Group model can deliver intended peer-led support to People Living with HIV, resulting in the achievement of favorable clinical outcomes. It is therefore recommendable to consider investing in this community-led model for a sustained HIV response in the country
Digital solutions to optimize guideline-directed medical therapy prescription rates in patients with heart failure: a clinical consensus statement from the ESC Working Group on e-Cardiology, the Heart Failure Association of the European Society of Cardiology, the Association of Cardiovascular Nursing & Allied Professions of the European Society of Cardiology, the ESC Digital Health Committee, the ESC Council of Cardio-Oncology, and the ESC Patient Forum
Get PDFThe 2021 European Society of Cardiology guideline on diagnosis and treatment of acute and chronic heart failure (HF) and the 2023 Focused Update include recommendations on the pharmacotherapy for patients with New York Heart Association (NYHA) class II–IV HF with reduced ejection fraction. However, multinational data from the EVOLUTION HF study found substantial prescribing inertia of guideline-directed medical therapy (GDMT) in clinical practice. The cause was multifactorial and included limitations in organizational resources. Digital solutions like digital consultation, digital remote monitoring, digital interrogation of cardiac implantable electronic devices, clinical decision support systems, and multifaceted interventions are increasingly available worldwide. The objectives of this Clinical Consensus Statement are to provide (i) examples of digital solutions that can aid the optimization of prescription of GDMT, (ii) evidence-based insights on the optimization of prescription of GDMT using digital solutions, (iii) current evidence gaps and implementation barriers that limit the adoption of digital solutions in clinical practice, and (iv) critically discuss strategies to achieve equality of access, with reference to patient subgroups. Embracing digital solutions through the use of digital consults and digital remote monitoring will future-proof, for example alerts to clinicians, informing them of patients on suboptimal GDMT. Researchers should consider employing multifaceted digital solutions to optimize effectiveness and use study designs that fit the unique sociotechnical aspects of digital solutions. Artificial intelligence solutions can handle larger data sets and relieve medical professionals’ workloads, but as the data on the use of artificial intelligence in HF are limited, further investigation is warranted
Re-evaluating the East-West divide in the European Union
Get PDFThis introduction argues that the East-West divide in Europe continues to be politically salient since the fall of the Berlin Wall and two decades since the accession of most East Central European (ECE) countries to the European Union. We re-evaluate the nature of the East-West divide in the EU, consider its sources, and examine the interplay between political variation and cross-border economic inequalities. The fundamental question posed here is whether such divisions are persistent, intractable, or transitional. We note that earlier scholarship on the East-West divide emphasised economic divergence as a primary explanatory factor. As relevant as the economy still is, our contribution is to argue that the divide also needs to be assessed against the broader political backdrop of democratic backsliding and new geopolitical developments. Although we find that the East-West divide is still highly salient, the articles here specify how fluid categories are and how variation has emerged – both between and within countries in the ECE region. Finally, the very perception of an East-West divide is politically consequential. If unaddressed, East-West divisions and tensions will impede future reforms of the EU’s internal governance processes and limit its power on the global stage
Attention-Based Hybrid Deep Learning Model for Intrusion Detection in IIoT Networks
Get PDFThe integration of Industrial Internet of Things (IIoT) technology into the industrial sector has produced numerous significant advantages. However, the notable concern remains the absence of robust security and privacy measures in these interconnected critical environments. To secure IIoT networks, several researchers and experts employ intrusion detection systems (IDS) for detecting cyberattacks. The current systems exhibit efficient performance when handling a few categories of attack classes, even in the presence of slight imbalances. However, these models face challenges when confronted with vast categories of attack classes and highly imbalanced data. To tackle these issues, this study introduces an attention-based hybrid deep learning (AB-HDL) model designed to monitor network traffic and predict cyberattacks within the network. The proposed model comprises an attention mechanism and a hybrid deep learning model that integrates convolutional neural networks (CNN) and an autoencoder (AE). The effectiveness of the proposed AB-HDL is assessed using publicly accessible datasets: Edge-IIoTset and X-IIoTID. To ascertain the efficacy of AB-HDL, a comparative analysis is conducted with various other machine learning (ML) and deep learning (DL) algorithms. The outcome analysis indicates that the proposed AB-HDL surpasses the performance of the other algorithms and exhibits optimal efficiency in detecting cyber attacks within IIoT networks
Type 2 Diabetes Mellitus - quality prescribing strategy: improvement guide 2024 to 2027
No full textThis quality prescribing guide is intended to support clinicians across the multidisciplinary team and people with Type 2 Diabetes Mellitus (T2DM) in shared decision-making and the effective use of medicines, and offers practical advice and options for tailoring care to the needs and preferences of individuals
Building a Good Digital Society from the Grassroots: Harnessing the Tradition of Community-led Initiatives in the Governance of Digital Services and Infrastructures
No full textOver the past two decades, community broadband networks, platform cooperatives, and data cooperatives have emerged as promising models to counterbalance market distortions and power asymmetries in the governance of digital infrastructures, services and data. Drawing on multidisciplinary academic debates, this paper investigates how these grassroots approaches to the development and governance of digital innovations can be further harnessed to foster a good digital society. Both their accomplishments and shortcomings are thoroughly reviewed and critically analysed to illustrate and appraise their potential application into diverse spheres of the digital society (from the governance of high-speed networks to the protection of nonpersonal data). The paper concludes with a research and policy agenda, designed to address the challenges emerging from the analysis. Academic researchers are urged to further advance both the empirical and theoretical investigation of these initiatives to develop a more coherent and robust understanding of their development and sustainability over time. A systemic change in the approach of policymakers is also advocated for, to devise regulatory interventions and policy measures capable of sustaining the diffusion and scaleup of grassroots digital innovations
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