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Happy to chat? Understanding older people’s attitudes and experiences of talking to strangers
Get PDFExtant literature shows that small conversations with strangers can help improve individuals’ wellbeing while reducing feelings of loneliness. Nevertheless, previous studies on talking to strangers tend to focus on young participants in controlled experimental settings, leaving a gap in understanding older adults’ experiences and their likelihood of adopting talking to strangers as part of their daily healthy ageing practices. Considering the problem of worsened social isolation and loneliness among older people during the Covid-19 pandemic, it is even more important to include them in the promotion of social inclusion through micro-conversations with strangers. To understand older adults’ attitudes and experiences of talking to strangers, this study interviewed 19 older people based on their trial of talking to strangers over a three-month period. Findings reveal that their willingness and confidence varied by age and gender, with retired individuals being more active in engaging with strangers. Time constraints and lack of self-efficacy were identified as barriers, particularly among those still working or with caregiving responsibilities. Rather than personal gains, the act of kindness towards others was emphasised as the key motive. These insights are valuable for policy makers and organisations supporting older people’s wellbeing, highlighting the potential for older individuals to serve as conversation initiators, promoting mutual kindness and wellbeing in communities.Brunel University London’s Institute of Health, Medicine and Environments and the Institute of Communities and Societies
As Summit Ends in Cop-Out, Can Social Tipping Points Change Climate Trajectory?
Get PDFCOP29 fizzled out as catastrophe looms. Could a mass change in political consciousness still stave off the worst
GENCODE 2025: reference gene annotation for human and mouse
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A new GENCODE release is produced up to four times each year for both human and mouse. Each release is made freely available immediately upon release from the Ensembl website (https://www.ensembl.org) and the GENCODE webportal (https//www.gencodegenes.org), with a release on the UCSC Genome Browser shortly after that (https://genome.ucsc.edu/). GENCODE is currently the default annotation in both genome browsers, and is embedded in numerous genomics and clinical projects. The current human release is GENCODE 47, and the current mouse release is GENCODE M36 (October 2024). Additional information and previous releases can be found at https//www.gencodegenes.org. MANE annotations are available from the Ensembl and RefSeq NCBI websites and can be viewed on both the Ensembl and UCSC genome browsers. To expedite public access to updated annotation between releases, all annotation changes are made freely available within 24 h via the ‘GENCODE Annotation Updates’ Track Hub, accessed at both the Ensembl and UCSC genome browsers. GENCODE has been designated a Global Core Biodata Resource by the Global Biodata Coalition.
GENCODE produces the human and mouse gene annotation for the Ensembl project, in collaboration with Ensembl. Human 47 and mouse M36 are contained within Ensembl release e113. Programmatic access to the GENCODE gene sets is possible via the extensive Ensembl Perl API and the language-agnostic Ensembl REST API (50). Programmatic access facilitates advanced genome-wide analysis such as retrieval of supporting features and associated gene trees. Examples of REST endpoint usage and starter scripts in different languages are at https://rest.ensembl.org. Other interfaces include the Ensembl FTP site (ftp://ftp.ensembl.org/pub/), which includes gene sets in GFF3, Genbank and GTF formats and full download of the complete Ensembl databases.
GENCODE-specific training materials and GENCODE-focused workshops from the Ensembl Outreach team are available via the Ensembl Training portal (http://training.ensembl.org) and EMBL-EBI (https://www.ebi.ac.uk/training/on-demand), and are regularly presented at online and in-person training events.
Further information on the results of the GENCODE CLS pipeline to produce a collection of full-length high-quality transcripts—including access to the human and mouse master tables of transcript models prior to full annotation—is available here: https://github.com/guigolab/gencode-cls-master-table. All raw transcriptomics data produced by GENCODE to support the CLS work have been uploaded to the ENCODE data repository (see https://www.encodeproject.org/about/data-access/) and will be made publicly available as part of a manuscript describing this work, currently in preparation.
Our resources are freely available at our web portal, www.gencodegenes.org, and via the Ensembl (https://www.ensembl.org) and UCSC genome browsers (https://genome.ucsc.edu/).GENCODE produces comprehensive reference gene annotation for human and mouse. Entering its twentieth year, the project remains highly active as new technologies and methodologies allow us to catalog the genome at ever-increasing granularity. In particular, long-read transcriptome sequencing enables us to identify large numbers of missing transcripts and to substantially improve existing models, and our long non-coding RNA catalogs have undergone a dramatic expansion and reconfiguration as a result. Meanwhile, we are incorporating data from state-of-the-art proteomics and Ribo-seq experiments to fine-tune our annotation of translated sequences, while further insights into function can be gained from multi-genome alignments that grow richer as more species’ genomes are sequenced. Such methodologies are combined into a fully integrated annotation workflow. However, the increasing complexity of our resources can present usability challenges, and we are resolving these with the creation of filtered genesets such as MANE Select and GENCODE Primary. The next challenge is to propagate annotations throughout multiple human and mouse genomes, as we enter the pangenome era. Our resources are freely available at our web portal www.gencodegenes.org, and via the Ensembl and UCSC genome browsers.National Human Genome Research Institute of the National Institutes of Health [U24HG007234, U24HG011451]; Wellcome Trust [WT222155/Z/20/Z]; European Molecular Biology Laboratory; National Science Center [2021/42/E/NZ2/00434 to B.U.-R.]. Funding for open access charge: National Institutes of Health
Multi-objective optimisation of hybrid renewable energy systems for Colombian non-interconnected zones
Get PDFData availability: Data will be made available on request.Colombia’s Atlantic coast wind and solar resources could enhance energy mix and self-generation. Renewable clean energy has been studied in response to fossil fuel pollution. Wind and solar photovoltaic systems have low initial, operational, and levelized energy costs. Variable wind and sun radiation may limit availability. In this regard, hybrid renewable energy systems (HRES) and energy storage have become crucial. These systems can effectively meet load demand by utilising complementary renewable resources. This study deals with sizing a wind and photovoltaic HRES with storage, using a Particle Swarm Optimisation algorithm for yearly variable resources in a non-interconnected zone in La Guajira, Colombia. The study evaluates the LCOE, probability of load loss, and the system’s CO2 emission. It develops a sensitivity analysis to determine the importance of each objective factor. From a life cycle analysis, an HRES configuration with low LCOE and environmental emission rates is associated with the size of the wind resource; the HRES configuration with minimum LCOE values is close to obtaining higher equivalent CO2e emissions. The study highlights the configuration obtained for the Colombian context, giving more importance to the environmental factor and reaching an LCOE of 0.754 USD/kWh and emission of 18.97 tCO2e/year; this configuration also increases the wind energy generation, reaching 41 % more share, compared to the configuration obtained when the economic factor is a priority.Universidad Pontificia Bolivariana through project 686C-08/21-19, “Study of the flexibility of generation and consumption systems for a sustainable energy transition”
Editorial: Adapting and building local resilience to sea level rise impacts on coastlines
Get PDFEditorial on the Research Topic: Adapting and building local resilience to sea level rise impacts on coastline
A copula-based whole system model to understand the environmental and economic impacts of grid-scale energy storage
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Data will be made available on request.Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S0306261924023183#:~:text=Appendix%20A.-,Supplementary%20data,-Data%20availability .Energy storage is important in future power systems. However, the role of grid-scale energy storage in the power system and in the whole socio-economic system is unclear. A copula-based whole system model is developed to explore the economic and environmental effects of grid-scale energy storage, thus supporting the decision-making at micro and macro levels. A power system optimisation model is linked with an input-output model, and the copula function is embedded in the model to reflect the multiple and interactive uncertainties from electricity demand, emission constraints, and sector disaggregation. We conducted case studies on China and the UK in 2025 considering different storage technologies (Pumped hydro, Battery, Flywheels storage) to show the differences related with power systems and economic structures. We find that increasing energy storage capacity leads to increase in renewable generation capacity (solar generation in China and wind generation in the UK). Thus, it can reduce their total economy-wide carbon emissions. Uncertainty in sector disaggregation will have a large impact on carbon emissions in some extreme cases, especially in those sectors closely linked to the power sector and with high emission intensity.The authors gratefully acknowledge financial support from the China Scholarship Council (No. 202206120057)
A Review on Transferability Estimation in Deep Transfer Learning
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The burgeoning field of deep transfer learning holds significant promise across various industries but encounters challenges in effective implementation. Mitigating negative transfer arising from dissimilarities between the source and target domains is crucial for successful deployment. This review delves into the realm of transferability estimation, a vital aspect in enhancing the efficacy of deep transfer learning approaches. By evaluating the transferability of data and models, these methods play a pivotal role in alleviating negative transfer effects. This review systematically categorizes and qualitatively analyzes four types of prominent transferability estimation methods, offering valuable insights for researchers to judiciously select appropriate methods. The significance of this work lies in guiding researchers in selecting appropriate deep transfer learning methods for different tasks, ensuring optimal performance across varied tasks. Furthermore, this review delineates several open problems in transferability estimation, charting a course for future research endeavors.Deep transfer learning has become increasingly prevalent in various fields such as industry and medical science in recent years. To ensure the successful implementation of target tasks and improve the transfer performance, it is meaningful to prevent negative transfer. However, the dissimilarity between the data from source domain and target domain can pose challenges to transfer learning. Additionally, different transfer models exhibit significant variations in performance for target tasks, potentially leading to a negative transfer phenomenon. To mitigate the adverse effects of the above factors, transferability estimation methods are employed in this field to evaluate the transferability of the data and the models of various deep transfer learning methods. These methods ascertain transferability by incorporating mutual information between the data or models of the source domain and the target domain. This paper furnishes a comprehensive overview of four categories of transferability estimation methods in recent years. It employs qualitative analysis to evaluate various transferability estimation approaches, assisting researchers in selecting appropriate methods. Furthermore, this paper evaluates the open problems associated with transferability estimation methods, proposing potential emerging areas for further research. Lastly, the open-source datasets commonly used in transferability estimation studies are summarized in this study.This work is partially supported by the Jiangsu Provincial Qinglan Project (2021), the Research Development Fund of XJTLU (RDF-20-01-18) and the Suzhou Science and Technology Programme (SYG202106)
Collaboration in Virtual Reality: Survey and Perspectives
Get PDFA preprint version of the article is available at arXiv, arXiv:2411.16124v1 [cs.HC] , https://arxiv.org/abs/2411.16124v1 . Comments: 22 pages, 10 figures, Appendix with a table. An earlier preprint version of the article is available at Research Square, https://doi.org/10.21203/rs.3.rs-3093370/v1 .The application of Virtual Reality Environments (VRE) has been gaining momentum as a relatively new tool to assist with mitigating various difficulties including abstractness of concepts, lack of user engagement, perception of disconnection from other users. A VRE may offer both synchronous and asynchronous experiences, in addition to an immersive environment which promotes users' engagement. Past research has shown that, in general, VRE do improve the experiences they try to enhance in many aspects of human activity. Terms like immersiveness and 3D representation of real life objects and environments are, as it appears, the two most obvious positive effects of Virtual Reality (VR) applications. However, despite these benefits it does not come without challenges. The main three concepts/challenges are the spatial design, the collaboration interaction between its members and the VRE, and the audio and video fidelity. Each of the three includes a number of other components that should be addressed for the total experience to be fine-tuned. These include mutual embodiment and shared perspectives, teleportation, gestural interaction, symmetric and asymmetric collaboration, physical and virtual co-location, inventory, and time and spatial synchronization. This paper comprises a survey of the literature, that identifies and explains the features introduced and the challenges involved with the VREs, and furthermore provides various interesting future research directions.The authors have not received any kind of funding for this paper from any source
Research on Indoor Positioning Technology of WSN based on T-RL Partition Path Model
Get PDFTo address the issues of unstable received signal strength indicator (RSSI) and low indoor positioning accuracy caused by walls and obstacles, the propagation conditions of the wireless communication system are categorized into two distinct environments: line-of-sight (LOS) and non-line-of-sight (NLOS). In the LOS environment, the traditional logarithmic path loss model is applied. For the NLOS environment, the impact of walls on signal transmission is considered, leading to the development of a multi-wall path loss model based on the T-RL method, with improvements made to the key parameter, the Fresnel coefficient R. The breakpoint value d = 2.3m in the partitioned model is determined, and the positional coordinates of the unknown nodes are calculated using the trilateration algorithm. Experimental results indicate that the T-RL based multi-wall model improves localization accuracy by 47% in NLOS environments compared to the traditional logarithmic path loss model. The average localization error using the T-RL partitioned path loss model is 0.702 1 m, representing a 55.9% improvement over the logarithmic path loss model and a 16.8% enhancement over the T-RL attenuation multi-wall model, thereby providing better environmental adaptability.Shanxi Provincial Natural Science Foundation General Project(202203021221117)
I'm a chatbot, ask me anything: using ChatGPT to improve learning experiences
Get PDFWithin Special Edition, ALDinHE Conference Proceedings and Reflections ALDCON24: Association for Learning Development Conference took place online and in person on 11 - 12 July 2024.Artificial Intelligence (AI) offers substantial opportunities and challenges in higher education. Given the evolving technological landscape, educators must ensure that students acquire a skill set encompassing both AI and traditional academic skills to enable them to succeed in their studies and future careers. We tested two groups of students, who each watched a recorded lecture on an unfamiliar topic. The first group used ChatGPT to ask questions and clarify content during the lecture, while the second group used Google search for the same purpose. We assessed the impact of these tools on the students’ cognitive load (germane and extraneous) and measured active learning through the number of questions students asked. We also used a post-test quiz, covering the breadth of Bloom’s Taxonomy, to evaluate the efficacy of each method. We expected that students using ChatGPT would experience lower extraneous cognitive load, higher germane cognitive load, and would learn content more effectively. Qualitative results demonstrated a notable preference for chatbots over search engines, due to the ease of locating specific information and obtaining insightful responses. Our findings suggest the potential of AI as a transformative tool in education, helping to enhance and deepen learning, while ensuring students retain ownership of their critical and creative processes. Leveraging the potential of AI and large language models may also serve a broader purpose: by personalising learning experiences to match individual students’ language skills, experience levels, and needs, AI may bridge the gap between the tailored support students want and the practical constraints that educators face
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