Scientific Applications Research Articles

5.B. Round table: Improving digital health literacy in the era of generative artificial intelligence

Abstract Improved access to generative Artificial Intelligence (AI) platforms and technologies has prompted greater awareness of their potential application in health communication. This workshop will examine this potential alongside the practical challenges and risks of using generative AI in health communication in the broader context of the European Union AI Act. The workshop will explore the implications for the concept of digital health literacy in ways that reduce or mitigate existing digital health inequities across and within Europe. Chatbots and conversational AI platforms such as ChatGPT have opened opportunities for people to find, understand and use health information in significantly different ways. Public health professionals are already using these technologies to simplify health and medical information and to make it available in different languages. AI assisted technologies are also being used to create interactive and engaging learning experiences that provide the public with real-time feedback and insights about their health as well as prompting and reinforcing actions they can take to improve health. These developments have fundamental implications for our understanding of digital health literacy and the methods we use to support people in optimising the potential benefits of AI. Different communities in Europe vary considerably in their - access to digital technology, - skills to discriminate the accuracy and reliability of information, and - trust and responsiveness to what digital technologies have to offer. Building these fundamental digital health literacy skills is a critical priority if we are to ensure that all people benefit equitably from the potential offered by AI. Those of us engaged in improving health literacy have an important role to play in influencing the future direction of AI in health communication, including and especially by engaging in research and development activities that build evidence of effectiveness and support the development of digital health literacy skills in parallel with the expansion of access to AI-assisted digital technologies. The workshop chairs will provide introductory and summary remarks, moderate the panel discussion, and facilitate audience participation in the workshop. The individual panel members will make 3-minute introductions to key issues from different perspectives on the emerging practical applications of generative AI in health communication as well as the implications for strategies to improve digital health literacy in different population groups. The panel is made up of individuals in different roles and at different career stages who can offer complementary perspectives on these issues. Following the presentations, the co-chair will present some follow-up questions to the Panel before engaging directly with the workshop attendees for an open but structured discussion scheduled to last for 30 minutes of the Workshop. Key messages • We will examine how generative artificial intelligence has opened new both new opportunities and risks in the ways people find, understand and use health information. • We will consider how we can optimise the benefits of generative AI in a way that is equitable through the development of digital health literacy within and between communities in Europe. Speakers/panelists Catherine Jenkins London South Bank University, London, UK Diane Levin-Zamir Tel Aviv University, Tel Aviv, Israel Christopher Le Inland Norway University of Applied Sciences, Innlandet, Norway Miguel Telo de Arriaga Direção-Geral da Saúde, Lisbon, Portugal

Integrating artificial intelligence in forensic science

Thesis. The thesis of this article is to explore the integration of artificial intelligence (AI) methodology in forensic science. An assessment of the potential implications of AI for improving investigative processes and outcomes will also be addressed. Concept. The concept focuses on exploring the application of AI technologies in ar- eas of science such as analysing evidence recognising patterns and supporting decision- making systems. The article emphasises the practical use of AI algorithms in investiga- tions. on exploring how artificial intelligence technologies can be implemented in various aspects of forensic science. Results. An analysis of existing literature and case studies shows that integrating AI into forensic science can improve efficiency, accuracy, and objectivity in investigations. AI tools can automate tasks analyse datasets and identify patterns that might be challeng- 16 “ABoUT The INTeRNeT” - TheoRy ing for humans to detect. However utilising AI in forensic science poses challenges like algorithms, privacy issues with data handling, and the necessity for oversight. Further- more, when employing intelligence for inquiries it is essential to prioritise transparency, and accountability and uphold integrity in decision-making processes. Originality. This article adds to the discussion about integrating AI into science by of- fering a thorough examination of its potential advantages and obstacles faced along, with ethical concerns. By merging research findings and offering perspectives on emerging trends we can gain insights, into the impact of AI on advancing investigations in the years ahead. Additionally, the theoretical structure presented here establishes a foundation for research studies and practical implementations, within the field of forensic science. Keywords: Artificial intelligence, forensic science, AI applications in forensic science, machine learning for evidence analysis, crime scene reconstruction with AI

Researching moral foundations using the Bayesian approach

Moral foundations theory describes a complex space of human morality along five moral foundations: care/harm, fairness/cheating, loyalty/betrayal, and sanctity/degradation. The first aim of this study is to explore the role of socio-demographic variables in endorsing the five moral foundations and to provide insight into the role of moral foundations in attitudes towards homeless people. The second objective of the study is to provide an example of employing the Bayesian statistical approach. There were 162 participants in our study (27% male, mean age 36 years). Convenience sampling method was used. Age and education found to be the most important predictors of moral foundations, with Bayesian factors showing the strongest evidence for age as a positive predictor of moral foundations. Gender, political affiliation, care and authority were found to be important predictors of attitudes towards homeless people, with Bayesian factors showing much stronger evidence for moral foundations than sociodemographic variables. This study lays the groundwork for research on moral foundations in Slovenian samples and provides a practical example of Bayesian approach application in social science.

Establishing the Reliability of the GaitON® Motion Analysis System: A Foundational Study for Gait and Posture Analysis in a Healthy Population

Background: Gait and posture analysis plays a crucial role in understanding human movement, with significant applications in rehabilitation, sports science, and clinical settings. The GaitON® system, a 2D motion analysis tool, provides an accessible and cost-effective method for assessing gait and posture. However, its reliability in clinical practice, particularly for intra-rater consistency, remains to be evaluated. This study aims to assess the intra-rater reliability of the GaitON® system in a healthy population, focusing on gait and posture parameters. Methods: A total of 20 healthy participants (10 males and 10 females) aged 18 to 50 years were recruited for the study. Each participant underwent gait and posture assessments using the GaitON® system on two separate occasions, spaced one week apart. Video recordings from anterior and posterior views were used to analyze gait, while images from anterior, posterior, and lateral views were captured to assess posture with markers placed on key anatomical landmarks. The reliability of the measurements was analyzed using intraclass correlation coefficients (ICC), a standard error of measurement (SEM), and the smallest detectable difference (SDD) method. Results: The GaitON® system demonstrated excellent intra-rater reliability across a wide range of gait and posture parameters. ICC values for gait parameters, including hip, knee, and ankle joint angles, ranged from 0.90 to 0.979, indicating strong consistency in repeated measurements. Similarly, ICC values for posture parameters, such as the head alignment, shoulder position, and ASIS alignment, were above 0.90, reflecting excellent reliability. SEM values were low across all parameters, with the smallest SEM recorded for the hip joint angle (0.37°), and SDD values further confirmed the precision of the system. Conclusion: The GaitON® system provides reliable and consistent measurements for both gait and posture analysis in healthy individuals. Its high intra-rater reliability and low measurement error make it a promising tool for clinical and sports applications. Further research is needed to validate its use in clinical populations and compare its performance to more complex 3D motion analysis systems.

Science application driven optimization of LSSTCam charge-coupled device clocking

Science application driven optimization of LSSTCam charge-coupled device clocking

Differential and integral elastic cross sections for electrons and positrons collisions with carbon dioxide molecules

This study investigates the elastic scattering of electrons and positrons by carbon dioxide (CO2) molecules over a wide energy range from 1 eV to 1 KeV. We have computed differential and integral elastic cross-sections (DCS and ICS) using partial wave analysis and compared the theoretical results with available experimental data. Our findings show a strong correlation with experimental results, particularly at mid to high-energy ranges, validating the employed theoretical frameworks. Additionally, the Sherman function was examined to explore spin polarisation effects during scattering events. This research provides crucial insights for applications in atmospheric physics, materials science, and molecular scattering processes, demonstrating its direct relevance to real-world problems and paving the way for further investigation into particle-molecule interactions.

Mixed and membrane-separated culturing of synthetic cyanobacteria-yeast consortia reveals metabolic cross-talk mimicking natural cyanolichens

Metabolite exchange mediates crucial interactions in microbial communities, significantly impacting global carbon and nitrogen cycling. Understanding these chemically-mediated interactions is essential for elucidating natural community functions and developing engineered synthetic communities. This study investigated membrane-separated bioreactors (mBRs) as a novel tool to identify transient metabolites and their producers/consumers in mixed microbial communities. We compared three co-culture methods (direct mixed, 2-chamber mBR, and 3-chamber mBR) to grow a synthetic binary community of the cyanobacterium Synechococcus elongatus PCC 7942 and the fungus Rhodotorula toruloides NBRC 0880, as well as axenic S. elongatus. Despite not being natural lichen constituents, these organisms exhibited interactions resembling those in cyanolichens. S. elongatus fixed CO2 into sugars as the primary shared metabolite, while R. toruloides secreted various biochemicals, predominantly sugar alcohols, mirroring the metabolite exchange observed in natural lichens. The mBR systems successfully captured metabolite gradients and revealed rapidly consumed compounds, including TCA cycle intermediates and amino acids. Our approach demonstrated that the 2-chamber mBR optimally balanced metabolite exchange and growth dynamics. This study provides insights into cross-species metabolic interactions and presents a valuable tool for investigating and engineering synthetic microbial communities with potential applications in biotechnology and environmental science.

Plant-mediated synthesis, characterization, and evaluation of a copper oxide/silicon dioxide nanocomposite by an antimicrobial study

Abstract This study presents an innovative, environmentally friendly method for biosynthesizing copper oxide–silica (Cu2O/SiO2) nanocomposites (CSNCs) utilizing an aqueous leaf extract of Callistemon viminalis (C. viminalis). The goal of this work is to fabricate CSNCs using a less hazardous and sustainable synthesis approach. Copper acetate and sodium metasilicate were used as precursors, whereas the C. viminalis green leaf extract was used as the reducing and stabilizing agent. Analysis of the plant extract using Fourier transform infrared spectroscopy indicated the presence of polyphenolic compounds, primarily phenolic acids, which functioned as both reducing and stabilizing agents in the synthesis of CSNCs. A combination of energy dispersive X-ray spectroscopy and scanning electron microscopy was used to study the formation of spherical copper–silica hybrid nanostructures. Powder X-ray diffraction analysis revealed the successful integration of silica with copper(i) oxide (Cu2O) through the presence of distinct Cu2O peaks and a broad amorphous SiO2 peak at 2θ = 22.77°. The thermal stability of the nanocomposites (NCs) was assessed using thermogravimetric analysis and differential thermal analysis under a nitrogen atmosphere. The biogenic NCs also successfully inhibited pathogenic strains of Staphylococcus aureus (S. aureus) and Candida albicans (C. albicans); however, S. aureus was found to be more susceptible to the biocidal activity of the NCs than P. aeruginosa. These findings suggest that this simple, cost-effective, and eco-friendly method for producing biologically active hybrid nanomaterials holds significant promise for future applications in both biological and materials sciences.

Indetermsoft-Set-Based D* Extra Lite Framework for Resource Provisioning in Cloud Computing

Cloud computing is an immensely complex, huge-scale, and highly diverse computing platform that allows the deployment of highly resource-constrained scientific and personal applications. Resource provisioning in cloud computing is difficult because of the uncertainty associated with it in terms of dynamic elasticity, rapid performance change, large-scale virtualization, loosely coupled applications, the elastic escalation of user demands, etc. Hence, there is a need to develop an intelligent framework that allows effective resource provisioning under uncertainties. The Indetermsoft set is a promising mathematical model that is an extension of the traditional soft set that is designed to handle uncertain forms of data. The D* extra lite algorithm is a dynamic heuristic algorithm that makes use of the history of knowledge from past search experience to arrive at decisions. In this paper, the D* extra lite algorithm is enabled with the Indetermsoft set to perform proficient resource provisioning under uncertainty. The experimental results show that the performance of the proposed algorithm is found to be promising in performance metrics such as power consumption, resource utilization, total execution time, and learning rate. The expected value analysis also validated the experimental results obtained.

Crystal Structure Prediction Using Generative Adversarial Network with Data-Driven Latent Space Fusion Strategy.

Crystal structure prediction (CSP) is an important field of material design. Herein, we propose a novel generative adversarial network model, guided by a data-driven approach and incorporating the real physical structure of crystals, to address the complexity of high-dimensional data and improve prediction accuracy in materials science. The model, termed GAN-DDLSF, introduces a novel sampling method called data-driven latent space fusion (DDLSF), which aims to optimize the latent space of generative adversarial networks (GANs) by combining the statistical properties of real data with a standard Gaussian distribution, effectively mitigating the "mode collapse" problem prevalent in GANs. Our approach introduces a more refined generation mechanism specifically for binary crystal structures such as gallium nitride (GaN). By optimizing for the specific crystallographic features of GaN while maintaining structural rationality, we achieve higher precision and efficiency in predicting and designing structures for this particular material system. The model generates 9321 GaN binary crystal structures, with 16.59% reaching a stable state and 24.21% found to be metastable. These results can significantly enhance the accuracy of crystal structure predictions and provide valuable insights into the potential of the GAN-DDLSF approach for the discovery and design of binary, ternary, and multinary materials, offering new perspectives and methods for materials science research and applications.

Electrochemical Sensors in the Food Sector: A Review.

In a world that is becoming increasingly concerned with health, safety, and the sustainability of food supply chains, the control and assurance of food quality have become of utmost importance. This review examines the application and potential of electrochemical sensors in the dynamic field of food science to meet these expanding demands. The article introduces electrochemical sensors and describes their operational mechanics and the components contributing to their function. A summary of the most prevalent electrochemical methods outlines the diverse food analysis techniques available. The review shifts to discussing the food science applications of these sensors, highlighting their crucial role in detecting compounds in food samples like meat, fish, juice, and milk for contemporary quality control. This paper showcases electrochemical sensors' utility in food analysis, underscoring their significance as powerful, efficient tools for maintaining food safety and how they could transform our approach to global food quality control and assurance.

Enhanced Singlet Oxygen Generation in Aggregates of Naphthalene-Fused BODIPY and Its Application in Photodynamic Therapy.

Several reports are available on aggregation-induced emission and its applications in biomedical imaging and other material sciences. However, enhancement of singlet oxygen generation in nanoaggregates is rarely reported. Here, we report the synthesis of Naph-BODIPY Br2, which absorbs at 661 nm (monomer) with a high molar absorption coefficient. The presence of bromine promotes intersystem crossing, thereby enhancing the singlet oxygen quantum yield (ΦΔ ∼ 0.50 in methanol). In order to increase hydrophilicity, we developed Naph-BODIPY Br2 nanoaggregates (∼100 nm), which demonstrated aggregation-induced properties and exhibited a bathochromic shift with an absorption maximum at 757 nm. The bathochromic shift in the UV-vis spectra due to aggregation is corroborated by TD-DFT analysis. The computational data also confirm the presence of a low-lying triplet state, which enhances the generation of singlet oxygen, making it effective for photodynamic therapy. These aggregates showed excellent singlet oxygen generation in aqueous media, compared to their monomeric form and standard methylene blue. Their hydrophilic nature and high singlet oxygen generation enabled significant phototoxicity against human carcinoma cells with IC50 values of 4.06 ± 0.01 and 4.09 ± 0.1 μM, respectively, for MCF-7 and A549 cells upon 5 min exposure to light. Moreover, their phototoxicity further increases with an increasing exposure time of light for both cell lines. Notably, Naph-BODIPY Br2 nanoaggregates exhibited nearly zero dark cell toxicity and effectively induced apoptosis in cancer cells upon light activation, highlighting their potential as powerful photosensitizers for photodynamic cancer therapy.

Genetically Engineered Filamentous Bacteriophages Displaying TGF-β1 Promote Angiogenesis in 3D Microenvironments

Combined 3D cell culture in vitro assays with microenvironment-mimicking systems are effective for cell-based screening tests of drug and chemical toxicity. Filamentous bacteriophages have diverse applications in material science, drug delivery, tissue engineering, energy, and biosensor development. Specifically, genetically modified bacteriophages have the potential to deliver therapeutic molecules or genes to targeted tumor tissues. The engineered bacteriophages in this study significantly enhanced endothelial cell migration and tube formation within the extracellular matrix (ECM). Compared to TGF-β1 alone and non-modified phages, the presence of TGF-β1 on the bacteriophages demonstrated superior performance as a continuous stimulant in the microenvironment, effectively promoting these angiogenic processes. Assays, including RT-qPCR, ELISA, and fluorescence microscopy, confirmed the expression of angiogenic markers such as CD31, validating the formation of 3D angiogenic structures. Our findings indicate that the TGF-β1 displayed by bacteriophages likely acted as a chemotactic factor, promoting the migration, proliferation, and tube formation of endothelial cells (ECs) within the ECM. Although direct contact between ECs and bacteriophages was not explicitly confirmed, the observed effects strongly suggest that TGF-β1-RGD bacteriophages contributed to the stimulation of angiogenic processes. The formation of angiogenic structures by ECs in the ECM was confirmed as three-dimensional and regulated by the surface treatment of microfluidic channels. These results suggest that biocompatible TGF-β1-displaying bacteriophages could continuously stimulate the microenvironment in vitro for angiogenesis models. Furthermore, we demonstrated that these functionalized bacteriophages have the potential to be utilized as versatile biomaterials in the field of biomedical engineering. Similar strategies could be applied to develop angiogenic matrices for tissue engineering in in vitro assays.

Tailored 3D Agarose-Well Integrated with Human Skin Equivalents for Enhanced Skin Penetration Assessment

We developed a tailored 3D Agarose-well system integrated with reconstructed human skin equivalents to enhance skin penetration assessments. This system addresses common limitations in traditional trans-well reconstructions, such as dermal layer contraction and limited lateral diffusion, by entangling collagen fibrils within the Agarose-well. We evaluated the penetration behavior of three peptides, with and without skin-penetrating peptide (SPP) sequences, alongside adenosine, a known anti-wrinkle agent. Despite a SPP having a molecular weight approximately four times greater than that of adenosine, its kinetic constant was similar, with values of about 39 and 34, respectively. Moreover, this living skin equivalent system not only allowed for the evaluation of adenosine penetration, but also demonstrated its biological effects, with adenosine significantly enhancing procollagen synthesis by approximately 23% compared to the control. Overall, this novel strategy holds the potential for tailoring 3D Agarose-wells and advancing high-performance gel development, making it a promising approach for applications in tissue engineering, medical science, regenerative medicine, and cosmetics.

Co-developing frameworks towards environmentally directed pharmaceutical prescribing in Scotland – A mixed methods study

The presence of human pharmaceuticals in the aquatic environment is recognised internationally as an important public health and environmental issue. In Scotland, healthcare sustainability targets call for improvements to medicine prescribing and use to reduce healthcare's impact on the environment. This proof-of-concept study aimed to develop a framework on the environmental impact of pharmaceuticals to use as a knowledge support tool for healthcare professionals, focussing on pharmaceutical pollution. Nominal Group Technique was applied to achieve consensus on pharmaceuticals and modelling factors for the framework, working with a panel of cross-sector stakeholders. Bayesian Belief Network modelling was applied to predict the environmental impact (calculated from hazard and exposure factors) of selected pharmaceuticals, with Scotland-wide mapping for visualisation in freshwater catchments. The model calculated the pollution risk score of the individual pharmaceuticals, using the ratio of prescribed mass vs. mass that would not exceed the predicted no-effect concentration in the freshwater environment. The pharmaceuticals exhibited different risk patterns, and spatial variation of risk was evident (generally related to population density), with the most catchments predicted to exceed the pollution risk score for clarithromycin (probability >80 % in 35 of 40 modelled catchments). Simulated risk scores were compared against observed risk calculated as the ratio of measured environmental concentrations from national regulatory and research monitoring and predicted no-effect concentrations. The model generally overpredicted risk, likely due to missing factors (e.g. solid-phase sorption, temporal variation), low spatial resolution, and low temporal resolution of the monitoring data. This work demonstrates a novel, trans-disciplinary approach to develop tools aiding collation and integration of environmental information into healthcare decision-making, through application of public health, environmental science, and health services research methods. Future work will refine the framework with additional clinical and environmental factors to improve model performance, and develop electronic interfaces to communicate environmental information to healthcare professionals.

Pengaruh Microteaching dan Dukungan Teman Sebaya terhadap Minat Mahasiswa Pendidikan Administrasi Perkantoran Universitas Negeri Jakarta Menjadi Guru

This study aims to see the influence of microteaching and peer support on students' interest in becoming teachers in Office Administration Education students of the State University of Jakarta 2022. The research approach used is quantitative through surveys with online questionnaire distribution. Data processing uses the Statistical Package for Social Sciences (SPSS) application. From the test results, it was found that partially microteaching directly affected students' interest in becoming teachers, this was evidenced by a significant value of 0.025. In addition, partially, peer support did not affect students' interest in the teaching profession, this was evidenced by a significant value of 0.268. Meanwhile, simultaneously microteaching and peer support did not simultaneously affect interest in becoming a teacher, this was evidenced by a significant value of 0.078. In addition, the results were also strengthened by the results of the determination coefficient test which showed that the two independent variables only explained 14.3% of the dependent variables.

Capabilities for Using Telemonitoring in Physiotherapy Treatment: Exploratory Qualitative Study.

Telemonitoring (TM), as part of telehealth, allows physiotherapists to monitor and coach their patients using remotely collected data. The use of TM requires a different approach compared with face-to-face treatment. Although a telehealth capability framework exists for health care professionals, it remains unclear what specific capabilities are required to use TM during physiotherapy treatments. This study aims to identify the capabilities required to use TM in physiotherapy treatment. An exploratory qualitative study was conducted following a constructivist semistructured grounded theory approach. Three heterogeneous focus groups were conducted with 15 lecturers of the School of Physiotherapy (Bachelor of Science Physiotherapy program) from the Amsterdam University of Applied Sciences. Focus group discussions were audiotaped and transcribed verbatim. Capabilities for using TM in physiotherapy treatment were identified during an iterative process of data collection and analysis, based on an existing framework with 4 different domains. Team discussions supported further conceptualization of the findings. Sixteen capabilities for the use of TM in physiotherapy treatment were found addressing 3 different domains. Four capabilities were identified in the "digital health technologies, systems, and policies" domain, 7 capabilities in the "clinical practice and application" domain, and 5 capabilities in the "data analysis and knowledge creation" domain. No capabilities were identified in the "system and technology implementation" domain. The use of TM in physiotherapy treatment requires specific skills from physiotherapists. To best use TM in physiotherapy treatment, it is important to integrate these capabilities into the education of current and future physiotherapists.

Pinwheel-like Curved Aromatics from the Cyclotrimerization of Strained Alkyne Cycloparaphenylenes.

Carbon nanomaterials composed of curved aromatics, such as carbon nanotubes, are difficult to selectively synthesize and modify precisely. Smaller molecular fragments of curved nanomaterials, such as cycloparaphenylenes, benefit from the precision of bottom-up synthesis, however, efforts to expand the curved molecular framework into even larger structures often rely on restrictive early stage synthetic strategies or difficult to control polymerizations. In this work we report a high yielding, strain-promoted, late-stage modification of a series of [n + 1]CPPs. We show that the conversion of these [n + 1]CPPs into soluble, pinwheel-like multipore carbon nanostructures is achievable via a straightforward and efficient metal-mediated alkyne cyclotrimerization reaction. We provide insight into suitable metals for this transformation, the photophysics of these trimeric molecules, as well as their strain profiles and crystal packing. We also demonstrate the strain-enhanced nature of the reaction under the optimized conditions, showing that strained internal-alkyne [n + 1]CPPs efficiently undergo complete conversion whereas unstrained diphenylacetylene remains completely unreacted. We anticipate that this work will have broader impacts on the study of reactivity in strained-alkyne-containing hydrocarbons, and that access to this new molecular architecture will inspire new research and applications in materials science and related fields.

Non-Classical Heat Transfer and Recent Progress

Abstract Heat transfer in solids has traditionally been described by Fourier's law, which assumes local equilibrium and a diffusive transport regime. However, advancements in nanotechnology and the development of novel materials have revealed non-classical heat transfer phenomena that extend beyond this traditional framework. These phenomena, which can be broadly categorized into those governed by kinetic theory and those extending beyond it, include ballistic transport, phonon hydrodynamics, coherent phonon transport, Anderson localization, and glass-like heat transfer, among others. Recent theoretical and experimental studies have focused on characterizing these non-classical behaviors using methods such as the Boltzmann transport equation, molecular dynamics, and advanced spectroscopy techniques. In particular, the dual nature of phonons, exhibiting both particle-like and wave-like characteristics, is fundamental to understanding these phenomena. This review summarizes state-of-the-art findings in the field, highlighting the importance of integrating both particle and wave models to fully capture the complexities of heat transfer in modern materials. The emergence of new research areas, such as chiral and topological phonons, further underscores the potential for advancing phonon engineering. These developments open up exciting opportunities for designing materials with tailored thermal properties and new device mechanisms, potentially leading to applications in thermal management, energy technologies, and quantum science.

The Role of Companionship in Volunteer Homelessness Support Services: A Qualitative Study

Drawing on developments in the application of occupational science to issues of homelessness and tenancy sustainability, this paper will argue for the recognition of authentic, everyday relationships as a core part of tenancy support. Data from a small-scale evaluation of a volunteer-led homelessness support service will be explored through a framework of social support to show the importance of companionship in enabling individuals to exit homelessness. The data show that individuals valued the provision of social support from volunteers in everyday locations, as it replicated authentic relationships which individuals had sometimes lost through their experiences of homelessness. This paper argues that the facilitation of authentic, everyday relationships should be considered an important part of homelessness support.