85000 research outputs found
Sort by
Participants’ baseline characteristics and feedback of the nature-based social intervention “friends in nature” among lonely older adults in assisted living facilities in finland: a randomised controlled trial of the RECETAS EU-project
No full textBackground: Loneliness is common among older adults in institutional settings. It leads to adverse effects on health and wellbeing, for which nature contact with peers in turn may have positive impact. However, the effects of nature engagement among older adults have not been studied in randomised controlled trials (RCT). The “Friends in Nature” (FIN) group intervention RCT for lonely older adults in Helsinki assisted living facilities (ALFs) aims to explore the effects of peer-related nature experiences on loneliness and health-related quality of life (HRQoL). In this study we aim describe the participants’ baseline characteristics of the RCT, feasibility of FIN intervention and intervention participants’ feedback on the FIN. Methods: Lonely participants were recruited from 22 ALFs in Helsinki area, Finland, and randomised into two groups: 1) nature-based social intervention once a week for nine weeks (n = 162) and 2) usual care (n = 157). Demographics, diagnoses and medication use were retrieved from medical records, and baseline cognition, functioning, HRQoL, loneliness and psychological wellbeing were assessed. Primary trial outcomes will be participants’ loneliness (De Jong Giervald Loneliness Scale) and HRQoL (15D). Results: The mean age of participants was 83 years, 73% were female and mean Minimental State Examination of 21 points. The participants were living with multiple co-morbidities and/or disabilities. The intervention and control groups were comparable at baseline. The adherence with intervention was moderate, with a mean attendance of 6.8 out of the nine sessions. Of the participants, 14% refused, fell ill or were deceased, and therefore, participated three sessions or less. General subjective alleviation of loneliness was achieved in 57% of the intervention participants. Of the respondents, 96% would have recommended a respective group intervention to other older adults. Intervention participants appreciated their nature excursions and experiences. Conclusions: We have successfully randomised 319 lonely residents in assisted living facilities into a trial about the effects of nature experiences in a group-format. The feedback from participants was favourable. The trial will provide important information about possibilities of alleviating loneliness with peer-related nature-based experiences in frail residents. Trial registration: ClinicalTrials.gov, ID: NCT05507684. Registration 19/08/2022.</p
Intermediated Legitimation: How Founders Build New Venture Legitimacy among Make-or-Break Audiences
No full textMany new ventures enter relationships with intermediaries, thereby ceding control to an organization that becomes a make-or-break audience for them. These settings foster intense experiences, suggesting that participating founders are likely to face a distinct set of challenges as they seek to build their venture’s legitimacy. Yet we lack a systematic analysis of new venture legitimation processes in the context of this critical audience type. To build new understanding of these important dynamics, we conducted an ethnographic study of three ventures in an Australian accelerator. Our study reveals three distinct legitimation pathways that ventures may follow when seeking legitimacy from a make-or-break audience—the obedient, pragmatic, and rebellious paths. We find that these pathways are jointly shaped by the expectations of the audience, the emotional experiences of founders, and founders’ reactions to these emotions in the context of perceived venture performance. We contribute to organizational scholarship by identifying a novel set of new venture legitimation pathways that incorporate emotion, conceptualizing “venture work” as a distinct type of social-symbolic work designed to legitimate startups and shedding new light on the role of new venture support organizations in entrepreneurial ecosystems.</p
Air quality monitoring using drones (UAV)
No full textAbstract
Air pollution causes many diseases and is a major environmental threat, therefore, it is essential to monitor and improve the air quality. With this work, we aim to assess atmospheric pollution using drone-mounted air sensors, with specific applications in Vietnam. We aim to measure Green-House Gas (GHG) emissions and other pollutants in urban and non-urban areas of interest including, but not limited to landfills and airports, to evaluate the effects of pollution on climate and health. The project will use a novel and creative approach to collect three-dimensional atmospheric data using smart sensors mounted on Unmanned Aerial Vehicles (UAVs). UAVs have been extensively applied in recent years, both in Vietnam and on a global scale, to multiple fields and with different scopes. Surveying, crop monitoring, irrigation and fertilization, surveillance and rescue support, and 2D and 3D mapping, are just a few examples of how UAVs can be used for agriculture, archaeology, forestry, urban planning, and architecture. In this project, we develop an integrated system of UAVs and smart sensors for air quality monitoring. To develop mitigation and adaptation strategies for reducing the environmental impact of transport, it is imperative to assess the emission sources and trends. Therefore, we are developing a system able to collect comprehensive atmospheric data using remote aerosol sampling, and chemical speciation. Furthermore, the potential of using smart sensors on other flying devices is explored.</p
Agriculture 4.0 and beyond: Evaluating cyber threat intelligence sources and techniques in smart farming ecosystems
No full textThe digitisation of agriculture, integral to Agriculture 4.0, has brought significant benefits while simultaneously escalating cybersecurity risks. With the rapid adoption of smart farming technologies and infrastructure, the agricultural sector has become an attractive target for cyberattacks. This paper presents a systematic literature review that assesses the applicability of existing cyber threat intelligence (CTI) techniques within smart farming infrastructures (SFIs). We develop a comprehensive taxonomy of CTI techniques and sources, specifically tailored to the SFI context, addressing the unique cyber threat challenges in this domain. A crucial finding of our review is the identified need for a virtual Chief Information Security Officer (vCISO) in smart agriculture. While the concept of a vCISO is not yet established in the agricultural sector, our study highlights its potential significance. The implementation of a vCISO could play a pivotal role in enhancing cybersecurity measures by offering strategic guidance, developing robust security protocols, and facilitating real-time threat analysis and response strategies. This approach is critical for safeguarding the food supply chain against the evolving landscape of cyber threats. Our research underscores the importance of integrating a vCISO framework into smart farming practices as a vital step towards strengthening cybersecurity. This is essential for protecting the agriculture sector in the era of digital transformation, ensuring the resilience and sustainability of the food supply chain against emerging cyber risks.</p
Unveiling Health Insights: Exploring the Link Between Virtual Engagement and Real-World Well-being
No full textThe overarching research objective of the thesis with publication aims to explore innovative, cost-effective assessment and treatment strategies incorporating virtual reality features. Guided by the World Health Organisation (WHO) and the American Psychological Association (APA), this thesis undertakes a comprehensive investigation into Internet Gaming Disorder (IGD) and its implications. Specifically, the thesis targets the evaluation of the under-researched User-Avatar Bond (UAB) and its influence on psychological outcomes, alongside the impact of pre-existing psychopathological conditions on treatment effectiveness. To achieve this overarching research question, the present thesis comprises three empirical studies, each with specific objectives.
Empirical Study One develops and validates the User Avatar Discrepancy Scale (UADS) to assess the gap between users' self-perceptions and their avatars in gaming environments. Validated among 477 Czech gamers, aged 11 to 21, the scale was found to have a unifactorial structure, with users typically viewing their avatars more favourably. This study contributes to exploring innovative assessment methods by providing a tool for assessing discrepancies that affect well-being, supporting the development of effective assessment strategies.
Empirical Study Two harnesses machine learning methodologies to evaluate the UAB as a digital phenotype predicting depression risks among 565 gamers. Findings revealed that AI models can learn to accurately and automatically identify depression risk cases, based on gamers reported UAB, both at present and six months later. The findings align with the overarching research objective of developing cost-effective strategies for early detection and intervention in mental health detection and interventions in digital environments.
Empirical Study Three examines how comorbid psychopathological symptoms influence treatment efficacy for excessive digital media use. Latent class analysis identified two psychopathological profiles among 203 treatment seekers, suggesting that those with lower psychopathology had better treatment outcomes. This study underscores the need for personalised treatment strategies that address complex psychological profiles, thereby improving treatment approaches for IGD.
Collectively, these studies addressed the overarching research question of addressing the overarching research gaps outlined by the APA and WHO and provide substantial contributions to the fields of digital media and mental health. Theoretically, this thesis enhances the deepens the understanding of the UAB, presenting it as a cyber-phenotype that predicts mental health risk, such as depression. Practically, these findings enhance mental health assessment and intervention strategies. The UADS allows clinicians to better assess individuals at risk for IGD and related conditions, while machine learning models leveraging UAB dimensions enhance early detection of depression risks. Identifying distinct psychopathological profiles informs the development of personalised, adaptive treatment strategies. From a societal viewpoint, this thesis emphasises the need to formally recognise and diagnose gaming-related disorders to mitigate harm and maximise the benefits of gaming. Incorporating user-avatar metrics into digital platforms aids early detection of mental health issues and may encourage healthier engagement. Game developers and platform designers are encouraged to implement harm-minimising strategies, fostering safer digital space that support mental health and well-being. This interdisciplinary approach enhances the understanding and treatment of gaming disorders, contributing to the broader conversation on digital well-being.</p
Application of Deep Learning in Parameter Estimation of Permanent Magnet Synchronous Machines
No full textThis paper presents a novel method for real-time identification of four parameters of the permanent magnet synchronous machines (PMSM) namely stator resistance, d-axis inductance, q-axis inductance and the rotor flux linkage. The proposed method is based on the utilization of the deep neural network to solve the problems of the existing model-based parameter estimation methods, which are caused by the non-linearity of the inverter and the inaccuracy of the measured rotor position. Extensive numerical simulations and experimental studies have been conducted to evaluate the robustness and the accuracy of the proposed online parameters identification solution, compared with the conventional methods such as recursive least square, extended Kalman filter and Adaline neural network.</p
Control of Bacterial Spoilage in Lamb Meat by Using Bacteriophage
No full textLamb meat, known for its distinctive taste and high nutritional value, is a significant part of the human diet worldwide. However, it is susceptible to bacterial contamination, which can compromise its quality and safety. This thesis explores the bacterial community dynamics of fresh and chilled backstrap lamb meat and investigates the feasibility of using bacteriophages (viruses that infect and kill bacteria), as a novel method to control bacterial growth, including spoilage and pathogenic bacteria, in lamb meat.
The research aimed to assess changes in bacterial communities, particularly specific spoilage organisms (SSO), in Modified Atmosphere Packaged (MAP) lamb meat stored at 4°C over 35 days. Methods used included 16S rRNA-gene-based sequencing, MALDI-TOF MS, and sequencing for bacterial identification. Culture-based analysis using Brain Heart Infusion (BHI) media was used to monitor changes in the bacterial population of Modified atmosphere packaging (MAP) lamb meat stored at 4°C. This comprehensive approach allowed for detailed bacterial community profiling and an understanding of bacterial stability and spoilage trajectories in stored meat products.
The investigation revealed a diverse culturable bacterial community in lamb backstrap meat packaged under modified atmosphere conditions over time in chilled storage. MALDI-TOF profiling identified spoilage-associated taxa such as Pseudomonas and Acinetobacter, which thrive in refrigerated, MAP meats. Quantitative assessments of viable counts depicted an increase over time in aerobic bacterial loads and a variable yet overall increasing anaerobic population. These trends were most pronounced post-day 14, indicating significant increases in bacterial numbers present on meat.
Principal Coordinate Analysis (PCoA) based on Bray-Curtis, Jaccard, unweighted emperor, and weighted emperor dissimilarities was used to chart successional changes in bacterial community structure and composition based on molecular analysis of total bacterial communities. Changes in bacterial diversity was assessed using Chao1 and Shannon indices, revealing a decrease in both richness and evenness, suggesting a simplification of the communities over time. This trend was mirrored in phylogenetic diversity measures, indicating a potential loss of less dominant taxa. The findings underscore significant shifts in bacterial community structure as typified by the loss and or appearance of new species during meat storage and highlight the importance of bacterial diversity in maintaining meat quality and shelf life.
High-throughput DNA sequencing data elucidated predominant and minor bacterial taxa, highlighting the dominance of bacterial groups belonging to the class Gammaproteobacteria and Firmicutes at the end of 35-day incubation period. The data also showed that the community composition changed over time as exemplified by the appearance or disappearance of new species.
Additionally, the study also explored isolating bacteriophages from lamb meat as biocontrol agents. Fresh lamb backstrap meat samples were collected, packed under MAP and non-MAP conditions, and used to attempt to isolate bacteriophages active against Pseudomonas fragi, Brochothrix thermosphacta, and Carnobacterium divergens. These taxa were selected for bacteriophage assay because they were the prevalent bacterial groups at the later stages of MAP and non-MAP incubated meat samples. Optimal growth conditions for these isolates were determined with growth curve studies of bacterial cultures incubated at 25°C. The results showed optimal OD600 values of 1.0 for Pseudomonas fragi after 6 hours, 0.65 for Brochothrix thermosphacta after 4 hours, and 0.44 for Carnobacterium divergens after 14 hours. Isolation of bacteriophages was performed on Double layer agar plates (DAL) using two methods: Direct isolation method and Isolation of phage using a phage amplification method. The direct isolation method detected plaques only from Pseudomonas fragi DAL plates incubated at 25°C and Carnobacterium divergens DAL plates incubated at 4°C. No plaques were seen on Brochothrix thermosphacta DAL plates. The isolation of phage using the amplification method, applied to samples from days 0 to 35, detected plaques only from Brochothrix thermosphacta DAL plates incubated at 25°C. Unfortunately, none of the plaques could be re-propagated despite many attempts, indicating a need to optimize propagation methodologies.
To investigate the potential use and efficacy of phage to reduce the numbers of spoilage bacteria present on meat, a commercially available phage (Pseudomonas phage vB_pfrM-S117) from a culture collection was used to investigate phage treatment of Pseudomonas fragi populations in irradiated meat samples at two time points (Day 2 and Day 7) using Tryptic Soy Agar (TSA) and Cetrimide Fucidin Nalidixic Acid (CFN) agar. On Day 2, bacteriophage treatment reduced bacterial counts by approximately 25% on TSA and 50% on CFN agar compared to samples in which phage were absent. (p < 0.05). By Day 7, the reduction increased to 70% on TSA and 57% on CFN agar. Control samples remained sterile, confirming the effectiveness of the irradiation process.
These findings underscore the potential of bacteriophage therapy as a promising biocontrol strategy to managing and reduce Pseudomonas fragi in meat products. Bacteriophage-treated samples showed significant reductions in bacterial counts, highlighting the efficacy of phages in lysing bacterial cells and reducing bacterial loads. This is particularly important for food safety, where controlling spoilage bacteria like Pseudomonas fragi is crucial for extending shelf life and ensuring meat quality. Additionally, bacteriophages preserve the organoleptic properties of food, unlike traditional preservation methods, by naturally controlling bacterial groups responsible for off-flavors and odors.
These findings align with previous studies demonstrating the prolonged efficacy of bacteriophages in reducing bacterial populations in various food matrices, highlighting the potential of bacteriophages as effective biocontrol agents. Specifically, the key spoilage bacterial genera on lamb meat after chilled storage were identified. Subsequent assays designed to reduce bacterial population numbers using bacteriophages was carried out and for one of them, P. fragi, the application of bacteriophage (P.phage vB pfrM-S117) successfully resulted in reductions in P. fragi numbers on packaged meat.
Overall, this study provides valuable insights into the potential application of bacteriophages for controlling Pseudomonas fragi in meat products. Given the significant reduction in bacterial count, the findings support the potential integration of bacteriophages into existing food safety protocols, offering a natural, targeted, and effective method for enhancing food microbiological safety. Future studies should refine bacteriophage application strategies, address potential resistance issues, and explore long-term stability and effectiveness of phage treatments in various food matrices.</p
Screening Techniques for Drug Discovery in Alzheimer’s Disease
No full textAlzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive and irreversible impairment of memory and other cognitive functions of the aging brain. Pathways such as amyloid beta neurotoxicity, tau pathogenesis and neuroinflammatory have been used to understand AD, despite not knowing the definite molecular mechanism which causes this progressive disease. This review attempts to summarize the small molecules that target these pathways using various techniques involving high-throughput screening, molecular modeling, custom bioassays, and spectroscopic detection tools. Novel and evolving screening methods developed to advance drug discovery initiatives in AD research are also highlighted.</p
Spatial digital twin framework for overheight vehicle warning and re-routing system
No full textOverhead road obstacles present a significant logistical hazard to the heavy vehicle industry. Traditional overheight vehicle warning systems such as passive warning systems (PWS) and active warning systems (AWS) have not adequately reduced the frequency and impact of overheight incidents, encouraging transportation agencies to employ intelligent transport system (ITS) strategies using state-of-the-art advanced technologies. This research takes an innovative approach in developing an immersive user-focused experience, harnessing multi-disciplinary methods and tools to engineer a spatial digital twin prototype for a novel Internet-of-Things (IoT)-based active warning alert and re-routing system (AWARS). LiDAR and 3D GIS were used to model the complex road environment, tailored to the strict fiscal objectives sought by economically mindful organisations. Tree crowns were extracted from near-Infrared aerial imagery and digital elevation models, supplying the dimensions necessary for 3D tree modelling. IoT connectivity was configured using a real-time analytics approach to deliver alerts and re-routing options. The World Traffic Service with live and predictive traffic data was used for the routing application programming interface (API). A standard-configuration common rigid truck (CRT) was inserted into the 3D road environment model to simulate overheight collisions and to ascertain the effect of re-routing on estimated time of arrival (ETA). Longer ETA durations were observed for routes computed by the digital twin. Theoretically, enhanced situational awareness and subsequent reduction of risk likelihood suggests an optimised response to industry demands, despite extended travel times, cultivating a favorable impact on the supply chain through enhanced safety management.</p
Exploring the factors affecting home dialysis patients' participation in telehealth-assisted home visits: A mixed-methods study
No full textBackground: Technology, such as telehealth, is increasingly used to support home dialysis patients. The challenges patients and carers face when home dialysis nursing visits are provided via telehealth have yet to be explored. Objectives: To explore patients' and carers' perspectives as they transition to telehealth-assisted home visits and identify the factors influencing their engagement in this modality. Design: A mixed-methods approach, guideed by the behaviour change wheel using the capability, opportunity, motivation-behaviour model to explore individual's perceptions of telehealth. Partcipants: Home dialysis patients and their carers. Measuruements: Suveys and qualitative interviews. Methods: A mixed-methods approach was undertaken, combining surveys and qualitative interviews. It was guided by the Behaviour Change Wheel using the Capability, Opportunity, Motivation- Behaviour model to explore individuals' perceptions of telehealth. Results: Thirty-four surveys and 21 interviews were completed. Of 34 survey participants, 24 (70%) preferred face-to-face home visits and 23 (68%) had previously engaged in telehealth. The main perceived barrier identified in the surveys was knowledge of telehealth, but participants believed there were opportunities for them to use telehealth. Interview results revealed that the convenience and flexibility of telehealth were perceived as the main advantages of telehealth. However, challenges such as the ability to conduct virtual assessments and to communicate effectively between clinicians and patients were identified. Patients from non-English speaking backgrounds and those with disabilities were particularly vulnerable because of the many barriers they faced. These challenges may further entrench the negative view regarding technology, as discussed by interview participants. Conclusion: This study suggested that a blended model combining telehealth and face-to-face services would allow patient choice and is important to facilitate equity of care, particularly for those patients who were unwilling or had difficulty adopting technology.</p