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The Anthro-Thumb: a biomimetic hybrid soft robotic carpometacarpal saddle joint for the thumb
Full text linkRobotic hand design is multifaceted, with the design of robotic thumbs often oversimplified to facilitate ease of manufacture, control, and reliability. Despite the extensive development of robotic hands, the need for a more dexterous and anthropomorphic thumb design remains significant, particularly for applications in prosthetics and rehabilitation robotics, where naturalistic movement and adaptability are essential. This paper addresses this gap by exploring the conception, evolution, and evaluation of a unique biomimetic soft thumb. The thumb plays a vital role in hand function, and its unique range of motion is enabled by the Carpometacarpal (CMC) saddle joint. By harnessing the biologically accurate mechanisms of the CMC joint, this research aims to enhance the functionality of tendon-driven robotic hands, offering improved dexterity and adaptability for tasks such as grasping and manipulation. The introduced Anthro-Thumb is a biomimetic soft robotic thumb that provides a comprehensive range of motion at the thumb's base while ensuring cost efficiency and reduced mechanical complexity. A comparative analysis with existing robotic thumb designs highlights the advancements of the Anthro-Thumb, particularly in terms of range of motion and cost-effectiveness. Additionally, we discuss the long-term durability and maintenance requirements of the soft robotic materials and components used. When subjected to the Kapandji physiotherapy test, the design secured a commendable score of 9 out of 10, with 10 representing the highest level of dexterity achievable by a human thumb. The findings affirm that employing biomimetic soft-structured robotic CMC saddle joints is a promising strategy to address the challenges associated with robotic thumb development in robotic hands
Development and validation of an LC-MS/MS intact C-peptide method and a protocol for testing dry blood spot samples for the diagnosis and management of Diabetes Mellitus
Full text linkC-peptide, which is produced in the pancreatic β-cells, is widely accepted as a surrogate marker for insulin secretion and estimation assist in the diagnosis and management of Diabetes Mellitus. Currently, C-peptide is predominantly measured by immunoassay methods, which have a number of potential limitations such as interferences from biotin, proinsulin, and heterophilic antibodies. We have developed and validated an LC-MS/MS intact C-peptide method for serum and plasma samples that overcomes these analytical limitations. We have also developed a new approach for using dry blood spot (DBS) as an alternative sample type.Assay calibrants and in-house internal quality control were prepared using Cerilliant C-peptide certified reference material spiked into charcoal stripped bovine serum. The internal standard used was (Tyr⁰)-C-Peptide. Sample preparation was based on simple protein precipitation followed by solid phase extraction using the Oasis HLB µElution plate. LC separation was done by the Waters Acquity UPLC system.The method was first validated for serum and plasma samples by the evaluation of accuracy, precision, recovery, carry over, sensitivity and interferences. DBS was verified against the serum method for systematic differences by method comparison studies and an evaluation of imprecision at medical decision points following CLSI guidance EP35.The assay calibration curve for serum and plasma samples was linear, showing R2 of >0.99, with a measurement range of 3.9 pmol/L to 8000 pmol/L. The within batch and between batch imprecision was 0.99. DBS imprecision were all below the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) biological variation allowable limits of 8.3%. The systematic difference of DBS was -15.7%, -17.3% and -9.6% at 0 – 300pmol/L, 300-700pmol/L, and 700-600pmo/L clinical decision target ranges respectively.This intact C-peptide LCMS-MS assay is sensitive with a low serum and plasma sample volume of 50 µl. DBS sampling and analytical workflow is convenient, and can support large scale clinical trials
We reject the use of generative artificial intelligence for reflexive qualitative research
No full textFour hundred and nineteen experienced qualitative researchers from 32 countries invite readers of Qualitative Inquiry to consider their position on use of generative artificial intelligence (GenAI) for qualitative research. We hold the position that analytic approaches such as reflexive thematic analysis are human research practices requiring a subjective, positioned, and reflexive researcher and therefore the use of GenAI in such approaches is not methodologically congruent. We additionally reject GenAI for reflexive qualitative approaches on the grounds of social and environmental justice
Is human body disposal an environmental issue?
No full textDespite growing global commitments to addressing climate change, the environmental impacts of human body disposal (HBD) remain poorly understood. In both the UK and the US, the contexts from which this paper is written, HBD is typically framed as a matter of personal choice shaped by preference, tradition, or belief rather than as a cumulative social or environmental concern. Given the relatively small emissions generated by an individual’s disposal, this framing is perhaps unsurprising. Yet when considered collectively, and when broader environmental consequences are included, the environmental impacts of HBD become significantly more substantial. This paper critically examines whether, and how, HBD can be measured, conceptualised, and addressed as a collective environmental issue. In doing so, it identifies the changes required to make the cumulative environmental consequences of HBD more visible to policymakers, industry providers, and the public
Optimising energy efficiency in Higher Education Institution buildings through denoise extreme learning machine occupancy estimation
Full text linkHigher education institutions (HEIs) face significant challenges related to energy inefficiency during operational stage, with heating, ventilation, and air conditioning (HVAC) systems accounting 40% of total energy consumption. However, HVAC systems in HEI buildings often operating inefficiently. To investigate the underlying issues and identify potential solutions, this research employed an extensive literature review and a phenomenological study. The analysis revealed that energy inefficiencies in HVAC management are primarily caused by discrepancies between estimated and actual occupancy, resulting in suboptimal indoor conditions that impact health, comfort, and learning outcomes. The findings suggest that integrating occupancy-based control strategies, such as predictive control model (PCM), into HVAC systems can significantly reduce energy consumption. However, accurately estimating real-time occupancy in higher education buildings remains a challenge due to complex and dynamic patterns. Therefore, this research developed, tested, and validated a new DELM (denoise extreme learning machine) algorithm for accurate real-time occupancy estimation using only occupancy data and CO2 measurements. This algorithm aims to enhance student health, comfort, and performance while reducing energy consumption and the associated economic cost and CO2 emissions. Thus, this research is timely and relevant amidst current efforts to reduce energy consumption and improve well-being. DELM is a variation of the standard extreme learning machine (ELM) algorithm, which combines extreme learning machine (ELM) and denoising stacked autoencoder (DSAE) to effectively remove noise from CO2 data and achieve high estimation accuracy while maintaining computational efficiency. The DELM algorithm’s ability to accurately estimate occupancy was validated across four different scenarios of use and number of occupants, demonstrating its accuracy in those scenarios, achieving 91% accuracy (15% improvement compared to ELM) in 300-person conference rooms; 93% accuracy (18% improvement) in 100-person conference rooms; and 98% accuracy in a 25-person office. The DELM algorithm outperforms ELM in stability and accuracy. This study also confirms that using CO2 concentration data can result in accurate occupancy estimation
Developing an informational intervention to reduce anxiety levels amongst patients referred for Single Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) imaging
Full text linkBackground: The development of advanced imaging technology such as Single Photon Emission Computed Tomography/ Computed Tomography (SPECT/CT), has provided improved imaging quality and diagnostic confidence. However, the complexity of the equipment, process and procedure can contribute to patient anxiety. This anxiety can have a negative impact on patient experience and increase the likelihood of non-adherence during the procedure, repeat scans and non-attendance. Limited research exists into how patient anxiety might be reduced in the SPECT/CT context. Aim: To inform the development of an informational intervention to reduce anxiety in patients referred for SPECT/CT imaging. Methods and findings: A sequential multiphase mixed methods design was used. Study 1, a systematic literature review, demonstrated the potential effectiveness of a range of anxiety reducing interventions, including written, verbal and/or visual information. However, the small number and overall low quality of studies made it difficult to draw clear conclusions regarding the application of any specific intervention in clinical practice. Study 2 consisted of a focus group with practitioners which highlighted the challenges created by patient anxiety and that patient preparation through information provision may be currently lacking. Study 3, in which patients were interviewed, highlighted the importance of clear and accessible information to prepare patients and supported the view of practitioners that current provision of information may not be effective. An online survey of patients in Study 4 demonstrated that current preparatory information could be difficult to understand and did not always include information that was important to patients, whilst highlighting patient preferences for a visual component to information provision. Study 4 was followed by a content analysis of online patient information which further highlighted inconsistencies and a lack of accessibility of patient information (study 5). Finally in study 6, real world examples of patient information leaflets (PILs) were evaluated through focus group discussions, suggesting that whilst patients may see the value of PILs, they feel they can be improved.Conclusions: This thesis has provided evidence for the need for an informational intervention to reduce anxiety amongst patients referred for SPECT/CT imaging. Key recommendations are that: imaging professionals should contribute to the process of designing patient information material; any information intervention should be co-produced with patients; and that written information may represent an appropriate approach, but further work around content and mode of delivery is still required
Justice through capabilities: An investigation into the social impacts of the East Bristol Liveable Neighbourhood (EBLN)
Full text linkThis study sits at the intersection of Just Transition and Just City scholarship, applying a green justice lens to public policymaking in the context of sustainable urban mobility. Through a qualitative case study of the East Bristol Liveable Neighbourhood (EBLN), a traffic regulation scheme implemented in one of Bristol’s most deprived and socioeconomically diverse areas, this research critically explores how such interventions shape equity and justice in low-carbon transitions.Through the Capability Approach (Sen, Nussbaum, Robeyns), the study interrogates whether residents impacted by the EBLN policy, particularly those experiencing social and economic vulnerability, possess the real opportunities and freedoms to convert this urban intervention into improved well-being. It links theoretical debates on just transitions with the lived realities of urban sustainability policies, investigating how class, race, gender, caregiving responsibilities, and disability mediate the experience of changing mobility.Drawing from extensive interviews and fieldwork, the research amplifies marginalised voices to reveal how policies designed to improve environmental outcomes may simultaneously deepen exclusion if unequal conversion factors are overlooked. This work contributes to critical urban scholarship by foregrounding the social justice implications of climate mitigation policies and challenging the assumption that sustainability initiatives are universally beneficial. Instead, it calls for a capabilities-based, people-centred approach to urban transitions, one that actively resists the reproduction of environmental privilege and gentrification outcomes.In doing so, it produces a narrative of the hidden voices to understand what more or else would need to happen to make the transition to a low-carbon economy and its profound revolutionary potential, an advantage to a more equal and inclusive society