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Biogas production from different food waste using small-scale floating-drum-type anaerobic digester
Full text linkThe generation of food waste poses an escalating societal challenge. Anaerobic digestion emerges as a sustainable and eco-friendly method for valorization and disposal. A small-scale floating-drum-type digester was developed, operating in batch mode to harness biogas from three distinct food waste categories. Potato waste, leftover cooked food, and fish waste were utilized as feedstock, maintained at an average temperature of 21 °C for a retention time of 10 days, with cow manure serving as the inoculum source. The advances of the current work are built upon comparing biogas production volume and methane content from mono-anaerobic digestion of these various wastes. Examining cow manure and different substrate samples offers insights into their composition, encompassing total solids, C/N ratio, and pH. Shredded raw wastes were wet fed into the digester at a 1:1 waste/water ratio. Cumulative production of biogas and the methane fraction from two experiments were monitored. The maximum average cumulative biogas production per kg of total solid was observed for leftover cooked food (up to 261.4 L/kgTS), followed by fish waste (up to 248.5 L/kgTS) and potato waste (up to 137.15 L/kgTS). The maximum methane percentage occurred in fish waste displaying the highest methane percentage (74%), trailed by leftover cooked food (59%) and potato waste (55.8%) from both experiments
Comparing plantar shear strain in patients with a previous diabetes‐related foot ulcer and those at low risk for ulceration using the STrain Analysis and Mapping of the Plantar Surface ( STAMPS ) system
Full text linkBackgroundSTrain Analysis and Mapping of the Plantar Surface (STAMPS) is an innovative system using a plastically deformable insole with a stochastic speckle pattern, developed to measure peak plantar shear strain (SMAG) in people with diabetes. The aim was to determine whether patients with a prior DFU exhibit higher SMAG than low-risk patients.MethodsParticipants walked 20 steps with the STAMPS insole within a standardised shoe and 10 m with the Pedar-X™ (Novel, Inc.) measurement insole. SMAG was compared in participants with either a recently healed diabetic foot ulcer (Prior DFU group) or diabetes and low risk for ulceration (NICE NG-19). Measurements were repeated three times. Images were analysed using the DIC software ‘GOM correlate’ (Zeiss, Inc.) and post-processed using MATLAB. Outcomes were overall and regional peak SMAG and peak plantar pressure (PPP). Consenting prior DFU participants subsequently repeated the walking assessments wearing a diabetic below-knee walker-boot. Overall and regional peak SMAG and PPP were compared between the standard shoe and walker-boot.ResultsTwenty participants with prior DFU and 14 at low risk were recruited. Overall peak SMAG within the prior DFU and low-risk groups was 27.9% (IQR – 17.3–37.5%) and 11.5% (IQR 9.6–20.3%) respectively, p = 0.003. Within the prior DFU group, SMAG was elevated at DFU sites compared with non-DFU sites; peak SMAG was 11.7% (IQR 7.6–25.6%) and 7.70% (4.4–13.1%). Sixteen participants completed the offloading assessments. Peak SMAG within the standard shoe and walker-boot was 27.4% (IQR 17.2–32.7) and 8.03% (IQR 6.3–12.2).ConclusionParticipants with a recently healed DFU exhibited elevated strain characteristics compared with the low-risk group. Furthermore, prospective work will explore the relationship between SMAG and DFU formation
How a sense of belonging allows nurses to develop and thrive at work
No full textThis article – the third in a series of four articles on compassionate leadership – discusses the need for nurses to have a sense of belonging to be able to flourish in the workplace, and provide compassionate and effective patient care. It looks at how compassionate leadership approaches, as promoted by the professional nurse advocate role, help to promote supportive and inclusive workplaces that support nurses’ wellbeing and help engender a sense of belonging
Developing a novel typology of unprofessional behaviours between healthcare staff: a best fit framework synthesis
Full text linkBackground: Unprofessional behaviours such as bullying, harassment, and microaggressions negatively affect patient safety and staff psychological wellbeing in healthcare systems globally. These behaviours do so by: (i) inhibiting health care professionals’ abilities to speak up to raise safety concerns; (ii) impairing team communication and individuals’ concentration; and (iii) promoting tolerance of bad practice. Unfortunately, there is little consensus in practice or academia about how these behaviours are defined. This can lead to an underestimation of the prevalence of these behaviours, inhibition of speaking up by victims and bystanders, and reduced accountability by those who enact these behaviours. We aimed to map definitions of unprofessional behaviours between staff to understand their similarities and differences and to develop a useful typology for theory-informed interventions. Methods: We used a six-step modified best-fit framework synthesis methodology to formulate our new typology, as a part of a wider realist review project. We employed a systematic approach to develop a framework for understanding UB. First, we identified relevant literature through a systematic search of Embase, CINAHL and MEDLINE databases (and more) (n = 146 sources). An initial framework outlining the dimensions of unprofessional behaviours was then constructed based on extracted definitions. Terms from included studies were then coded against this framework, with new dimensions introduced as needed to accommodate terms that did not align with existing categories. The resulting framework was refined iteratively and validated through stakeholder engagement, enhancing its relevance and validity. Results: We identified 37 behaviours drawing on 146 literature sources and found little consensus in how unprofessional behaviours between staff are defined in the academic literature. By collating definitions, we identified five dimensions inherent to unprofessional behaviours between staff namely: visibility; inherent frequency; whether they are highly targeted; if behaviours target protected characteristics (personal attributes that are legally safeguarded against discrimination in the UK and many other countries, such as race, sex or religion); if behaviours are physical; and if hierarchy is required. These dimensions enabled formulation of the typology with increased understanding of the differences between unprofessional behaviour types. Conclusions: We found that poor and inconsistent understanding of unprofessional behaviour could undermine interventions by inhibiting speaking up, enabling instigators to avoid accountability, and inhibiting ability to measure unprofessional behaviour and address it. Our typology provides a useful resource for academics, healthcare organisations, intervention architects, and individuals who are seeking to understand and clarify the range of unprofessional behaviours that may be encountered in healthcare settings
Ibrutinib versus acalabrutinib in fixed-duration chronic lymphocytic leukemia therapy: a comparative analysis of efficacy
Full text linkExploring the interaction between ethnicity, deprivation and the use of CGM on diabetes outcomes—A study from the Association of British Clinical Diabetologists
No full textAims: Ethnic and socioeconomic disparities in diabetes outcomes persist despite widespread adoption of technologies such as continuous glucose monitoring (CGM). We examined the independent and interactive effects of ethnicity and area-level deprivation on baseline glycaemic control and CGM effectiveness in a large UK diabetes cohort. Materials and Methods: This retrospective observational study used data from the Association of British Clinical Diabetologists (ABCD) national audit, including 18,139 adults with type 1 or type 2 diabetes initiating FreeStyle Libre. Ethnicity was categorised into White, Black, Indian, Pakistani/Bangladeshi, and Mixed/Other groups. Deprivation was assessed using hospital-level Index of Multiple Deprivation (IMD) rankings. Baseline and follow-up HbA1c and diabetes distress scores (DDS) were analysed. Linear regression assessed associations between predictors and HbA1c, and interaction effects were evaluated using both regression and Gradient Boosting Machine (GBM) models, with Friedman's H-statistic quantifying interaction strength. Results: Compared to White individuals, Black participants had significantly higher baseline HbA1c (β = 6.19, SE = 1.87, p 0.05); GBM analysis supported this with a low H-statistic of 0.088. CGM use resulted in significant HbA1c reductions across all IMD groups, most notably in very high deprivation areas (−8.54%, p < 0.001). Reduction was similar across ethnicities (all p = 1.0). Conclusions: Ethnicity and deprivation independently influence glycaemic control. However, CGM yields equitable HbA1c improvements across all subgroups
Book Review: Promoting inclusive systems for migrants in education edited by Paul Downes, Jim Anderson, Alireza Behtoui and Lore Van Praag, London, Routledge, 2024, 224 pp., £133.24 (hard cover), ISBN: 978-1-032-19304-5
No full textOpening paragraph:Promoting Inclusive Systems for Migrants in Education, edited by Paul Downes, Jim Anderson, Alireza Behtoui and Lore Van Praag, is an essential and timely addition to the current debate surrounding migrants in education. This edited volume stands out because of its focus on critical perspectives and qualitative research, providing a nuanced and complex analysis of both painful and positive realities in creating inclusive educational environments for migrants across different phases of learning, from early years to higher education and workplace transitions
Comprehensive Health Tracking Through Machine Learning and Wearable Technology
Full text linkThe accurate interpretation of data from wearable devices is paramount in advancing personalized healthcare and disease prevention. This study explores the application of machine learning techniques to improve the interpretation of health metrics from wearable technology, focusing on heart rate and activity prediction. The study conducts a device-wise comparison of data from popular devices, namely the Apple Watch and Fitbit, using both tree-based and boosting algorithms. The outcome of the experiment shows that the Random Forest model is a better predictor for heart rate, with the lowest error rate across devices and a prediction accuracy of 98% on the combined dataset. Conversely, the classification result for activity prediction showed that all models used have better accuracy with Fitbit data, and accuracy drops with Apple Watch data. The Random Forest achieves a consistent performance of 87% for accuracy and F1 score on the combined data. However, after cross-validated hyperparameter tuning, this result on the combined dataset is superseded by the boosted models, with both Gradient Boosting and XGBoost achieving the same level of performance (90%) across metrics
Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning
Full text linkRecent advances in deep learning have significantly improved the accuracy of skin lesion classification models, supporting medical diagnoses and promoting equitable healthcare. However, concerns remain about potential biases related to skin color, which can impact diagnostic outcomes. Ensuring fairness is challenging due to difficulties in classifying skin tones, high computational demands, and the complexity of objectively verifying fairness, given the continuous and context-dependent nature of skin tone and the dependence of fairness conclusions on metric choice and subgroup representation. To address these challenges, we propose a fairness algorithm for skin lesion classification that overcomes the challenges associated with achieving diagnostic fairness across varying skin tones. By calculating the skewness of the feature map in the convolution layer of the Visual Geometry Group network (VGG) and the patches and the heads of the Vision Transformer (ViT), our method reduces unnecessary channels related to skin tone, focusing instead on the lesion area. Application on VGG11 and ViT-B16, showed improved fairness metrics by 15-20% on average while maintaining accuracy and F1-score within 0.01 of the baseline. Additionally, the method reduced model size by 16% for VGG11 and decreased memory footprint for ViT-B16, without requiring skin tone labels at inference. Thus, the approach lowers computational costs and mitigates bias without relying on conventional statistical methods. It potentially reduces model size while maintaining fairness, making it more practical for real-world applications
A Model is a Map: Reimagining the Placement-Layering-Integration Model
No full textThe placement-layering-integration model was designed to explain money laundering operations in cash-intensive economies. It continues to influence how money laundering is conceptualized. Increasingly, both the model’s utility and relevance are being questioned. This paper cautions against discarding the model. Instead, we recontextualize the model to demonstrate its applicability to digital economies. Specifically, we argue that the placement stage has two key characterizing properties: the type of illicit proceeds being laundered and the environment in which layering begins. To assess the merit of the recontextualized model, an empirical analysis of Counter-Strike skin sales is combined with crime script analysis to investigate a modern-day money laundering operation. It is demonstrated how the placement-layering-integration model continues to provide meaningful analytic categories with which to analyze certain types of money laundering operations