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The Intertemporal Analysis of Jamaica’s Energy Landscape in the Industrial Age
No full textThis chapter critically examines the three distinctive periods in Jamaica’s energy landscape. The analysis initiates with the use of coal-burning steam generators in 1892 and the energy use transition to the early 2000s through the integration of significant installed power capacity of wind and solar energy and finally a glimpse into the future outlook of the overall energy mix with enhanced energy storage mechanisms.Therefore, a deep-dive analysis into the role of fossil fuels, renewable energy and more so transition pathways to a low carbon economy through renewables higher penetration vis a vis the energy mix.This will be presented in tandem with three future timelines; 2027, 2030 and 2055. These timelines have been selected through their regional, local and internationally established importance of carbon dioxide mitigation targets
Diagnosis and Treatment of Acute Ischemic Stroke Using Modern Neuroimaging and Artificial Intelligence
No full textThe early diagnosis and personalized treatment of acute ischemic stroke (AIS) are unmet clinical challenges. The recent development of neuroimaging technologies provides more in-depth information on brain circulation that can be used for the clinical management of AIS. Artificial intelligence (AI), including machine learning and deep learning models, enables the data-driven early diagnosis of AIS. Radiomic analysis can extract AIS-associated deep features from neuroimaging data. The fusion of multimodal data further enhances the diagnostic power of AI models. Meanwhile, many AI models are limited by the sample size with a lack of validation on big data. This chapter provides an updated review on recent works and summarizes the advantages and challenges of AI models based on neuroimaging data in the diagnosis of treatment of AIS, offering a reference for clinicians, data scientists, and biomedical engineers
Neuroimaging Biomarkers for Diagnosing Cerebral Small Vessel Disease
No full textCerebral small vessel disease (CSVD) is a term that relates to a large number of pathological changes in the microvessels of the brain. With advances in technology in terms of hardware, imaging processing algorithms, and data science, new biomarkers associated with CSVD have been extracted from neuroimaging data. This chapter provides an overview of neuroimaging modalities for CSVD, including computed tomography, magnetic resonance imaging (MRI), diffusion MRI, iron imaging, myelin imaging, cerebrovascular reactivity imaging, and vessel wall imaging. It also discusses existing and emerging or novel biomarkers from the different investigation modalities. We evaluate the diagnostic values of these neuroimaging biomarkers from a clinical perspective and summarize the limitations as well as future directions
Routledge Handbook on Political Science: Nationalist and Republican Perspectives of the Conflict in Ireland 1948-2018
No full textSEM images data set development of ML models to predict hydrogen embrittlement in welded 316 L/304 L stainless steels
Full text linkThis study focuses on the machine learning (ML)-based identification of defects caused by hydrogen embrittlement (H2E) in the welded zones of 316 L/304 L stainless steels. It involves developing a robust SEM image dataset to train ML models for accurate defect identification. Initially, Gas Metal Arc Welding (GMAW) samples were manufactured with weld gap variations of 0.8 mm, 1.2 mm, and 1.5 mm. The welding parameters used were: (i) welding speeds of 15 mm/sec and 10 mm/sec, (ii) wire feed rate of 5.5 m/min, and (iii) voltage of 15.5 V. The samples were then exposed to a hydrogen gas environment at a pressure of 80 bar for 150 h. When analyzed using scanning electron microscopy (SEM) & electron backscatter diffraction (EBSD), H2E was observed on the surfaces of the welded zones (WZ) and heat-affected zones (HAZ). These defects, validated through literature, were segregated / sectioned as defect-based feature images and stored as a dataset. A preliminary analysis of the images validated after with 16 DOE's using AlexNet, a convolutional neural network (CNN)-based ML model, showed significant identification of these defects with 90 % accuracy. The trained models helped identify areas and understand previously unidentified defects. Through a focused discussion on defect detection, supported by validation using classification (CNN Accuracy, Precision, Recall, and F1-Score) and regression metrics (R2 and Success Rate), the article demonstrates the potential of ML-based approaches in advancing welding diagnostics.</p
Agroecological cashew cultivation increases pollinator abundance, diversity and flower visitation rates, with potential yield benefits
Full text linkAgroecological approaches have the potential to reduce the adverse impacts of agriculture on the environment whilst sustaining productivity, yet rigorous assessments of associated policies’ ability to achieve these dual aims at farm scale remain scarce. Here, we evaluate the impacts of the Zero Budget Natural Farming (ZBNF) programme – a large-scale government-led agroecological strategy in South India – on the ecological and productivity performance of an emerging commodity crop associated with high deforestation-risk: cashew (Anacardium occidentale L.). ZBNF increased the abundance and species richness of insects visiting cashew flowers (including known cashew pollinators) by almost 400 % and 250 % respectively, with visitation rates to cashew flowers rising nearly fivefold compared to conventional, agrichemical-based systems. Whilst there was strong support for these positive effects, estimates of their magnitudes were imprecise. Around 40 % of all species were exclusively found at ZBNF orchards. ZBNF's effect on cashew nut yield was uncertain due to high variability in the data, yet our results indicate a positive trend, with yields averaging over 70 % higher under ZBNF. Thus, ZBNF likely enhanced cashew pollination service provisioning and had insect conservation benefits, although more targeted actions may be needed for rare, specialist species. Whilst ZBNF can help shift cashew production towards sustainability, we stress that it must be paired with land-use planning and strengthened conservation efforts to prevent further cashew expansion into natural ecosystems.</p
NICE’s waist-to-height ratio guidelines “to have a waist<0.5 of your height”, are suitable for children but misleading for adolescents
No full textBackground and aim: National Institutes of Clinical Excellence (NICE)’s “at-risk” guidelines (waist-to-height ratio (WC/HT)<0.5) over penalizes shorter adults and fails to alert taller adults who may be at risk. The aim is to assess whether the “at-risk” guidelines recommended by NICE are appropriate for children, by assessing whether their WCs increase in proportion to their height, thus obeying the principle of “geometric similarity.”.Methods: Cross-sectional study including 11018 participants aged 7-17 years. We assessed whether the children’s waist circumferences (WC) increased in proportion to their heights (HT) using the allometric power law, WC=a.HT^b. We also cross-tabulated children (7-13 yrs.) and adolescents (14-17 yrs.) by height categories (short <145 cm, average 145 to 175 cm, and tall>0.175 cm) to identify whether taller or shorter individuals were equally “at-risk” (WHTR>0.5).Results: The power law identified children’s height exponents was approximately 1 (geometrically similar), but older adolescents’ height exponents were approximately 0.5. We also identified that the frequency of children “at-risk” was evenly spread across the 3 height groups. In contrast, shorter adolescents were more frequently “at-risk” compared with their taller peers.Conclusions: NICEs guideline (WC/HT<0.5) is suitable for, and fairly classifies children (aged 7-12 years) “at-risk” irrespective of their height. In contrast, shorter adolescents are consistently more likely to be unfairly classified as “at-risk” compared with taller adolescents, i.e., NICEs guideline (WC/HT<0.5) will unfairly classify many adolescents as being “at-risk”, with shorter adolescents being consistently over-penalized compared with their taller peers who may well be lulled into a false sense of security.<br/
Formation and revival:Entanglements in older men’s clothing narratives
No full textThis article draws on two consecutive qualitative research projects. In the first study, the aim was to develop an in-depth understanding of older British men’s experiences of ageing through the lens of fashion and clothing. The follow-up study built on the previous findings, by exploring various points of continuity and change in the social performance of older men through their evolving fashion and clothing practices. Between 2013-2014, a series of in-depth, semi-structured interviews were undertaken with five mature fashion-conscious men (based in the Midlands, UK), which alongside wardrobe methods revealed the various ways in which they used their embodied relationship with clothing as a mechanism for articulating and negotiating their ageing bodies, individual and collective identities. Now, over a decade later, a series of follow-up interviews and personal inventories with three of the original study participants, shines new light on the richness and complexity of the men’s lived experiences, and the interconnectedness of their social and bodily performance as they continue to age. Drawing upon Barad’s seminal work on agential realism, and particularly the concept of entanglement (2007), which proposes the emergence of individual traits through interactions and intra-relationships, we analyse the participants’ past and present accounts, garments and photographs of their selected favourite fashion objects as inseparable, intertwined, and iterative. This approach to analysis revealed various entanglements in the older men’s unfolding clothing narratives in relation to their fashion identities, social performance and agency. <br/
Toward extended durability and power output of high temperature proton exchange membrane fuel cells with Gd2Zr2O7-C3N4 composite membrane
No full textState-of-the-art proton exchange membrane fuel cell (PEMFC) that operate at 80 °C and 100% relative humidity (RH) requires an external humidifier, noble electrocatalyst, and expensive Nafion membrane to obtain appreciable power output and durability. High temperature (HT) operation over 100 °C provides an ideal solution to avoid costly components in PEMFC application. However, HT-PEMFC frequently loses its performance excessively because of phosphoric acid leaching from the conventional polybenzimidazole-based membranes. Herein, we present a gadolinium zirconium oxide (Gd 2Zr 2O 7)-carbon nitride (C 3N 4) (GdZr-CN) additive that reasonably improves the power output, chemical durability, and operational stability of sulfonated poly(ether ether ketone) (SPEEK) membrane in HT-PEMFC. When use SPEEK/GdZr-CN composite membrane in HT-PEMFC, the metal cations (Zr 4+ and Gd 3+) decompose the free radicals, while the acid–base interactions between functional groups (-SO 3H, -NH, -NH 2, and -OH) involve the anhydrous proton conduction. Using SPEEK/GdZr-CN composite membrane, we obtain a HT-PEMFC exhibiting a maximal power output of 315 mW cm −2 at 110 °C under 15% RH, with minimal chemical degradation after 300 h of operation. Although the incorporation of GdZr-CN significantly enhances the durability of the composite membrane by scavenging free radicals and increasing glass transition temperature, the minimal degradation observed is primarily attributed to the inherent vulnerability of ether linkages in SPEEK backbones to free radical attacks and hygrothermal stress during prolonged operation. This study unveils that SPEEK/GdZr-CN composite membrane is a cost-competitive, energy-efficient, and durable PEM from the perspective of HT-PEMFC.</p
A model-agnostic ordinal regression pipeline for length of stay prediction
No full textThe prediction of hospitalization duration, known as length of stay (LoS), is a critical aspect of optimizing healthcare resource allocation. To solve this problem, several earlier studies divided LoS into different buckets and predicted them using classification methods. Nonetheless, these studies overlook the skewed distribution and the intrinsic ordinal nature of the various categories. Besides, the highly sparse Electronic Health Records (EHRs) degrade the prediction accuracy. To overcome the aforementioned challenges, in this paper, we propose a model-agnostic ordinal regression pipeline for length of stay prediction (MORE) in ICUs. Initially, we introduce a variable selection module aimed at pruning marginal and sparse features from the original input data. This approach directs the model’s focus toward important features, thereby reducing noise influence and enhancing computational efficiency. Subsequently, we present a multi-task learning-based optimization module where we integrate cross-entropy loss and an accumulated link loss into a unified loss function. Finally, we carry out a comprehensive series of experiments across two publicly available datasets, MIMIC-III and PhysioNet. The experimental results show that MORE can improve the performance of existing classification methods in terms of mean absolute error and accuracy.</p