Data-driven models for microscopic vehicle emissions

In this paper, a new approach for describing the relationship between tailpipe emissions and vehicle movement variables is presented, called generalized additive model for location, scale and shape (GAMLSS). The dataset for this model is second-by-second emission laboratory measurements, following a real driving cycle that were recorded in urban, suburban and motorway areas of London. The GAMLSS emission model estimates each of CO_{2}, CO and NO_{x} in each second for two different vehicle types (petrol or diesel) using instantaneous speed and acceleration as the explanatory variables. Comparing the results with current emission models indicates substantial improvement in accuracy and quality of estimation by this approach

The role of particle size and other properties on silo discharge behaviour of chipped wood biomass

To achieve net-zero carbon emissions by 2050, the UK government emphasizes the pivotal role of sustainable bioenergy in electricity, transportation, and heating. However, challenges persist in handling biomass particulate solids in production facilities, leading to economic impacts. This study investigates the flow characteristics of stemwood chips from four tree species using a novel drum chipper. Experimental analyses include bulk density measurements, silo discharge studies, biomass flow property assessments, and wall friction measurements. Comparative analyses are performed using Jenike’s procedure for building wedge-shaped silos, with a focus on predicting the critical opening size to prevent arching. Additionally, the paper delves into the creation of statistical models aimed at identifying key factors influencing the flow behaviour during silo discharge. Emphasis is placed on understanding potential discrepancies between theoretical predictions and experimental results concerning critical silo openings for arch-free discharge. The results contribute to understanding the factors influencing the flow behaviour of wood chips, informing silo design considerations. Our findings suggest limitations in applying traditional silo design methods, urging further research for more accurate predictions

Aqueous Al2O3 nanofluids: the important factors impacting convective heat transfer

A high accuracy, counter flow double pipe heat exchanger system is designed for the measurement of convective heat transfer coefficients with different nanofluids. Both positive and negative enhancement of convective heat transfer of alumina nanofluids are found in the experiments. A modified equation was proposed to explain above phenomena through the physic properties of nanofluids such as thermal conductivity, special heat capacity and viscosity

Bi-view semi-supervised active learning for cross-lingual sentiment classification

Recently, sentiment classification has received considerable attention within the natural language processing research community. However, since most recent works regarding sentiment classification have been done in the English language, there are accordingly not enough sentiment resources in other languages. Manual construction of reliable sentiment resources is a very difficult and time-consuming task. Cross-lingual sentiment classification aims to utilize annotated sentiment resources in one language (typically English) for sentiment classification of text documents in another language. Most existing research works rely on automatic machine translation services to directly project information from one language to another. However, different term distribution between original and translated text documents and translation errors are two main problems faced in the case of using only machine translation. To overcome these problems, we propose a novel learning model based on active learning and semi-supervised co-training to incorporate unlabelled data from the target language into the learning process in a bi-view framework. This model attempts to enrich training data by adding the most confident automatically-labelled examples, as well as a few of the most informative manually-labelled examples from unlabelled data in an iterative process. Further, in this model, we consider the density of unlabelled data so as to select more representative unlabelled examples in order to avoid outlier selection in active learning. The proposed model was applied to book review datasets in three different languages. Experiments showed that our model can effectively improve the cross-lingual sentiment classification performance and reduce labelling efforts in comparison with some baseline methods

Effects of Cu and Ag nano-particles on flow and heat transfer from permeable surfaces

Consideration is given to flow and heat transfer of nano-fluids over a permeable flat plate with convective boundary condition. The governing partial differential equations are transformed into ordinary differen- tial equations using similarity solutions, before being solved numerically. Two types of nano-fluids, Cu– water and Ag–water are considered. The effects of nano-particles volume fraction, the type of nano-par- ticles and permeability parameter on skin friction and convection heat transfer coefficient are studied and discussed. It is shown that the increment in skin friction is a considerable drawback imposed by Cu–water and Ag–water nano-fluids, especially in case of injection. In the cases of injection and imper- meable surface, increasing the nano-particles volume fraction results in augmentation of convection heat transfer rate. However, in the case of suction, adding Cu and Ag particles reduces the convection heat transfer coefficient at the surface in spite of thermal conductivity enhancement imposed by the nano- particles

Reducing SABA overprescribing in asthma: lessons from a Quality Improvement prescribing project in East London

BACKGROUND: Excess prescription and use of short-acting beta-agonist (SABA) inhalers is associated with poor asthma control and increased risk of hospital admission. AIM: To quantify the prevalence and identify the predictors of SABA overprescribing. DESIGN AND SETTING: A cross-sectional study using anonymised clinical and prescribing data from the primary care records in three contiguous East London boroughs. METHOD: Primary care medical record data for patients aged 5–80 years, with ‘active’ asthma were extracted in February 2020. Explanatory variables included demography, asthma management, comorbidities, and prescriptions for asthma medications. RESULTS: In the study population of 30 694 people with asthma, >25% (1995/7980), were prescribed ≥6 SABA inhalers in the previous year. A 10-fold variation between practices (25% of those taking ≥6 SABAs/year were underusing ICSs, this rose to >80% (18 170/22 713), for those prescribed <6 SABAs/year. Prescription modality was a strong predictor of SABA overprescribing, with repeat dispensing strongly linked to SABA overprescribing (odds ratio 6.52, 95% confidence interval = 4.64 to 9.41). Increasing severity of asthma and multimorbidity were also independent predictors of SABA overprescribing. CONCLUSION: In this multi-ethnic population a fifth of practices demonstrate an overprescribing rate of <20% a year. Based on previous data, supporting practices to enable the SABA ≥12 group to reduce to 4–12 a year could potentially save up to 70% of asthma admissions a year within that group

Characteristics of asthma patients overprescribed short-acting beta-agonist (SABA) reliever inhalers stratified by blood eosinophil count in North East London - a cross-sectional observational study.

BACKGROUND: Over-prescription of short-acting beta-agonist (SABA) inhalers and blood eosinophil count have strong associations with exacerbation risk in asthma. However, in our recent publication only a minority of SABA-overprescribed patients (≥6 inhalers in 12 months) were eosinophilic (≥0.3x109 cells/L). AIM: To compare the characteristics of eosinophilic and non-eosinophilic SABA over-prescribed patients, and identify latent classes using clinical variables available in primary care. DESIGN & SETTING: Cross-sectional analysis of asthmatic patients in North East London using primary care electronic health record data. METHOD: Unadjusted and adjusted multi-variate regression models and latent class analysis. RESULTS: Eosinophilia was significantly less likely in female patients, those with multiple mental health comorbidities and those with SABA on repeat prescription. Latent class analysis identified 3 classes of SABA over-prescribed patients representing those with classical Uncontrolled Asthma (oral-steroid requiring exacerbations, step 2-3 asthma medications, high probability of being eosinophilic), Mild Asthma (low exacerbation frequency, low asthma medication step, low probability of being eosinophilic), and Difficult Asthma (high exacerbation frequency despite high-strength preventer inhalers, low probability of being eosinophilic). The Mild Asthma class was the largest. CONCLUSION: Many patients being over-prescribed SABA are non-eosinophilic with a low exacerbation frequency suggesting disproportionately high SABA prescription compared to other asthma control markers. Potential reasons for high SABA prescription in these patients include repeat prescription (being dispensed but not taken) and use of SABA for non-asthma breathlessness (eg, breathing pattern disorders with anxiety). Further research is needed into management of SABA overuse in patients without other markers of uncontrolled asthma