Multivariate Generalizations of the Multiplicative Binomial Distribution: Introducing the MM Package

We present two natural generalizations of the multinomial and multivariate binomial distributions, which arise from the multiplicative binomial distribution of Altham (1978). The resulting two distributions are discussed and we introduce an R package, MM, whichincludes associated functionality

Benchmarking to trigger and sustain the introduction of cleaner production in small to medium sized enterprises

This thesis investigates benchmarking (and associated capacity building activities) as a trigger for the diffusion and implementation of Cleaner Production. The critical success factors for the environmental benchmarking process are: The identification of gaps in environmental performance in areas important to the long-term future of the businesses; Providing and/or promoting the drivers to close the performance gaps; Ensuring business managers possess the ability and tools to close the performance gap. A program was developed implementing these factors and delivered to the drycleaning industry in Western Australia. This program identified large performance gaps for the different Eco-Efficiency indicators. The participants accepted the benchmarks (which are amended for 'economies of scale' if required) as suitable targets and committed their businesses to achieving these in their action plans. Economic benefits, managing environmental risk and maintaining their licence to operate were found to be important drivers. Participants on average reduced hazardous waste generation by 48%, improved their chemical efficiency by 30% and their energy efficiency by 9%, while individual business manager's levels of Eco-Efficiency improvements varied widely. The businesses with the higher levels of productivity and the greatest experience in the industry obtained the greatest improvements in Eco-Efficiency from the program. Furthermore, the business managers involved in the program had a significantly higher uptake of Cleaner Production in comparison with control groups, both inside the drycleaning sector as well as in 3 other sectors dominated by small to medium-sized enterprises.This research indicates that benchmarking for small businesses needs to be part of an on-going industry specific capacity building program with the opportunity to network in a supportive atmosphere. When this is the case, improved environmental accounting practices and benchmarking can trigger and sustain the uptake of Cleaner Production to improve the Eco-Efficiency of small businesses

Tracer Studies in Heterogeneous Catalysis

The reactions of ethylene, propylene, acetylene and hydrogen over alumina- and silica-supported catalysts from 2

The ethics of uncertainty for data subjects

Modern health data practices come with many practical uncertainties. In this paper, I argue that data subjects’ trust in the institutions and organizations that control their data, and their ability to know their own moral obligations in relation to their data, are undermined by significant uncertainties regarding the what, how, and who of mass data collection and analysis. I conclude by considering how proposals for managing situations of high uncertainty might be applied to this problem. These emphasize increasing organizational flexibility, knowledge, and capacity, and reducing hazard

Machine learning for the detection and diagnosis of cognitive impairment in Parkinson’s Disease: a systematic review

Background: Parkinson's Disease is the second most common neurological disease in over 60s. Cognitive impairment is a major clinical symptom, with risk of severe dysfunction up to 20 years post-diagnosis. Processes for detection and diagnosis of cognitive impairments are not sufficient to predict decline at an early stage for significant impact. Ageing populations, neurologist shortages and subjective interpretations reduce the effectiveness of decisions and diagnoses. Researchers are now utilising machine learning for detection and diagnosis of cognitive impairmentbased on symptom presentation and clinical investigation. This work aims to provide an overview of published studies applying machine learning to detecting and diagnosing cognitive impairment, evaluate the feasibility of implemented methods, their impacts, and provide suitable recommendations for methods, modalities and outcomes.Methods: To provide an overview of the machine learning techniques, data sources and modalities used for detection and diagnosis of cognitive impairment in Parkinson's Disease, we conducted a review of studies published on the PubMed, IEEE Xplore, Scopus and ScienceDirect databases. 70 studies were included in this review, with the most relevant information extracted from each. From each study, strategy, modalities,sources, methods and outcomes were extracted.Results: Literatures demonstrate that machine learning techniques have potential to provide considerable insight into investigation of cognitive impairment in Parkinson's Disease. Our review demonstrates the versatility of machine learning in analysing a wide range of different modalities for the detection and diagnosis of cognitive impairment in Parkinson's Disease, including imaging, EEG, speech and more, yielding notable diagnostic accuracy. Conclusions: Machine learning based interventions have the potential to glean meaningful insight from data, and may offer non-invasive means of enhancing cognitive impairment assessment, providing clear and formidable potential for implementation of machine learning into clinical practice

Hand and Eye Dominance in Sport: Are Cricket Batters Taught to Bat Back-to-Front?

Background: When first learning to bimanually use a tool to hit a target (e.g., when chopping wood or hitting a golf ball), most people assume a stance that is dictated by their dominant hand. By convention, this means that a ‘right-handed’ or ‘left-handed’ stance that places the dominant hand closer to the striking end of the tool is adopted in many sports. Objective: The aim of this study was to investigate whether the conventional stance used for bimanual hitting provides the best chance of developing expertise in that task. Methods: Our study included 43 professional (international/first-class) and 93 inexperienced (<5 years’ experience) cricket batsmen. We determined their batting stance (plus hand and eye dominance) to compare the proportion of batters who adopted a reversed stance when batting (that is, the opposite stance to that expected based on their handedness). Results: We found that cricket batsmen who adopted a reversed stance had a stunning advantage, with professional batsmen 7.1 times more likely to adopt a reversed stance than inexperienced batsmen, independent of whether they batted right or left handed or the position of their dominant eye. Conclusion: Findings imply that batsmen who adopt a conventional stance may inadvertently be batting ‘back-to-front’ and have a significant disadvantage in the game. Moreover, the results may generalize more widely, bringing into question the way in which other bimanual sporting actions are taught and performed

Detecting non-binomial sex allocation when developmental mortality operates

Optimal sex allocation theory is one of the most intricately developed areas of evolutionary ecology. Under a range of conditions, particularly under population sub-division, selection favours sex being allocated to offspring non-randomly, generating non-binomial variances of offspring group sex ratios. Detecting non-binomial sex allocation is complicated by stochastic developmental mortality, as offspring sex can often only be identified on maturity with the sex of non-maturing offspring remaining unknown. We show that current approaches for detecting non-binomiality have limited ability to detect non-binomial sex allocation when developmental mortality has occurred. We present a new procedure using an explicit model of sex allocation and mortality and develop a Bayesian model selection approach (available as an R package). We use the double and multiplicative binomial distributions to model over- and under-dispersed sex allocation and show how to calculate Bayes factors for comparing these alternative models to the null hypothesis of binomial sex allocation. The ability to detect non-binomial sex allocation is greatly increased, particularly in cases where mortality is common. The use of Bayesian methods allows for the quantification of the evidence in favour of each hypothesis, and our modelling approach provides an improved descriptive capability over existing approaches. We use a simulation study to situations where current methods fail, and we illustrate the approach in real scenarios using empirically obtained datasets on the sexual composition of groups of gregarious parasitoid wasps demonstrate substantial improvements in power for detecting non-binomial sex allocation in situations where current methods fail, and we illustrate the approach in real scenarios using empirically obtained datasets on the sexual composition of groups of gregarious parasitoid wasps

Automatic Detection of the CaRS Framework in Scholarly Writing Using Natural Language Processing

Many academic introductions suffer from inconsistencies and a lack of comprehensive structure, often failing to effectively outline the core elements of the research. This not only impacts the clarity and readability of the article but also hinders the communication of its significance and objectives to the intended audience. This study aims to automate the CaRS (Creating a Research Space) model using machine learning and natural language processing techniques. We conducted a series of experiments using a custom-developed corpus of 50 biology research article introductions, annotated with rhetorical moves and steps. The dataset was used to evaluate the performance of four classification algorithms: Prototypical Network (PN), Support Vector Machines (SVM), Naïve Bayes (NB), and Random Forest (RF); in combination with six embedding models: Word2Vec, GloVe, BERT, GPT-2, Llama-3.2-3B, and TEv3-small. Multiple experiments were carried out to assess performance at both the move and step levels using 5-fold cross-validation. Evaluation metrics included accuracy and weighted F1-score, with comprehensive results provided. Results show that the SVM classifier, when paired with Llama-3.2-3B embeddings, consistently achieved the highest performance across multiple tasks when trained on preprocessed dataset, with 79% accuracy and weighted F1-score on rhetorical moves and strong results on M2 steps (75% accuracy and weighted F1-score). While other combinations showed promise, particularly NB and RF with newer embeddings, none matched the consistency of the SVM–Llama pairing. Compared to existing benchmarks, our model achieves similar or better performance; however, direct comparison is limited due to differences in datasets and experimental setups. Despite the unavailability of the benchmark dataset, our findings indicate that SVM is an effective choice for rhetorical classification, even in few-shot learning scenarios

Adaptive MUlti-scale Superpixel Embedding Convolutional Neural Network (AMUSE-CNN) for Land Use Classification

Currently, a large number of remote sensing images with different resolutions are available for Earth observation and land monitoring, which are inevitably demanding intelligent analysis techniques for accurately identifying and classifying land use (LU). This article proposes an adaptive multiscale superpixel embedding convolutional neural network architecture (AMUSE-CNN) for tackling LU classification. Initially, the images are parsed via the superpixel representation so that the object-based analysis (via a superpixel embedding convolutional neural network scheme) can be carried out with the pixel context and neighborhood information. Then, a multiscale convolutional neural network (MS-CNN) is proposed to classify the superpixel-based images by identifying object features across a variety of scales simultaneously, in which multiple window sizes are used to fit to the various geometries of different LU classes. Furthermore, a proposed adaptive strategy is applied to best exert the classification capability of the MS-CNN. Subsequently, two modules are developed to fully implement the AMUSE-CNN architecture. More specifically, Module I is to determine the most suitable classes for each window size (scale) by applying majority voting to a series of MS-CNNs Module II carries out the classification of the classes identified in Module I for the given scale used in the MS-CNN and, therefore, complete the LU classification of the entire classes. The proposed AMUSE-CNN architecture is both quantitatively and qualitatively validated using remote sensing data collected from two cities, Kano and Lagos in Nigeria, due to the spatially complex LU distribution. Experimental results show the superior performance of our approach against several state-of-the-art techniques.</p

Letter from Georgina Altham, Winchester, England, to Jeannette Marks : typed manuscript copy unsigned, 1942 November 19

Letterhead: 7 Kingsgate Street, Winchester.https://repository.wellesley.edu/autographletters/1038/thumbnail.jp