Invasive animal and plant species can be a serious threat to the health, well-being or even survival of indigenous local species. In much of Western Europe, the Asian “yellow legged” hornet, Vespa velutina, is causing serious problems for vital pollinator species, including bees, whilst the Oriental hornet, Vespa orientalis, is causing similar problems in $n$ the Southern Mediterranean. If left unchecked, these invasive hornets could post a major threat to native pollinators, and hence to the farming of many crops, to many wildflower plants and to the availability of bee products such as honey, beeswax and propolis. People are encouraged to report sightings of such invasive species, but non-experts may often misidentify superficially similar-looking harmless insects as the dangerous species, leading to the wasting expert time and effort of experts, and considerable expense to investigate such “false alarms”. In this paper, we describe the use of image processing and machine learning to distinguish between the invasive Asian hornet from relatively harmless, but superficially similar in appearance, common wasp and European hornet in digital photographs and “home videos”. The initial results are very encouraging and should help authorities locate and control such invasions of dangerous insects with fewer false alarms and less wastage of resources.

Honeybees and other pollinating insects are of vital importance to both the agricultural industry and the wider ecosystem, but they have been in serious decline over recent years. The spread of parasites and predators such as the varroa mite and the Asian hornet create additional threats to the beneficial species. Hence, monitoring the health and well-being of benign insects and the prevalence and location of pests becomes of great importance. In this paper, we describe the use of image processing and machine learning approaches to identify and count honeybees in both still images and videos, with a view to monitoring the activity and health of bees close to a hive. Possible extensions of the work to identify and monitor parasites, predators and other species are also discussed.

Environmental factors, including air pollution, noise, and decline in biodiversity, have become issues of major concern over recent decades. Air pollution and other environmental contaminants (such as pesticides) have led to concerns relating to the health and well-being of human, animal and plant populations, whilst changes in temperature and rainfall patterns raise issues of possible rises in sea levels, coastal erosion and changes to sustainable plant and animal populations. For example, the population of bees has experienced a marked decline in many countries, which is likely to have very serious consequences for agriculture and other plant life. Bees could also be sensitive to other environmental factors such as pollution, and our recent and present work is a first step towards monitoring bees for obtaining information from the wider environment. In this paper, we discuss the analysis and processing of multi-modal (sound, temperature, humidity, natural light level and air quality) signals recorded over several months from a sensor system of our own design. This sensor system was originally planned and constructed to monitor the health and well-being of honeybees in a beehive. However, we noted that same sensor system could additionally provide useful information concerning the local natural environment – for example, variations in air quality over time. We apply various signal processing methodologies both to individual signals and to the relationships between them, and discern some interesting patterns within the signals, including some relating to interactions between the environment and the activities of people living and working in the area. This work shows how a relatively simple and low-cost sensor system can be used to perform monitoring of the local environment, with a view to improving or preserving its quality, or at least limiting damage to it due to human interventions. Our sensor network (with Raspberry Pi microcomputer) cost approximately GBP £ 100 per system, or approximately GBP £ 200 per unit if audio and video recording, plus additional local data storage, were required. This should make the system reasonably affordable to farmers or environmental NGOs (e.g) in developing countries, for whom commercially-produced environmental monitoring systems may be too expensive.

Honeybees are vital to both the agricultural industry and the wider ecological system, most importantly for their role as major pollinators of flowering plants, many of which are food crops. Honeybee colonies are dependent on having a healthy queen for their long-term survival since the queen bee is the only reproductive female in the colony. Thus, as the death or loss of the queen is of great negative impact for the well-being of a honeybee colony, beekeepers need to be aware if a queen has died in any of their hives so that appropriate remedial action can be taken. In this paper, we describe our approaches to using acoustic signals recorded in beehives and machine learning algorithms to identify whether beehives do or do not contain a healthy queen. Our results are extremely positive and should help beekeepers decide whether intervention is needed to preserve the colony in each of their hives.

. This paper describes the early phase of a framework being developed to identify infestation levels of pests and parasites of the Western Honey Bee. Image processing techniques and two classical machine learning algorithms have been used to examine close-up images of the material falling on a varroa board beneath the mesh floor of a bee hive and identify Varroa Destructor mites versus other debris to a high level of accuracy.

Difficulties in accessibility to resources for, and writing documents in, mathematical notation, has limited the educational and career opportunities of people with disabilities such as visual impairments and/or limited (or no) use of their hands or arms.In this paper, we describe a system, TalkMaths, aiming to help address these issues.TalkMaths is a speech interface allowing users, particularly those with the type of disabilities noted, to dictate and edit mathematical text, using relatively simple natural language commands.Although a PC-based version of TalkMaths was developed a few years ago, the latest version uses a web-based client-server architecture, which reduces the demands on the user's own computer, and potentially making the system usable on mobile devices.We discuss the technologyincluding automatic speech recognition, statistical language models for prediction and correction, and novel parsing strategies -underpinning TalkMaths, and present results from some initial evaluations of its use both as a dictation/editing system and as an instructive tool.

Honeybees are of vital importance to both agriculture and ecology. Unfortunately, their populations have been in serious decline over recent years. Swarms from hives are both of great importance to wider success of a colony and of major significance to beekeepers. In this paper, we contribute to the challenge of predicting when a swarm is going to occur. We have employed a Convolutional Neural Network (CNN) approach applied to audio data recorded from hives. Our initial results are extremely encouraging, since they allow us to distinguish hives which are preparing to swarm from those which are not with high levels of accuracy.

About fifty years ago, the world's first fully automated system for trading securities was introduced by Instinet in the US. Since then the world of trading has been revolutionised by the introduction of electronic markets and automatic order execution. Nowadays, financial institutions exploit the associated flow of daily data using more and more advanced analytics to gain valuable insight on the markets and inform their investment decisions. In particular, time series of Open High Low Close prices and Volume data are of special interest as they allow identifying trading patterns useful for forecasting both stock prices and volumes. Traditionally, relational databases have been used to store this data; however, the ever-growing volume of this data, the adoption of the hybrid cloud model, and the availability of novel non-relational databases which claim to be more scalable and fault tolerant raise the question whether relational databases are still the most appropriate. In this study, we define a set of criteria to evaluate performance of a variety of databases on a hybrid cloud environment. There, we conduct experiments using standard and custom workloads. Results show that migration to a MongoDB database would be most beneficial in terms of cost, storage space, and throughput. In addition, organisations wishing to take advantage of autoscaling and the maintenance power of the cloud should opt for a cloud native solution.

We develop a discrete model of type-token dynamics based on random type selection from the Zipf–Mandelbrot probability distribution, with a view to examining the relationships between the constants of Zipf’s and Heaps’ laws. Analysis of items randomly selected items from the Standardised Project Gutenberg Corpus (SPGC) reveal a significant low-frequency “droop” in the β-slope of the types vs. frequency distribution, inconsistent with the model when vocabulary is unlimited: when a finite vocabulary limit is imposed, optimal parameter selection allows the droop to be reproduced. We adjust the parameters of both the limited and unlimited vocabulary models to obtain optimal agreement with the vocabulary growth curves: the limited vocabulary model usually yields the best optimised agreement, but a sizeable minority of items are better represented by an unlimited vocabulary. While the optimised Zipf α indices correlate strongly with the corresponding values obtained directly from document statistics, the former are generally larger than the latter (though this is partially explained by the distorting effect of large values of the Mandelbrot parameter m). The β indices optimised from the limited vocabulary model are also compared with their directly measured equivalents, showing significant positive correlation. The relationship between optimised α and β agrees plausibly with the well-known continuum model, though the degree of agreement depends on how β is defined. The experiments yield repeatable results from each of three 100-item samples, demonstrating the statistical significance of the experiments.

Osteoarthritis is a major cause of mobility problems in older people and is a particular problem in former sportspeople. The objective of this study was to develop and characterise a new system for the detection, monitoring and analysis of acoustic emissions from knee joints. 15 adult volunteers participated in the study. The participants performed six sets of three sit-stand-sit cycles. Reflective markers were placed at specific body landmarks recorded by 3D cameras. The exercise was performed with one foot on a force platform. A sensitive condenser microphone with a wide frequency response was connected to a dedicated acoustic analysis unit. Preliminary results provide clear acoustic signals showing a distinctive sequence of impulse-decay forms occurring naturally during each sit-stand-sit cycle. There are distinct differences between the acoustic signals emitted from younger healthy knees and those from aged knees. This work demonstrates the potential for this system to be used as an indication of the state of health of a human knee during movement.

Honeybees are of vital importance to both agriculture and ecology, but honeybee populations have been in serious decline over recent years. The queen bee is of crucial importance to the success of a colony. In this paper, we contribute to addressing these problems by employing Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP) Neural Networks and Logistic Regression approaches applied to audio data recorded from “queen-absent” and “queen-present” hives to provide a method of prompt detection of a hive lacking a queen. The initial results-particularly from the LSTM - are highly encouraging.

We introduce here a new index of diversity based on consideration of reasonable propositions that such an index should have in order to represent diversity. The behaviour of the index is compared with that of the Gini-Simpson diversity index, and is found to predict more realistic values of diversity for small communities, in particular when each species is equally represented and for small communities. The index correctly provides a measure of true diversity that is equal to the species richness across all values of species and organism numbers when all species are equally represented, as well as Hill's more stringent 'doubling' criterion when they are not. In addition, a new graphical interpretation is introduced that permits a straightforward visual comparison of pairs of indices across a wide range within a parameter space based on species and organism numbers.

We investigate the predictive capability of mathematical models of the type-token relationship applied to the vocabulary growth profiles of selected English language documents. We compare the existing Good-Toulmin and Heaps formulae with an alternative approach based on Bernoulli trial word selection from a fixed finite vocabulary using the Zipf and Zipf-Mandelbrot probability distributions. We make two major observations: firstly, while the ZipfMandelbrot model makes better predictions of vocabulary growth than the Zipf model, the optimized parameters of the latter correlate better than those of the former with statistics gleaned independently from the data. Secondly, the mean of the Zipf-Mandelbrot, GoodToulmin and Heaps models provides a more consistent and unbiased prediction of vocabulary than any individual model alone. (c) 2021 Elsevier Ltd. All rights reserved. There have been many attempts to quantify the relationship between the number of "types" observed amongst a population of "tokens"; an early example was based on Corbet's 1940s survey of butterflies in the Malay peninsula (Fisher et al., 1943), types representing the species observed and tokens the individuals captured. The aim has generally been to predict how many types exist beyond those observed in a limited sample; this is called the "unseen species" problem. Some workers have used text documents ("corpora") as models of type/token systems, with word instances as tokens and unique "vocabulary" words as types. This practice goes back at least to 1956 when Good and Toulmin (1956) used word-samples from Dickens and Macauley, and in 1976 Efron and Thisted (1976) attempted to estimate how many words Shakespeare knew. Some have even found inherent value in studying linguistic type/token systems, for example in enumerating the language development of young children (Richards, 1987). This type of study falls under what is more generally known as "statistical language modelling". The current paper assesses the predictive abilities of several different vocabulary growth models. Each model is trained using

Conventional approaches to diagnosing and monitoring osteoarthritis and other conditions affecting the performance of the human knee (and the comfort of its owner) tend to either involve invasive surgery and/or ionising radiation such as X-rays. However, analysis of the sounds produced – so-called “acoustic emissions” – produced as the knee flexes during movements of the leg – offer opportunities to performing such diagnosis and monitoring in a non-invasive, and potentially low cost way, using portable equipment which can be taken outside the hospital or clinic. In this paper, we describe and discuss our progress so far on analysing both acoustic and biomechanical data obtained from volunteer participants of various ages and differing knee health histories performing squad – stand – squat exercise cycles.

Honeybees, in their role as pollinators, are vital to both agriculture and the wider ecosystem. However, they have experienced a serious decline across much of the world over recent years. Monitoring their well-being, and taking appropriate action if that is in jeopardy, has thus become a matter of great importance. In this paper, we present an approach based on computer vision to monitor bee activity and motion in the vicinity of an entrance/exit to a hive, including identifying and counting the number of bees approaching or leaving the hive in a given image frame or sequence of image frames.

This study investigates the application and evaluation of existing indirect methods, namely point-based registration techniques, for the estimation and compensation of observed motion included in the 2-D image plane of contrast-enhanced ultrasound (CEUS) cine-loops recorded for the characterization and diagnosis of focal liver lesions (FLLs). The value of applying motion compensation in the challenging modality of CEUS is to assist in the quantification of the perfusion dynamics of an FLL in relation to its parenchyma, allowing for a potentially accurate diagnostic suggestion. Towards this end, this study also proposes a novel quantitative multi-level framework for evaluating the quantification of FLLs, which to the best of our knowledge remains undefined, notwithstanding many relevant studies. Following quantitative evaluation of 19 indirect algorithms and configurations, while also considering the requirement for computational efficiency, our results suggest that the "compact and real-time descriptor" (CARD) is the optimal indirect motion compensation method in CEUS.

While Shannon’s differential entropy adequately quantifies a dimensioned random variable’s information deficit under a given measurement system, the same cannot be said of differential weighted entropy in its existing formulation. We develop weighted and residual weighted entropies of a dimensioned quantity from their discrete summation origins, exploring the relationship between their absolute and differential forms, and thus derive a “differentialized” absolute entropy based on a chosen “working granularity” consistent with Buckingham’s Π-theorem. We apply this formulation to three common continuous distributions: exponential, Gaussian, and gamma and consider policies for optimizing the working granularity.

We present the result of a small study where we investigate what types of resources current students of Mathematics and Mathematics for Engineering prefer for assisting them with their studies of those topics. We found that modern students seem to have a clear preference for on-line resources over traditional textbooks. However, there is currently a lack of good quality resources of that type which allow students to carry-out conventional mathematics exercises on-line and still get appropriate, meaningful and informative feedback on their answers. We then describe our efforts towards addressing this problem through the development of an “intelligent” tutorial system for Calculus which provides feedback tailored to the student’s responses, noting where and how they have made common errors.

There has been much debate regarding the “best” approach for students to learn Mathematics, although it has been acknowledged that this may vary between students and between specialised subject areas. In this study, we investigate similarities and differences in students’ self-perceived preparedness for their degree studies and their preferred approaches to studying mathematics between three different groups – specialist students of Mathematics, Aeronautical Engineering students and Civil Engineering students – all of whom have to study Mathematics as part of their degrees. We interviewed a number of students from each group, asking them about their previous Mathematical background, how much of the first year (level 4) material was new to them, whether resources provided where sufficient, the extent to which they found MathsAid drop-in support sessions useful and their preferred type of resources (lecture notes, text books, on-line, etc) for revision.