5,539 research outputs found

    Convolutional neural network ensemble learning for hyperspectral imaging-based blackberry fruit ripeness detection in uncontrolled farm environment

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    Fruit ripeness estimation models have for decades depended on spectral index features or colour-based features, such as mean, standard deviation, skewness, colour moments, and/or histograms for learning traits of fruit ripeness. Recently, few studies have explored the use of deep learning techniques to extract features from images of fruits with visible ripeness cues. However, the blackberry (Rubus fruticosus) fruit does not show obvious and reliable visible traits of ripeness when mature and therefore poses great difficulty to fruit pickers. The mature blackberry, to the human eye, is black before, during, and post-ripening. To address this engineering application challenge, this paper proposes a novel multi-input convolutional neural network (CNN) ensemble classifier for detecting subtle traits of ripeness in blackberry fruits. The multi-input CNN was created from a pre-trained visual geometry group 16-layer deep convolutional network (VGG16) model trained on the ImageNet dataset. The fully connected layers were optimized for learning traits of ripeness of mature blackberry fruits. The resulting model served as the base for building homogeneous ensemble learners that were ensemble using the stack generalization ensemble (SGE) framework. The input to the network is images acquired with a stereo sensor using visible and near-infrared (VIS-NIR) spectral filters at wavelengths of 700 nm and 770 nm. Through experiments, the proposed model achieved 95.1% accuracy on unseen sets and 90.2% accuracy with in-field conditions. Further experiments reveal that machine sensory is highly and positively correlated to human sensory over blackberry fruit skin texture

    Faster inference from state space models via GPU computing

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    Funding: C.F.-J. is funded via a doctoral scholarship from the University of St Andrews, School of Mathematics and Statistics.Inexpensive Graphics Processing Units (GPUs) offer the potential to greatly speed up computation by employing their massively parallel architecture to perform arithmetic operations more efficiently. Population dynamics models are important tools in ecology and conservation. Modern Bayesian approaches allow biologically realistic models to be constructed and fitted to multiple data sources in an integrated modelling framework based on a class of statistical models called state space models. However, model fitting is often slow, requiring hours to weeks of computation. We demonstrate the benefits of GPU computing using a model for the population dynamics of British grey seals, fitted with a particle Markov chain Monte Carlo algorithm. Speed-ups of two orders of magnitude were obtained for estimations of the log-likelihood, compared to a traditional ‘CPU-only’ implementation, allowing for an accurate method of inference to be used where this was previously too computationally expensive to be viable. GPU computing has enormous potential, but one barrier to further adoption is a steep learning curve, due to GPUs' unique hardware architecture. We provide a detailed description of hardware and software setup, and our case study provides a template for other similar applications. We also provide a detailed tutorial-style description of GPU hardware architectures, and examples of important GPU-specific programming practices.Publisher PDFPeer reviewe

    A reinforcement learning recommender system using bi-clustering and Markov Decision Process

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    Collaborative filtering (CF) recommender systems are static in nature and does not adapt well with changing user preferences. User preferences may change after interaction with a system or after buying a product. Conventional CF clustering algorithms only identifies the distribution of patterns and hidden correlations globally. However, the impossibility of discovering local patterns by these algorithms, headed to the popularization of bi-clustering algorithms. Bi-clustering algorithms can analyze all dataset dimensions simultaneously and consequently, discover local patterns that deliver a better understanding of the underlying hidden correlations. In this paper, we modelled the recommendation problem as a sequential decision-making problem using Markov Decision Processes (MDP). To perform state representation for MDP, we first converted user-item votings matrix to a binary matrix. Then we performed bi-clustering on this binary matrix to determine a subset of similar rows and columns. A bi-cluster merging algorithm is designed to merge similar and overlapping bi-clusters. These bi-clusters are then mapped to a squared grid (SG). RL is applied on this SG to determine best policy to give recommendation to users. Start state is determined using Improved Triangle Similarity (ITR similarity measure. Reward function is computed as grid state overlapping in terms of users and items in current and prospective next state. A thorough comparative analysis was conducted, encompassing a diverse array of methodologies, including RL-based, pure Collaborative Filtering (CF), and clustering methods. The results demonstrate that our proposed method outperforms its competitors in terms of precision, recall, and optimal policy learning

    Isotope reconstructions of East Asian Monsoon behaviour across Glacial Terminations I and II from Lake Suigetsu, Japan (IAP2−18−54)

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    Understanding the response of the East Asian Monsoon to rising temperatures is crucial in light of recent anthropogenic climate change and the vulnerability of East Asia to future climatic hazards. However, East Asian Monsoon dynamics during warming periods in the late Quaternary are poorly understood, particularly on decadal to millennial timescales. Significant sources of this uncertainty are the spatially and temporally heterogeneous responses of the East Asian Monsoon to submillennial temperature fluctuations. The conflicting patterns observed in available reconstructions of East Asian Monsoon strength suggest that the teleconnections acting during these intervals were complex. Understanding the behaviours of the East Asian Monsoon by accounting for links to remote climatic perturbations allows for a more holistic understanding of deglacial climate changes. A means of tackling this ambiguity is by contributing well−dated, high−resolution records of East Asian Monsoon evolution spanning Glacial Terminations I and II (which typify accessible, contrasting examples of rapid global warming) to the growing network of reconstructions from across the region. The aim of this thesis is to deconvolve East Asian Monsoon evolution during the last two glacial terminations by utilising the unique hydrological distribution of East Asian Monsoon precipitation over Japan to reconstruct both seasonal modes of the system (i.e., the East Asian Winter Monsoon and East Asian Summer Monsoon). This aim is met by the construction of isotope−based, season−specific East Asian Monsoon records across Glacial Terminations I and II using materials from the Lake Suigetsu sediment cores. This thesis is comprised of four interconnected research papers, preceded by an introduction and succeeded by a summary of findings, discussion of relevance, suggestions for future work and conclusions. In the first research paper, we utilise extended contemporary monitoring of the stable isotope composition of precipitation, river water and lake water in the Lake Suigetsu catchment to understand the factors affecting these variables and aid robust interpretation of isotope−based proxy reconstructions from the Lake Suigetsu sediment cores. Our results show that the composition of precipitation was influenced by the dual East Asian Monsoon system, and that these signals were then transferred to the lake system where they were combined with secondary local influences on lake water composition. Based on our knowledge of late Quaternary catchment dynamics, these observations suggest that the palaeo−isotope composition of Lake Suigetsu was closely related to the East Asian Monsoon. In the second research paper, we examine the influence of remote climatic processes on the East Asian Winter Monsoon and East Asian Summer Monsoon in Japan during Glacial Termination I by reconstructing trends in the strength of each seasonal mode. This is achieved using oxygen isotope analysis of diatom silica and compound−specific hydrogen isotope analysis of n−alkanoic acids from the Lake Suigetsu sediment cores. Our results support distinctive seasonal behaviours of the East Asian Monsoon during Glacial Termination I, with evidence for East Asian Winter Monsoon weakening and East Asian Summer Monsoon strengthening. The East Asian Summer Monsoon also exhibited variations in strength which were synchronous with Antarctic temperature fluctuations after 16,000 years ago, which supports a temporally restricted climatic link between Japan and the Southern Hemisphere at this time. In the third research paper, we reconstruct the East Asian Summer Monsoon in Japan during Glacial Termination II, and contrast the findings to those from Glacial Termination I. The reconstruction presented in this chapter, which is based on compound−specific hydrogen isotope analysis of n−alkanoic acids, provides evidence for early East Asian Summer Monsoon strengthening followed by a gradual weakening phase with submillennial−scale variability. Comparison of this record to others derived from mainland China supports the assertion that East Asian Summer Monsoon behaviours during Glacial Termination II were spatially heterogenous. Additionally, the different evolutions of the East Asian Summer Monsoon during Glacial Terminations I and II indicate that the system operated distinctively under contrasting boundary conditions, although the new reconstructions from Japan were consistently more closely linked with Southern Hemisphere (Antarctic) temperatures than Northern Hemisphere (Greenlandic) temperatures during both intervals. The fourth research paper was motivated by a lack of an absolute chronology for the oldest (pre−50,000 years ago) parts of the Lake Suigetsu sediment cores (which includes Glacial Termination II). In this paper, we appraise the luminescence characteristics of the cores using rapid profiling techniques. These are employed across four key time periods in order to assess the application of these methods for the detection of local and environmental shifts, and to assess the suitability of the core materials for luminescence dating. We show that the luminescence characteristics of the cores were susceptible to a range of environmental perturbations, best illustrating local changes by using high−resolution contiguous sampling. The feasibility of future luminescence dating is supported by quantifiable luminescence signals, and first order approximate ages suggest that blue light optically stimulated luminescence dating of feldspar provides the most accurate and most practical assessment of burial age. This technique should be the subject of dating efforts in pursuit of refinements to the Suigetsu core chronology before 50,000 years ago. The findings of this thesis contribute to our collective knowledge of East Asian Monsoon behaviours during glacial terminations. Critically, they represent a geographical expansion of the regional high−resolution record network to include Japan. The value of this process is demonstrated by the decoupled evolutions of each seasonal mode during Glacial Termination I, and a remote link between Antarctic temperatures and East Asian Summer Monsoon evolution in Japan during Glacial Terminations I and II, which were hitherto unconstrained by high resolution analysis. These findings acknowledge and begin to rationalise spatial and temporal heterogeneities in East Asian Monsoon behaviours by comparison to other records. This work highlights the complexity of the East Asian Monsoon, and the value of long records from contrasting deglacial periods for a better comprehension of this system in the context of anthropogenic climate change

    Lessons to be learned by comparing integrated fisheries stock assessment models (SAMs) with integrated population models (IPMs)

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    AEP was partially funded by the Cooperative Institute for Climate, Ocean, & Ecosystem Studies (CICOES) under NOAA Cooperative Agreement NA15OAR4320063, Contribution No. 2023-1331.Integrated fisheries stock assessment models (SAMs) and integrated population models (IPMs) are used in biological and ecological systems to estimate abundance and demographic rates. The approaches are fundamentally very similar, but historically have been considered as separate endeavors, resulting in a loss of shared vision, practice and progress. We review the two approaches to identify similarities and differences, with a view to identifying key lessons that would benefit more generally the overarching topic of population ecology. We present a case study for each of SAM (snapper from the west coast of New Zealand) and IPM (woodchat shrikes from Germany) to highlight differences and similarities. The key differences between SAMs and IPMs appear to be the objectives and parameter estimates required to meet these objectives, the size and spatial scale of the populations, and the differing availability of various types of data. In addition, up to now, typical SAMs have been applied in aquatic habitats, while most IPMs stem from terrestrial habitats. SAMs generally aim to assess the level of sustainable exploitation of fish populations, so absolute abundance or biomass must be estimated, although some estimate only relative trends. Relative abundance is often sufficient to understand population dynamics and inform conservation actions, which is the main objective of IPMs. IPMs are often applied to small populations of conservation concern, where demographic uncertainty can be important, which is more conveniently implemented using Bayesian approaches. IPMs are typically applied at small to moderate spatial scales (1 to 104 km2), with the possibility of collecting detailed longitudinal individual data, whereas SAMs are typically applied to large, economically valuable fish stocks at very large spatial scales (104 to 106 km2) with limited possibility of collecting detailed individual data. There is a sense in which a SAM is more data- (or information-) hungry than an IPM because of its goal to estimate absolute biomass or abundance, and data at the individual level to inform demographic rates are more difficult to obtain in the (often marine) systems where most SAMs are applied. SAMs therefore require more 'tuning' or assumptions than IPMs, where the 'data speak for themselves', and consequently techniques such as data weighting and model evaluation are more nuanced for SAMs than for IPMs. SAMs would benefit from being fit to more disaggregated data to quantify spatial and individual variation and allow richer inference on demographic processes. IPMs would benefit from more attempts to estimate absolute abundance, for example by using unconditional models for capture-recapture data.Publisher PDFPeer reviewe

    An explainable deep-learning architecture for pediatric sleep apnea identification from overnight airflow and oximetry signals

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    Producción CientíficaDeep-learning algorithms have been proposed to analyze overnight airflow (AF) and oximetry (SpO2) signals to simplify the diagnosis of pediatric obstructive sleep apnea (OSA), but current algorithms are hardly interpretable. Explainable artificial intelligence (XAI) algorithms can clarify the models-derived predictions on these signals, enhancing their diagnostic trustworthiness. Here, we assess an explainable architecture that combines convolutional and recurrent neural networks (CNN + RNN) to detect pediatric OSA and its severity. AF and SpO2 were obtained from the Childhood Adenotonsillectomy Trial (CHAT) public database (n = 1,638) and a proprietary database (n = 974). These signals were arranged in 30-min segments and processed by the CNN + RNN architecture to derive the number of apneic events per segment. The apnea-hypopnea index (AHI) was computed from the CNN + RNN-derived estimates and grouped into four OSA severity levels. The Gradient-weighted Class Activation Mapping (Grad-CAM) XAI algorithm was used to identify and interpret novel OSA-related patterns of interest. The AHI regression reached very high agreement (intraclass correlation coefficient > 0.9), while OSA severity classification achieved 4-class accuracies 74.51% and 62.31%, and 4-class Cohen’s Kappa 0.6231 and 0.4495, in CHAT and the private datasets, respectively. All diagnostic accuracies on increasing AHI cutoffs (1, 5 and 10 events/h) surpassed 84%. The Grad-CAM heatmaps revealed that the model focuses on sudden AF cessations and SpO2 drops to detect apneas and hypopneas with desaturations, and often discards patterns of hypopneas linked to arousals. Therefore, an interpretable CNN + RNN model to analyze AF and SpO2 can be helpful as a diagnostic alternative in symptomatic children at risk of OSA.Ministerio de Ciencia e Innovación /AEI/10.13039/501100011033/ FEDER (grants PID2020-115468RB-I00 and PDC2021-120775-I00)CIBER -Consorcio Centro de Investigación Biomédica en Red- (CB19/01/00012), Instituto de Salud Carlos IIINational Institutes of Health (HL083075, HL083129, UL1-RR-024134, UL1 RR024989)National Heart, Lung, and Blood Institute (R24 HL114473, 75N92019R002)Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación- “Ramón y Cajal” grant (RYC2019-028566-I

    Equity and spatial accessibility of healthcare resources in online health community network

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    Introduction: This study investigates the geographical distribution and fractal characteristics of the medical service network in China, using the “Good Doctor website” as a case study.Methods: Data for this study were extracted from the Good Doctor website Health Community. A two-tiered hierarchical network model was developed to analyze the geographical distribution and fractal characteristics of the medical service network in China.Results: Results unveil the hierarchical nature of hospital distribution and the interconnectivity among healthcare institutions. Shandong Province as a central node within the national hospital network, and networks of secondary hospitals show significant self-similarity and scale-free properties.Discussion: The small world and fractal characteristics shed light on the rapid dissemination of medical information and the robustness of the healthcare network. The results offer a novel perspective for understanding and optimizing the distribution of medical resources, and help improve the efficiency of healthcare services supply

    The medical applications of hyperpolarized Xe and nonproton magnetic resonance imaging

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    Hyperpolarized 129Xe (HP 129Xe) magnetic resonance imaging (MRI) is a relatively young field which is experiencing significant advancements each year. Conventional proton MRI is widely used in clinical practice as an anatomical medical imaging due to its superb soft tissue contrast. HP 129Xe MRI, on the other hand, may provide valuable information about internal organs functions and structure. HP 129Xe MRI has been recently clinically approved for lung imaging in the United Kingdom and the United States. It allows quantitative assessment of the lung function in addition to structural imaging. HP 129Xe has unique properties of anaesthetic, and may transfer to the blood stream and be further carried to the highly perfused organs. This gives the opportunity to assess brain perfusion with HP 129Xe and perform molecular imaging. However, the further progression of the HP 129Xe utilization for brain perfusion quantification and molecular imaging implementation is limited by the absence of certain crucial milestones. This thesis focused on providing important stepping stones for the further development of HP 129Xe molecular imaging and brain imaging. The effect of glycation on the spectroscopic characteristics of HP 129Xe was studied in whole sheep blood with magnetic resonance spectroscopy. An additional peak of HP 129Xe bound to glycated hemoglobin was observed. This finding should be implemented in the spectroscopic HP 129Xe studies in patients with diabetes. [...

    Correlation between systemic inflammatory response index and thyroid function: 2009-2012 NHANES results

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    AimsThis study investigates the relationship between the Systemic Inflammatory Response Index (SIRI) and thyroid function.MethodsUtilizing data from the National Health and Nutrition Examination Survey (NHANES) 2009-2012, we excluded participants lacking SIRI or thyroid function data, those under 20 years, and pregnant individuals. SIRI was determined using blood samples. We conducted weighted multivariate regression and subgroup analyses to discern the independent relationship between SIRI and thyroid function.ResultsThe study included 1,641 subjects, with an average age of 47.26±16.77 years, including 48.65% males and 51.35% females. The population was divided into three SIRI-based groups (Q1-Q3). Q3, compared to Q1, exhibited higher age-at-onset, greater male prevalence, and increased levels of FT3, FT4, TT4, leukocytes, and triglycerides. This group also showed a higher incidence of diabetes, hypertension, and smoking. Notably, Q1 had lower LDL and HDL levels. SIRI maintained a positive association with FT4 (β = 0.01, 95% CI = 0.00-0.03, P for trend = 0.0071), TT4 (β = 0.20, 95% CI = 0.10, 0.31, P for trend=0.0001), and TPOAb (β = 8.0, 95% CI = 1.77-14.30, P for trend = 0.0120), indicating that each quartile increase in SIRI corresponded to a 0.01 ng/dL increase in FT4, a 0.2 g/dL increase in TT4, and an 8.03 IU/mL rise in TPOAb. The subgroup analysis suggested the SIRI-thyroid function correlation was influenced by hypertension.ConclusionInflammation may impact the development and progression of thyroid function disorders. Proactive anti-inflammatory treatment might mitigate thyroid abnormalities
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