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Drought Response in Miscanthus:Breeding Increases Radiation and Water Use Efficiency Over Three Contrasting Years in Central Germany
Get PDFMore and new sources of biomass are needed for renewable energy and renewable products for the bioeconomy. A leading new source of biomass is the highly sustainable perennial grass crop Miscanthus. The majority of the Miscanthus crop comprises a clone of Miscanthus × giganteus (M × g) of limited genetic variation and poor yield under dry growth conditions. The parental species of M × g, M. sacchariflorus and M. sinensis , are distributed over a large geographical range in Eastern Asia and may be used to improve on M × g. From breeding trials, we selected seven novel hybrids and two control genotypes including M × g. We grew these in a field experiment on drought‐prone soil in Germany with and without irrigation. To identify superior Miscanthus types, we estimated radiation use efficiency (RUE), yield and water use efficiency (WUE) from within‐season measurements made over three contrasting growing seasons. Temporal variations in RUE and WUE for different genotypes varied significantly and two novel hybrids, WAT6 and WAT8, achieved the highest yields. To achieve goodness of fit to yield measurements, genotype‐specific parameters for process descriptions in the model MiscanFor were adjusted for the two superior genotypes. These parameters included earlier shooting and an increased threshold of overheating. When the model was run over ten years, despite generating the highest yield values, WAT8 accumulated less biomass than WAT6 over the longer term. The response of WUE to variation in soil capillary pressure and vapour pressure deficit was examined. WUE of M × g increased with the severity of water stress then declined again. The superior yielding genotypes were more able to sustain biomass accumulation and/or water use under the highest stress. We believe that combining physiology with crop modelling is a powerful way to inform genetic and agronomic improvements needed to secure the future supply of biomass for the bioeconomy
Feature Selection for High-Dimensional Imbalanced Class Datasets Using Harmony Search and Kullback-Leibler Divergence
No full textHigh-dimensional imbalanced datasets pose significant challenges in pattern recognition, often leading to overfitting and classifier bias toward majority classes. While numerous feature selection algorithms exist, most struggle to effectively address both high dimensionality and class imbalance simultaneously. This paper introduces Harmony search Kullback-Leibler (HKL), a novel feature selection algorithm that integrates Kullback-Leibler divergence with the Harmony Search metaheuristic to specifically address these dual challenges. HKL establishes an information-theoretic foundation by employing KL divergence as a statistical framework to evaluate feature subsets based on their ability to separate minority and majority classes. Unlike existing Harmony Search variants that operate as class-blind optimizers treating feature selection as a generic optimization problem, HKL fundamentally shifts the paradigm by incorporating direct class distribution awareness into the optimization process. The algorithm implements a dual optimization approach that simultaneously balances classification performance metrics with class distribution divergence measures. This design specifically enhances minority class discrimination by prioritizing features that maximize the divergence between class distributions, ensuring that selected features provide discriminative power for underrepresented classes rather than simply favoring the majority class. Experimental validation across multiple high-dimensional biomedical datasets demonstrates that HKL consistently outperforms existing state-of-the-art methods in terms of AUC and G-mean metrics, with particular improvements for minority class classification. The algorithm achieves optimal performance while using substantially reduced feature subsets, often requiring only a quarter to half of the original features to maintain or exceed baseline classification accuracy. Statistical significance testing confirms that these performance improvements represent genuine algorithmic advantages rather than random variation. The proposed approach offers an effective solution to both dimensionality reduction and class imbalance challenges, providing a valuable tool for complex classification tasks across various domains
Reconceptualizing the Nation in Sanctuary Practices:Toward a Progressive, Relational National Politics?
No full textThis article explores sanctuary in Wales, focusing on the Welsh Government’s recent declaration to become a Nation of Sanctuary (NoS), and identifying how the national scale provides an alternative locus for progressive sanctuary measures. In revealing the nation’s emergence as another crucial site of sanctuary, the work reconceptualizes the nation’s place in sanctuary policies and practices in two ways: (i) it locates sanctuary through a national scale, thus moving beyond the city/state dichotomy that has dominated explanations of sanctuary, and (ii) it shows the importance of decoupling the nation-state compound while simultaneously integrating the nation(al) into discussions on sanctuary without being bound to the state or xenophobic populism. In showing how “nations against the state” can participate in sanctuary measures, we expand the current understanding of where sanctuary can be found, and capture the various forms of national belonging and identities that exist within plurinational states, including alternative, progressive forms of civic belonging. This is particularly significant in light of the tightening of state immigration policies, greater regulation of immigrant entry at state borders, and continuation of restrictive citizenship policies witnessed in recent years, which have ignited sanctuary measures aimed at creating safe spaces beyond the reach of state measures
Trust-enhanced POI recommendation algorithm using expectation-maximization
Get PDFPoint-of-interest (POI) recommendation systems have become increasingly important as travelers rely on mobile technologies and location-based social networks to discover new places. However, existing approaches often struggle with static user preferences, inadequate trust modeling, and extreme data sparsity. This paper introduces ExMax, a dynamic trust-enhanced recommendation framework leveraging Expectation-Maximization theory to address these limitations. ExMax employs a novel check-in matrix representation that adapts to evolving user interests, incorporates friendship network information to enhance recommendation quality, and integrates sentiment analysis to capture nuanced satisfaction signals beyond ratings. The framework’s iterative probabilistic model discovers latent features within sparse data, enabling meaningful recommendations even with limited interaction history. The algorithm exhibits time complexity for offline learning, where T represents EM iterations (typically 20–30), |R| denotes observed ratings, F indicates features, and K represents gradient steps. While sparse matrix operations and parallelization potential provide some mitigation, the iterative nature poses scalability challenges for platforms with hundreds of millions of users. Experimental evaluation on Yelp, Gowalla, and Brightkite datasets demonstrates that ExMax performs favorably compared to existing approaches across various metrics. The results suggest that integrating dynamic preference modeling, social trust signals, and contextual information offers a promising direction for location-based recommendation systems, particularly where recommendation quality justifies the computational cost. This work demonstrates how probabilistic modeling can effectively capture the dynamic and social nature of location discovery while acknowledging the inherent computational trade-offs of iterative optimization.</p
"In This Country There are Many Thousands To Whom the Act ... is a Sealed Book":Locality, Centre and the Welsh Language In the New Poor Law, 1834–1850s
No full textThe 1834 New Poor Law saw the reordering of local government across England and Wales. Wales experienced an imposition of reformed poor law administrative structures designed to combat changing economic and social conditions in Midland and Southern England. Such reform fed into the building of modern centralised state power against Welsh traditions of parochial management providing a deep point of conflict. As with several European states new welfare legislation was imposed where the population (including those responsible for delivering it) spoke a different language to those tasked with introducing it. However, language was more than a ‘practical’ consideration. Some paupers, advocates and union officers continued corresponding and translating official guidance into Welsh: it was clear this was also a means of lingering contestation against an administrative imposition.</p
Language-Guided Change Detection for high-resolution remote sensing imagery with limited labelled data
No full textDeep learning has been extensively applied in the field of remote sensing for tasks such as change detection (CD). However, since CD is a pixel-level task, the high cost of data annotation and often limited availability of labelled data significantly restrict the performance of existing deep learning-based CD methods. To mitigate this problem, a novel Language-Guided Change Detection (LGCD) framework is introduced. Within this LGCD network, text information is leveraged to precisely locate changed areas, addressing the shortcomings associated with insufficient labelled image data. Also, augmentation semi-supervised learning techniques are employed to generate high-quality pseudo-labels, further reducing the reliance on labelled samples. Additionally, the utilisation of Fusion UNet (FUNet) and Transformer capitalises on their sensitivity to local and global features respectively, offering a comprehensive examination of change features in high-resolution bi-temporal remote sensing imagery. For evaluation purposes, three publicly available CD datasets are exploited. Experimental results demonstrate that the proposed LGCD framework achieves exceptional detection performance in both fully supervised and semi-supervised settings, despite the constraints of limited labelled data.</p
Cataloguing Interviews
Get PDFThis project was set up for a conference paper for the Metadata & Discovery Group Conference in September 2020 - presentation abstract included. Brief description: Many library services have reduced in-house expertise in bibliographical cataloguing, in some cases to lone metadata specialist librarians. We are one such small service here in Information Services in Aberystwyth and having been temporally even smaller for six months, we are now in the position to train new cataloguers – one as a permanent part time job-share and two as temporary part time support to tackle our backlog. This interview project aims to informally document the training process and in so doing, develop an understanding of what librarians new to cataloguing think about metadata and librarianship
Optimisation of multiple clustering based undersampling using artificial bee colony:Application to improved detection of obfuscated patterns without adversarial training
Get PDFAttack detection is one of the main features required in modern defence systems. Despite the ongoing research, it remains challenging for a typical mechanism like network-based intrusion detection system (NIDS) to catch up with evolving adversarial attacks. They specifically aim to confuse a machine-learning based predictor. Without the knowledge of adversarial patterns, the best approach is generalising signatures learned from a dataset of legitimate connections and known intrusions. This work focuses on analysing non-payload traffics so that the resulting techniques can be exploited to a range of network-based applications. It investigates a novel means to deal with the problem of imbalanced classes. An optimised undersampling method is introduced to select a subset of majority-class representatives initially created through an ensemble clustering procedure. A weighted combination of criteria representing distributions within and between classes is proposed as the objective function for a global optimisation using the artificial bee colony (ABC). This approach usually outperforms its baselines and other state-of-the-art undersampling models, with ABC being more effective using the global best strategy than a random selection of solutions or an iterative greedy search. The paper also details the parameter analysis offering a heuristic guide for potential taking up of the proposed techniques.</p
Theoretical calculation methods of stable bearing capacity for thin-walled shells with corrosion and variable temperature
Get PDFThin-wall shells (steel plates, steel cylindrical shells, steel spherical shells, etc.) are widely used in many engineering fields such as construction, machinery, chemical industry, navigation, and aviation because of their light weight and high strength. Their failure modes under static pressure or impact dynamic load are mostly buckling instability, and the failure is very sudden, often causing structural failure or even catastrophic accidents without obvious symptoms. In this framework, the significance of this paper is that it considers the influence of external environment corrosion on steel shells' bearing capacity using plate and shell classical stability theory, and investigates the stable bearing capacity of thin-wall steel shells in view of corrosion impact. By this approach, a theoretical calculating method for the time-varying stable bearing capacity of plate and shell thin-walled steel members under the simultaneous action of corrosion and temperature changes is obtained, providing a useful theory for complex engineering practices such as corrosion and temperature changes, including fire actions. Noted that for this method with no analytical solution found, its numerical solutions are given in the appendixes.</p