Advanced optimisation software framework for floating offshore wind farm logistics and operations

Offshore wind farms are being built farther from shore to maximise energy production, but this creates significant logistical challenges for maintaining these remote turbines. Service Operation Vessels (SOVs) and Crew Transfer Vessels (CTVs) play a vital role in keeping turbines operational. However, their routes need to be carefully planned to reduce travel distances and fuel consumption while meeting tight maintenance schedules. This study introduces a Python-based optimisation framework designed to streamline both day-to-day and campaign-style maintenance operations. By integrating geospatial analysis, clustering algorithms, and multi-day scheduling, the framework generates efficient vessel routes considering different turbine locations, weather windows, technician capacity, and vessel availability. When full daily servicing is unfeasible, the framework prioritises tasks and creates multi-day schedules to ensure efficient use of resources. Clustering techniques further streamline the process by grouping nearby turbines for maintenance. A real-world case study demonstrated a 36 % reduction in fuel consumption compared to conventional methods, underscoring the framework's potential to lower operational costs and enhance sustainability. In addition to this efficiency gains, the solution mitigates risks by ensuring timely maintenance, thereby supporting the offshore wind sector's capacity to meet escalating energy demands reliably

Distribution of statistics on separable permutations restricted by a flat POP

Finding distributions of statistics in pattern-avoiding permutations has attracted significant attention in the literature. In particular, Chen, Kitaev, and Zhang derived functional equations for the joint distributions of any subset of classical minima and maxima statistics, as well as for the joint distributions of ascents and descents in separable permutations. Meanwhile, partially ordered patterns (POPs) have also been extensively studied. Notably, so-called flat POPs played a key role, via the notion of shape-Wilf-equivalence, in proving a conjecture on pattern-avoiding permutations. In this paper, we study flat POP-avoiding separable permutations, where the maximum element in a flat POP receives the largest label. Avoiding such a POP imposes restrictions on the position of the maximum element in a separable permutation, forcing it to be positioned to the left. We establish a system of functional equations describing the joint distribution of six classical statistics in the most general case, extending the work of Chen, Kitaev, and Zhang. As a specialization, when the POP has length 3, we recover a joint distribution result of Han and Kitaev on permutations avoiding classical patterns of length 3. As another specialization, for the flat POP of length 4, we derive an explicit rational generating function that captures the distribution of six statistics, with a numerator containing 100 monomials and a denominator containing 19 monomials

A step towards ultrasonic guided wave monitoring for resin infusion front position estimation in composites manufacturing

Resin infusion paired with Out of Autoclave (OoA) curing offers an alternative to infrastructure-heavy autoclave-based manufacturing. However, lower fibre volume fractions and increased porosity from uneven resin flow limit the adoption of OoA processes in safety-critical applications. Consequently, there is demand for in-situ monitoring tools to track resin progression and ensure full permeation. Prior methods, including optical fibres and electromagnetic sensors, can infer front position but are intrusive or hard to scale. This research investigates leaky Lamb waves generated by ultrasonic transducers embedded in the top lid of an infusion mould. To isolate wave-fluid interactions, liquid-only measurements in a 2.0 mm thick infusion box are collected, removing laminate heterogeneity and enabling acquisition of controllable consecutive measurements, enabling the development and validation of predictive models under well-defined conditions. Attenuation of the fundamental antisymmetric mode (A0) as resin reaches the sensing region was demonstrated through theoretical and simulation-based analysis, highlighting the potential of Ultrasonic Guided Waves (UGW) for real-time fluid tracking. A custom experimental setup enabled consistent repeatable measurements of an advancing liquid front. A parametric study investigated the effects of geometry and fluid on signal amplitude, determining sensor spacing for sensitivity and areal coverage. Ultrasonic measurements were correlated with time-stamped images of the resin front through a machine-vision algorithm. Several functional approximation methods were applied to estimate liquid position from ultrasonic data, capturing the general trends in flow behaviour. The models yielded robust predictions, with mean errors of 5-7% of the sensor spacing, despite environmental variations and system nonlinearities contributing to data variability

Generative self-supervised learning for seismic event classification

Deep learning has been widely applied to seismic signal classification, predominantly through supervised learning, typically relying on large labeled datasets. However, since the process of labeling large volumes of seismic data by domain experts is time-consuming and prone to human error, labeled seismic datasets are scarce. To address the problem of limited labeled data availability, a novel approach for seismic event classification is proposed employing self-supervised learning techniques. Initially, a generative-based self-supervised learning model, specifically an auto-encoder, is designed to extract informative features from the Short Time Fourier Transform of seismic recordings. These features are classified into four categories: earthquakes, micro-earthquakes, rockfalls, and anthropogenic noise. Classification is performed using (a) unsupervised K-means clustering on unlabeled data and (b) semi-supervised approaches, where only 5 to 33.3% of the data are labeled. The proposed semi-supervised method achieves high performance on a publicly available Résif dataset with recall of 0.90 for earthquakes, 0.65 for micro-earthquakes, 0.91 for rockfalls, and 0.84 for noise signals when trained with 20% of the labeled data. Additionally, we introduce a novel method to improve data labeling efficiency by using Self-Organizing Maps to cluster features from large datasets into multiple nodes. Our results demonstrate that the experts can more effectively and confidently label a small number of nodes instead of labeling all the events in the large dataset, thereby reducing the experts’ workload to just 4.6% of the original effort and our study reveals that this approach provides an excellent trade-off between expert labeling effort and classification accuracy, making it a highly effective solution for seismic event labeling. To evaluate the generalization capability of our proposed self-supervised learning model, we tested it on two unseen seismic datasets: the globally distributed Stanford Earthquake Dataset and the regionally focused Pacific Northwest Curated Seismic Dataset. On Stanford Earthquake Dataset, the pre-trained model effectively extracted discriminative earthquake and noise features, achieving high clustering accuracies. The Pacific Northwest Curated Seismic Dataset further challenges generalization with heterogeneous and previously unseen event types such as explosions, and thunder. Despite this diversity, the pre-trained model still preserved meaningful feature separability and captured inter-class relationships among acoustically similar events. Overall, these findings highlight the model’s ability to generalize effectively across both global and regional seismic datasets, underscoring its potential for wide deployment in seismological monitoring and event characterization without extensive retraining

What is the connection between employee voice and job quality?

Though concerns with job quality have a long genesis in social science research, the subject has gained increased attention from policymakers in recent decades. Job quality is a multidimensional concept and a multidisciplinary area of research. The core concern of this chapter is the connection between employee voice and job quality, an area which is underemphasized in job quality debates. The focus is primarily upon the experience of the UK, a nation which has achieved strong employment levels in recent decades but where concerns have increasingly been expressed by various stakeholders regarding the quality of jobs. The chapter therefore considers the relationship between employee voice and job quality by reviewing current evidence before considering the implications for the future. The key argument is that, to improve understanding and policymaking in the context of developments in working patterns, a better theorization and conceptualization of voice and its relationship with job quality is necessary

Adaptive compensation for in-process ultrasonic cladding inspection

Throughout the early 21st century, the rise in manufacturing costs has led to economic and industrial drivers to develop novel solutions to tackle the increasing costs of high-integrity manufacturing. A key driver to reduce costs is to implement product quality conformance inspections, such as Non-Destructive Testing (NDT) at the point of manufacture, rather than at the end of the process, reducing manufacturing rework, improving schedule certainty, and increasing manufacturing throughput within industrial facilities. Welding is a highly utilised process deployed in the manufacture of high-value components such as nuclear pressure vessels, which are then clad with a corrosion-resistant alloy, with preferential attributes onto a cheaper base material to reduce the cost of manufacture. Traditional code-compliant ultrasonic inspection methodology commonly requires the machining of any non-planar surfaces prior to inspection, preventing the inspection of cladding methods during manufacture. Until now, in-process inspection has not been applied to weld cladding applications with non-planar surface profiles. This paper presents a novel approach to optimising ultrasonic imaging through the as-clad surface, consisting of multiple angled transmission and reception beams. Representative cladding trials, with artificial ultrasonic reflectors representing typical cladding defects, were introduced to assess the sensitivity of the ultrasonic inspection to defects under various non-planar surfaces. The approach demonstrated a reduction in variability of defect amplitude due to surface profile compensation alone, from 9.42dB to 1.37dB, demonstrating the methodology that can be applied agnostically of complex ray-tracing methods

Numerical investigations of aerodynamic performance for flettner rotors in the presence of full-scale ship-rotor interaction

This study presents numerical investigations of aerodynamic characteristics (lift and drag coefficients) for Flettner rotors in the presence of the interaction between the rotor and the full-scale merchant ship. For this purpose, several numerical investigations have been conducted for two different isolated rotors in model and full-scale conditions using Reynolds Averaged Navier-Stokes (RANS) based Computational Fluid Dynamics (CFD) approaches. The effects of different turbulence models, mesh types and sizes, and boundary conditions on the domain's bottom surface have been investigated for a reference rotor in isolation and model-scale conditions. After that, selected methods were implemented on a full-scale isolated rotor geometry. The results of the computations were compared with experimental and computational results from the open literature and showed good agreement. As a result of the validation studies in isolated conditions, a similar CFD approach was applied on a full-scale rotor, which is operating on a capsize bulk carrier (merchant ship) to investigate the interaction between the rotor and the ship. During these numerical calculations, different ship and wind speeds, rotation rates for rotor and Thom, and also different wind profiles such as Straight and Atmospheric Boundary Layer (ABL) were investigated for the Flettner rotors in interaction with the full-scale ship. In conclusion, not only the aerodynamic characteristics of the Flettner rotor but also the effects of this complex interaction between the rotor and ship were analysed and investigated computationally. Results show that rotor-ship interaction significantly affects aerodynamic performance at spin ratios above 3, with drag forces increasing and lift forces decreasing compared to isolated conditions. Moreover, ABL profiles consistently led to lower lift coefficients than uniform wind conditions, underlining the importance of realistic environmental modeling

“I want to be honest…but how much can I share?” : Sustainable influencing and experiences of moral residue

Transparency is the cornerstone of social media influencing. Research has explored how influencers disclose commercial interests, yet little is known about influencers’ self-disclosure of private consumption. Building on the transparency management and moral hypocrisy literatures, this paper explores how sustainable influencers navigate moral dilemmas as they communicate about sustainability. Through interviews and analysis of media articles, we find that sustainable fashion influencers experience persistent emotional baggage that we frame as ‘moral residue’, in navigating three moral dilemmas related to (anti)consumption, (non)promotion, and (non)commercialization. To reconcile this, sustainable fashion influencers engage in transparency management, choosing between strategies of ‘confessing’, ‘concealing’ and/or ‘conning’. These strategies may inadvertently exacerbate moral hypocrisy, evidencing how sustainable influencers are locked in perpetual cycles of moral residue. In explicating the process and potential outcomes of managing transparency around moral dilemmas, we provide an intrapersonal view of moral hypocrisy and offer implications for theory and practice

Administrating crisis is just a transition : interventions on bureaucratic activity in the United Kingdom, 1987-2022

The process of leaving the European Union set off a disruptive transformation of the UK’s system of government. Central to implementing this process was secondary legislation, called statutory instruments, which received unparalleled levels of attention by the public due to the government’s use of them to untangle UK and EU law. Yet, the legislative crisis caused by Brexit, appeared in many ways just another form of government transition. We propose that understanding how this process affected bureaucratic activity requires a broad theory of regular partisan transitions. Large changes in the ideological goals and demands of the government redirect the priority of policies developed through instruments. To examine this perspective, we analyse the most prominent partisan and political transitions in the UK from 1987 to 2022 using time series intervention analyses. The results indicate that crises and transitions alike led to lasting changes in the bureaucracy’s agenda. Transitions in 2010 and 2015 not only exhibited shifts in the topical focus of secondary legislation, but also dramatic reductions in productivity. This paper’s findings further suggest that partisan effects on issue attention may have more to do with the organisation of government than the broader distribution of issues addressed using public policy