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Robust feedback control of collisional plasma dynamics in presence of uncertainties
No full textMagnetic fusion aims to confine high-temperature plasma within a device, enabling the fusion of deuterium and tritium nuclei to release energy. Due to the very large temperatures involved, it is essential to isolate the plasma from the device walls to prevent structural damage and the external magnetic fields play a fundamental role in achieving this confinement. In realistic settings, the physical mechanisms governing plasma behavior are highly complex, involving numerous uncertain parameters and intricate particle interactions, such as collisions, that significantly affect both confinement efficiency and overall stability. In this work, we address particularly these challenges by proposing a robust feedback control strategy designed to steer the plasma towards a desired spatial region, despite the presence of uncertainties. From a modeling perspective, we consider a collisional plasma described by a Vlasov-Poisson-BGK system, which accounts for a self-consistent electric field and a strong external magnetic field, while incorporating uncertainty in the model. A key feature of the proposed control strategy is its independence from the random parameter, making it particularly suitable for practical applications. A series of numerical simulations confirms the effectiveness of our approach and demonstrates the ability of external magnetic fields to successfully confine plasma away from the device boundaries, even in the presence of uncertain conditions
Strategies enhancing the implementation of design for adaptability in the Ghanaian construction industry: An exploratory and confirmatory factor analyses
No full textDespite the benefits design for adaptability (DfA) contributes to achieving sustainability and circularity in the construction industry, studies have demonstrated that construction professionals are yet to realize its full potential. This study examines the strategies that can enhance the practice of DfA among design professionals in the Ghanaian construction industry (GCI). A quantitative approach was used to achieve the aim of the study by soliciting the views of 236 design professionals in the GCI through structured questionnaires. Data gathered were analyzed via descriptive and inferential statistics. The findings revealed six key categories of strategies (i.e., management strategies, economic strategies, governmental regulations and policy strategies, design strategies, technological strategies and social strategies) to enhance the implementation of DfA practices in the GCI. This study highlights the theoretical and practical implications of DfA implementation, offering actionable insights for construction stakeholders to foster sustainability and resilience in the built environment. It contributes to academic discourse by categorizing strategies and proposing an implementation framework relevant to developing economies like Ghana
AutoMedTS: Automated Modeling of Physiological Time Series for Surgical Suturing Action Recognition
No full textIn laparoscopic surgical training and evaluation, real-time recognition of surgical actions with transparency outputs is crucial for automated, objective, and immediate instructional feedback to support skills improvement. However, we face challenges due to limited dataset sizes and variability in surgical environments. This study presents AutoMedTS, an end-to-end automated machine learning framework customized for medical time-series data, enabling rapid deployment using surgical suturing trajectories collected from both expert and novice surgeons. The proposed method features key improvements including: (i) a novel temperature-scaled Softmax resampling technique effectively addressing severe class imbalance, and (ii) an uncertainty-aware ensemble selection mechanism ensuring robust predictions across surgeons with varying skill levels. Additionally, the approach emphasizes model transparency to meet the high standards of reliability and transparency required in medical applications. Compared to deep learning methods, traditional machine learning models not only facilitate efficient rapid deployment but also offer significant transparency advantages. Experimental results demonstrate that our method provides fast, stable, and reliable real-time surgical action recognition in clinical training environments. Code and data are publicly available at https://github.com/baobingzhang/AutoMedTS
ActDroid: An active learning framework for Android malware detection
No full textThe growing popularity of Android requires malware detection systems that can keep up with the pace of new software being released. According to a recent study, a new piece of malware appears online every 12 seconds. To address this, we treat Android malware detection as a streaming data problem and explore the use of active online learning as a means of mitigating the problem of labelling applications in a timely and cost-effective manner. Specifically, we develop a semi-supervised active learning framework that incrementally trains online learning models using only samples with low prediction confidence, while detecting concept drift and retraining the models when drift is observed. Our resulting framework achieves accuracies of up to 96% on a balanced dataset, requires as little as 24% of the training data to be labelled, and compensates for concept drift that occurs between the release and labelling of an application. We also consider the broader practicalities of online learning within Android malware detection, and systematically explore the trade-offs between using different static, dynamic and hybrid feature sets to classify malware. We find that features derived from static API calls lead to the best performing models, though models based around lower-dimensional permission and opcode feature sets provide a potentially more practical basis for deployment, with only a marginal deficit in accuracy. Dynamic and hybrid feature sets are found to significantly increase feature extraction costs with no net benefit to predictive performance
AIS underrepresents vessel traffic in Scotland's Marine Protected Areas
No full textMaritime traffic poses a variety of risks to both the marine environment and marine wildlife. To quantify and predict risk, accurate data on the distribution and densities of vessel traffic is required, yet currently there is no single data type that captures all vessel traffic. Most commonly, AIS (Automatic Identification System) vessel tracking data is used, despite awareness that AIS data does not fully capture all vessels present. Therefore, evaluations using only AIS likely underestimate the potential impacts. To estimate the scale of underestimation, vessel presence within six of Scotland's Marine Protected Areas (MPAs) were recorded during >1800 h of land-based and at-sea surveys, and compared with AIS data collected from a network of receivers deployed around Scotland. Non-AIS vessels were present within MPAs during 62 % of the surveyed period, with 64 % of vessels sighted not broadcasting AIS. AIS transmission rates varied between MPA, season and vessel type. Given that AIS data is the most commonly used data type for quantifying vessel activity and predicting associated impacts, consideration must be given to the volume of vessel traffic not represented within AIS datasets, particularly within MPAs. Underestimation of actual vessel traffic is likely leading to insufficient management or mitigation efforts within areas designated for protection
Impaired nuclear PTEN function drives macrocephaly, lymphadenopathy and late-onset cancer in PTEN Hamartoma Tumour Syndrome
No full textPTEN hamartoma tumour syndrome (PHTS), a rare disease caused by germline heterozygous PTEN variants, is associated with multi-organ/tissue overgrowth, autism spectrum disorder and increased cancer risk. Phenotypic variability in PHTS is partly due to diverse PTEN variants and the protein's multifaceted functions. PTEN is primarily a phosphatidylinositol(3,4,5)trisphosphate (PIP3) phosphatase regulating PI3K/AKT signalling but also maintains chromosomal stability through nuclear functions such as double-stranded (ds)DNA damage repair. Here, we show that PTEN-R173C, a pathogenic variant frequently found in PHTS and somatic cancer, has elevated PIP3 phosphatase activity that effectively regulates canonical PI3K/AKT signalling. However, PTEN-R173C is unstable and excluded from the nucleus. We generated Pten+/R173C mice which developed few tumours during their lifetime, aligning with normal PI3K/AKT signalling. However, they exhibited lymphoid hyperplasia, macrocephaly and brain abnormalities, associated with impaired nuclear functions of PTEN-R173C, demonstrated by reduced dsDNA damage repair. We integrated PHTS patient data with our mouse model results, and propose that defective nuclear functions of PTEN variants can predict the onset of PHTS phenotypes and that late-onset cancer in these individuals may arise from secondary genetic alterations, facilitated by compromised dsDNA repair
Storage v. production: challenges for reservoir modelling and simulation practitioners
No full textThe rising interest in subsurface CO2 storage makes new calls on reservoir modelling skills, most of which have been developed for hydrocarbon production scenarios. The question for practitioners is: to what extent can the familiar production tools be transferred to the world of storage? In this paper, areas requiring attention are highlighted and high-resolution models are used to compare the behaviour of simulators for production v. storage for two reservoir analogue examples. It is concluded that modelling for storage makes a significant call on multi-scale modelling, to a much greater extent than in production scenarios, and the simplification or omission of reservoir heterogeneities (sometimes tolerable in production scenarios) are much less tolerable when modelling storage. Key static model heterogeneities include the modelling of faults as 3D features, the inclusion of fine-scale reservoir permeability contrasts and the avoidance of net reservoir cut-offs. For dynamic models, use of equation of state is necessary for storage in depleted fields, and correct representation of hysteretic effects of plume migration are a requirement for modelling in aquifers (always) and depleted fields (usually). Modelling for storage, especially for saline aquifers, sets the challenge of modelling volumes previously considered to be at exploration scale, but with an effective resolution more typical of production scales
Purpose versus profit: How institutions shape entrepreneurial success across countries
No full textWhat does ‘success’ mean for entrepreneurs in different institutional environments? Drawing on institutional theory, we explore how the success perceptions of entrepreneurs are shaped by their interpretations of the institutional environment across countries. Based on 87 semi-structured, in-depth interviews with digital start-ups in China, Germany, and the United Kingdom (UK), we find that success perceptions differ substantially across institutional contexts. Our findings suggest that success perceptions balance the individual preferences of entrepreneurs and the need to adapt to the institutional environment. We contribute to understanding on differences in entrepreneurship across countries by examining how institutions can influence entrepreneurial response strategies and outcomes. In addition, we provide a novel perspective on the role of entrepreneurial agency in the context of strong institutional influences
Natural deep eutectic solvents as a green inhibitor of carbon steel corrosion in sulphuric acid
No full textThis study reports two distinct natural deep eutectic solvents (NADESs) as novel corrosion inhibitors of carbon steel in diluted sulphuric acid (H2SO4). The investigated NADES was synthesised from lactic acid as hydrogen bond donor and glucose or D-fructose as hydrogen bond acceptors. The effects of temperature, time, inhibitor concentration, and H2SO4 concentration were optimised. Then, the corrosion rate was assessed in the presence and absence of the inhibitors. The inhibition efficiency of the NADES was investigated at different temperatures. The inhibition mechanism was investigated using adsorption isotherms and kinetic models. The results indicated that 120 min, 298 K, and 0.5 mol/L of H2SO4 achieved the lowest corrosion rate. NADES prepared from lactic acid and D-fructose was the most efficient solvent as a corrosion inhibitor, demonstrating an inhibition efficiency of 82% at 120 min in 0.5 mol/L H2SO4 and 298 K. Furthermore, the Langmuir isotherm model closely fit the inhibition process, which suggests monolayer adsorption of green NADES on the carbon steel surface. The kinetic analysis reveals a mixed-type mechanism involving the physisorption of the green NADES inhibitors. The results provided insights into the development of benign and environmentally friendly inhibitors such as NADES in acidic media
Strong Convergence of a Splitting Method for the Stochastic Complex Ginzburg–Landau Equation
No full textWe consider the numerical approximation of the stochastic complex Ginzburg-Landau equation with additive noise on the one-dimensional torus. The complex nature of the equation means that many of the standard approaches developed for stochastic partial differential equations cannot be directly applied. We use an energy approach to prove an existence and uniqueness result as well as to obtain moment bounds on the stochastic partial differential equation (SPDE) before introducing our numerical discretization. For such a well-studied deterministic equation, it is perhaps surprising that its numerical approximation in the stochastic setting has not been considered before. Our method is based on a spectral discretization in space and a Lie-Trotter splitting method in time. We obtain moment bounds for the numerical method before proving our main result: strong convergence on a set of arbitrarily large probability. From this, we also obtain the convergence in probability. The numerical experiments illustrate the effectiveness of our method
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