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Non-LTE analysis of pre-eruptive prominence plasma parameters’ effects on the Lyman β and Lyman γ lines with solar orbiter SPICE observations
Get PDFThe first dedicated observation of an off-limb prominence by Solar Orbiter took place on April 15, 2023. Our aim is to determine the range of different physical parameters of this prominence and to examine how these parameters affect the formation of the Lyman β and Lyman γ lines of hydrogen. We have found a way to refine key physical parameters by observational data. We will test the method by this prominence observation. We generate 200 random non-LTE models using these observational constraints and compute the Lyman β line and the Lyman γ line profiles. We use the Spectral Imaging of the Coronal Environment (SPICE) full-disk mosaic from November 13, 2023 to constrain the incident radiation. We present the parameters and results of 200 random models using parallel coordinate plots to explore how different parameters affect the results. This allows us to infer the key physical parameters (e.g., central pressure, column mass and temperature gradient) that impact the formation of the Lyman β line and the Lyman γ line in this observation
Adaptive sensing and the future of industrial autonomy
No full textSmart acoustic and ultrasonic systems that can adjust and optimise to their environments are changing the way connected factories work. By rapidly reacting to their surroundings using a combination of physical triggers including sound or temperature, combined with artificial intelligence or machine learning algorithms, they can help reduce operational delays, boost reliability and keep machines connected – even in tough or fast-changing conditions. Physically adaptive sensing constitutes a major step towards more autonomous, efficient industrial operations
Comparison of haptoglobin concentrations between microfilaremic and amicrofilaremic dogs infected by Dirofilaria immitis
No full textBackground:
Dirofilariosis, a zoonotic disease caused by Dirofilaria immitis, is associated with cardiovascular damage and systemic inflammation in dogs.
Objectives:
This study aimed to present preliminary data on the evaluation of serum haptoglobin (Hp) concentration as a potential biomarker of inflammation in dogs naturally infected with D. immitis, with and without microfilaremia.
Methods:
Thirty dogs were categorized into three groups: microfilaremic seropositives (G1, n = 10), amicrofilaremic seropositives (G2, n = 10), and negative controls (CG, n = 10). Serum Hp concentrations were measured using a colorimetric assay and analyzed via one-way ANOVA with Tukey's post-test.
Results:
Median Hp levels were 10.0 mg/dL (G1), 9.1 mg/dL (G2), and 13.7 mg/dL (CG), with no significant differences among groups. Additionally, no significant correlation was found between microfilarial burden and Hp levels (p = 0.651).
Conclusions:
Despite D. immitis infection, Hp concentration did not provide evidence of an inflammatory response in G1 and G2. While previous studies reported decreased Hp in microfilaremic dogs, our findings did not confirm this trend. The seropositive dogs in this study did not show clinical signs, indicating they had relatively mild infections, which may at least in part explain these results. The small sample size and lack of other acute-phase protein assessments restrict the generalizability of our findings and, thus, this study provides limited information about acute phase response dynamics. Nevertheless, these preliminary results highlight the complexity of Hp behavior in D. immitis infection and emphasize the need for further research
The player in copyright law
No full textThis chapter examines the legal status of the video game player in copyright law, showing how copyright doctrine attempts – and fails – to categorise the player within traditional binaries of author/user, and production/consumption. Through a doctrinal analysis, it considers whether playing a game might constitute authorship, performance, or legitimate ‘use’ in copyright law. However, as game environments become increasingly complex and players’ contributions become more socially and economically significant, the limitations of existing legal categories become evident. This chapter argues that play resists categorisation and that attempts to subject it to copyright regulation can mischaracterise its ludic and participatory nature. It concludes that “the player” should be understood as a distinct ontological figure, and that recognising play as beyond the scope of copyright better reflects both doctrinal consistency and cultural practice
Studying with GenAI: student views on the opportunities and risks of GenAI in higher education
No full textThe emergence of Generative Artificial Intelligence (GenAI) technology and the increasing sophistication of such tools have raised concerns about their impact on teaching, learning, and assessment in higher education. To gain an understanding of how students are using GenAI tools for studying and assessments, and to explore their perceptions of its benefits and challenges in higher education, this qualitative study examined the views of Psychology students from two UK universities. Six focus groups (N = 28), consisting of postgraduate psychology students and a few UG students were conducted using semi-structured interview questions to explore their experiences and attitudes toward the use of GenAI during their studies. Students reported using GenAI to summarise content, clarify complex ideas, and ease the writing process. While they recognised its value, especially for accessibility and time management, they also expressed concerns about plagiarism, reduced critical thinking, and unclear boundaries of ethical use. Many felt anxious about academic integrity due to inconsistent guidance. Notably, some students viewed AI use as ethically problematic, particularly in group work. The findings underscore the urgent need for clearer institutional policies and targeted training to promote ethical, effective engagement with GenAI in academic contexts
Marginal diffusion slope as a prognostic imaging biomarker of infiltrating phenotype in glioblastoma; a cancer imaging biomarker roadmap study
No full textBackground:
Conventional MRI protocols fail to probe marginal tumour infiltration in glioblastoma, hindering surgery and radiotherapy planning. This study aimed to demonstrate development and biological validation of a putative imaging biomarker (IB) for characterising glioblastoma infiltration, following principles outlined in the cancer imaging biomarker roadmap.
Methods:
This IB is based upon spatial change in apparent diffusion coefficient (ADC) measures across a macroscopic tumour boundary. We systematically assessed whether ADC slope at the tumour margin (marginal diffusion slope-MDS) could (a) describe an underlying infiltrative phenotype based on reported links between ADC and tumour cellularity validated using a preclinical model, and (b) predict clinical outcome in a single-centre exemplar prospective human cohort study.
Results:
Preclinical results showed a strong, spatially-resolved, negative correlation between marginal ADC and underlying tumour cell density in coregistered MRI-histology datasets from a glioblastoma model. Clinical results (n = 18) showed a positive linear correlation between MDS and clinical outcome, with higher MDS (i.e. steeper marginal ADC slope) associated with longer survival (Pearson's correlation was 0.636, p < 0.005). Cox proportional hazard analysis yielded a survival model (p = 0.013) with MDS significantly associated with overall survival (OS) controlling for age (age p = 0.35, MDS p = 0.010). The hazard ratio for each MDS standard deviation was 0.47 (range 0.25–0.89), indicating that higher MDS predicts longer survival.
Conclusions:
In alignment with the Imaging Biomarker Roadmap consensus, we biologically validated MDS as a biomarker of infiltrative phenotype in a preclinical model and demonstrated its predictive value for OS in humans as a prelude to larger clinical validation studies
Dominant contribution of fossil fuel combustion to carbonaceous aerosol pollution in Delhi: insights from radiocarbon and organic tracers
No full textDelhi experiences some of the highest levels of fine particulate matter (PM2.5) pollution among megacities worldwide. Here, we integrated radiocarbon (14C) analysis with organic molecular tracers to quantify the sources of carbonaceous aerosols in Delhi. Through time-resolved seasonal and diurnal PM2.5 sampling at two representative urban sites and using 14C as an unambiguous tracer, we provide robust quantitative constraints on source contributions. We found that fossil fuel combustion is the dominant contributor, accounting for 62–65 % of organic carbon and 64–66 % of elemental carbon in PM2.5. Crucially, primary organic carbon from fossil fuels (POCFF) constituted the largest fraction of PM2.5 organic carbon (31–44 %). Its contribution peaked in the post- monsoon season, driven mainly by traffic emissions and coal combustion. Secondary organic carbon from fossil sources (SOCFF), biomass burning (OCBB), and cooking emissions (OCCK) contributed 21–29 %, 10–18 % and 3–7 % of PM2.5 organic carbon, respectively. Furthermore, comparisons with Positive Matrix Factorization (PMF) results suggest that conventional methods may overestimate the biomass burning contribution, underscoring the value of the 14C-based approach for accurate apportionment in this complex environment. This study underscores the critical need to reduce fossil fuel reliance and accelerate the shift toward clean energy infrastructure to effectively combat carbonaceous aerosol pollution in Delhi
Machine learning–based multi-objective optimisation of low-carbon and profitable hydrogen and diesel production from non-recycled municipal plastic waste: an integrated life cycle assessment and cost–benefit analysis
No full textSustainable plastic waste management is essential for net zero trajectory, potentially transforming the sector from an emissions source to a circular asset. MPWs (Municipal Plastic Wastes) that are not mechanically recycled can go through pyrolysis-based chemical recycling to produce hydrogen and diesel. There is limited understanding about the optimal configuration and design of pyrolysis-based chemical recycling of plastic waste. Associated attempts to optimise the recycling is rare. In this study, a reliable optimisation framework incorporating machine learning, life cycle assessment and cost-benefit analysis was developed for the design of the pyrolysis of Non-Recycled Municipal Plastic Waste (NMPW). Specifically, the global warming potential (GWP) and net-present value (NPV) of 900 diesel and hydrogen-producing scenarios for the pyrolysis of NMPW were calculated. Associated transportation and pyrolysis process were modelled using ArcGIS Pro and Aspen Plus, respectively. The long short-term memory recurrent neural network (LSTM-RNN) was applied to define temporal dependencies and dynamics of the system, which was integrated with Monte Carlo simulations to expand scenarios from 900 to 700,000. A Pareto curve was derived from the GWPs and NPVs, from which the optimal scenario in terms of environmental and economic performance was identified based on the comparison of two multi-criteria decision-making approaches, i.e., TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and LINMAP (Linear Programming Technique for Multidimensional Analysis of Preference). The solutions by TOPSIS and LINMAP achieved GWPs of -2,570.42 and -1,025.28 kg CO2-eq per tonne NMPW, and NPVs of £300.32 and £-1,402.92 per tonne NMPW, respectively. Thus, the TOPSIS scenario is preferable to the LINMAP scenario due to its lower carbon footprint and higher economic feasibility. This study showed that the proposed optimisation framework has the capacity to facilitate the design of pyrolysis-based processing of NMPW that is profitable and carbon-saving. Such systems could be deployed widely across the UK, where a large share of NMPW is currently either landfilled or incinerated
Virus-dependent geographic structure of co-circulating viruses in a single bat species
No full textUnderstanding the spatial spread of viruses within wildlife populations is often a key component of disease management efforts. Viral spread is likely constrained by host ecology, but inter-virus differences in infection strategy might allow some viruses to overcome these constraints, leading to divergent population structures within a common host environment. We studied the phylogeographic structure of six virus taxa (dependoparvovirus, deltavirus, mastadenovirus, betaherpesvirus and two lineages of rabies virus) circulating in common vampire bats (Desmodus rotundus) in Peru, finding that viral population structure is inconsistently constrained by host ecology. Specifically, while bat travel distance structured the genetic diversity of betaherpesvirus and two lineages of rabies virus, other viruses were instead constrained by anthropogenic factors (dependoparvovirus), or had weakly defined population structure (mastadenovirus). The genetic structure of all viruses was affected by a measure of human travel difficulty between sites, but effects varied in size and direction. Distinct drivers of viral population structure within the same host species imply that virus infection strategy can outweigh host ecological connectivity, acting as a key determinant of geographic spread. Because barriers to gene flow generalise poorly between viruses, whether a tractable virus can illuminate host population structure or predict the spread of high-impact viruses depends on individual virus biology
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