University of Lincoln
  • Lincoln, Lincolnshire, United Kingdom
Recent publications
Particulate Matter (PM) emissions from passenger vehicles have attracted considerable interest over the last decade. In order to reduce PM emissions, improving maximum injection pressure has been a developing trend for new generation GDI engines. However, comparing gasoline and ethanol impingement spray characteristics from a GDI injector under high injection pressure is still unclear. In this paper, a comparative investigation on both the macroscopic and microscopic characteristics of impingement spray from a GDI injector fuelled with gasoline and ethanol was performed under injection pressure up to 50 MPa, providing new findings to promote a more homogeneous air–fuel mixture and reduce PM emissions. The experimental results show that under the same PI (injection pressure), rebound height of gasoline impingement spray is a bit higher than ethanol. AS (spray area) of gasoline is slightly higher than ethanol under PI=10MPa. However, under PI=30MPa and PI=50MPa, AS of gasoline is gradually exceeded by that of ethanol as time progresses. By increasing PI to 50 MPa, the difference in DN (diffusion distance of the near side) between gasoline and ethanol is greatly reduced, meantime DF (diffusion distance of the far side) becomes weaker than ethanol. For both gasoline and ethanol, with the increase PI from 10 MPa to 50 MPa, VN (average normal component of droplet velocity) and VT (average tangential component of droplet velocity) of incident droplets increase by around 1 m/s. Meantime, there is a slight decrease in the absolute value of VN and VT of reflected droplets. DSMD (Sauter mean diameter of droplets) presents a significant decreasing trend with the increase of PI. Besides, a smaller DSMD can be seen for the gasoline impingement spray compared to ethanol under the same PI.
The inherent nature of personality serves as a predisposing, and possible maintaining factor of insomnia. However, methodological differences limit the ability to draw causal conclusions regarding the specific traits involved in the aetiology of the disorder. This systematic review of the relationship between insomnia and personality provides a narrative synthesis of the literature to date. Here, we identified N=76 studies meeting the inclusion/exclusion criteria. The outcomes reliably evidenced the experience of insomnia to be associated with personality traits which are typically considered to be negative or maladaptive in nature. More specifically, insomnia was related to neuroticism, introversion, perfectionistic doubts and concerns, elevated personal standards, negative affect, social inhibition and avoidance, hysteria, hypochondriasis, psychasthenia, impulsive behaviour, anger, hostility and psychopathic tendencies, schizotypal and borderline traits, reduced conscientiousness and self-directedness, and negatively perceived perception of the self. Several studies examined the role of personality plays in predicting the treatment efficacy and adherence of CBTi. Moving forward, longitudinal research, methodological consistency, the mediating role of treatment outcomes and adherence, and clinical and population representative samples should be prioritised. Methodological strengths and limitations of the literature are discussed alongside the next steps which should be taken to advance our understanding of the literature.
The SARs-CoV-2 (hereafter Covid-19) pandemic represents the most significant and disruptive event of the twenty-first century so far (Briggs et al. in Lockdown: Social harm in the Covid-19 era. Palgrave Macmillan, 2021a; Hall in Journal of Extreme Anthropology. 6:44–62, 2022). The consequences of this event were so far reaching that there are very few individuals alive today who lived through the pandemic that could say their life was not in some way, however small, affected by it.
Given the wider context outlined in the book so far, ‘vaccine hesitancy’ has always existed but had grown prior to the pandemic with fewer people around the world getting vaccinated, sometimes even for basic and treatable diseases.
Ben told us this halfway through 2021 when the vaccination campaign was well under way in Germany. He had doubts about the vaccine, particularly how quickly it was conceived, made available, and distributed which was why he refused it.
The Covid-19 pandemic, or perhaps more accurately the lockdown policies enacted to reduce Covid-19 transmission, hospitalisations, and mortalities, fundamentally reordered social life from 2020 to 2022. The rationale behind lockdown was relatively simple and pushed from several perspectives early into the pandemic: lockdown could buy time and save lives while a vaccine was manufactured (Green and Fazi in The Covid consensus: The global assault on democracy and the poor—A critique from the left. Hurst Publishers, 2023; Woolhouse in The year the world went mad: A scientific memoir. Sandstone Press, 2022).
The continual presence of ‘anti-vaxxer’ opposition was met with heavy-handed governmental responses which became iron fisted. Numerous governments including Western liberal democracies refused to listen to dissenting voices; instead, they intensified the legal exclusion of significant numbers of people who for various reasons were frustrated and angry about Covid-19 vaccine mandates. While this occurred in many nations including Austria, Netherlands, Italy, Germany, France, New Zealand, and Australia, perhaps the clearest example was in Canada where tens of thousands of truckers and protestors blocked highways, bridges, border crossings and occupied the space outside parliament in protest at the persistence of Covid-19 restrictions during early 2022.
This statement made by healthcare professionals and academics in the UK and Australia positions ‘immunity passports’ within the context of severe restrictions on movement and the opportunities presented by technology. Vaccination and immunity status for overseas travel is not new.
We write this just after the 3rd anniversary of the WHO declaration that the global spread of Covid-19 was a pandemic and the subsequent announcement of lockdowns around the world. Yet by May 2023, the very same organisation (WHO) announced that Covid-19 is no longer a public health emergency of international concern. Unsurprisingly, perhaps then that, for many people, Covid-19 has receded into the background of social life. It has all but vanished from public consciousness.
The phenomenon of liquid droplet impingement on solid surfaces is particularly important in industrial applications related to spray coating, thermal spraying, inkjet printing, spray cooling, and powder generation industries. Atomized liquid metal droplet impact over surfaces where impingement on both stationary and rotating surfaces, such as rotating disks, can be used to carefully control droplet sizes. Furthermore, several other aspects, such as liquid properties (especially its surface tension), falling height, surface roughness, and wettability, play a vital role in controlling characteristics that not only affect droplet size but also influence droplet trajectories and spread. These parameters were studied in fine detail in a previous article, where a series of experiments were conducted to investigate the phenomenon of transient liquid spreading under varying conditions. In this paper, we further extend the previous study by demonstrating the effect of surface roughness, ra, the droplet Reynolds, and Weber numbers and the contact angle by fitting 342 data points to obtain a high-fidelity model using an artificial neural network (ANN) for predicting βmax, the dimensionless spreading diameter. By comparing the obtained model with ten models in the literature, the authors demonstrated the development of a more precise neural network-based predictive model and its accuracy using a large set of experimental data. It is shown that the spreading is strongly affected in an inverse manner by the impinged surface roughness, which the ANN modeling well captures along with the complex interaction of the other independent factors.
Post-traumatic stress disorder (PTSD) is an anxiety condition caused by exposure to severe trauma. It is characterised by nightmares, flashbacks, hyper-vigilance and avoidance behaviour. These all lead to impaired functioning reducing quality of life. PTSD affects 2-5% of the population globally. Most sufferers cannot access effective treatment, leading to impaired psychological functioning reducing quality of life. Eye movement desensitisation and reprocessing (EMDR) is a non-invasive brain stimulation treatment that has shown significant clinical effectiveness in PTSD. Another treatment modality, that is, trauma-focused cognitive behavioural therapy is also an effective intervention. However, both evidence-based treatments are significantly resource intensive as they need trained therapists to deliver them. A concept of a neuro-digital tool for development is proposed to put to clinical practice of delivering EMDR to improve availability, efficiency and effectiveness of treatment. The evidence in using new technologies to measure sleep, geolocation and conversational analysis of social media to report objective outcome measures is explored. If achieved, this can be fed back to users with data anonymously collated to evaluate and improve the tool. Coproduction would be at the heart of product development so that the tool is acceptable and accessible to people with the condition.
In geospatial data interpolation, as in mapping, mineral resource estimation, modeling and numerical modeling in geosciences, kriging has been a central technique since the advent of geostatistics. Here, we introduce a new method for spatial interpolation in 2D and 3D using a block discretization technique (i.e., microblocking) using purely machine-learning algorithms and workflow design. This paper addresses the challenges of modeling spatial patterns and regularities in nature, and how different approaches have been used to cope with these challenges. We specifically explore the advantages and drawbacks of kriging while highlighting the long and complex sequence of procedures associated with block kriging. We argue that machine-learning techniques offer opportunities to simplify and streamline the process of mapping and mineral resource estimation, especially in cases of strong spatial relationships between sample location and resource concentration. To test the new method, synthetic 2D and 3D data were used for both 2D block modeling and geometallurgical modeling of a synthetic porphyry Cu deposit. The synthetic porphyry Cu data were very useful in validating the performance of the proposed microblocking technique as we were able to reproduce known values at unsampled locations. Our proposed method delivers the benefits of a machine learning-based block modeling approach, which includes its simplicity (a minimum of 2 hyperparameters), speed and familiarity to data scientists. This enables data scientists working on spatial data to employ workflows familiar to their training, to tackle problems that were previously solely in the domain of geoscience. In exchange, we expect that our method will be a gateway to attract more data scientist to become geodata scientists, benefitting the modern data-driven mineral value chain.
Small artificial waterbodies are larger emitters of carbon dioxide (CO2) and methane (CH4) than natural waterbodies. The Intergovernmental Panel on Climate Change (IPCC) recommends these waterbodies are accounted for in national emission inventories, yet data is extremely limited for irrigated landscapes. To derive a baseline of their greenhouse gas footprint, we investigated 38 irrigation farm dams in horticulture and broadacre cropping in semi-arid NSW, Australia. Dissolved CO2, CH4, and nitrous oxide (N2O) were measured in spring and summer, 2021–2022. While all dams were sources of CH4 to the atmosphere, 52% of irrigation farm dams were sinks for CO2 and 70% were sinks for N2O. Relationships in the linear mixed effect models indicate that CO2 concentrations were primarily driven by dissolved oxygen (DO), ammonium, and sediment carbon content, while N2O concentration was best explained by an interaction between DO and ammonium. Methane concentrations did not display any relationship with typical biological variables and instead were related to soil salinity, trophic status, and size. Carbon dioxide-equivalent emissions were highest in small (< 0.001 km²) dams (305 g CO2-eq m⁻² season⁻¹) and in those used for recycling irrigation water (249 g CO2-eq m⁻² season⁻¹), with CH4 contributing 70% of average CO2-eq emissions. However, irrigation dams had considerably lower CH4 emissions (mean 40 kg ha⁻¹ yr⁻¹) than the IPCC emission factor (EF) of 183 kg CH4 ha⁻¹ yr⁻¹ for constructed ponds and lower N2O EF of 0.06% than the indirect EF for agricultural surface waters (0.26%). This synoptic survey reveals existing models may be severely overestimating (4–5 times) farm dam CH4 and N2O emissions in semi-arid irrigation areas. Further research is needed to define these artificial waterbodies in emissions accounting.
Surface finishing of aluminium is a challenging task due to its soft nature and achieving nano level surface finish on aluminium surface is a daunting task even for the most advanced of the finishing processes. The material removal demands precise control of forces from these processes. Ball end magnetorheological finishing (BEMRF) process is one such process that can deliver precisely controlled forces and that too at confined and localized spaces. However, as aluminium is non-magnetic in nature it does not allow formation of an opposite magnetic pole for the process and hence the applied forces are low. To overcome this, a permanent magnet opposite pole is introduced beneath the sample surface which facilitates better forces and magnetic flux between the work surface and the tool tip. This research study is on the optimization of polishing fluid composition for the finishing of aluminium using BEMRF process. With the optimum fluid composition, the surface roughness of the aluminium sample is brought from 95.7 nm down to 53.6 nm in 32 minutes of finishing. The topographical analysis shows that the light scratches are completely removed from the surface and the deep scratches have turned into light discontinuous scratches after finishing. The finished surface is also free from the embedment of abrasive particles.
Techniques for mapping and quantifying ecosystem services are gaining increased traction in recent years. They include powerful computational and visual tools for representing ecosystem service supply and for facilitating policy, planning, and management decisions. This chapter describes, evaluates, and critiques the tools and approaches for quantifying ecosystem service supply that are commonly used by both academics and practitioners. Drawing on relevant case studies, this chapter introduces the mapping methods available for characterizing and measuring both single and multiple ecosystem services and offers new insights for the identification of priority areas for ecosystem services management. Despite the growing use of approaches to ecosystem service modeling, current research and application challenges include: (1) Gaps in data availability; (2) inconsistency in mapping approaches; (3) Assessing uncertainties in ecosystem services mapping; and (4) Translating supply into actual benefits. The chapter concludes with suggestions to overcome these challenges through future research, engagement with end users, and integrating ecosystem service quantification and mapping into decision-making processes.
In recent decades wildfires have occurred frequently over eastern Siberia, which favors increased atmospheric carbon dioxide and air pollution. However, it is not well understood what factors have contributed to the recent increase in these wildfires. Here, we analyzed meteorological and satellite data to show that background Arctic warming related to summer Russian Arctic sea-ice decline can account for about 84% of the increase in eastern Siberian wildfires during 2004-2021 with the remaining 16% related to internal atmospheric variability associated with changes in Siberian blocking events. We further demonstrate that Siberian blocking events occur at higher latitudes, are more persistent and have larger zonal scales over 2004-2021 than during 1979-2003 due to reduced meridional potential vorticity gradients caused by background Arctic warming during the latter period. These changes lead to more persistent, widespread and intense wildfires and their poleward migation, thus contributing to the recent increases in eastern Siberian wildfires.
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Christos Frantzidis
  • School of Computer Science
Lambros Lazuras
  • School of Sport and Exercise Science
Barry Turner
  • Lincoln School of Pharmacy, Lincoln International Business School, Lincoln School of English and Journalism
Andra le Roux-Kemp
  • Lincoln Law School
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