The potential of plasma-derived hard carbon for sodium-ion batteries

Sodium-ion batteries (SIB) are receiving wider attention due to sodium abundance and lower cost. The application of hard carbon to SIB electrodes has shown their significant potential to increase rates, capacities, stability, and overall performance. This article describes the significance of hard carbon, its structural models, and mechanisms for SIB applications. Further, this work unveils the potential of plasma methods as a scalable and sustainable manufacturing source of hard carbon to meet its increasing industrial demands for energy storage applications. The working mechanisms of major plasma technologies, the influence of their parameters on carbon structure, and their suitability for SIB applications are described. This work summarises the performance of emerging plasma-driven hard carbon solutions for SIB, including extreme environments, and revolves around the flexibilities offered by plasma methods in a wider spectrum such as multi-materials doping, in-situ multilayer fabrication, and a broad range of formulations and environments to deposit hard carbon-based electrodes for superior SIB performance. It is conceived the challenges around the stable interface, capacity fading, and uplifting SIB capacities and rates at higher voltage are currently being researched, Whereas, the development of real-time monitoring and robust diagnostic tools for SIB are new horizons. This work proposes a data-driven framework for plasma-driven hard carbon to make high-performance energy storage batteries

Cloud-to-ground lightning in cities : seasonal variability and influential factors

Urban-induced land use changes have a significant impact on local weather patterns, leading to increased hydro-meteorological hazards in cities. Despite substantial threats posed to humans, understanding atmospheric hazards related to urbanisation, such as thunderstorms, lightning, and convective precipitation, remains unclear. This study aims to analyse seasonal variability of cloud-to-ground (CG) lightning in the five large metropolitans in Bangladesh utilising six years (2015–2020) of Global Lightning Detection Network (popularly known as GLD360) data. It also investigates factors influencing CG strokes. The analysis revealed substantial seasonal fluctuations in CG strokes, with a noticeable increase in lightning activity during the pre-monsoon months from upwind to metropolitan areas across the five cities. Both season and location appear to impact the diurnal variability of CG strokes in these urban centres. Bivariate regression analysis indicated that precipitation and particulate matter (PM) significantly influence lightning generation, whilst population density, urban size, and mean surface temperature have negligible effects. A sensitivity test employing a random forest (RF) model underscored the pivotal role of PM in CG strokes in four of the five cities assessed, highlighting the enduring impact of extreme pollution on lightning activity. Despite low causalities from CG lightning, the risk of property damage remains high in urban environments. This study provides valuable insights for shaping public policies in Bangladesh, a globally recognised climate hotspot

Triglyceride glucose index as an indicator of cardiovascular risk in Syrian refugees

Background: The triglyceride glucose (TyG) index is a quick and inexpensive approach to measure insulin resistance. The aim of this study was to evaluate the TyG index’s ability to predict cardiovascular risk and determine the TyG index cutoff values in Syrian refugees. Methods: A retrospective research study was conducted with 756 Syrian refugees. Data on demographics and clinical laboratory assessments were obtained from refugee’s files. The formula Ln [fasting triglycerides (mg/dL) × fasting plasma glucose (mg (dL)/2] was used to calculate the TyG index. The Framingham risk score was used to calculate ten-year cardiovascular risk. The TyG index cutoff point was determined using the receiver operating characteristic curve (ROC). Results: Included participants had a mean age of 56.76 ± 10.78 years and a mean body mass index (BMI) of 27.42 ± 4.03 kg/m2. 28.57% of the subjects were smokers, and the majority were female (56.75%). A significant moderate correlation was observed between TyG index and Framingham score (r = 0.428, p < 0.001). ROC curve analysis for TyG index and Framingham score showed an area under the curve (AUC) of 0.741 (95% CI = 0.691–0.791; p < 0.001). The cutoff value of the TyG index to recognize intermediate/high risk Framingham risk score was 9.33, with a sensitivity of 64.3%, and specificity of 75.0%. Conclusion: Our findings determine that, given a TyG index cutoff value of 9.33, the TyG index has a predictive ability to assess ten-year cardiovascular risk by comparison to the Framingham risk score in a high-risk group of Syrian refugees and can be used as an independent indicator of cardiovascular risk

Probing scrambling and operator size distributions using random mixed states and local measurements

The dynamical spreading of quantum information through a many-body system, typically called scrambling, is a complex process that has proven to be essential to describe many properties of out-of-equilibrium quantum systems. Scrambling can, in principle, be fully characterized via the use of out-of-time-ordered correlation functions, which are notoriously hard to access experimentally. In this work, we put forward an alternative toolbox of measurement protocols to experimentally probe scrambling by accessing properties of the operator size probability distribution, which tracks the size of the support of observables in a many-body system over time. Our measurement protocols require the preparation of separable mixed states together with local operations and measurements, and combine the tools of randomized operations, a modern development of near-term quantum algorithms, with the use of mixed states, a standard tool in NMR experiments. We demonstrate how to efficiently probe the probability-generating function of the operator distribution and discuss the challenges associated with obtaining the moments of the operator distribution. We further show that manipulating the initial state of the protocol allows us to directly obtain the individual elements of the distribution for small system sizes

The power flow algorithm for AC/DC microgrids based on improved unified iteration method

In response to the complexity of the Jacobian matrix inversion process in the power flow algorithm for AC/DC microgrids, leading to large memory requirements and susceptibility to convergence issues, a novel power flow algorithm based on an improved unified iteration method for AC/DC microgrids is proposed. Firstly, the fundamental equations of the unified iteration method and the characteristics of DC systems are analyzed. The reactive power correction terms and voltage phase correction differences are removed from the modified equations of the unified iteration method, and result in a reduction in the order of the Jacobian matrix in the power flow algorithm. Subsequently, the improved IEEE 11-node system is subjected to simulation verification to attain precise power flow solutions for hybrid AC/DC microgrids. The theoretical analysis identifies the main influencing parameters of active and reactive power errors and assesses their impact factors. Finally, experimental validation of the improved power flow algorithm is carried out on a physical platform, clarifying the applicability range of the proposed method. The research results indicate that within allowable error margins, the proposed approach reduces the difficulty of Jacobian matrix inversion, resulting in an 80% increase in computational speed compared to the unified iteration method. It is suitable for microgrid systems with short electrical distances and small magnitudes of node voltage amplitudes and phase differences

Exploring the Impacts of the Adopted Carbon Capture Approach to the Scottish Chemical Industry and the Wider Scottish Economy

The UK Climate Change Committee identifies carbon capture utilisation and storage (CCUS) as essential to achieve net zero by 2050 and the UK Government is providing support to the rollout of CCUS in four of UK’s industrial clusters. However, carbon capture can be introduced in industries either post-combustion (post-production more generally) or pre-combustion with a substitution towards low or zero carbon fuels. Each approach has its own capital and energy requirements and therefore impacts the adopting industries in different ways. In this brief we discuss how the Scottish chemical industries, and by extension the wider Scottish economy, may be affected by the introduction of pre- or post-combustion carbon capture. We also discuss the implications of a UK-wide adoption of carbon capture in chemical industries versus unilateral actions by the Scottish chemical sector, while we explore the potential effects of government subsidies aiming to ease some of the price pressures associated with the introduction of carbon capture

Principles for inclusive assessment design in cybersecurity education

When designing cybersecurity assessments, educators aim to achieve several objectives. Primarily, we need to measure how well students meet the intended learning outcomes while ensuring that the assessments are both authentic and valid. This involves providing opportunities for students to develop their security skills and knowledge. Achieving these objectives is challenging and often requires substantial time and effort. In addition to these goals, we must also ensure that the assessments are inclusive since, arguably, a non-inclusive assessment is not truly valid. In cybersecurity, designing inclusive assessments without inadvertently omitting key knowledge and skills can be particularly difficult. This chapter reflects on relevant research on inclusive assessment and identifies core principles that can be applied to cybersecurity assessment design in higher education to improve inclusivity

Determination of zooplankton absorption spectra and their contribution to ocean color

Zooplankton are keystone organisms that provide a critical link between primary production and higher order predators in the marine food web, as well as facilitating the sequestration of carbon within the ocean. In this context, there is considerable interest in the detection of zooplankton swarms from satellite ocean colour signals. However, for this to be possible, accurate inherent optical property characterisation of key zooplankton groups is first required. In this study, spectral absorption properties of six epipelagic zooplankton groups have been measured using a novel serial addition technique carried out with a Point Source Integrating Cavity Absorption Meter. The measured absorption spectra were used to model the impact of each group on remote sensing reflectance signals and determine a concentration threshold that would generate a distinguishable signal from ocean colour data. Results indicate that the spectral shape of absorption did not vary much between species, with most organisms showing a peak at around 480 nm, characteristic of the pigment astaxanthin. Conversely, the magnitude of absorption did vary considerably between species, with larger organisms typically producing stronger absorption signals than smaller species. Thus, detection thresholds also varied for each group measured and were additionally influenced by background constituents within the water column. The calculated concentration thresholds indicate the feasibility of identifying zooplankton from ocean colour, but owing to the spectral similarity in absorption properties, knowledge of in situ populations would be required to determine species abundances from satellite signals

Sparse time-varying parameter VECMs with an application to modeling electricity prices

In this paper we propose a time-varying parameter (TVP) vector error correction model (VECM) with heteroskedastic disturbances. We propose tools to carry out dynamic model specification in an automatic fashion. This involves using global–local priors and postprocessing the parameters to achieve truly sparse solutions. Depending on the respective set of coefficients, we achieve this by minimizing auxiliary loss functions. Our two-step approach limits overfitting and reduces parameter estimation uncertainty. We apply this framework to modeling European electricity prices. When considering daily electricity prices for different markets jointly, our model highlights the importance of explicitly addressing cointegration and nonlinearities. In a forecasting exercise focusing on hourly prices for Germany, our approach yields competitive metrics of predictive accuracy