Suppression of the nuclear rainbow in the inelastic nucleus-nucleus scattering

The nuclear rainbow observed in the elastic $\alpha$-nucleus and light heavy-ion scattering is proven to be due to the refraction of the scattering wave by a deep, attractive real optical potential. The nuclear rainbow pattern, established as a broad oscillation of the Airy minima in the elastic cross section, originates from an interference of the refracted far-side scattering amplitudes. It is natural to expect a similar rainbow pattern also in the inelastic scattering of a nucleus-nucleus system that exhibits a pronounced rainbow pattern in the elastic channel. Although some feature of the nuclear rainbow in the inelastic nucleus-nucleus scattering was observed in experiment, the measured inelastic cross sections exhibit much weaker rainbow pattern, where the Airy oscillation is suppressed and smeared out. To investigate this effect, a novel method of the near-far decomposition of the inelastic scattering amplitude is proposed to explicitly reveal the coupled partial-wave contributions to the inelastic cross section. Using the new decomposition method, our coupled channel analysis of the elastic and inelastic $^{12}$C+$^{12}$C and $^{16}$O+$^{12}$C scattering at the refractive energies shows unambiguously that the suppression of the nuclear rainbow pattern in the inelastic scattering cross section is caused by a destructive interference of the partial waves of different multipoles. However, the inelastic scattering remains strongly refractive in these cases, where the far-side scattering is dominant at medium and large angles like that observed in the elastic scattering.Comment: Dedicated to the memory of Jacques Raynal; to be published in EPJ

Maintenance grouping for multi-component systems with availability constraints and limited maintenance teams

International audienceThe paper deals with a maintenance grouping approach for multi-component systems whose components are connected in series. The considered systems are required to serve a sequence of missions with limited breaks/stoppage durations while maintenance teams (repairmen) are limited and may vary over time. The optimization of the maintenance grouping decision for such multi-component systems leads to a NP-complete problem. The aim of the paper is to propose and to optimize a dynamic maintenance decision rule on a rolling horizon. The heuristic optimization scheme for the maintenance decision is developed by implementing two optimization algorithms (genetic algorithm and MULTIFIT) to find an optimal maintenance planning under both availability and limited repairmen constraints. Thanks to the proposed maintenance approach, impacts of availability constraints or/and limited maintenance teams on the maintenance planning and grouping are highlighted. In addition, the proposed grouping approach allows also updating online the maintenance planning in dynamic contexts such as the change of required availability level and/or the change of repairmen over time. A numerical example of a 20-component system is introduced to illustrate the use and the advantages of the proposed approach in the maintenance optimization framework

Fundamentals of micellar electrokinetic chromatography (MEKC)

Micellar electrokinetic chromatography (MEKC) is a useful branch of capillary electrophoresis (CE) that utilizes surfactant above critical micelle concentration (CMC) as pseudo-stationary phase. MEKC can be employed to separate both charged and neutral molecules, individually or simultaneously, including chiral compounds. MECK benefits from high peak efficiency due to electroosmotic flow (EOF) in the separation capillary, compounded with large variety of synthetic surfactants, organic modifiers, temperature and variable separation voltage has made MECK the method of choice for separation scientists. In this review, we present the introduction of CE, fundamentals of surfactant chemistry as it relates to MEKC, separation principles in MECK including equations involved in calculating separation parameters (capacity factor, resolution etc.)

The impact of acute surgical unit rostering on National Emergency Access Targets during the COVID-19 pandemic: a single hospital experience

Background: Surgical departments have been dramatically impacted by the novel coronavirus 19 (COVID-19) pandemic, with the cancellation of elective cases and changes to the provision of emergency surgical care. The aim of this study was to determine whether structural changes made within our facility's surgical department during COVID-19 altered National Emergency Access Target (NEAT) times and impacted on patient outcomes. Methods: Emergency surgical cases over a 4-month time period were retrospectively collected and statistically analysed, divided into pre- and mid-COVID-19 pandemic. Results: Baseline characteristics between the groups were comparable. There was a significant increase in consultant presence in theatre in the COVID group. There were also statistically significant reductions in NEAT times at each timepoint, although these did not meet national guidelines. There was no change in emergency surgical workload, complication rate or mortality rates within 30 days. Conclusion: Any significant change to services requires a coordinated hospital-wide approach, not just from a single department, and clinicians must continue to be wary of benchmarked times as the overall feasibility and safety of NEAT times has also been highlighted again

POTENTIOMETRIC SENSOR FOR HYDROGENE ION BASED ON NEUTRAL CARRIER IN A POLY (VINYL CHLORIDE) MEMBRANE WITH POLYANILINE SOLID CONTACT

Joint Research on Environmental Science and Technology for the Eart

A long range, energy efficient internet of things based drought monitoring system

The climate change and global warning have been appeared as an emerging issue in recent decades. In which, the drought problem has been influenced on economics and life condition in Vietnam. In order to solve this problem, in this paper, we have designed and deployed a long range and energy efficient drought monitoring based on IoT (Internet of Things) for real time applications. After being tested in the real condition, the proposed system has proved its high dependability and effectiveness. The system is promising to become a potential candidate to solve the drought problem in Vietnam

Maintenance optimisation for systems with multi-dimensional degradation and imperfect inspections

In this paper, we develop a maintenance model for systems subjected to multiple correlated degradation processes, where a multivariate stochastic process is used to model the degradation processes, and the covariance matrix is employed to describe the interactions among the processes. The system is considered failed when any of its degradation features hits the pre-specified threshold. Due to the dormancy of degradation-based failures, inspection is implemented to detect the hidden failures. The failed systems are replaced upon inspection. We assume an imperfect inspection, in such a way that a failure can only be detected with a specific probability. Based on the degradation processes, system reliability is evaluated to serve as the foundation, followed by a maintenance model to reduce the economic losses. We provide theoretical boundaries of the cost-optimal inspection intervals, which are then integrated into the optimisation algorithm to relieve the computational burden. Finally, a fatigue crack propagation process is employed as an example to illustrate the effectiveness and robustness of the developed maintenance policy. Numerical results imply that the inspection inaccuracy contributes significantly to the operating cost and it is suggested that more effort should be paid to improve the inspection accuracy

Planning the restoration of membranes in RO desalination using a digital twin

This paper describes the development of a decision support system (DSS) for evaluating membrane restoration strategy. The engine of the DSS is a digital twin (DT), a virtual representation of wear (degradation) and restoration of membrane elements in a reverse osmosis (RO) pressure vessel. The basis of the DT is a mathematical model that describes an RO vessel as a novel multi-component system in which the wear-states of individual elements (components) are quantified and elements can be swapped or replaced. This contrasts with the contemporary presentation in the literature of a membrane system as a single system. We estimate the parameters of the model using statistical methods. We describe our approach in the context of a case study on the Carlsbad Desalination Plant in California, which suffers from biofouling due to seasonal algae blooms. Our results show a good fit between the observed and the modelled wear-states. Competing policies are compared based on risk, cost, downtime, and number of stoppages. Projections indicate that a significant cost-saving can be achieved while not compromising the integrity of plant