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Microplastics (MPs) draw international attention owing to their widespread distribution in water ecosystems, but whether MPs cause neurotoxic effects in aquatic animals at environmentally realistic concentrations is still controversial. This meta-analysis recompiled 35 studies to determine whether MPs could change the levels of brain (in vivo) neurotransmitters in aquatic animals at environmentally realistic concentrations (≤1 mg/L, median = 0.100 mg/L). Then, a group comparison was conducted to compare the effects of different factors on the effect size and to explore the significant factors affecting the neurotoxicity of MPs. The results demonstrated that MP exposure could considerably decrease the levels of acetylcholinesterase (AchE) in the brain of aquatic animals by 16.2%. However, the effects of MPs on cholinesterase (CHE), acetylcholine (ACh), dopamine (DA) and γ-aminobutyric acid (GABA) were not statistically significant due to the small number of studies and samples. The neurotoxicity of MPs was closely linked with particle size and exposure time but independent of animal species, MP compositions, MP morphology and MP concentrations. Further literatures review indicated that MP-induced neurotoxicity and behavioral changes are related with multiple biological processes, including nerve damage, oxidative stress, intestinal flora disturbance and metabolic disorder. Furthermore, some factors influencing MP neurotoxicity in the real environment (e.g. the aging of MPs, the release of MP additives, and the co-exposure of MPs and pollutants) were discussed. Overall, this study preliminarily explored whether MPs induced changes in neurotoxicity-related indicators in aquatic animals through meta-analysis and provided scientific evidence for evaluating the health risks and neurotoxicity of MPs at the environmental level.

The optical attractive force in tapered single-mode fibers (SMFs) is usually uniformly distributed around the tapered section and has been found to be important for trapping and manipulating targeted atoms and nanoparticles. In contrast, a peculiar phenomenon of the evanescent field splitting along the azimuth axis can be experimentally observed by tapering a weakly-coupled MCF into a strongly-coupled MCF to generate supermode interference. Moreover, the supermode interference produces a hexagonally distributed evanescent field and its six vertices give rise to the multiline optical attractive force. For such spectral resonances, the optimum extinction ratio for the transmission dips is given by 47.4 dB, this being determined using an index liquid to cover the tapered MCF. The resonant dips move to a greater extent at longer wavelengths, with the optimum tuning efficiency of 392 nm/RIU for index sensing. The split evanescent fields respectively attract the excited upconversion nanoparticles in the liquid to be linearly aligned and running down the tapered region over the fiber surface, emitting green light with 60° symmetry. The charged nanoparticles were periodically self-organized, with a period of around 1.53 µm. The parallel lines, with 60° rotational symmetry, can be useful for (1) indicating the exact locations of the side-cores or orientations of the tapered MCF; (2) as precision alignment keys for micro-optical manipulation; and (3) enhancing the upconversion light, or for use in lasers, coupling back to the MCF. The split evanescent fields can be promising for developing new evanescent field-based active and passive fiber components with nano-structures.

By tapering a weakly-coupled MCF into a strongly-coupled MCF, supermode interference effects can be generated with the best extinction ratio, 47.4dB and tuning efficiency, 392nm/RIU. Up-conversion nanoparticles are attracted and aligned by hexagonal evanescent fields.

We study inventory rationing in a system with multiple demand classes and lost sales. It is assumed to have at most one outstanding order, resulting in two periods in an order cycle separated by the time of order release. We review the most related work by Melchiors (2001, 2003) (Ph.D. thesis, University of Aarhus, Int. J. Prod. Econ. 81–82, (11), 461–468), and find that the existing approximated and optimal policies are not easy to obtain due to computational complexity. Also as the rationing issue before order release is not well addressed in literature, in this paper we prove the static rationing being optimal. Furthermore in such a system with two distinct periods, the optimal rationing policy is a combination of a dynamic policy during the replenishment lead time and a static policy before order release. In order to make the rationing policies to be readily used in practice, we introduce two approximated methods for calculating the rationing levels in two periods, respectively. The results, in particular the combination of static and dynamic rationing, outperform the existing approximations in literature. In addition, the computation is obviously simplified due to the efficient algorithm of dynamic rationing and the explicit expressions of static rationing.

This paper examines a periodic-reviewed lot sizing problem with random yields, disruptions and limited inventory capacity. To characterise the continuous production, an additive random yield model is considered rather than a multiplicative one. Disruptions cause breakdowns to production. Inventory capacity is included since the production has to be shut down when the inventory buffer is full. Both disruptions and shutdowns lead to a start-up cost and a stochastic lead time to recover the production. These compound factors of uncertainty are encountered in practical planning decisions in process industries. We review the existing random yield models, which are then compared with the additive model. With a linear production cost, the additive model has an order-up-to policy to be optimal. Disruptions deteriorate the expected actual production quantity and the fill-rate dramatically, even though the optimal order-up-to level increases compared with the cases of no disruption. Considering inventory capacity makes the problem to be a non-convex dynamic programming problem. Numerical analysis shows that the performance is dramatically deteriorated when the inventory capacity is rather tight, which indicates the importance of selecting a proper inventory capacity to reduce the negative impacts and avoid redundant investment on capacity. Moreover, the start-up cost plays an important role in determining the level of inventory capacity.

Customers may react differently when stockouts occur. In this paper we investigate the rationing policy for an inventory system with a mixture of demand classes of backorder type and lost sales type. Since the penalty cost of backorders varies with time, the priorities of demand classes also alter with time. This totally changes the problem structure compared with the classic rationing models. A dynamic rationing policy is studied in this paper by considering the dynamics of demand priorities. A Markov decision model is developed to obtain the optimal dynamic rationing levels for multiple demand classes. The results indicate that between the priority switching points, rationing levels often exhibit different patterns. For lost sales demand classes, the rationing levels always decrease as the remaining time approaches to zero. For backorder demand classes, the rationing levels increase in some parts due to declining of the priorities. The rationing levels of all demand classes finally decline to zero to reduce the inventory holding cost. The application of dynamic rationing is further extended from a single period model to a multi-period ( S , T ) model where unit cost has to be included. The optimal ordering policy is proved to be a myopic base stock policy and the dynamic rationing policy in the single period model can still be applied with modified time-independent penalty costs for lost sales classes. To overcome the computational complexity, a heuristic dynamic rationing policy is introduced. Due to its good outcome, implementing such a heuristic dynamic rationing policy can be a practical solution for inventory system with mixed backorders and lost sales, in order to enhance the system performance.