Highlights d 1.6 million tests identified 1,388 SARS-CoV-2 infections in Guangdong by 19 March d Virus genomes can be recovered using a variety of sequencing approaches d Analyses reveal multiple viral importations with limited local transmission d Effective control measures helped reduce and eliminate chains of viral transmission

A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. ABSTRACTRecovering value from carbon fibre reinforced polymers waste can help to address the high cost and environmental burden of producing carbon fibres, but there is limited understanding of the cost and environmental implications of potential recycling technologies. The objective of this study is to assess the environmental and financial viability of mechanical recycling of carbon fibre composite waste. Life cycle costing and environmental assessment models are developed to quantify the financial and environmental impacts of alternative composite waste treatment routes, comparing landfilling, incineration with energy recovery, and mechanical recycling in a UK context. Current Landfill Tax results in incineration becoming the lowest cost composite waste treatment option; however, incineration is associated with high greenhouse gas emissions as carbon released from composite waste during combustion exceeds CO2 emissions savings from displacing UK electricity and/or heat generation, resulting in a net greenhouse gas emissions source. Mechanical recycling and fibre reuse to displace virgin glass fibre can provide the greatest greenhouse gas emissions reductions of the treatment routes considered (378 kg CO2eq./t composite waste), provided residual recyclates are landfilled rather than incinerated. However, this pathway is found to be unfeasible due to its high cost, which exceeds £2,500/t composite waste ($3,750/t composite waste). The financial performance of mechanical recycling is impaired by the high costs of dismantling and recycling processes; low carbon fibre recovery rate; and low value of likely markets. To be viable, carbon fibre recycling processes must achieve near-100% fibre recover rates and minimise the degradation of fibre mechanical properties to enable higher-value applications (e.g., virgin carbon fibre displacement). Ongoing development of carbon fibre recovery technologies and composite manufacturing techniques using recycled carbon fibres leading to improved material properties is therefore critical to ensuring financial viability and environmental benefit of carbon fibre reinforced polymer recycling.

Ruibin (2018) Decision-making in cold chain logistics using data analytics: a literature review.

Highlights: 1) 1.6 million molecular diagnostic tests identified 1,388 SARS-CoV-2 infections in Guangdong Province, China, by 19th March 2020; 2) Virus genomes can be recovered using a variety of sequencing approaches from a range of patient samples. 3) Genomic analyses reveal multiple virus importations into Guangdong Province, resulting in genetically distinct clusters that require careful interpretation. 4) Large-scale epidemiological surveillance and intervention measures were effective in interrupting community transmission in Guangdong Summary: COVID-19 is caused by the SARS-CoV-2 coronavirus and was first reported in central China in December 2019. Extensive molecular surveillance in Guangdong, China's most populous province, during early 2020 resulted in 1,388 reported RNA positive cases from 1.6 million tests. In order to understand the molecular epidemiology and genetic diversity of SARS-CoV-2 in China we generated 53 genomes from infected individuals in Guangdong using a combination of metagenomic sequencing and tiling amplicon approaches. Combined epidemiological and phylogenetic analyses indicate multiple independent introductions to Guangdong, although phylogenetic clustering is uncertain due to low virus genetic variation early in the pandemic. Our results illustrate how the timing, size and duration of putative local transmission chains were constrained by national travel restrictions and by the province's large-scale intensive surveillance and intervention measures. Despite these successes, COVID-19 surveillance in Guangdong is still required as the number of cases imported from other countries is increasing.

Abstract. One of the main motivations for investigating hyper-heuristic methodologies is to provide a more general search framework than is currently available. Most of the current search techniques represent approaches that are largely adapted for specific search problems (and, in some cases, even specific problem instances). There are many real-world scenarios where the development of such bespoke systems is entirely appropriate. However, there are other situations where it would be beneficial to have methodologies which are more generally applicable to more problems. One of our motivating goals is to underpin the development of more flexible search methodologies that can be easily and automatically employed on a broader range of problems than is currently possible. Almost all the heuristics that have appeared in the literature have been designed and selected by humans. In this paper, we investigate a simulated annealing hyper-heuristic methodology which operates on a search space of heuristics and which employs a stochastic heuristic selection strategy and a short-term memory. The generality and performance of the proposed algorithm is demonstrated over a large number of benchmark data sets drawn from three very different and difficult (NP-hard) problems: nurse rostering, university course timetabling and onedimensional bin packing. Experimental results show that the proposed hyper-heuristic is able to achieve significant performance improvements over a recently proposed tabu search hyper-heuristic without lowering the level of generality. We also show that our hyper-heuristic is capable of producing competitive results against bespoke meta-heuristics methods for these problems. In some cases, the simulated annealing hyper-heuristic has even obtained considerable improvements over some of the current best problem-specific meta-heuristic approaches. The contribution of this paper is to present a method which can be readily (and automatically) applied to very different problems whilst still being able to produce results on benchmark problems which are competitive with bespoke human designed tailor made algorithms for those problems.

A significant amount of work has investigated inventory control problems associated with fresh produce. Much of this work has considered deteriorating inventory control with many models having been proposed for the various situations that exist. However, no researchers have specifically studied fresh produce which has its own special characteristics. Most research categorise fresh produce into more general deteriorating categories with random lifetimes and nondecaying utilities. However, this classification is not reasonable or practical because the freshness condition usually plays a very important role in influencing the demand for the produce, which drops gradually over time. In this paper, a single-period inventory and shelf space allocation model is proposed for fresh produce. These items usually have a very short lifetime. The demand rate is assumed to be deterministic and dependent on both the displayed inventory (the number of facings of items on the shelves) and the items' freshness conditions. The freshness condition drops continuously over time according to a known function. Several problem instances of different sizes are given and solved by a modified generalised reduced gradient (GRG) algorithm. Scope and PurposeThe profit on general foods, such as cans, frozen vegetables, fruit juice, etc., is gradually decreasing due to highly competitive retail conditions. The demand for these products is also slowing. On the other hand, the demand for some other merchandise, such as fresh produce, organic food and children clothes, has increased dramatically owing to improving living standards. This requires retailers to concentrate more in these areas (Johnson 2002). In this paper, we formulate a mathematical model in order to assist in ordering and shelf allocation decisions for the retail of fresh produce, such as vegetables, fruits, fresh meats, etc. The main characteristics of these items are their very short shelf-life and decaying utilities over time. Preliminary experiments are conducted (using a modified generalised reduced gradient algorithm) in order to demonstrate that good quality solutions can be found. Most of the literature have treated fresh produce as deteriorating items with a random lifetime and non-decaying utilities (Nahmias 1982, Goyal andGiri 2001). In this paper, we assume that the produce has a continuous utility and physically deteriorates over time. Freshness is one of the main criteria to evaluate a product's quality and could dramatically affect its demand if its condition is inferior. To obtain a good financial performance from fresh goods requires the adoption of strict temperature control and intelligent inventory and shelf management systems. Furthermore, although a large number of deteriorating inventory models have been proposed in previous research, most of them are based on the analysis of a single item excluding the constraints of shelf space which come when considering a range of goods. No researchers have integrated a deteriorating inventory model with a shel...

Service network design under uncertainty is fundamentally crucial for all freight transportation companies.The main challenge is to strike a balance between two conflicting objectives: low network setup costs and low expected operational costs. Together these have a significant impact on the quality of freight services.Increasing redundancy at crucial network links is a common way to improve network flexibility. However, in a highly uncertain environment, a single predefined network is unlikely to suit all possible future scenarios, unless it is prohibitively costly. Hence, rescheduling is often an effective alternative. In this paper, we proposed a new stochastic freight service network design model with vehicle rerouting options. The proposed model explicitly introduces a set of integer variables for vehicle rerouting in the second stage of the stochastic program. Although computationally more expensive, the resultant model provides more options (i.e. rerouting) and flexibility for planners to deal with uncertainties more effectively. The new model was tested on a set of instances adapted from the literature and its performance and characteristics are studied through both comparative studies and detailed analyses at the solution structure level. Implications for practical applications are discussed and further research directions are also provided.

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