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Abstract A two-step approach is proposed to investigate the shear band formation in cold-rolled aluminum alloy sheet during plane strain tension, compression and simple shear tests. In the first step, a finite element model with only one element using Taylor type polycrystal plasticity model is applied to compute the stress-strain curve of the deformation. In the second step, a full scale mesoscopic simulation is applied for a further study of the details of the shear band development for cases with higher possibility of shear band formation as judged from the results in step 1. Systematic studies show that the hardening/softening features of stress-strain curves obtained by one-element method well agree with the extent of macroscale shear bands development in mesoscopic scale simulation: softening or flat stress-strain curves correspond to the formation of severe macroscale shear band in mesoscopic scale simulation, and the stronger the softening is, the more severe the macroscale shear band is, whereas no macroscale shear band is developed when the curves exhibit continuous and obvious hardening. Therefore, when it is needed to investigate probability and features of shear band formation in many samples or loading modes, e.g. loading along different directions, people can use the first step to efficiently estimate the possibility of macroscale shear band development for all the cases concerned, and then apply the full scale mesoscopic simulation only for those cases where stress-strain curves exhibit little hardening or softening features. Our calculations show that the computation efficiency can be substantially improved by the two-step approach.

Dam building and reservoir operations alter the downstream hydrological regime, and as a result, affect the health of the river aquatic ecosystem, particularly for large-scale cascade reservoirs. This study investigated the impact of the Gezhouba Reservoir (GR) and the Three Gorges Reservoir (TGR) on the spawning conditions of two critical taxa, i.e., the endemic four major carps and the endangered Chinese sturgeon in the Yangtze River. We analyzed the flow, sediment, and thermal regime in these two taxa spawning seasons and compared their features between the predam and postdam periods. Our results revealed that the GR and the TGR had altered the frequency distributions of flow, sediment, and water temperature to different degrees, with the impact by the GR on the carps and Chinese sturgeon ranked as water temperature > water temperature. For the GR, the satisfying degree of the suitable flow and water temperature of the carps increased, whilst the suitable flow, sediment, and water temperature for the Chinese sturgeon decreased. These changes in TGR showed a significant ascending (descending) trend in the suitable flow (water temperature) for the carps, and a clear decreasing trend in the flow, sediment, and temperature for Chinese sturgeon. Both the TGR and the GR had negative impacts on the spawning of these two taxa in terms of the rising/falling flow characteristics. flow, and the effect of the TGR on these two taxa were ordered as flow > water temperature, sediment > water temperature > flow, sediment > flow >

Ribonuclease 6 (RNase6 or RNase K6) is a protein that belongs to a superfamily thought to be the sole verte-brate-specific enzyme known for a wide range of physiological functions, including digestion, cytotoxicity, angiogenesis, male reproduction and host defense. In our study, 51 functional genes and 11 pseudogenes were identified from 27 Rodentia species. Intriguingly, in the 3 main lineages of rodents there were multiple RNase6s identified in all species of Ctenohystrica, whereas only a single RNase6 was observed in other Rodentia species examined except for 2 species in the mouse-related clade. The evolutionary scenario of "birth (gene duplication) and death (gene deactivation)" and gene sorting have been demonstrated in Ctenohystrica. In addition, bursts of positive selection, diversification of isoelectric point and positive net charge have been identified in Ctenohystrica, especially at two key sites that are involved in antimicrobial function. Site Trp30 has undergone positive selection and Ile45 has changed into other residues in Group B and Group C of the Ctenohystrica. Our results demonstrated a complex and intriguing evolutionary pattern of rodent RNase6, and indicated that functional modification may have occurred, which establishes an important theoretical foundation for future functional assays in rodent RNase6.

Neurocognitive and Psychosocial Outcomes of Obstructive Sleep Apnea in Hong Kong Chinese: Similar to or Different from Western Populations? Purpose: Obstructive sleep apnea (OSA) has long been associated with daytime consequences. However, systematic and comprehensive studies on the neuropsychological functioning among Chinese patients with OSA were lacking. This study aimed to investigate the functioning of individuals with OSA using a comprehensive neuropsychological battery, experimental tasks based on a well-specified paradigm of working memory (WM), and questionnaires measuring a wide spectrum of psychosocial functioning, thereby establishing a neurocognitive and psychosocial profile of Hong Kong Chinese with OSA, in comparison to western populations. Methods: Twenty-five patients with moderate to severe OSA and 30 healthy controls were recruited from the Sleep Disorders Centre of Queen Mary Hospital and the community, respectively. Participants were tested on attention and working memory, verbal and visual learning and recall, executive functions, and processing speed, and completed self-reported measures on daytime sleepiness, sleep quality, mood, functional outcomes, and quality of life.

Abstract Inspired by the α -maxmin expected utility, we propose a new class of mean-variance criterion, called α -maxmin mean-variance criterion, and apply it to the reinsurance-investment problem. Our model allows the insurer to have different levels of ambiguity aversion (rather than only consider the extremely ambiguity-averse attitude as in the literature). The insurer can purchase proportional reinsurance and also invest the surplus in a financial market consisting of a risk-free asset and a risky asset, whose dynamics is correlated with the insurance surplus. Closed-form equilibrium reinsurance-investment strategy is derived by solving the extended Hamilton–Jacobi–Bellman equation. Our results show that the equilibrium reinsurance strategy is always more conservative if the insurer is more ambiguity-averse. When the dependence between insurance and financial risks are weak, the equilibrium investment strategy is also more conservative if the insurer is more ambiguity-averse. However, in order to diversify the portfolio, a more ambiguity-averse insurer may adopt a more aggressive investment strategy if the insurance market is very ambiguous. For an ambiguity-neutral insurer, the investment strategy is identical to the non-robust investment strategy.

Nowadays, blockchain has become a promising tamper-evident and tamper-resistant distributed ledger technology that achieves the security and privacy through the cryptography, consensus mechanism and chained data structure. In this paper, we propose a general blockchain-based smart manufacturing system (BSM) that utilizes the decentralization, immutability, auditability of blockchain to achieve flexible manufacturing that responds to on-demand services in time. For management, manufacturing services are divided into tasks and for unified scheduling, these tasks are queued along the logical flow in the transaction pool. Considering the contradiction between large scale manufacturing and limited transactional throughput, a joint task scheduling over blockchains and supply-demand configuration design is proposed to obtain the maximum customers’ net profit while balancing the timeliness as well as the production and blockchain payoff. Moreover, a maximum weight matching based Alternating Optimization framework (MWMAO) is proposed as the solution. Simulation results show that the proposed framework has superiority on the profitability.

Despite widespread anthropogenic nutrient enrichment, it remains unclear how nutrient enrichment influences plant-arbuscular mycorrhizal fungi (AMF) symbiosis and ecosystem multifunctionality at the global scale. Here, we conducted a meta-analysis to examine the worldwide effects of nutrient enrichment on AMF and plant diversity and ecosystem multifunctionality using data of field experiments from 136 papers. Our analyses showed that nutrient addition simultaneously decreased AMF diversity and abundance belowground and plant diversity aboveground at the global scale. The decreases in AMF diversity and abundance associated with nutrient addition were more pronounced with increasing experimental duration, mean annual temperature (MAT) and mean annual precipitation (MAP). Nutrient addition-induced changes in soil pH and available phosphorus (P) predominantly regulated the responses of AMF diversity and abundance. Furthermore, AMF diversity correlated with ecosystem multifunctionality under nutrient addition worldwide. Our findings identify the negative effects of nutrient enrichment on AMF and plant diversity and suggest that AMF diversity is closely linked with ecosystem function. This study offers an important advancement in our understanding of plant-AMF interactions and their likely responses to ongoing global change.

Nowadays, because of the emergence of many online logistics platforms – some of which function as virtual marketplaces – airfreight forwarders are facing an increasing number of shipment requests and many choices of collaborating agents. They are also expected to handle a greater variety of shipments from an increasing number of origins and to an increasing number of destinations. In this paper, we address the three inter-related decisions of job selection, route selection and agent selection in the design of a shipment plan. We formulate this problem as a mixed 0-1 LP, with the objective of maximising profit. Using a numerical example, we analyse the effects of job and route selection and provide insights into various aspects of shipment planning.

We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRI) from highly undersampled k-space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and the patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov Chain Monte Carlo (MCMC) for the Bayesian model, and use the alternating direction method of multipliers (ADMM) for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.