Self-organized rock textures and multiring structure in the Duolun crater

The Duolun impact crater is a multiring basin located 200 km north of Beijing. From the center to the edge of the crater there are innermost rim, inner ring, outer rim, and outermost ring. Recently, we have found some self-organized textures or chaos phenomena in shock-metamorphic rocks from the Duolun impact crater, such as turbulence in matrices of impact glass, oscillatory zoning, or chemical chaos of spherulites in spherulitic splashed breccia, fractal wavy textures or self-similar wavy textures with varied scaling in impact glass, and crystallite beams shaped like Lorentz strange attractors. The rare phenomena indicate that the shock-metamorphic rocks from Duolun crater are formed far from equilibrium. If impact cratering generates momentarily under high-pressure and superhigh-temperature, occurrence of those chaos phenomena in shock-metamorphic rock is not surprising

Research Progress of Laboratory Diagnosis of TB

Early, rapid, and accurate identification of Mycobacterium tuberculosis is crucial to the treatment and management of the disease, and laboratory diagnosis is an important means for its diagnosis, treatment, and prevention control. Common methods include pathogenic methods based on bacterial smear and culture, molecular methods based on polymerase chain reaction (PCR), immunological methods such as tuberculin skin test and gamma-interferon (IFN-γ) release test, and the latest emergence of molecular methods, such as Xpert MTB/RIF and CRISPR technology have provided new perspectives for TB diagnosis. This review focuses on the main research advances in laboratory diagnosis of TB

Development of Robust and Standardized Cantilever Sensors Based on Biotin/Neutravidin Coupling for Antibody Detection

A cantilever-based protein biosensor has been developed providing a customizable multilayer platform for the detection of antibodies. It consists of a biotin-terminated PEG layer pre-functionalized on the gold-coated cantilever surface, onto which NeutrAvidin is adsorbed through biotin/NeutrAvidin specific binding. NeutrAvidin is used as a bridge layer between the biotin-coated surface and the biotinylated biomolecules, such as biotinylated bovine serum albumin (biotinylated BSA), forming a multilayer sensor for direct antibody capture. The cantilever biosensor has been successfully applied to the detection of mouse anti-BSA (m-IgG) and sheep anti-BSA(s-IgG) antibodies. As expected, the average differential surface stress signals of about 5.7 +/- 0.8 x 10(-3) N/m are very similar for BSA/m-IgG and BSA/s-IgG binding, i.e., they are independent of the origin of the antibody. A statistic evaluation of 112 response curves confirms that the multilayer protein cantilever biosensor shows high reproducibility. As a control test, a biotinylated maltose binding protein was used for detecting specificity of IgG, the result shows a signal of bBSA layer in response to antibody is 5.8 x 10(-3) N/m compared to bMBP. The pre-functionalized biotin/PEG cantilever surface is found to show a long shelf-life of at least 40 days and retains its responsivity of above 70% of the signal when stored in PBS buffer at 4 degrees C. The protein cantilever biosensor represents a rapid, label-free, sensitive and reliable detection technique for a real-time protein assay

Research on the emission reduction decision of cost-sharing logistics service supply chain in the O2O model

As an effective way to realize energy savings and environmental protection, cost sharing is gradually becoming an important measure to reduce emissions in the logistics service supply chain under O2O mode in recent years. How to conduct contract selection and design optimization under the cost-sharing situation, and then improve the operational efficiency of the logistics service supply chain is an important issue that needs to be addressed. Firstly, based on the initial market demand for logistics, this paper involves the influence of both online logistics service integrators and onsite functional logistics service providers on logistics market demand in terms of emission reduction and platform brand image and develops a model based on the logistics service demand function in the O2O mode. Secondly, for the role of online and onsite emission reduction services in multi-cycle continuous cooperation to enhance the platform integrator’s brand image, a cost-sharing differential game model between online and onsite services is developed to facilitate providers’ adoption of high-quality emission reduction services. Finally, the HJB equation is used to compare the non-cooperative Nash game, the cost-sharing Stackelberg game, and the cooperative game to make the optimal abatement decision, the optimal benefit, and the cost-sharing ratio of the abatement service supply chain in the non-cooperative Nash game, the cost-sharing Stackelberg game, and the cooperative game. By comparing the results of the three games, we find that the optimal onsite and online abatement service decision is related to the cost, marginal revenue, and the impact of the service on demand; the abatement cost-sharing contract and cooperation are both better than the non-cooperative independent decision state, which can effectively guide the provision of high-quality onsite abatement service and improve the revenue of both parties involved in the logistics service supply chain and the total system’s revenue in the O2O mode. Compared with the cooperative game model, the cost-sharing contract can more effectively facilitate close cooperation between the actors, and the relationship between onsite and online marginal revenue affects the improvement of both parties’ revenue. The findings of the study can provide useful managerial insights for the selection and design optimization of abatement contracts for logistics service supply chains considering cost-sharing via the O2O model

Blacklight: Defending Black-Box Adversarial Attacks on Deep Neural Networks

The vulnerability of deep neural networks (DNNs) to adversarial examples is well documented. Under the strong white-box threat model, where attackers have full access to DNN internals, recent work has produced continual advancements in defenses, often followed by more powerful attacks that break them. Meanwhile, research on the more realistic black-box threat model has focused almost entirely on reducing the query-cost of attacks, making them increasingly practical for ML models already deployed today. This paper proposes and evaluates Blacklight, a new defense against black-box adversarial attacks. Blacklight targets a key property of black-box attacks: to compute adversarial examples, they produce sequences of highly similar images while trying to minimize the distance from some initial benign input. To detect an attack, Blacklight computes for each query image a compact set of one-way hash values that form a probabilistic fingerprint. Variants of an image produce nearly identical fingerprints, and fingerprint generation is robust against manipulation. We evaluate Blacklight on 5 state-of-the-art black-box attacks, across a variety of models and classification tasks. While the most efficient attacks take thousands or tens of thousands of queries to complete, Blacklight identifies them all, often after only a handful of queries. Blacklight is also robust against several powerful countermeasures, including an optimal black-box attack that approximates white-box attacks in efficiency. Finally, Blacklight significantly outperforms the only known alternative in both detection coverage of attack queries and resistance against persistent attackers