Blockchain provides new technologies and ideas for the construction of agricultural product traceability system (APTS). However, if data is stored, supervised, and distributed on a multiparty equal blockchain, it will face major security risks, such as data privacy leakage, unauthorized access, and trust issues. How to protect the privacy of shared data has become a key factor restricting the implementation of this technology. We propose a secure and trusted agricultural product traceability system (BCST-APTS), which is supported by blockchain and CP-ABE encryption technology. It can set access control policies through data attributes and encrypt data on the blockchain. This can not only ensure the confidentiality of the data stored in the blockchain, but also set flexible access control policies for the data. In addition, a whole-chain attribute management infrastructure has been constructed, which can provide personalized attribute encryption services. Furthermore, a reencryption scheme based on ciphertext-policy attribute encryption (RE-CP-ABE) is proposed, which can meet the needs of efficient supervision and sharing of ciphertext data. Finally, the system architecture of the BCST-APTS is designed to successfully solve the problems of mutual trust, privacy protection, fine-grained, and personalized access control between all parties.
Given the growing prevalence of gun control policies in service settings, this study aims to investigate how the adoption of a gun control policy by a service businesses influences consumers’ evaluations of the service businesses.
Three experiments were conducted to examine how the adoption of a gun control policy by a service businesses influences consumers’ brand favorability of that service businesses and how value congruence (i.e. the alignment between a consumer’s own personal values and perceptions of the brand’s values) is the underlying mechanism.
This study documents several major findings. First, the authors find that the adoption of a gun control policy by a service businesses increases consumers’ brand favorability. Second, the authors highlight a boundary condition to this effect, such that a gun control policy actually decreases consumers’ brand favorability for people high (vs low) in support for gun rights. Third, the authors show that value congruence is the psychological process underlying these effects. Fourth, the authors generalize the focal effects to a real-world brand and demonstrate that the adoption of a gun control policy increases brand favorability for consumers low (vs high) in patronage behavior of the brand. Finally, the authors find that a pioneer brand strategy in the adoption of a gun control policy significantly increases brand favorability, whereas a follower brand strategy in the adoption of such a policy is less effective.
To the best of the authors’ knowledge, this research is the first to provide critical insight to service businesseses as to how their position regarding guns influences consumers’ evaluations of the service businesses.
The Chinese government has made great efforts to combat air pollution through the reductions in SO2, NOx and VOCs emissions, as part of its socioeconomic Five-Year Plans (FYPs). China aims to further reduce the emissions of VOCs and NOx by 10% in its upcoming 14th FYP (2021–2025). Here, we used a regional chemical transport model (e.g., WRF/CMAQ) to examine the responses of PM2.5 and O3 to emission control policies of the 14th FYP in the Yangtze River Delta (YRD) region. The simulation results under the 4 emission control scenarios in the 2 winter months in 2025 indicate that the average concentrations of city mean PM2.5 in 41 cities in the YRD were predicted to only decrease by 10% under both S1 and S1_E scenarios, whereas the enhanced emission control scenarios (i.e., S2_E and S3_E) could reduce PM2.5 in each city by more than 20%. The model simulation results for O3 in the 3 summer months in 2025 show that the O3 responses to the emission controls under the S1 and S1_E scenarios show different control effects on O3 concentrations in the YRD with the increase and decrease effects, respectively. The study found that both enhanced emission control scenarios (S2_E and S3_E) could decrease O3 in each city by more than 20% with more reductions in O3 under the S3_E emission control scenario because of its higher control strengths for both NOx and VOCs emissions. It was found that emission reduction policies for controlling high emission sectors of NOx and VOCs such as S2_E and S3_E were more effective for decreasing both PM2.5 and O3 in the YRD. This study shows that O3 controls will benefit from well-designed air pollution control strategies for reasonable control ratios of NOx and VOCs emissions.
We describe a fluid model with time-varying input that approximates a multiclass many-server queue with general reneging distribution and multiple customer classes (specifically, the multiclass G/GI/N+GI queue). The system dynamics depend on the policy, which is a rule for determining when to serve a given customer class. The class of admissible control policies are those that are head-of-the-line (HL) and nonanticipating. For a sequence of many-server queues operating under admissible HL control policies and satisfying some mild asymptotic conditions, we establish a tightness result for the sequence of fluid scaled queue state descriptors and associated processes and show that limit points of such sequences are fluid model solutions almost surely. The tightness result together with the characterization of distributional limit points as fluid model solutions almost surely provides a foundation for the analysis of particular HL control policies of interest. We leverage these results to analyze a set of admissible HL control policies that we introduce, called weighted random buffer selection (WRBS), and an associated WRBS fluid model that allows multiple classes to be partially served in the fluid limit (which is in contrast to previously analyzed static priority policies).
Reinforcement learning has been established over the past decade as an effective tool to find optimal control policies for dynamical systems, with recent focus on approaches that guarantee safety during the learning and/or execution phases. In general, safety guarantees are critical in reinforcement learning when the system is safety-critical and/or task restarts are not practically feasible. In optimal control theory, safety requirements are often expressed in terms of state and/or control constraints. In recent years, reinforcement learning approaches that rely on persistent excitation have been combined with a barrier transformation to learn the optimal control policies under state constraints. To soften the excitation requirements, model-based reinforcement learning methods that rely on exact model knowledge have also been integrated with the barrier transformation framework. The objective of this paper is to develop safe reinforcement learning method for deterministic nonlinear systems, with parametric uncertainties in the model, to learn approximate constrained optimal policies without relying on stringent excitation conditions. To that end, a model-based reinforcement learning technique that utilizes a novel filtered concurrent learning method, along with a barrier transformation, is developed in this paper to realize simultaneous learning of unknown model parameters and approximate optimal state-constrained control policies for safety-critical systems.