Perturbative interpretation of relativistic symmetries in nuclei

Perturbation theory is used systematically to investigate the symmetries of the Dirac Hamiltonian and their breaking in atomic nuclei. Using the perturbation corrections to the single-particle energies and wave functions, the link between the single-particle states in realistic nuclei and their counterparts in the symmetry limits is discussed. It is shown that the limit of S-V=const and relativistic harmonic oscillator (RHO) potentials can be connected to the actual Dirac Hamiltonian by the perturbation method, while the limit of S+V=const cannot, where S and V are the scalar and vector potentials, respectively. This indicates that the realistic system can be treated as a perturbation of spin-symmetric Hamiltonians, and the energy splitting of the pseudospin doublets can be regarded as a result of small perturbation around the Hamiltonian with RHO potentials, where the pseudospin doublets are quasidegenerate.Comment: 5 pages, 4 figures, Phys. Rev. C in pres

Pseudospin symmetry: Recent progress with supersymmetric quantum mechanics

It is an interesting and open problem to trace the origin of the pseudospin symmetry in nuclear single-particle spectra and its symmetry breaking mechanism in actual nuclei. In this report, we mainly focus on our recent progress on this topic by combining the similarity renormalization group technique, supersymmetric quantum mechanics, and perturbation theory. We found that it is a promising direction to understand the pseudospin symmetry in a quantitative way.Comment: 4 pages, 1 figure, Proceedings of the XX International School on Nuclear Physics, Neutron Physics and Applications, Varna, Bulgaria, 16-22 September, 201

Business sustainability for competitive advantage: identifying the role of green intellectual capital, environmental management accounting and energy efficiency

Themanufacturing organizations are threatening the earth and its wildlife because of their growing concern about environmental pollution and industrial waste. Hence, in the present study, the three potential solutions, Green Intellectual Capital, Environmental Management Accounting and Energy Efficiency, are evaluated for excelling the organizational operations towards business sustainability and attaining the Competitive Advantage. With the assistance of ‘Partial Least Square-Structural Equation Modelling’ on the dataset of 364 respondents from the manufacturing organizations in China, the outcome reported the positive and significant impact of all of the studied potential solutions in excelling and enhancing business sustainability and competitive advantage. Based on the findings, it is proposed that manufacturing organizations need to apportion due attention to developing the green intellectual capital, improve the level of consumption of energy and need to disclose their environmentalmanagement through proper Environmental Management Accounting

Temperature Matrix-Based Data Placement Optimization in Edge Computing Environment

The scale of data shows an explosive growth trend, with wide use of cloud storage. However, there are challenges such as network latency and energy consumption. The emergence of edge computing brings data close to the edge of the network, making it a good supplement to cloud computing. The spatiotemporal characteristics of data have been largely ignored in studies of data placement and storage optimization. To this end, a temperature matrix-based data placement method using an improved Hungarian algorithm (TEMPLIH) is proposed in this work. A temperature matrix is used to reflect the influence of data characteristics on its placement. A data replica matrix selection algorithm based on temperature matrix (RSA-TM) is proposed to meet latency requirements. Then, an improved Hungarian algorithm based on replica matrix (IHA-RM) is proposed, which satisfies the balance among the multiple goals of latency, cost, and load balancing. Compared with other data placement strategies, experiments show that the proposed method can effectively reduce the cost of data placement while meeting user access latency requirements and maintaining a reasonable load balance between edge servers. Further improvement is discussed and the idea of regional value is proposed

Decelerating Airy pulse propagation in highly non-instantaneous cubic media

The propagation of decelerating Airy pulses in non-instantaneous cubic medium is investigated both theoretically and numerically. In a Debye model, at variance with the case of accelerating Airy and Gaussian pulses, a decelerating Airy pulse evolves into a single soliton for weak and general non- instantaneous response. Airy pulses can hence be used to control soliton generation by temporal shaping. The effect is critically dependent on the response time, and could be used as a way to measure the Debye type response function. For highly non- instantaneous response, we theoretically find a decelerating Airy pulse is still transformed into Airy wave packet with deceleration. The theoretical predictions are confirmed by numerical simulations

Selective gas detection using Mn3O4/WO3 composites as a sensing layer

Pure WO3 sensors and Mn3O4/WO3 composite sensors with different Mn concentrations (1 atom %, 3 atom % and 5 atom %) were successfully prepared through a facile hydrothermal method. As gas sensing materials, their sensing performance at different temperatures was systematically investigated for gas detection. The devices displayed different sensing responses toward different gases at specific temperatures. The gas sensing performance of Mn3O4/WO3 composites (especially at 3 atom % Mn) were far improved compared to sensors based on pure WO3, where the improvement is related to the heterojunction formed between the two metal oxides. The sensor based on the Mn3O4/WO3 composite with 3 atom % Mn showed a high selective response to hydrogen sulfide (H2S), ammonia (NH3) and carbon monoxide (CO) at working temperatures of 90 degrees C, 150 degrees C and 210 degrees C, respectively. The demonstrated superior selectivity opens the door for potential applications in gas recognition and detection

BVFB: Training Behavior Verification Mechanism for Secure Blockchain-Based Federated Learning

There are still two problems of the existing methods of defending against poisoning attacks of the blockchain-based federated learning: 1) It is difficult to accurately identify the nodes under attack; 2) The effect of the model is greatly affected when the number of malicious nodes exceeds a half. So, an innovative secure mechanism is proposed for blockchain-based federated learning, which is called the training behavior verification mechanism. The mechanism describes the consistent training behavior rules of nodes by constructing the training behavior model, and distinguishes honest nodes from malicious nodes by comparing the differences in training behavior models on the training behavior verification algorithm. Experiments show that the new mechanism can effectively resist more than half of the label-flipping attacks and backdoor attacks, and has the advantages of higher stability and higher accuracy than methods such as Krum, Trimmed Mean, and Median

Optimizing Data Placement for Cost Effective and High Available Multi-Cloud Storage

With the advent of big data age, data volume has been changed from trillionbyte to petabyte with incredible speed. Owing to the fact that cloud storage offers the vision of a virtually infinite pool of storage resources, data can be stored and accessed with high scalability and availability. But a single cloud-based data storage has risks like vendor lock-in, privacy leakage, and unavailability. Multi-cloud storage can mitigate these risks with geographically located cloud storage providers. In this storage scheme, one important challenge is how to place a user's data cost-effectively with high availability. In this paper, an architecture for multi-cloud storage is presented. Next, a multi-objective optimization problem is defined to minimize total cost and maximize data availability simultaneously, which can be solved by an approach based on the non-dominated sorting genetic algorithm II (NSGA-II) and obtain a set of non-dominated solutions called the Pareto-optimal set. Then, a method is proposed which is based on the entropy method to determine the most suitable solution for users who cannot choose one from the Pareto-optimal set directly. Finally, the performance of the proposed algorithm is validated by extensive experiments based on real-world multiple cloud storage scenarios