The Price Decision of Order With Third-Prty Logistics Based on Incomplete Information Game

As the price is always a prime factor taken into consideration when a 3PL provides logistics service. With increasingly cases of logistics service providing to retailers rather than manufacturers nowadays, quantity may be not as an important factor. For retailers always have certain amounts of goods, they can hardly expand the scale at their will to obtain a quantity discount. To logistics companies, most of them are medium-sized and small enterprises, under limitations of its fields and capability. They can neither provide enough warehousing area for retailers. This paper builds the price decision model in expectation of utility obtaining by service contract instead of the whole revenueof pricemultiplying quantity, and would be more suitable for 3PL to apply when working for a retailer. Besides, this paper sets the opponent’s acceptable price as uncertain information, which is more realistic than the usual complete and perfect information assumption. Based on the incomplete information game and introducing variables of service capability and service level, this paper will give the best decision of pricing order and a specific amount in quotingthe price stage. Further discussion will quantify the benefits of pricing first and factors affect the benefits which aremore conducive to the third -party logisticsenterprises to provide services for external companies

Illegal Intrusion Detection of Internet of Things Based on Deep Mining Algorithm

In this study, to reduce the influence of The Internet of Things (IoT) illegal intrusion on the transmission effect, and ensure IoT safe operation, an illegal intrusion detection method of the Internet of Things (IoT) based on deep mining algorithm was designed to accurately detect IoT illegal intrusion. Moreover, this study collected the data in the IoT through data packets and carries out data attribute mapping on the collected data, transformed the character information into numerical information, implemented standardization and normalization processing on the numerical information, and optimized the processed data by using a regional adaptive oversampling algorithm to obtain an IoT data training set. The IoT data training set was taken as the input data of the improved sparse auto-encoder neural network. The hierarchical greedy training strategy was used to extract the feature vector of the sparse IoT illegal intrusion data that were used as the inputs of the extreme learning machine classifier to realize the classification and detection of the IoT illegal intrusion features. The experimental results indicate that the feature extraction of the illegal intrusion data of the IoT can effectively reduce the feature dimension of the illegal intrusion data of the IoT to less than 30 and the dimension of the original data. The recall rate, precision, and F1 value of the IoT intrusion detection are 98.3%, 98.7%, and 98.6%, respectively, which can accurately detect IoT intrusion attacks. The conclusion demonstrates that the intrusion detection of IoT based on deep mining algorithm can achieve accurate detection of IoT illegal intrusion and reduce the influence of IoT illegal intrusion on the transmission effect

FENDI: High-Fidelity Entanglement Distribution in the Quantum Internet

A quantum network distributes quantum entanglements between remote nodes, which is key to many quantum applications. However, unavoidable noise in quantum operations could lead to both low throughput and low quality of entanglement distribution. This paper aims to address the simultaneous exponential degradation in throughput and quality in a buffered multi-hop quantum network. Based on an end-to-end fidelity model with worst-case (isotropic) noise, we formulate the high-fidelity remote entanglement distribution problem for a single source-destination pair, and prove its NP-hardness. To address the problem, we develop a fully polynomial-time approximation scheme for the control plane of the quantum network, and a distributed data plane protocol that achieves the desired long-term throughput and worst-case fidelity based on control plane outputs. To evaluate our algorithm and protocol, we develop a discrete-time quantum network simulator. Simulation results show the superior performance of our approach compared to existing fidelity-agnostic and fidelity-aware solutions

Untangling the chemical evolution of Titan's atmosphere and surface–from homogeneous to heterogeneous chemistry

The arrival of the Cassini-Huygens probe at Saturn's moon Titan - the only Solar System body besides Earth and Venus with a solid surface and a thick atmosphere with a pressure of 1.4 atm at surface level - in 2004 opened up a new chapter in the history of Solar System exploration. The mission revealed Titan as a world with striking Earth-like landscapes involving hydrocarbon lakes and seas as well as sand dunes and lava-like features interspersed with craters and icy mountains of hitherto unknown chemical composition. The discovery of a dynamic atmosphere and active weather system illustrates further the similarities between Titan and Earth. The aerosol-based haze layers, which give Titan its orange-brownish color, are not only Titan's most prominent optically visible features, but also play a crucial role in determining Titan's thermal structure and chemistry. These smog-like haze layers are thought to be very similar to those that were present in Earth's atmosphere before life developed more than 3.8 billion years ago, absorbing the destructive ultraviolet radiation from the Sun, thus acting as 'prebiotic ozone' to preserve astrobiologically important molecules on Titan. Compared to Earth, Titan's low surface temperature of 94 K and the absence of liquid water preclude the evolution of biological chemistry as we know it. Exactly because of these low temperatures, Titan provides us with a unique prebiotic 'atmospheric laboratory' yielding vital clues - at the frozen stage - on the likely chemical composition of the atmosphere of the primitive Earth. However, the underlying chemical processes, which initiate the haze formation from simple molecules, have been not understood well to date

Study on the teaching Korean subject honorific to Chinese intermediate learners

학위논문 (석사)-- 서울대학교 대학원 : 국어교육과 한국어교육전공, 2011.8. 구본관.Maste

Methionine can reduce the sublethal risk of Chlorantraniliprole to honeybees (Apis mellifera L.): Based on metabolomics analysis

Bees, essential for pollination in agriculture and global economic growth. However, the great wax moth (Galleria mellonella, GWM), a Lepidopteran insect, poses a substantial threat to bee colonies, contributing to a global decline in bee populations. Chlorantraniliprole (CH) is one of the primary insecticide used to control GWM due to its efficacy and low toxicity to bees. To improve beekeeping safety and reduce the risk of GWM developing resistance to prolonged use of CH, we investigated the potential of combining methionine (MET) which has been found to have insecticidal activity against certain Lepidoptera pests, with chlorantraniliprole for use in the apiculture industry. This study assessed the combined effects of MET and CH on GWM and honeybees by employing the maximum concentration of MET (1 %, w/w), previously reported as safe for honeybees, and the practical concentration of CH (1 mg/kg) for GWM control. The results revealed limited acute lethal toxicity of MET to GWM and honeybees, whereas the combined chronic exposure of MET and CH (MIX) led to significant synergistic lethal effects on GWM mortality. Nevertheless, the protective effect of MET on honeybees exposed to CH was significant under chronic exposure. Potential mechanisms underlying the synergistic actions of MET and CH may stem from MET-induced protection of the ''Cysteine and methionine'' and the ''Glycine, serine, and threonine'' metabolism pathways. Furthermore, immune stress mitigation was also observed in honeybee immune-related gene transcripts treated by the combination of MET and CH under both acute and chronic exposure. The effects of MET on CH activity in GWM and honeybees are likely due to metabolic regulation. This study suggests the potential of developing MET as a promising biopesticide or protective agent in the future

Light-weight and highly flexible TaC modified PyC fiber fabrics derived from cotton fiber textile with excellent electromagnetic shielding effectiveness

For the first time, tantalum carbide (TaC) nanoparticles modified pyrolytic carbon fiber (TaC/PyC)(f) fabrics were elaborately designed and fabricated through in-situ formation of TaC nanophase on the surface of PyCf fabrics derived from pyrolysis of cotton fiber clothes. The introduction of highly electrically conductive TaC nanophase significantly improved the electrical conductivity and interaction capability of (TaC/PyC)(f) fabrics with electromagnetic waves in terms of reflection and absorption, which leads to a significant increase in the total shielding effectiveness (SET). With a thickness of similar to 260 mu m, the best SET of the resultant (TaC/PyC )(f) fabrics can reach 75.0 dB compared to 24.4 dB of the PyCf fabrics with otherwise identical conditions. Through employing both the advantages of TaC and C-f, flexible, thin and light-weight (TaC/PyC)(f) fabrics possessing excellent SE were obtained

Effects of goal orientation on environment management in technology-based physics learning

The purpose of the current study is to propose and examine a comprehensive model that uses motivational and self-regulated variables to explain factors affecting environment management in technology-based physics learning among Chinese secondary school students. Data were collected from 726 grade-eight secondary school students in Southeast China, who were learning physics. Structural equation modeling was used to analyze the relationships among students’ goal orientations, environment management, and time management. Results suggest that students were more likely to manage their environment if they had learning-oriented goals and if they managed their time, but they were less likely to do so if they had social-oriented goals. Implications for teachers’ technology integration in physics class were discussed

Highly flexible and ultrathin Mo2C film via in-situ growth on graphene oxide for electromagnetic shielding application

Molybdenum carbide (β-Mo2C) possesses excellent electrical conductivity, good thermal and chemical stability, and is, therefore, a promising candidate material for electromagnetic (EM) shielding in diverse harsh environments. Herein, for the first time, β-Mo2C based ultra-thin films were prepared through in-situ growth of the carbide on a self-assembled graphene oxide (GO) film. The remaining reduced GO (RGO) layers located in between two adjacent Mo2C nanoparticles not only can work as flexible binder to impart the resultant films excellent flexibility but also enable the formation of heterogeneous nano-interface which is beneficial for the improvement of shielding by absorption. The amounts of β-Mo2C in the resultant films were adjusted through varying the Mo precursor contents. With the introduction of β-Mo2C phase in the resultant film, the shielding effectiveness (SE) increased significantly. At the thickness of ∼25 μm, the highest SE reaches 46.8 dB compared to 21.6 dB of the pristine RGO film. With the increase of the Mo2C content in the resultant materials, the density increased from 0.32 to 1.23 g/cm3. The resultant Mo2C film exhibit an SSE/t (specific SE by thickness) value of as high as 15,971 dB cm2 g−1

Optimization Method of Combined Multi-Mode Bus Scheduling under Unbalanced Conditions

In view of the spatial and temporal imbalance of residents’ travel demands and challenges of optimal bus capacity allocation, in this paper the grand station express bus scheduling mode is introduced in the direction of heavy passenger flow during peak hours. Coordinated scheduling combining whole-journey and grand station express buses is adopted, and the station correlation calculation model is used to determine the optimal stops of the grand station express bus. Thus, a two-way bus scheduling optimization model for peak passenger flow is established with the goal of minimizing the total cost of passenger travel and enterprise operation. Finally, the nonlinear inertia weight dynamic cuckoo search algorithm is selected for the model’s solution, and the established scheduling optimization model is solved by combining basic data such as the study line’s bus Integrated Circuit (IC) card data. The effectiveness of the model is verified through a comparative study and evaluation of the solution