Portuguese cultural standards from the Chinese perspective

O objetivo da tese é identificar os padrões culturais portugueses a partir da perspectiva chinesa. A metodologia da tese usa o Método de Padrões Culturais, que é uma abordagem qualitativa inovadora de pesquisa transcultural e particularmente usada para estudar as diferenças culturais entre duas culturas específicas. O método dos padrões culturais consiste em três fases principais: entrevistas narrativas, análise e categorização, bem como feedback e discussão. Os principais dados da tese foram recolhidos de trinta entrevistados chineses que têm experiência de viver e trabalhar em Portugal. Os resultados foi confirmado pelo feedback e discussão dos entrevistados e de terceiros. Os padrões culturais portugueses na perspectiva chinesa são identificados a seguir: Orientação do ritmo lento, Falta de ambições no desempenho profissional, Importância do tempo de lazer, Cultura alimentar diferente, Conservadorismo, Relacionamento social entusiasta e superficial. Os resultados da tese pode servir como base para o treino especial de interações transculturais para ajudar os chineses a entender melhor as diferenças culturais com os portuguêses e diminuir os choques culturais resultantes de diferenças culturais. Dois estudos de caso baseados nas experiências reais são apresentados como exemplos da aplicação do resultado.The completion of the thesis is attributed to many people's support and encouragement. First and foremost, I want to extend my heartfelt gratitude to my supervisor, Professor António Robalo, whose gentle guidance and constant encouragement make me accomplish this dissertation. When I was an exchange student in ISCTE three years ago, Professor António Robalo got me very interested in cross-cultural research through his brilliant lectures and profound knowledge. When I decided to study cross-cultural interactions as my final thesis, Professor António Robalo was very friendly to accept me to be my supervisor, gave me a lot of help and encouragements during the whole process of my studying. I am very grateful for Professor António Robalo to lead me into the cross-cultural research and provide a lot of guidelines so that I could finish my dissertation successfully. Secondly, I would like to express my sincere gratitude to all the professors who have taught me during my master stage. Their instructions have helped broaden my horizon and have provided me with a solid foundation to accomplish this dissertation, which will also be greatly helpful for my future career and academic research. Moreover, I would like to thank all the interviewees who were willing to share their time and experiences for me to accomplish my research. Last but not least, I would like to express my special thanks to my parents, whose care and support motivate me to move on and make me want to be a better person

On the fractional abstract Schrodinger type evolution equations on the Hilbert space and its applications to the fractional dispersive equations

In this paper we prove the local and global well-posedness of the time fractional abstract Schr\"odinger type evolution equation on the Hilbert space and as an application, we prove the local and global well-posedness of the fractional dispersive equation with static potential under the only assumption that the symbol of P(D) behaves like a polynomial of highest degree m at infinity. In appendix, we also give the Holder regularities and the asymptotic behaviors of the mild solution to the linear time fractional abstract Schr\"odinger type equation. Because of the lack of the semigroup properties of the solution operators, we employ a strategy of proof based on the spectral theorem of the self-adjoint operators and the asymptotic behaviors of the Mittag-Leffler functions.Comment: 44 pages, 0 figure

Exponential stability of highly nonlinear neutral pantograph stochastic differential equations

In this paper, we investigate the exponential stability of highly nonlinear hybrid neutral pantograph stochastic differential equations(NPSDEs). The aim of this paper is to establish exponential stability criteria for a class of hybrid NPSDEs without the linear growth condition. The methods of Lyapunov functions and M-matrix are used to study exponential stability and boundedness of the hybrid NPSDEs

Ampere-hour-scale soft-package potassium-ion hybrid capacitors enabling 6-minute fast-charging

Extreme fast charging of Ampere-hour (Ah)-scale electrochemical energy storage devices targeting charging times of less than 10 minutes are desired to increase widespread adoption. However, this metric is difficult to achieve in conventional Li-ion batteries due to their inherent reaction mechanism and safety hazards at high current densities. In this work, we report 1 Ah soft-package potassium-ion hybrid supercapacitors (PIHCs), which combine the merits of high-energy density of battery-type negative electrodes and high-power density of capacitor-type positive electrodes. The PIHC consists of a defect-rich, high specific surface area N-doped carbon nanotube-based positive electrode, MnO quantum dots inlaid spacing-expanded carbon nanotube-based negative electrode, carbonate-based non-aqueous electrolyte, and a binder- and current collector-free cell design. Through the optimization of the cell configuration, electrodes, and electrolyte, the full cells (1 Ah) exhibit a cell voltage up to 4.8 V, high full-cell level specific energy of 140 Wh kg-1 (based on the whole mass of device) with a full charge of 6 minutes. An 88% capacity retention after 200 cycles at 10 C (10 A) and a voltage retention of 99% at 25 ± 1 °C are also demonstrated

Magneto-Thermal Coupling Simulation of Flowing Liquid Induction Heating through Static Mixer-Type Susceptors

As a new non-contact heating technology, induction heating technology has very broad application prospects in the field of fluid food heating. However, its application is inevitably affected by the heat concentration caused by uneven energy distribution. The uneven temperature distribution of the heating process will lead to the decrease in the quality of heating products. Therefore, based on the previous research, in order to improve the uniformity of heat distribution in the heating process, this study selected the susceptor with the greatest potential for efficient and the most uniform heating fluid to carry out the coupling simulation of electromagnetic heat transfer. The susceptor was simulated and optimized in three aspects: different power comparisons, the influence of structural change on temperature distribution uniformity, and the influence of physical property change of metal material on temperature distribution uniformity. The results show that the simulation results are in good agreement with the experimental results, and the error between the experimental and simulation values of the outlet temperature at Kelvin temperature is less than 0.18%. The change of geometric structure had a great influence on the uniformity of temperature distribution, and the uniformity of temperature distribution was inversely proportional to the conductivity. During the simulation, the temperature of the fluid heated by the susceptor was increased from 284.75 K to about 333K. The temperature distribution of the fluid at the outlet of the susceptor was uniform, and the temperature difference was about 1 K

Clothing Image Classification with DenseNet201 Network and Optimized Regularized Random Vector Functional Link

To ameliorate the precision of clothing image classification, we proposed a clothing image classification method via the DenseNet201 network based on transfer learning and the optimized regularized random vector functional link (RVFL). First, the formula extracts weight’s parameters about DenseNet201 that is pre-trained on the ImageNet dataset for transfer learning, thereby obtaining an incipient network，after that trim this model parameters. The modified network is utilized to pick up the clothing image features output by the DenseNet201’s global average pooling layer. Second, regularization coefficient is introduced to control RVFL’s model complexity and solve the problem of over-fitting. Then, the generated solution vector of aquila optimizer (AO) is produced by marine predators algorithm (MPA). The input weights, biases of hidden layer and renormalization modulus of regularized RVFL are optimized using the improved AO algorithm. Finally, we use the optimized RVFL to assort abstracted fashion graphics traits. We use Accuracy, Macro-F1, Macro-R and Macro-P to assess the algorithm’s ability and compare this algorithm with ResNet50 network, ResNet101 network, DenseNet201 network, InceptionV3 network and different classifiers, which use DenseNet201 as the feature extractor to get the input. From the experimental results, this algorithm proposed has excellent classification power and generalization ability

Research on Harmonic Current Amplification Effect of Parallel APF Compensating Voltage Source Nonlinear Load

A parallel active power filter (APF) is generally used to suppress dynamic harmonic current and compensate reactive power in the grid. However, parallel APF may have a negative effect on the load current when compensating the nonlinear load of a voltage source type, which may lead to the amplification effect of the load harmonic current. In this paper, the fundamental causes of harmonic current amplification were analyzed by studying the harmonic current amplification effect when a parallel APF compensates a nonlinear load. According to the results of the theoretical derivation, a feasible method to limit this current amplification effect by changing the system structure and the APF’s own control was proposed, and the corresponding design scheme is given. Finally, the correctness of the theoretical derivation of the harmonic current amplification effect and the feasibility of the proposed solution were proven through simulation and experiment

Embedding differential privacy in decision tree algorithm with different depths

Differential privacy (DP) has become one of the most important solutions for privacy protection in recent years. Previous studies have shown that prediction accuracy usually increases as more data mining (DM) logic is considered in the DP implementation. However, although one-step DM computation for decision tree (DT) model has been investigated, existing research has not studied the scenarios when the DP is embedded in two-step DM computation, three-step DM computation until the whole model DM computation. It is very challenging to embed DP in more than two steps of DM computation since the solution space exponentially increases with the increase of computational complexity. In this work, we propose algorithms by making use of Markov Chain Monte Carlo (MCMC) method, which can efficiently search a computationally infeasible space to embed DP into DT generation algorithm. We compare the performance when embedding DP in DT with different depths, i.e., one-step DM computation (previous work), two-step, three-step and the whole model. We find that the deep combination of DP and DT does help to increase the prediction accuracy. However, when the privacy budget is very large (e.g., ϵ = 10), this may overwhelm the complexity of DT model, and the increasing trend is not obvious. We also find that the prediction accuracy decreases with the increase of model complexity

Energy-Efficient Online Resource Management and Allocation Optimization in Multi-User Multi-Task Mobile-Edge Computing Systems with Hybrid Energy Harvesting

Mobile Edge Computing (MEC) has evolved into a promising technology that can relieve computing pressure on wireless devices (WDs) in the Internet of Things (IoT) by offloading computation tasks to the MEC server. Resource management and allocation are challenging because of the unpredictability of task arrival, wireless channel status and energy consumption. To address such a challenge, in this paper, we provide an energy-efficient joint resource management and allocation (ECM-RMA) policy to reduce time-averaged energy consumption in a multi-user multi-task MEC system with hybrid energy harvested WDs. We first formulate the time-averaged energy consumption minimization problem while the MEC system satisfied both the data queue stability constraint and energy queue stability constraint. To solve the stochastic optimization problem, we turn the problem into two deterministic sub-problems, which can be easily solved by convex optimization technique and linear programming technique. Correspondingly, we propose the ECM-RMA algorithm that does not require priori knowledge of stochastic processes such as channel states, data arrivals and green energy harvesting. Most importantly, the proposed algorithm achieves the energy consumption-delay trade-off as [ O ( 1 / V ) , O ( V ) ] . V, as a non-negative weight, which can effectively control the energy consumption-delay performance. Finally, simulation results verify the correctness of the theoretical analysis and the effectiveness of the proposed algorithm