Complexity最新文献

Conventional synergy theory explains the inhibitory effects of drug combinations at specific times. Determining the magnitude of inhibition is crucial for exploring the synergy effect. In the results of previous studies, the Chou–Talalay multiple drug effect analysis demonstrated that the combination of a mutant oncolytic herpes virus (G207) and the chemotherapeutic agent (paclitaxel) is the most effective strategy for treating anaplastic thyroid cancer, compared to other combinations such as G207 and NV1023 or paclitaxel and doxorubicin. However, the mechanism behind the synergy effect of G207 and paclitaxel remains unknown, and measuring the synergy effect over time is challenging and expensive. In this study, we formulated a mathematical model to quantify the synergy of G207, paclitaxel, and both over time using the dataset. We conducted a Bayesian estimation of tumor cell proliferation over 16 days using Markov chain Monte Carlo sampling. The Bliss independence was incorporated into the model to compare the observed and expected responses to combination therapy. The expected antitumor effect was significantly lower than the experimental data, suggesting a synergistic effect. Our result showed that the antitumor effect was influenced by the rate of inhibition of tumor growth and the absolute growth delay. Additionally, we found that combination therapy achieved an additional 24% antitumoral effect and a 12-day delay in cell growth. This modeling approach suggests the possibility of quantifying synergistic effects.

The recent progress in the fields of sensor miniaturization, light materials, automatic control, and battery management systems has opened up new opportunities for low-cost unmanned aerial vehicles (UAVs), such as quadrotors. In fact, quadrotors have transitioned from a primarily military application to being widely used almost everywhere. Evidently, controlling such robots requires a deep understanding of their dynamic behavior and the use of robust strategies to accomplish the flight missions without compromising users’ safety. This study presents a comprehensive survey of control strategies for unmanned quadrotors. In our examination, the performance assessment of widely used control algorithms is discussed. Furthermore, the concept of model-based design is presented as a solution for bridging the gap between simulation and experimental validation of control systems. It is anticipated that the present study will provide the reader with a clear vision of quadrotor UAV control theory.

Image encryption schemes are predominantly software-based. Only a select few have been implemented in real-life communication systems. This paper introduces a novel chaotic image encryption scheme based on a modified Z-order curve, a modified Josephus problem, and an improved Vigenère cipher–based ribonucleic acid (RNA) operation. It is implemented and assessed within a light-fidelity (Li-Fi) infrastructure, comprising two core components: software and hardware. The software component manages data encryption and decryption, while the hardware ensures efficient data transmission. The proposed encryption scheme starts with a pixel-level permutation based on an improved Z-order curve, applicable to rectangular images, optimizing efficiency and increasing permutation ability. This is followed by a bit-level permutation using a modified Josephus problem, which enhances the diversity of generated sequences and introduces additional dislocation effects. Subsequently, a Vigenère cipher–based RNA operation serves for diffusion alongside basic RNA operations and the cipher block chaining (CBC) mode. Theoretical analyses and experimental findings demonstrate that the proposed encryption scheme is highly robust, outperforming several existing cryptosystems. Moreover, owing to its successful implementation, the proposed encryption scheme signifies a compelling stride toward bolstering secure visible light communication systems.

This article investigates the finite-time (FT) boundedness problem for the time delay (TD) Takagi–Sugeno fuzzy model (TSFM) with conformable derivative (CD) and in the presence of certain actuator faults. Through the reconstruction of an appropriate Lyapunov–Krasovskii functional, some sufficient conditions expressed by the linear matrix inequalities (LMIs) are given to ensure the FT boundedness of the proposed model not only during regular operation but also when encountering certain actuator faults. Finally, a numerical example and an inverted pendulum system are presented to illustrate our theoretical results.

The focus on high-quality development in regional tourism involves not only transforming the previous extensive development model and improving the efficiency of tourism development but also promoting the coordinated development of the tourism industry across different regions. Taking the 21 prefecture-level cities in Guangdong Province as the research object and guided by high-quality development, a tourism efficiency measurement index system that includes carbon emissions as an unexpected output has been established. By comprehensively applying methods such as the Super-SBM model, LISA temporal path, and standard deviation ellipse, this study addresses the neglect of spatial relationships in the existing literature and measures the tourism efficiency of Guangdong’s 21 prefecture-level administrative units from 2009 to 2019, exploring its spatiotemporal evolution and collaborative trends. The results show that during the research period, the average tourism efficiency in Guangdong Province was 0.807, at a medium–high efficiency level. Spatially, the tourism efficiency of the province is composed of a main peak and side peaks, with a general leftward shift of the main peak, a fluctuating decrease in peak height, and an expanding width. The evolution of spatial patterns reveals that regions with similar tourism efficiency in Guangdong tend to be spatially concentrated, with strong local stability and clear spatial dependency in the change process of tourism efficiency. The study’s insights suggest strategies for Guangdong’s tourism sector, advocating for technological innovation, sustainable development practices, and a robust evaluation framework. It emphasizes leveraging regional tourism assets, fostering collaboration, and promoting the “Great Lingnan Tourism Circle” for a balanced industry growth.

Regionalization is the basic feature of cruise shipping network organization. We insist that the cruise networks of Alaska, Hawaii, etc., have developed into a whole with the scaling up of cruise tourism. To prove it, we used complex network analysis methods to explore the port connections and the spatial structure of the cruise shipping network in these regions. We found that Alaska, Hawaii, and the west coast of Mexico all belong to seasonal cruise market areas. Cruise itineraries in these areas are categorized into one-way and round-trip itineraries, and more than 70% of the itineraries are short duration and medium duration. These areas build cruise shipping networks used in Vancouver, Los Angeles, Anchorage, San Francisco, Honolulu, and other cruise ports, which can be subdivided into nine single-core cruise shipping network systems and two dual-core cruise shipping network systems. The interconnection of different systems forms a T-shaped cruise shipping network in geographical space.

Financial technology is crucial for the sustainable development of financial systems. Algorithmic trading, a key area in financial technology, involves automated trading based on predefined rules. However, investors cannot manually analyze all market patterns and establish rules, necessitating the development of supervised learning trading systems that can discover market patterns using machine or deep learning techniques. Many studies on supervised learning trading systems rely on up–down labeling based on price differences, which overlooks the issues of nonstationarity, complexity, and noise in stock data. Therefore, this study proposes an N-period volatility trading system that addresses the limitations of up–down labeling systems. The N-period volatility trading system measures price volatility to address uncertainty and enables the construction of a stable, long-term trading system. Additionally, an instance-selection technique is utilized to address the limitations of stock data, including noise, nonlinearity, and complexity, while effectively reducing the data size. The effectiveness of the proposed model is evaluated through trading simulations of stocks comprising the NASDAQ 100 index and compared with up–down labeling trading systems. The experimental results demonstrate that the proposed N-period volatility trading system exhibits higher stability and profitability than other trading systems.

Q&A platforms are vital sources of information but often face challenges related to their high ratios of passive to active contributors, which can impede knowledge construction and information exchange on the platforms. This study introduced a novel method for identifying trend discoverers, key users who can detect and initiate discussions on emerging question trends, through response order analysis of data from Zhihu and Stack Overflow. This study underscores the significant role of trend discoverers in influencing question popularity. Trend discoverers not only exhibit higher engagement in knowledge-sharing activities but also participate in discussions across a broader range of topics compared to regular users. The insights derived from this research have crucial implications for improving the development and functionality of Q&A platforms.

With the phasor measurement units (PMUs) being widely utilized in power systems, a large amount of data can be stored. If transient stability assessment (TSA) method based on the deep learning model is trained by this dataset, it requires high computation cost. Furthermore, the fact that unstable cases rarely occur would lead to an imbalanced dataset. Thus, power system transient stability status prediction has the bias problem caused by the imbalance of sample size and class importance. Faced with such a problem, a TSA model based on the sample selection method is proposed in this paper. Sample selection aims to optimize the training set to speed up the training process while improving the preference of the TSA model. The typical samples which can accurately express the spatial distribution of the raw dataset are selected by the proposed method. Primarily, based on the location of training samples in the feature space, the border samples are selected by trained support vector machine (SVM), and the edge samples are selected by the assistance of the approximated tangent hyperplane of a class surface. Then, the selected samples are input to stacked sparse autoencoder (SSAE) as the final classifier. Simulation results in the IEEE 39-bus system and the realistic regional power system of Eastern China show the high performance of the proposed method.

This paper begins by analyzing the key mathematical properties of diffusive vaccinated models, including existence, uniqueness, positivity, and boundedness. Equilibria are identified, and the basic reproductive number is calculated. The Banach contraction mapping principle is applied to rigorously establish the solution existence and uniqueness. In order to understand the disease’s time transmission, it is important to examine the global stability of the equilibrium points. Disease-free equilibrium and endemic equilibrium are the two equilibria in this model. Here, we demonstrate that the endemic equilibrium is worldwide asymptotic stable when the basic reproductive number is greater than 1, and the disease-free equilibrium is globally asymptotic stable whenever the basic reproductive number is less than 1. Moreover, based on the Caputo fractional derivative of order and the implicit Euler’s approximation, we offered an unconditionally stable numerical solution for the resultant system. This work explores the solution of some significant population models of noninteger order using an approach known as the iterative Laplace transform. The proposed methodology is developed by effectively combining Laplace transformation with an iterative procedure. A series form solution that exhibits some convergent behavior towards the precise solution can be attained. It is noted that there is a close contact between the obtained and precise solutions. Moreover, the suggested method can handle a variety of fractional order derivative problems because it involves minimal computations. This information will be helpful in further studies to determine the ideal strategy of action for preventing or stopping the spread disease transmission.