This paper aims to tackle the problem of low accuracy in predicting financial distress in Chinese industrial enterprises, attributable to data imbalance and insufficient information. It utilizes annual data on systemic risk indicators and financial metrics of Chinese industrial enterprises listed on the China’s A-share market between 2008 and 2022 to construct the adaptive weighted XGBoost-Bagging model for corporate financial distress prediction. Empirical findings demonstrate that systemic risk indicators possess predictive potential independent of traditional financial information, rendering them valuable non-financial early warning indicators for China’s industrial sector; moreover, they help to enhance the predictive accuracy of various comparative models. The adaptive weighted XGBoost-Bagging model incorporating systemic risk indicators effectively addresses challenges arising from data imbalance and information scarcity, significantly improving the accuracy of financial distress prediction in Chinese industrial enterprises under the 2015 Chinese stock market crash, the Sino-US trade friction, and the COVID-19 epidemic; as such, it can be used as an efficient risk early warning tool for China’s industrial sector.

Coverage is a fundamental issue in the research field of wireless sensor networks (WSNs). Connected target coverage discusses the sensor placement to guarantee the needs of both coverage and connectivity. Existing works largely leverage on the Boolean disk model, which is only a coarse approximation to the practical sensing model. In this paper, we focus on the connected target coverage issue based on the probabilistic sensing model, which can characterize the quality of coverage more accurately. In the probabilistic sensing model, sensors are only be able to detect a target with certain probability. We study the collaborative detection probability of target under multiple sensors. Armed with the analysis of collaborative detection probability, we further formulate the minimum ϵ-connected target coverage problem, aiming to minimize the number of sensors satisfying the requirements of both coverage and connectivity. We map it into a flow graph and present an approximation algorithm called the minimum vertices maximum flow algorithm (MVMFA) with provable time complex and approximation ratios. To evaluate our design, we analyze the performance of MVMFA theoretically and also conduct extensive simulation studies to demonstrate the effectiveness of our proposed algorithm.

We study the realized power variations for the fourth order linearized Kuramoto–Sivashinsky (LKS) SPDEs and their gradient, driven by the space–time white noise in one-to-three dimensional spaces, in time, have infinite quadratic variation and dimension-dependent Gaussian asymptotic distributions. This class was introduced-with Brownian-time-type kernel formulations by Allouba in a series of articles starting in 2006. He proved the existence, uniqueness, and sharp spatio-temporal Hölder regularity for the above class of equations in $d=1,2,3$. We use the relationship between LKS-SPDEs and the Houdré–Villaa bifractional Brownian motion (BBM), yielding temporal central limit theorems for LKS-SPDEs and their gradient. We use the underlying explicit kernels and spectral/harmonic analysis to prove our results. On one hand, this work builds on the recent works on the delicate analysis of variations of general Gaussian processes and stochastic heat equation driven by the space–time white noise. On the other hand, it builds on and complements Allouba’s earlier works on the LKS-SPDEs and their gradient.

In H.264/AVC, new coding techniques introduce many optional macroblock encoding modes. Exhaustive search over all possible modes can achieve optimal coding efficiency but is highly computationally expensive. In this paper, a fast macroblock mode selection (FMMS) algorithm based on the motion content classification is proposed. Each macroblock is categorized into complex motion or simple motion contents by a fuzzy classifier, then different mode search orders with distinct early termination schemes are employed according to the classification. The proposed method can be readily incorporated with fast motion estimation algorithms and simulation results show that it can further save 40%-70% of the block distortion calculations on the basis of conventional fast motion estimation algorithms, while maintaining similar rate distortion performance.

Coal has always been an important foundation for China’s economy. The booming and declining of the coal industry can directly impact the regional economy. This study researches the impact of the rise and fall of the coal industry on the regional economy, finance, and employment by establishing a panel data model. The results show that the economic growth rate of coal-producing areas is vulnerable to the rise and fall of the coal industry, and the impact is uneven. That is, the prosperity of the coal industry has a slightly greater impact on regional economic growth than that of a recession. The fiscal revenue and expenditure of coal-producing areas has been substantially affected during the coal industry recession, and the fiscal deficit has risen remarkably. The employment situation of urban workers in coal-producing areas is almost unaffected by the rise and fall of the coal industry.