Drilling conditions serve as reliable indicators for evaluating drilling system performance. The recognition methodology forms a critical foundation for intelligent drilling and plays a significant role in preventing borehole accidents such as pipe sticking. This study proposes a recognition method for typical drilling conditions based on bit motion characteristics, developing intelligent algorithms that utilize fundamental motion parameters and drive signals as inputs to achieve accurate condition identification. Firstly, micro-drilling experiments were conducted under various pressure and rotational speed combinations. The variation characteristics of drilling speed, rotational speed, torque, and drilling pressure during the process were analyzed. Based on rotational and feed motion features at different stages, four typical drilling conditions were defined: stable drilling, slowing-down drilling, intermittent jamming, and pipe sticking. Subsequently, using actual coal mine drilling data, feature indicators for recognition models were selected through collective statistical characteristics and rolling statistical properties. Recognition models employing support vector machine (SVM), convolutional neural network (CNN), and long short-term memory (LSTM) networks were constructed and trained. Test results demonstrate that the LSTM-based model achieved the highest recognition accuracy (84% overall). It attained 100% accuracy in identifying slowing-down drilling and intermittent jamming conditions, though recognition accuracy for stable drilling remained relatively low. This research contributes to enhanced intelligent recognition of drilling conditions and provides theoretical support for advancing intelligent drilling technologies.

Wearable microfluidic sensors represent a promising advancement in the field of noninvasive health monitoring, providing continuous and personalized insights into the physiological status of an individual. With all-in-one wearable devices, human sweat can be collected, transported, and analyzed noninvasively through a large number of biochemical biomarkers in human sweat including glucose, lactate, uric acid, cortisol, and various ions. In this review, the state-of-art wearable microfluidic noninvasive sensors for biomarker monitoring in sweat are classified into four subtopics according to the sweat analysis method: electrochemical sweat analysis, colorimetric sweat analysis, fluorometric and plasmonic sweat analysis, and hybrid sweat analysis. In addition, the research trend of wearable microfluidic noninvasive sensors for sweat analysis is outlooked.

Many have argued that cultural factors are the main reasons for the high level of IP infringement in China. Confucianism has been often regarded as the main factor that leads to the nation’s negative attitudes towards IPR protection. Based on an interpretation of the Five Constant Virtues of Confucianism under their historical contexts, we hypothesize a positive relationship between Confucian ethics and attitude towards IPR protection. Our survey of 515 Chinese participants supports the hypothesized relationship. The results suggest that the strong influence of Confucian ethics can contribute to positive attitudes towards IPR protection. Furthermore, our findings strongly suggest that a low level of public IP awareness is one of the leading causes of the low acceptance of the concept of IPR and the high level of IP infringement in China. JEL: O31, O33, O34, O35, O36

Wearable microfluidic sweat biosensors have drawn great attention for their noninvasive ability to detect various biomarkers in human sweat. However, traditional microfluidic biosensors are always complicated and inflexible, limiting modification after production. This work proposed a cost-effective and easy-to-fabricate method inspired by traditional Chinese mortise-tenon structure joints to develop a wearable modular sweat sensing system without glue, nails, or any other materials. Both electrochemical sensors and colorimetric sensors can be integrated into the wearable modular microfluidic sensing system to meet different requirements. The proposed microfluidic sensing system can avoid flow-through mixing effects using capillary bursting valves and detect pH, together with Na+ and Cl in collected sweat. The detected biomarkers can be added or changed in this microfluidic sensing system according to distinct applications. Therefore, this work provides a novel way to fabricate multifunctional wearable sensing systems and sheds light on the future of point-of-care testing and personalized healthcare.

This study examines how CEO political power influences merger and acquisition (M&A) decisions in China, focusing on the propensity to complete deals despite negative market reactions. Analyzing 1346 M&A deals by Chinese listed firms from 2008 to 2024, we find that politically powerful CEOs are more likely to finalize ‘bad’ deals (negative CARs), driven by personal motives such as compensation gains and reduced turnover risk, often leveraging private information for insider trading. Conversely, boards with greater political power relative to CEOs (higher PPD) enhance oversight, increasing the likelihood of canceling value-destroying M&As. These effects are more pronounced in non-SOEs, where state control is lower. Long-term performance analysis (OPER and BHAR) confirms that completed ‘bad’ deals under powerful CEOs harm firm value, while strong boards mitigate losses. Our findings highlight the critical role of political power dynamics in M&A governance, extending agency and network theories in China’s unique institutional context.

In China, the prevalence of strong political connections among a significant number of boards of directors and CEOs highlights the importance of cultivating such relationships. This is particularly relevant when considering the Chinese political system where officers holding higher political ranks wield dominant, or even absolute, power in decision-making. Our findings reveal that the political power differential (PPD) between a board of directors and its CEO plays a pivotal role in mitigating CEO entrenchment associated with political power. Specifically, when directors possess more political power than their CEOs, they can effectively fulfil their disciplinary role, leading to the dismissal of underperforming CEOs. Our study substantiates a significantly positive relationship between PPD and the probability of a forced CEO turnover, as well as the sensitivity of CEO turnover to performance. Notably, as PPD increases by one standard deviation from its mean level, we observe an approximate 30% increase in CEO turnover-performance sensitivity. These findings confirm a higher likelihood of replacing underperforming CEOs in firms with a politically powerful board. Our results also highlight that a higher proportion of either independent or female directors alone does not guarantee effective monitoring. The key lies in ensuring that these directors possess stronger political power than the CEO.

The biosensor is an instrument that converts the concentration of biomarkers into electrical signals for detection. Biosensing technology is non-invasive, lightweight, automated, and biocompatible in nature. These features have significantly advanced medical diagnosis, particularly in the diagnosis of mental disorder in recent years. The traditional method of diagnosing mental disorders is time-intensive, expensive, and subject to individual interpretation. It involves a combination of the clinical experience by the psychiatrist and the physical symptoms and self-reported scales provided by the patient. Biosensors on the other hand can objectively and continually detect disease states by monitoring abnormal data in biomarkers. Hence, this paper reviews the application of biosensors in the detection of mental diseases, and the diagnostic methods are divided into five sub-themes of biosensors based on vision, EEG signal, EOG signal, and multi-signal. A prospective application in clinical diagnosis is also discussed.

Prior literature shows that corporate culture matters for firm performance and enhance corporate resilience in crises. We construct a text-based measure of corporate culture for Chinese listed firms and study whether and how strong corporate culture improves corporate resilience during the Sino-US trade war. Empirical analyses suggest that a strong corporate culture mitigates the deteriorating stock returns due to the trade war exposure through two potential influencing mechanisms, enhancing operating performance and mitigating financial constraint. Results also indicate that strong culture works more effectively for privately owned enterprises (POEs) than their state-owned peers, and individual cultures of innovation, hardworking, teamwork, and quality have material effects on stock performance of the POEs. The evidence pinpoints that strong corporate culture helps insulate Chinese private firms more from external shocks and achieve a more sustainable growth.

Nanosheets, a classification of two-dimensional nanomaterials with extremely high surface-to-volume ratios, have attracted great attention in various fields with vast applications. Owing to their specific structures, nanosheets possess prominent sensing properties including catalyst, electrical properties, etc., together with unique physical properties like remarkable adhesion, stretchability, and flexibility. Therefore, nanosheet materials could play a significant role in the field of wearable biohazard gas sensors which is one of the rising research topics in the field of biosensors. In this review, the state-of-the-art development of wearable biohazard gas sensors based on nanosheet materials is discussed and classified into four categories including wearable biohazard gas sensors based on nanosheets towards the monitoring of NO2, NH3, other gases, and multiple gases. Finally, the research trend in wearable biohazard gas sensors based on nanosheets is prospected.

This study provides empirical evidence that firms with a higher proportion of board directors who are politically more powerful than their CEOs can significantly reduce stock performance variability, but not on accounting performance variability. Our findings show that among independent (female, non-coopted) directors, only those who are politically more powerful than CEOs are effective in their monitoring role. In our additional tests, we show that our findings are not driven by an endogeneity bias. We find some mechanisms through which politically superior boards can mitigate stock performance variability.

Human detection within the operating area of electromechanical equipment is essential to ensure safe production and avoid accidents in the underground coal mine. Low light intensity and uneven light distribution in its environment surrenders the traditional color image based methods for human detection. In this paper, we focus on accurate detection of human in the operating area of electromechanical mining equipment using RGBD image. A novel network framework for miner detection based on YOLOv3 is proposed to fuse color image and depth image with enhanced attention mechanism. In the Pre-Backbone, feature extraction of both Depth and RGB branches are developed as the preliminary feature extractor with convolutional layer and residual block. Then the Convolutional Block Attention Module (CBAM) is improved to select and fuse RGB and Depth features by defining channel weights. Finally, the features are further inputted to Post-Backbone and used for multi-scale prediction in Head. The experiments demonstrate the superiority of the proposed method over some classical methods on miner detection with different light intensities and distributions.

In recent years, a huge number of individuals all over the world, elderly people, in particular, have been suffering from Alzheimer's disease (AD), which has had a significant negative impact on their quality of life. To intervene early in the progression of the disease, accurate, convenient, and low-cost detection technologies are gaining increased attention. As a result of their multiple merits in the detection and assessment of AD, biosensors are being frequently utilized in this field. Behavioral detection is a prospective way to diagnose AD at an early stage, which is a more objective and quantitative approach than conventional neuropsychological scales. Furthermore, it provides a safer and more comfortable environment than those invasive methods (such as blood and cerebrospinal fluid tests) and is more economical than neuroimaging tests. Behavior detection is gaining increasing attention in AD diagnosis. In this review, cutting-edge biosensor-based devices for AD diagnosis together with their measurement parameters and diagnostic effectiveness have been discussed in four application subtopics: body movement behavior detection, eye movement behavior detection, speech behavior detection, and multi-behavior detection. Finally, the characteristics of behavior detection sensors in various application scenarios are summarized and the prospects of their application in AD diagnostics are presented as well.

伪谱法将连续的最优控制问题转换为离散的非线性规划问题,通过非线性规划离散解插值获得原问题的解,即连续的控制函数与状态函数.首先,梳理伪谱法解最优控制问题的基础理论与求解的一般过程;其次,归纳伪谱法求解的一致性原则,包括解的存在性、可行性和最优性;然后,推导保证解一致性的方法,通过3种情况算例仿真验证了一致性原则与方法的适用性;最后,总结伪谱法的特点,提出不同应用场景下的一般求解思路和新的研究挑战.

As Intellectual Property (IP) protection can nurture innovation, and since innovation is one of the critical sources of economic growth, it has become especially important since China surpassed a certain economic development stage, because China now has a growing number of its own innovations which need to be protected. This paper describes the construction of a new research model with which to explore and examine the impact of potential factors on attitudes towards Intellectual Property Rights (IPR) in China in the context of the creative design industry. The findings of a quantitative study of Chinese design business owners reveal the significant roles of Confucianism, perceived economic loss and perceived effectiveness of IPR law enforcement in shaping their attitudes towards IPR. Our findings support the idea that promoting Confucianism can help to develop an internalised respect for IPR, while sizable penalties for IPR infringement can enhance the effectiveness of IPR protection.

In this paper, we first prove that the local time associated with symmetric alpha-stable processes is of bounded p-variation for any p > 2/ alpha-1 partly based on Barlow's estimation of the modulus of the local time of such processes. The fact that the local time is of bounded p-variation for any p > 2/ alpha-1 enables us to define the integral of the local time integral(infinity)(-infinity) del(alpha-1)(-) f(x)d(x) L-t(x) as a Young integral for less smooth functions being of bounded q-variation with 1 <= q < 2/3-alpha. When q >= 2/3-alpha Young's integration theory is no longer applicable. However, rough path theory is useful in this case. The main purpose of this paper is to establish a rough path theory for the integration with respect to the local times of symmetric alpha-stable processes for 2/3-alpha <= q <4. (C) 2017 Elsevier B.V. All rights reserved.

In this paper, we describe an efficient stereo matching algorithm which is inspired by the excellent performances of convolutional neural network (CNN) on vision problems in recent years. Our algorithm applies adaptive smoothness constraints making use of disparity discontinuous information to optimize the overall disparity map. First, we define a CNN architecture called DD-CNN to classify whether disparities of pixels in the image is continuous or not. The training data set is constructed from Middlebury stereo data sets. Once we obtain the disparity discontinuous map, different penalizes are applied to the energy function which takes the whole disparity map as argument. The algorithm imposes large penalizes to disparity differences between pixels and their neighborhoods when disparities of the center pixels are predicted to be discontinuous and small penalizes otherwise. Experiments show that the proposed algorithm performs better than the state-of-art algorithm.