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Abnormal conditions greatly reduce the efficiency of hydraulic fracturing of unconventional gas reservoirs. Optimizing the fracturing scheme is crucial to minimize the likelihood of abnormal operational conditions, such as pressure channeling, casing deformation, and proppant plugging. This paper proposes a novel machine learning-based method for optimizing abnormal conditions during hydraulic fracturing of unconventional natural gas reservoirs. Firstly, the main controlling factors of abnormal conditions are selected through a hybrid controlling analysis, upon which a surrogate model is established for predicting the occurrence probability of abnormal conditions, rather than whether abnormal conditions happen or not. Subsequently, a machine learning-based optimization algorithm is developed to minimize the occurrence probability of abnormal conditions, acknowledging their inevitability during the fracturing process. The optimal results demonstrate the proposed method outperforms traditional methods, on average. The proposed methodology is more in line with the needs of practical operation in an environment full of uncertainty. [ABSTRACT FROM AUTHOR]

Artificial intelligence of things systems equipped with flexible sensors can autonomously and intelligently detect the condition of the surroundings. However, current intelligent monitoring systems always rely on an external computer with the capability of machine learning rather than integrating it into the sensing device. The computer-assisted intelligent system is hampered by energy inefficiencies, privacy issues, and bandwidth restrictions. Here, a flexible, large-scale sensing array with the capability of low-power in-sensor intelligence based on a compression hypervector encoder is proposed for real-time recognition. The system with in-sensor intelligence can accommodate different individuals and learn new postures without additional computer processing. Both the communication bandwidth requirement and energy consumption of this system are significantly reduced by 1,024 and 500 times, respectively. The capability for in-sensor inference and learning eliminates the necessity to transmit raw data externally, thereby effectively addressing privacy concerns. Furthermore, the system possesses a rapid recognition speed (a few hundred milliseconds) and a high recognition accuracy (about 99%), comparing with support vector machine and other hyperdimensional computing methods. The research holds marked potential for applications in the integration of artificial intelligence of things and flexible electronics., Competing Interests: Competing interests: The authors declare that they have no competing interests., (Copyright © 2024 Zhangyu Xu et al.)

The facial expressions are a mirror of the elusive emotion hidden in the mind, and thus, capturing expressions is a crucial way of merging the inward world and virtual world. However, typical facial expression recognition (FER) systems are restricted by environments where faces must be clearly seen for computer vision, or rigid devices that are not suitable for the time-dynamic, curvilinear faces. Here, we present a robust, highly wearable FER system that is based on deep-learning-assisted, soft epidermal electronics. The epidermal electronics that can fully conform on faces enable high-fidelity biosignal acquisition without hindering spontaneous facial expressions, releasing the constraint of movement, space, and light. The deep learning method can significantly enhance the recognition accuracy of facial expression types and intensities based on a small sample. The proposed wearable FER system is superior for wide applicability and high accuracy. The FER system is suitable for the individual and shows essential robustness to different light, occlusion, and various face poses. It is totally different from but complementary to the computer vision technology that is merely suitable for simultaneous FER of multiple individuals in a specific place. This wearable FER system is successfully applied to human-avatar emotion interaction and verbal communication disambiguation in a real-life environment, enabling promising human-computer interaction applications., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this article., (Copyright © 2021 Meiqi Zhuang et al.)

Epidermal electrophysiology is widely carried out for disease diagnosis, performance monitoring, human-machine interaction, etc. Compared with thick, stiff, and irritating gel electrodes, emerging tattoo-like epidermal electrodes offer much better wearability and versatility. However, state-of-the-art tattoo-like electrodes are limited in size (e.g., centimeters) to perform electrophysiology at scale due to challenges including large-area fabrication, skin lamination, and electrical interference from long interconnects. Therefore, we report large-area, soft, breathable, substrate- and encapsulation-free electrodes designed into transformable filamentary serpentines that can be rapidly fabricated by cut-and-paste method. We propose a Cartan curve-inspired transfer process to minimize strain in the electrodes when laminated on nondevelopable skin surfaces. Unwanted signals picked up by the unencapsulated interconnects can be eliminated through a previously unexplored electrical compensation strategy. These tattoo-like electrodes can comfortably cover the whole chest, forearm, or neck for applications such as multichannel electrocardiography, sign language recognition, prosthetic control or mapping of neck activities., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

Objective: Overtraining is a serious problem in sports, assessed by comprehensive multi-index evaluation, but so far there is still no sensitive, specific monitoring indicator or simple evaluation method to evaluate it. This research established a method for detecting plasma cell free DNA (cfDNA) of rats by real time PCR and discuss edits significance: a new molecular marker of overtraining?, Methods: Twelve male SD rats were randomly divided into control group and overtraining group. The overtraining group rats were undertaken overtraining on a motor-driven treadmill for 5 weeks, while the control group rats kept quiescent. All the rats were drawn blood at pre-and after-5 weeks to detect plasma levels of cfDNA, testosterone (T) and corticosterone (Cort) as well as peroxidation/antioxidation parameters (T-AOC, MDA, SOD, GSH-Px) and creatin kinase (CK)., Results: (1) Plasma cfDNA of rat was detected specifically by our real time PCR. (2) Compared with control group rats, the plasma cfDNA of overtraining rats increased obviously (about 5.43 fold). (3) Plasma cfDNA was related to plasma T, Cort, T/C ratio and MDA (correlation coefficent were -0.729, 0.854, -0.655 and 0.720, respectively) rather than plasma T-AOC, GSH-Px, SOD and CK., Conclusion: (1) A real time PCR method was established successfully to determine plasma cfDNA of rat. (2) A remarkable raises of plasma levels of cfDNA were found in overtraining rats which were associated with T, Cort and T/C, suggested that plasma cfDNA might be a new molecular marker of overtraining. (3) The increase of plasma cfDNA of overtraining rat might correlate with enhanced oxidative stress induced by overtraining instead of muscle damage.