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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]

Six chest leads are the standardized clinical devices of diagnosing cardiac diseases. Emerging epidermal electronics technology shift the dangling wires and bulky devices to imperceptible wearing, achieving both comfortable experience and high-fidelity measuring. Extending small areas of current epidermal electronics to the chest scale requires eliminating interference from long epidermal interconnects and rendering the data acquisition (DAQ) portable. Herein, we developed a chest-scale epidermal electronic system (EES) for standard precordial-lead ECG and hydration monitoring, including the only μm-thick substrate-free epidermal sensing module and the soft wireless DAQ module. An electrical compensation strategy using double channels within the DAQ module and epidermal compensated branches (ECB) is proposed to eliminate unwanted signals from the long epidermal interconnects and to achieve the desired ECG. In this way, the EES works stably and precisely under different levels of exercise. Patients with sinus arrhythmias have been tested, demonstrating the prospect of EES in cardiac diseases. [ABSTRACT FROM AUTHOR]

The internal availability of silent speech serves as a translator for people with aphasia and keeps human–machine/human interactions working under various disturbances. This paper develops a silent speech strategy to achieve all-weather, natural interactions. The strategy requires few usage specialized skills like sign language but accurately transfers high-capacity information in complicated and changeable daily environments. In the strategy, the tattoo-like electronics imperceptibly attached on facial skin record high-quality bio-data of various silent speech, and the machine-learning algorithm deployed on the cloud recognizes accurately the silent speech and reduces the weight of the wireless acquisition module. A series of experiments show that the silent speech recognition system (SSRS) can enduringly comply with large deformation (~45%) of faces by virtue of the electricity-preferred tattoo-like electrodes and recognize up to 110 words covering daily vocabularies with a high average accuracy of 92.64% simply by use of small-sample machine learning. We successfully apply the SSRS to 1-day routine life, including daily greeting, running, dining, manipulating industrial robots in deafening noise, and expressing in darkness, which shows great promotion in real-world applications. [ABSTRACT FROM AUTHOR]