Search strategy and line association analysis of cascading failure accident chain in new energy power systems

As the penetration rate of new energy in the power system gradually increases and the complexity of cascading faults increases, it is of great significance for the power system to comprehensively explore the chain of cascading faults in the new energy power system and quickly determine the closely related lines in the cascading faults. In response to the lack of consideration in existing research of the changes in the importance of transmission lines after the introduction of new energy, this paper proposes a cascading failure prediction index that integrates the importance and operational status of transmission lines in new energy power systems and applies it to the search for cascading failures in new energy power systems. First, the development characteristics of cascading faults were analyzed, and the main factors influencing cascading faults were identified: the importance of the transmission line and operating status of the new energy power system. Based on these factors, a prediction index for cascading faults was established, and the accident chain was searched using this index. Then, the FP-growth algorithm was used to analyze the lines in the fault chain concentration, and based on the analysis results, the correlation relationship suitable for the cascading failure lines in the new energy power system was determined. Finally, a simulation was conducted on an IEEE 10 machine 39 node system containing new energy wind turbines, and the results verified the effectiveness of the proposed indicators and strategies

ArguGPT: evaluating, understanding and identifying argumentative essays generated by GPT models

AI generated content (AIGC) presents considerable challenge to educators around the world. Instructors need to be able to detect such text generated by large language models, either with the naked eye or with the help of some tools. There is also growing need to understand the lexical, syntactic and stylistic features of AIGC. To address these challenges in English language teaching, we first present ArguGPT, a balanced corpus of 4,038 argumentative essays generated by 7 GPT models in response to essay prompts from three sources: (1) in-class or homework exercises, (2) TOEFL and (3) GRE writing tasks. Machine-generated texts are paired with roughly equal number of human-written essays with three score levels matched in essay prompts. We then hire English instructors to distinguish machine essays from human ones. Results show that when first exposed to machine-generated essays, the instructors only have an accuracy of 61% in detecting them. But the number rises to 67% after one round of minimal self-training. Next, we perform linguistic analyses of these essays, which show that machines produce sentences with more complex syntactic structures while human essays tend to be lexically more complex. Finally, we test existing AIGC detectors and build our own detectors using SVMs and RoBERTa. Results suggest that a RoBERTa fine-tuned with the training set of ArguGPT achieves above 90% accuracy in both essay- and sentence-level classification. To the best of our knowledge, this is the first comprehensive analysis of argumentative essays produced by generative large language models. Machine-authored essays in ArguGPT and our models will be made publicly available at https://github.com/huhailinguist/ArguGP

Case report: Beneficial effects of visual cortex tDCS stimulation combined with visual training in patients with visual field defects

BackgroundVisual field defect (VFD) refers to the phenomenon that the eye is unable to see a certain area within the normal range of vision, which may be caused by eye diseases, neurological diseases and other reasons. Transcranial direct current stimulation (tDCS) is expected to be an effective treatment for the recovery or partial recovery of VFD. This paper describes the potential for tDCS in combination with visual retraining strategies to have a positive impact on vision recovery, and the potential for neuroplasticity to play a key role in vision recovery.MethodsThis case report includes two patients. Patient 1 was diagnosed with a right occipital hemorrhage and homonymous hemianopia. Patient 2 had multiple facial fractures, a contusion of the right eye, and damage to the optic nerve of the right eye, which was diagnosed as a peripheral nerve injury (optic nerve injury). We administered a series of treatments to two patients, including transcranial direct current stimulation; visual field restoration rehabilitation: paracentric gaze training, upper and lower visual field training, VR rehabilitation, and perceptual training. One time per day, 5 days per week, total 6 weeks.ResultsAfter 6 weeks of visual rehabilitation and tDCS treatment, Patient 1 Humphrey visual field examination showed a significant improvement compared to the initial visit, with a reduction in the extent of visual field defects, increased visual acuity, and improvement in most visual functions. Patient 2 had an expanded visual field, improved visual sensitivity, and substantial improvement in visual function.ConclusionOur case reports support the feasibility and effectiveness of tDCS combined with visual rehabilitation training in the treatment of occipital stroke and optic nerve injury settings

InterFormer: Interactive Local and Global Features Fusion for Automatic Speech Recognition

The local and global features are both essential for automatic speech recognition (ASR). Many recent methods have verified that simply combining local and global features can further promote ASR performance. However, these methods pay less attention to the interaction of local and global features, and their series architectures are rigid to reflect local and global relationships. To address these issues, this paper proposes InterFormer for interactive local and global features fusion to learn a better representation for ASR. Specifically, we combine the convolution block with the transformer block in a parallel design. Besides, we propose a bidirectional feature interaction module (BFIM) and a selective fusion module (SFM) to implement the interaction and fusion of local and global features, respectively. Extensive experiments on public ASR datasets demonstrate the effectiveness of our proposed InterFormer and its superior performance over the other Transformer and Conformer models.Comment: Accepted by Interspeech 202

Novel role for non-invasive neuromodulation techniques in central respiratory dysfunction

Respiration is a crucial steady-state function of human life. Central nervous system injury can damage the central respiratory pattern generator (CRPG) or interrupt its outflow, leading to central respiratory paralysis and dysfunction, which can endanger the patient's life. At present, there is no effective means to reverse this process. Commonly used non-invasive neuromodulation techniques include repetitive transcranial magnetic stimulation (rTMS), transcranial direct current stimulation (tDCS) and so forth, which have been widely applied in nervous system diseases and their various secondary symptoms, but rarely in respiratory function. Clinical and animal studies have confirmed that TMS is also suitable for investigating the excitability and plasticity of ascending corticospinal respiratory pathways. In addition, although rTMS and tDCS differ in their respective mechanisms, both can regulate respiratory networks in healthy individuals and in diseased states. In this review, we provide an overview of the physiology of respiration, the use of TMS to assess the excitability of corticophrenic pathways in healthy individuals and in central respiratory disorders, followed by an overview of the animal and clinical studies of rTMS, tDCS and so forth in regulating respiratory circuits and the possible mechanisms behind them. It was found that the supplementary motor area (SMA) and the phrenic motor neuron (PMN) may be key regulatory areas. Finally, the challenges and future research directions of neuroregulation in respiratory function are proposed. Through understanding how neuromodulation affects the respiratory neural circuit non-invasively, we can further explore the therapeutic potential of this neuromodulation strategy, so as to promote the recovery of respiratory function after central nervous system diseases or injury

A Long-Lived Accretionary Process during the Amalgamation of the North China Craton: Insights from Neoarchean–Paleoproterozoic Polyphase Magmatism in the Lüliang Complex

There has been a long debate regarding the timing of the final amalgamation of the North China Craton, which is considered to have occurred either during the Neoarchean or Paleoproterozoic era. One major point of contention is whether there existed a long-lived subduction lasting through the Neoarchean to Paleoproterozoic. The Lüliang Complex contains multiphases of magmatism and thus represents the most viable region to address this controversy. In this study, we carried geochronological and geochemical analysis on the representative granitoids. Secondary ion mass spectrometry U–Pb dating revealed four distinct granitoid groups emplaced at 2531 ± 4, 2189–2173, 2027 ± 25, and 1852 ± 41 Ma, respectively. Notably, the 2531 Ma granitic gneiss was identified for the first time in this region. Based on the geochemical characteristics, the granitoids can be divided into two types. The 2531 and 2027 Ma groups display I-type features, while the 2189–2173 and 1852 Ma groups exhibit A-type geochemical affinities. Both I-type groups exhibit enrichment in Rb, depletion in Nb, Ta, and Ti, moderate fractionated REE patterns, substantial negative Eu anomalies, low Sr/Y ratios, and positive εHf(t) (+3.51 to +5.53 and +5.59 to +7.32, respectively), indicating that they were generated from partial melting of the juvenile mafic crust. In contrast, the 2189–2173 Ma granitoids belong to A2-type and were most likely generated by the partial melting of felsic rocks in the back-arc region, while the 1852 Ma granitoids belong to A1-type and were most possibly the result of partial melting of mafic-intermediate rocks during the post-collisional stage. Based on the records of A-type granitic magmatism and the ~1950 Ma peak metamorphism throughout the Trans-North China Orogen, we propose that a long-lived subduction process (2531–1950 Ma) can mostly explain the existing geological phenomena. It is likely that the subduction between the Eastern and Western Blocks should have commenced at ~2531 Ma, followed by a long-lived subduction. The two blocks ultimately collided with each other to form the North China Craton at ~1950 Ma, which triggered post-collisional exhumation and partial melting at ~1852 Ma