Polarized Tunneling Transistor for Ultralow-Energy-Consumption Artificial Synapse toward Neuromorphic Computing.

Neural networks based on low-power artificial synapses can significantly reduce energy consumption, which is of great importance in today's era of artificial intelligence. Two-dimensional (2D) material-based floating-gate transistors (FGTs) have emerged as compelling candidates for simulating artificial synapses owing to their multilevel and nonvolatile data storage capabilities. However, the low erasing/programming speed of FGTs renders them unsuitable for low-energy-consumption artificial synapses, thereby limiting their potential in high-energy-efficient neuromorphic computing. Here, we introduce a FGT-inspired MoS2/Trap/PZT heterostructure-based polarized tunneling transistor (PTT) with a simple fabrication process and significantly enhanced erasing/programming speed. Distinct from the FGT, the PTT lacks a tunnel layer, leading to a marked improvement in its erasing/programming speed. The PTT's highest erasing/programming (operation) speed can reach ∼20 ns, which outperforms the performance of most FGTs based on 2D heterostructures. Furthermore, the PTT has been utilized as an artificial synapse, and its weight-update energy consumption can be as low as 0.0002 femtojoule (fJ), which benefits from the PTT's ultrahigh operation speed. Additionally, PTT-based artificial synapses have been employed in constructing artificial neural network simulations, achieving facial-recognition accuracy (95%). This groundbreaking work makes it possible for fabricating future high-energy-efficient neuromorphic transistors utilizing 2D materials.

PZT-Enabled MoS2 Floating Gate Transistors: Overcoming Boltzmann Tyranny and Achieving Ultralow Energy Consumption for High-Accuracy Neuromorphic Computing.

Low-power electronic devices play a pivotal role in the burgeoning artificial intelligence era. The study of such devices encompasses low-subthreshold swing (SS) transistors and neuromorphic devices. However, conventional field-effect transistors (FETs) face the inherent limitation of the "Boltzmann tyranny", which restricts SS to 60 mV decade-1 at room temperature. Additionally, FET-based neuromorphic devices lack sufficient conductance states for highly accurate neuromorphic computing due to a narrow memory window. In this study, we propose a pioneering PZT-enabled MoS2 floating gate transistor (PFGT) configuration, demonstrating a low SS of 46 mV decade-1 and a wide memory window of 7.2 V in the dual-sweeping gate voltage range from -7 to 7 V. The wide memory window provides 112 distinct conductance states for PFGT. Moreover, the PFGT-based artificial neural network achieves an outstanding facial-recognition accuracy of 97.3%. This study lays the groundwork for the development of low-SS transistors and highly energy efficient artificial synapses utilizing two-dimensional materials.

Short-Term Effect of Lumbar Traction on Intervertebral Discs in Patients with Low Back Pain: Correlation between the T2 Value and ODI/VAS Score.

OBJECTIVE: The effect of lumbar traction on low back pain (LBP) patients is controversial. Our study aims to assess changes in the intervertebral disc water content after lumbar traction using T2 mapping and explore the correlation between changes in the T2 value and Oswestry Disability Index (ODI)/visual analogue scale (VAS) score. DESIGN: Lumbar spine magnetic resonance imaging was performed, and the ODI/VAS scores were recorded in all 48 patients. Midsagittal T2-weighted imaging and T2 mapping were performed to determine the Pfirrmann grade and T2 value. Then, the T2 values were compared between pre- and posttraction, and the correlation between changes in the T2 value and ODI/VAS scores were examined. RESULTS: In the traction group, the changes in the nucleus pulposus (NP) T2 values for Pfirrmann grades II-IV and the annulus fibrosus (AF) T2 values for Pfirrmann grade II were statistically significant after traction (P < 0.05). Changes in the mean NP T2 value of 5 discs in each patient and in the ODI/VAS score showed a strong correlation (r = 0.822, r = 0.793). CONCLUSION: T2 mapping can be used to evaluate changes in the intervertebral disc water content. Ten sessions of traction resulted in a significant increase in quantitative T2 measurements of the NP in discs for Pfirrmann grade II-IV degeneration and remission of the patients' clinical symptoms in the following 6 months. Changes in the mean NP T2 value of 5 discs in each patient were strongly correlated with changes in the ODI/VAS score.

Whole-lesion histogram analysis of the apparent diffusion coefficient: Evaluation of the correlation with subtypes of mucinous breast carcinoma.

PURPOSE: To evaluate the utility of the whole-lesion histogram apparent diffusion coefficient (ADC) for characterizing the heterogeneity of mucinous breast carcinoma (MBC) and to determine which ADC metrics may help to best differentiate subtypes of MBC. MATERIALS AND METHODS: This retrospective study involved 52 MBC patients, including 37 pure MBC (PMBC) and 15 mixed MBC (MMBC). The PMBC patients were subtyped into PMBC-A (20 cases) and PMBC-B (17 cases) groups. All patients underwent preoperative diffusion-weighted imaging (DWI) at 1.5T and the whole-lesion ADC assessments were generated. Histogram-derived ADC parameters were compared between PMBC vs. MMBC and PMBC-A vs. PMBC-B, and receiver operating characteristic (ROC) curve analysis was used to determine optimal histogram parameters for differentiating these groups. RESULTS: The PMBC group exhibited significantly higher ADC values for the mean (P = 0.004), 25th (P = 0.004), 50th (P = 0.004), 75th (P = 0.006), and 90th percentiles (P = 0.013) and skewness (P = 0.021) than did the MMBC group. The 25th percentile of ADC values achieved the highest area under the curve (AUC) (0.792), with a cutoff value of 1.345 × 10-3 mm2 /s, in distinguishing PMBC and MMBC. The PMBC-A group showed significantly higher ADC values for the mean (P = 0.049), 25th (P = 0.015), and 50th (P = 0.026) percentiles and skewness (P = 0.004) than did the PMBC-B group. The 25th percentile of the ADC cutoff value (1.476 × 10-3 mm2 /s) demonstrated the best AUC (0.837) among the ADC values for distinguishing PMBC-A and PMBC-B. CONCLUSION: Whole-lesion ADC histogram analysis enables comprehensive evaluation of an MBC in its entirety and differentiating subtypes of MBC. Thus, it may be a helpful and supportive tool for conventional MRI. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:391-400.

[Retrieval of Copper Pollution Information from Hyperspectral Satellite Data in a Vegetation Cover Mining Area].

Heavy metal mining activities have caused the complex influence on the ecological environment of the mining regions. For example, a large amount of acidic waste water containing heavy metal ions have be produced in the process of copper mining which can bring serious pollution to the ecological environment of the region. In the previous research work, bare soil is mainly taken as the research target when monitoring environmental pollution, and thus the effects of land surface vegetation have been ignored. It is well known that vegetation condition is one of the most important indictors to reflect the ecological change in a certain region and there is a significant linkage between the vegetation spectral characteristics and the heavy metal when the vegetation is effected by the heavy metal pollution. It means the vegetation is sensitive to heavy metal pollution by their physiological behaviors in response to the physiological ecology change of their growing environment. The conventional methods, which often rely on large amounts of field survey data and laboratorial chemical analysis, are time consuming and costing a lot of material resources. The spectrum analysis method using remote sensing technology can acquire the information of the heavy mental content in the vegetation without touching it. However, the retrieval of that information from the hyperspectral data is not an easy job due to the difficulty in figuring out the specific band, which is sensitive to the specific heavy metal, from a huge number of hyperspectral bands. Thus the selection of the sensitive band is the key of the spectrum analysis method. This paper proposed a statistical analysis method to find the feature band sensitive to heavy metal ion from the hyperspectral data and to then retrieve the metal content using the field survey data and the hyperspectral images from China Environment Satellite HJ-1. This method selected copper ion content in the leaves as the indicator of copper pollution level, using stepwise multiple linear regression and cross validation on the dataset which is consisting of 44 groups of copper ion content information in the polluted vegetation leaves from Dexing Copper Mine in Jiangxi Province to build up a statistical model by also incorporating the HJ-1 satellite images. This model was then used to estimate the copper content distribution over the whole research area at Dexing Copper Mine. The result has shown that there is strong statistical significance of the model which revealed the most sensitive waveband to copper ion is located at 516 nm. The distribution map illustrated that the copper ion content is generally in the range of 0-130 mg · kg⁻¹ in the vegetation covering area at Dexing Copper Mine and the most seriously polluted area is located at the South-east corner of Dexing City as well as the mining spots with a higher value between 80 and 100 mg · kg⁻¹. This result is consistent with the ground observation experiment data. The distribution map can certainly provide some important basic data on the copper pollution monitoring and treatment.