Physical reservoir computing systems with novel nanomaterials and devices have emerged as research hotspots in this field in recent years. To target intelligent hardware integration at the edge, a study on reservoir computing using single-node carbon nanotube (CNT) heterojunction was conducted. The Au/POM-SWCNT/Au devices were fabricated on silicon substrate via standard semiconductor process. On the basis of the rich nonlinear electrical dynamics arising from electron exchange between polyoxometalate (POM) molecules and single-walled carbon nanotubes (SWCNTs), a reservoir computing architecture was constructed through vector-mask multiplication and dimension expansion. With hyperparameters such as feedback gain, the number of past states of linearly or nonlinearly transformed nodes concatenated to the current state, the ridge regression regularization coefficients, etc., searched by an adaptive genetic algorithm, a normalized mean square error (NMSE) of 0.0532 on the 10th-order nonlinear autoregressive moving average (NARMA10) task was achieved at best. The kernel rank (KR), generalization rank (GR), and memory capacity (MC) of the optimal reservoir were calculated to be 49, 46, and 22.9, respectively. The performance is comparable to that of similar multi-node physical reservoir systems whereas more hard-ware friendly in terms of system integration.

Reservoir computing (RC) is a very lightweight machine learning framework, suitable for edge information processing in the era of IOT. Recently, increasing efforts on RC to be implemented using various novel materials and devices are emerging, such as carbon nanotube, memristor, etc. However, how to configure the reservoir’s behavior suitable for a certain application is still an open problem. In this paper, our attention is paid to the behavior space of the reservoir including its all the currently available task-independent evaluation metrics, i.e., rank of kernel quality, rank of generalization capacity, information processing capacity, and memory capacity. A genetic algorithmis used to explore the space in order to identify the performance boundary of the reservoir. It is applied to characterize the computing capabilities of a carbon nanotube based in-materio reservoir system, especially to find its critical metric to implement the prediction of nonlinear series such as Nonlinear Autoregressive Moving Average with 10th order timelag (NARMA 10).

Reservoir computing (RC) has the advantages of fast learning and low training cost, as only the readout weights need training. Furthermore, it is suitable for multi-task processing or continuous learning as there could be no interferences between tasks. In this paper, we prepared a carbon nanotubes (CNTs)/PBMA reservoir modified with phosphomolybdic acid molecules (POM). The dynamical tracking of dual gas concentration in the environment was carried out by our lab-made RC experimental platform with the CNT reservoir under atmosphere. Through grid search of several RC parameters in this implementation, we obtained the optimized minimum tracking accuracy of less than 1 ppm for NH 3 and NO 2 when they were randomly mixed under a closed measurement environment or an open environment. This demonstrates a very promising potential of the POM/CNT/PBMA reservoir to perform near/in gas sensor computing.

Stress cardiovascular magnetic resonance (CMR) imaging is a well-validated non-invasive stress test to diagnose significant coronary artery disease (CAD), with higher diagnostic accuracy than other common functional imaging modalities. One-stop assessment of myocardial ischemia, cardiac function, and myocardial viability qualitatively and quantitatively has been proven to be a cost-effective method in clinical practice for CAD evaluation. Beyond diagnosis, stress CMR also provides prognostic information and guides coronary revascularisation. In addition to CAD, there is a large body of literature demonstrating CMR’s diagnostic performance and prognostic value in other common cardiovascular diseases (CVDs), especially coronary microvascular dysfunction (CMD). This review focuses on the clinical applications of stress CMR, including stress CMR scanning methods, practical interpretation of stress CMR images, and clinical utility of stress CMR in a setting of CVDs with possible myocardial ischemia.

CaS:Eu2+,Ce3+ are widely used in various fields, which is due to their excellent luminescent properties. However, the traditional high-temperature solid state synthesis of CaS has the disadvantage of large particle size and easy agglomeration. Also, the CaS-based material is unstable and decomposes easily in humid air. In the present study, CaS:Eu2+, Ce3+(here denoted as CaS:EuCe) phosphors with a uniform particle size, good dispersion, and small particle size (Dv50 = 13.46 mu m, abbreviated as D50) have been obtained using a novel coprecipitationmesophase decomposition (CMD) method. In addition, the inorganic-organic bilayer modification technology has been used to form core-shell structured CaS:EuCe@CaZnOS@Si69 phosphors, which could resist acidic/basic solutions and moist air. Its surface property changed from hydrophilicity (water contact angle 37.28 degrees) to hydrophobicity (water contact angle 112.23 degrees). Finally, the optimized phosphor was used to prepare luminescent film laminated glass, which showed the potential for use in plant growth. The reaction process of the CaS phosphor, as prepared by the CMD method, and the mechanism of the bilayer surface modification, have here been discussed in detail. We believe that the here-developed synthesis method provides a new strategy for the preparation of high-performance chalcogenide phosphors.

Phosphor-converted materials play a very important role in modern facility agriculture, both in converting the excess green light and low-efficiency ultraviolet (UV) into the red and blue light that is beneficial for the plant growth, and in slowing down the aging of polymer films. In this work, UVC absorption of Zn0.5Mg0.5Al2O4 (ZMAO):Cr3+ was significantly improved by co-doping Tb3+. The energy transfer (ET) from Tb3+ to Cr3+ occurred through a dipole-quadrupole interaction mechanism and the ET efficiency (eta(ET)) from was up to 99.90% in the ZMAO:0.5%Tb3+, 3.0%Cr3+ phosphor, which improved the match between the phosphor emission and absorption spectra of the phytochrome P-FR. Finally, light-conversion polymeric film is achieved by coating ZMAO:Tb3+, Cr3+ phosphor. This study provides a strategy to efficiently covert the UVC and green components of the sunlight to far-red light promoting the plant growth in horticulture and extending the service life of polymer film.

Electroencephalogram (EEG) data classification is still a complex and time-consuming task so far. In this paper, a liquid state machine (LSM), a bio-inspired computing model, was investigated for classification of the epileptic seizure EEG data. To enhance the classification performance of the random connected LSM, the particle swarm optimization (PSO) algorithm was used to optimize its topology and nonlinear dynamics through the search of the liquid hyperparameters such as the scaling of synaptic strength, connection probability and time constant of membrane potential. The effect of the inertia weight in PSO on the performance of searched LSM was studied. The best accuracy of 95% for EEG classification was achieved by an optimized LSM with 160 neurons combined with a Softmax classifier via 10-fold cross-validation. In addition, the margin of the searched parameters for hardware implementation of LSM was given.

Reservoir Computing (RC), a compact recurrent neural network (RNN), is an efficient artificial neural network suitable for processing timing signals. In our previous work on a physical reservoir consisting of single-walled carbon nanotubes (SWCNTs) network, its computing performance was presented to be improved by phosphomolybdic acid-modification on CNTs. In this paper, a genetic algorithm was used to search some hyperparameters related to its computing architecture, including input gain, input position, auxiliary input, regularization coefficient and leak rate, for two test benches, i.e., NARMA10 time series prediction and epileptic seizure EEG classification. The parametric setting for improved computing capability of this physical system were found. The best performances were achieved at normalized root-mean-square error of 0.0877 for NARMA10 and an accuracy of 98.33% for EEG classification, respectively.

It has been experimentally shown that metallic zigzag graphene nanoribbon (ZGNR) can be obtained by unzipping a carbon nanotube (CNT) along the chiral direction of CNT. This makes it possible to fabricate a unique AGNR-ZGNR-CNT heterojunction so that the semi-metallic ZGNR is between semiconducting AGNR and CNT. Here we demonstrate that such a unique all-carbon heterojunction may be utilized to obtain a barrier-free tunneling field-effect transistor (TFET). By performing a self-consistent first-principle calculation based on density functional theory combined with none-equilibrium Green's function (DFT-NEGF), we show that such a barrier-free TFET may reduce subthreshold swing below the classical limit while increase on-state current by diminishing tunneling barrier, which provides an promising route toward ultralow-power, high-performance carbon heterojunction electronics.

Physical reservoir computing (PRC) is a recently introduced framework using the complex dynamics of physical systems for information processing. In this paper, a physical reservoir based on a molecular network of gas molecules/and single-walled carbon nanotubes (SWCNTs)/polyoxometalate (POM) is proposed. The simulation of electronic properties and preliminary experiments on the electrical behavior of the POM-decorated SWCNT composite structure modified by redox gas molecules present its high potential as a configurable neuromorphic unit to construct the physical reservoir.

Physical reservoir computing (RC) is a recently introduced framework for information processing using the complex dynamics of physical systems. In this paper, a physical reservoir based on a molecular network of polyoxometalate (POM) decorated single-walled carbon nanotubes (SWCNT) with PBMA composite is fabricated. By the lab-built hardware platform, we experimentally demonstrate its excellent performance in a time series prediction benchmark with large short-term memory capacity (MC), indicating SWCNT/POM network as a promising substrate for reservoir computing because abundant inner charge and discharge in junctions leading to special electron transport characteristics that make rich dynamic and high-dimensional mapping properties appear in the POM-decorated SWCNT composite structure.

Crucible lead smelting,a traditional technology unique to China,refers to the production of lead by reducing lead sulfide with iron metal in crucibles.In recent years,a number of crucible lead production sites of the Liao-Jin-Yuan periods (tenth-fourteenth centuries CE) have been found in northern China,providing opportunities for the study of the technology.This paper provides a comprehensive overview of this technology based on the historical and archaeological evidence,with particular emphasis on the crucibles used.Firstly,it reviews the historical records on crucible lead smelting,and introduces,in detail,the technology used in Gansu during the Qing period (1644-1911)as well as indigenous methods used in the twentieth century;secondly,it summarizes the discoveries of crucible lead smelting sites in recent years,and reconstructs the manufacturing of crucibles and the iron reduction method by analysis of the crucible and slag;finally,it expounds the technical characteristics of crucible lead smelting,and explores the origin and development of the technology.

OBJECTIVE:This study aimed to investigate the diagnostic performance of radiomics features derived from coronary computed tomography angiography (CCTA) in the identification of ischemic coronary stenosis plaque using invasive fractional flow reserve (FFR) as the reference standard. MATERIALS AND METHODS:174 plaques of 149 patients (age: 62.21 ± 8.47 years, 96 males) with at least one lesion stenosis degree between 30 % and 90 % were retrospectively included. Stenosis degree and plaque characteristics were recorded, and a conventional multivariate logistic model was established. Over 1000 radiomics features of the plaque were derived from CCTA images. The plaques were randomly divided into training set (n = 139) and validation set (n = 35). A random forest model was built. The area under the curve (AUC) of the models was compared. RESULTS:Fifty-eight radiomics features were correlated with functionally significant stenosis (p < 0.05), wherein 56 features had an AUC of >0.6. NCP volume, NRS, remodeling index, and spotty calcification were included in the conventional model. Ultimately, 14 features were integrated to build the radiomics model. The AUC showed an improvement: 0.71 vs 0.82 for the training set and 0.70 vs 0.77 for the validation set (conventional model and radiomics model, respectively); however, it was not statistically significant (p = 0.58). CONCLUSION:The radiomics analysis of plaques showed improvement compared with conventional plaques assessment in identifying hemodynamically significant coronary stenosis. The statistical advancement of machine learning for plaques to predict hemodynamic stenosis with a noninvasive approach still needs further studies on a large-scale dataset.

Low-dimensional materials such as carbon nanotubes (CNTs) are promising candidates for gas sensing. Surface modification with specific molecules is considered an effective approach to enhance gas s...

郴桂矿厂是清代湖南最重要的铜、铅、锌等币材原料产地.该矿厂使用一种先炼铅、再炼铜的铅铜共生矿冶炼技术,史料中称其为"铅渣炼铜".2016年,桂阳桐木岭遗址发现了多金属冶炼遗迹、遗物,为复原郴桂矿厂铅渣炼铜技术提供了重要的实物证据.文章通过对铅渣炼铜的史料记载、桐木岭炼铅遗存以及炼铅炉渣的研究,判断郴桂矿厂采用的技术是:先用铁还原法在竖炉中炼铅,将冶炼得到的冰铜和铅分离开,最后用冰铜炼铜.该技术不同于其他铜铅共生矿冶炼技术,是铁还原法炼铅、冰铜炼铜两种技术的结合,为清代郴桂矿厂特有,在冶金史上系首次发现.这种技术产生的原因,是郴桂矿厂铜矿资源少,但又要尽可能满足宝南局铸钱对铜料的需求.

As an important part of the Internet of Things, mobile wireless sensor network (MWSN) will generate traffic with different service quality requirements due to the multi-sensor integration of nodes and application diversity. Due to the topological changes, resource constraints and self-organizing characteristics of MWSN, the Per-Hop Behavior (PHB) approach in the traditional Diff-Serv model has many challenges in providing differential service. In this paper, a path reservation multipath routing (PRMR) protocol is proposed, which can provide a suitable path for each type of traffic with service requirements. PRMR protocol includes path discovery algorithm and packet scheduling algorithm. In addition, the path scheduling model and scheduling algorithm in PRMR solve the problem of load imbalance among reserved paths caused by different types of traffic. In scenarios with different number of nodes, three types of traffic, integrity sensitive data, delay sensitive data and normal data, are used to verify the performance of PRMR differential service and load balancing. Simulation experiments not only compare the quality of service provided by each reserved path in PRMR, but also compare the differential service performance between PRMR and several multipath routing algorithms. Simulation results show that PRMR routing algorithm can guarantee high packet delivery rate and low delay for integrity-sensitive data and delay-sensitive data, respectively. In addition, the simulation results of the average residual energy index show that the protocol can achieve network energy balance.

Background Stress cardiovascular magnetic resonance (CMR) to screen for silent myocardial ischaemia in asymptomatic high risk patients with type 2 diabetes mellitus (DM) has never been performed, and its effectiveness is unknown. Our aim was to determine the feasibility of a screening programme using stress CMR by obtaining preliminary data on the prevalence of silent ischaemia caused by obstructive coronary artery disease (CAD) and quantify myocardial perfusion in asymptomatic high risk patients with type 2 diabetes. Methods In this prospective cohort study, we recruited 63 asymptomatic DM patients (mean age 66 years ± 4.4 years; 77.8% male); with Framingham risk score ≥ 20% from 3 sites from June 2017 to August 2018. Normal volunteers were recruited to determine normal global myocardial perfusion reserve index (MPRI). Adenosine stress CMR and global MPRI was performed and measured in all subjects. Positive stress CMR cases were referred for catheter coronary angiography (CCA) with/without fractional flow reserve (FFR) measurements. Positive CCA was defined as an FFR ≤ 0.8 or coronary narrowing ≥ 70%. Patients were followed up for major adverse cardiovascular events. Prevalence is presented as patient numbers and percentage. Mann–Whitney U test was used to compare global MPRI between patients and normal volunteers. Results 13 patients had positive stress CMR with positive CCA (20.6% of patient population), while 9 patients with positive stress CMR examinations had a negative CCA. 5 patients (7.9%) had infarcts detected of which 2 patients had no stress perfusion defects. 12 patients had coronary artery stents inserted, whilst 1 patient declined stent placement. DM patients had lower global MPRI than normal volunteers (n = 7) (1.43 ± 0.27 vs 1.83 ± 0.31 respectively; p < 0.01). After a median follow-up of 653 days, there was no death, heart failure, acute coronary syndrome hospitalisation or stroke. Conclusion 20.6% of asymptomatic DM patients (with Framingham risk ≥ 20%) had silent obstructive CAD. Furthermore, asymptomatic patients have reduced global MPRI than normal volunteers. Trial Registration: ClinicalTrials.gov Registration Number: NCT03263728 on 28th August 2017; https://clinicaltrials.gov/ct2/show/NCT03263728 .

It is difficult to distill metal zinc partly due to the reduction temperature of zinc oxide ores close to the boiling point of metallic zinc. The treatment of zinc sulfide ores is more complicated since they have to be roasted before smelting. Previous archaeometallurgical studies on zinc smelting technology in China mainly focus on the distillation of zinc oxide ores. This paper, for the first time, presents analytical results of archaeological evidence about the distillation of zinc sulfide ores in Guiyang in southern China dated back to the Qing Dynasty (CE 1636-1912). The smelting remains including ores, distillation retorts and slags, especially the roasting hearths and zinc calcine firstly discovered and confirmed in zinc smelting sites were characterized comprehensively by p-XRF, OM, SEM-EDS and XRD. It was revealed that the zinc smelting technology in the Tongmuling site and the Doulingxia site was mainly based on the distillation of zinc sulfide ores, which should be oxidized by a lengthy roasting processing at the lower temperature before the distilling. In order to enhance the condensation efficiency, the height of the condensers in the distillation retorts has been significantly increased. Most of the zinc products were ordered by the Minting sub-Bureau of Baonan in Changsha.

The ultrahigh carrier mobility and matchable work function of graphene have positioned this material as a leading candidate for the ideal contact material for carbon nanotubes (CNTs). Highly efficient carrier transport through CNT–graphene junctions is facilitated by covalently bonded contacts. This paper, therefore, proposes covalently bonded CNT–graphene junctions and investigates their characteristics theoretically. In these junctions, partially unzipped CNTs are longitudinally or radially bonded with graphene. By exploiting nonequilibrium Green's functions with density-functional theory, we examine ballistic electron transport (∼1.38 × 105 cm2/V s) and edge-dependent transport. Moreover, the contact properties of the junctions with adsorbed Cu atoms are investigated. Electron transfer from Cu to the junction turns the p-type Schottky contact into an n-type contact and decreases the Schottky barrier height from 0.2 to 0.08 eV. Furthermore, the junction resistance decreases by one to three orders of magnitude. The proposed design of Cu-decorated CNT–graphene junctions and first-principles calculations suggest an approach for low-power, high-performance CNT-based electronics.

OBJECTIVES This study investigated the prognosis of coronary microvascular disease (CMD) as determined by stress perfusion cardiac magnetic resonance (CMR) in patients with ischemic symptoms but without significant coronary artery disease (CAD). BACKGROUND Patients with CMD have poorer prognosis with various cardiac diseases. The myocardial perfusion reserve index (MPRI) derived from noninvasive stress perfusion CMR has been established to diagnose microvascular angina with a threshold MPRI <1.4. The prognosis of CMD as determined by MPRI is unknown. METHODS Chest pain patients without epicardial CAD or myocardial disease from January 2009 to December 2017 were retrospectively included from 3 imaging centers in Hong Kong (H K). Stress perfusion CMR examinations were performed using either adenosine or adenosine triphosphate. Adequate stress was assessed by achieving splenic switch-off sign. Measurement of MPRI was performed in all stress perfusion CMR scans. Patients were followed for major adverse cardiovascular events defined as all-cause death, acute coronary syndrome (ACS), epicardial CAD development, heart failure hospitalization and non-fatal stroke. RESULTS A total of 218 patients were studied (mean age 59 +/- 12 years; 49.5% male) and the average MPRI of that cohort was 1.56 +/- 0.33. Females and a history of hyperlipidemia were predictors of lower MPRI. Major adverse cardiovascular events (MACE) occurred in 15.6% of patients during a median follow-up of 5.5 years (interquartite range: 4.6 to 6.8 years). The optimal cutoff value of MPRI in predicting MACE was found with a threshold MPRI <= 1.47. Patients with MPRI <= 1.47 had three-fold increased risk of MACE compared with those with MPRI >1.47 (hazard ratio [HR]: 3.14; 95% confidence interval [CI]: 1.58 to 6.25; p = 0.001). Multivariate Cox regression after adjusting for age and hypertension demonstrated that MPRI was an independent predictor of MACE (HR: 0.10; 95% CI: 0.03 to 0.34; p < 0.001). CONCLUSIONS Stress perfusion CMR-derived MPRI is an independent imaging marker that predicts MACE in patients with ischemic symptom and no overt CAD over the medium term. (C) 2021 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation.