A real-time field bus architecture for multi-smart-motor servo system.

The multi-motor servo system (MMSS) is an electro-mechanical system widely used in various fields, including electric vehicles, robotics, and industrial machinery. Depending on the application, the number of motors in the system can range from several dozens to tens of thousands, which imposes additional communication demands. Thus, ensuring synchronization and control precision of the system requires addressing the challenge of guaranteeing the performance and reliability of communication among motors in the MMSS. In this paper, we design a smart servo motor (SSM) to upgrade the system to the multi-smart-motor servo system (MSMSS) based on a distributed real-time field bus architecture, namely, Multi-Motor Bus (MMB) architecture. The proposed MMB architecture is lightweight and stable, providing real-time support for Control Area Network connections to a central user computer and inter-integrated circuit connections to SSM units. This MMB architecture facilitates the synchronization of command transmission across SSMs and ensures the consistency of motors in the MSMSS. Additionally, a serial experiments to examine 3 key system performance and reliability characteristics are conducted, including command transmission time, transmission jitters, and rotation consistency. The analysis of these characteristics demonstrates the system's potential and feasibility to be applicable in industry.

Polymer removal and dispersion exchange of (10,5) chiral carbon nanotubes with enhanced 1.5 µm photoluminescence.

Singe-chirality single-walled carbon nanotubes (SWCNTs) produced by selective polymer extraction have been actively investigated for their semiconductor applications. However, to fulfil the needs of biocompatible applications, the organic solvents in polymer-sorted SWCNTs impose a limitation. In this study, we developed a novel strategy for organic-to-aqueous phase exchange, which involves thoroughly removing polymers from the sorted SWCNTs, followed by surfactant covering and redispersing of the cleaned SWCNTs in water. Importantly, the obtained aqueous system allows us to perform sp3 functionalization of the SWCNTs, leading to a strong photoluminescence emission at 1550 nm from the defect sites of (10,5) SWCNTs. These functionalized SWCNTs as infrared light emitters show considerable potential for bioimaging applications. This exchange-and-functionalization strategy opens the door for future biocompatible applications of polymer-sorted SWCNTs.

Iterative Strategy for Sorting Single-Chirality Single-Walled Carbon Nanotubes from Aqueous to Organic Systems.

Significant advancements in electronic devices and integrated circuits have been facilitated by semiconducting single-walled carbon nanotubes (SWCNTs) sorted by conjugated polymers (CPs). However, the variety of CPs with single-chirality selectivity is limited, and the sorting results are strongly dependent on the chiral distribution of the starting materials. To address this, we develop an iterative strategy to achieve single-chirality SWCNT separation from aqueous to organic systems, based on a multistep tandem extraction technique that allows a gentle and nondestructive separation of surfactants from SWCNTs, ensuring an efficient system transfer. In parallel, we refined the iterative sorting process between CPs. Employing two starting materials with narrow diameter distributions, using three CPs, we successfully sorted out five single-chirality SWCNTs of the (9,5), (8,6), (10,5), (8,7), and (11,3) species in organic systems. This strategy bridges the gap between aqueous and organic separation systems, achieving efficient complementarity between them.

Infrared Light-Emitting Diodes Based on Chirality-Sorted Carbon Nanotube Films.

An important goal in carbon nanotube optoelectronics is to achieve a high-performance near-infrared light source. But there are still many challenges such as the purity of single-walled carbon nanotube (SWCNT) chirality, nonradiative defects, thin-film quality, and device structure design. Here, we realize infrared light-emitting diodes (LEDs) based on chirality-sorted (10, 5) SWCNT network films, which operate at a low bias voltage and emit at a telecom O band of 1290 nm. Asymmetric palladium (Pd) and hafnium (Hf) contacts are used as electrodes for hole and electron injection, respectively. However, the large Schottky barrier at the interface of the SWCNTs and the Hf electrode, primarily resulting from the polymer wrapped on the nanotube surface during the sorting process, leads to inefficient electron injection and thus a low electroluminescence efficiency. We find that the efficiency of electron injection can be improved by the local doping of the nanotubes with dielectric layers of YOX-HfO2, which reduces the Schottky barrier at the SWCNT/Hf interface. Accordingly, the (10, 5) SWCNT film-based LED achieves an external quantum efficiency of larger than 0.05% without any optical coupling structure. With further improvement, we expect that such an infrared light source will have great application potential in the carbon nanotube monolithic optoelectronic integrated system and on-chip optical interconnection, especially in the field of short-distance optical fiber communications and data center.

Interlayer Charge Transfer Induced Electrical Behavior Transition in 1D AgI@sSWCNT van der Waals Heterostructures.

The emergence of one-dimensional van der Waals heterostructures (1D vdWHs) opens up potential fields with unique properties, but precise synthesis remains a challenge. The utilization of mixed conductive types of carbon nanotubes as templates has imposed restrictions on the investigation of the electrical behavior and interlayer interaction of 1D vdWHs. In this study, we efficiently encapsulated silver iodide in high-purity semiconducting single-walled carbon nanotubes (sSWCNTs), forming 1D AgI@sSWCNT vdWHs. We characterized the semiconductor-metal transition and increased the carrier concentration of individual AgI@sSWCNTs via sensitive dielectric force microscopy and confirmed the results through electrical device tests. The electrical behavior transition was attributed to an interlayer charge transfer, as demonstrated by Kelvin probe force microscopy. Furthermore, we showed that this method of synthesizing 1D heterostructures can be extended to other metal halides. This work opens the door for the further exploration of the electrical properties of 1D vdWHs.

Review of Physical Layer Security in Molecular Internet of Nano-Things.

Molecular networking has been identified as a key enabling technology for Internet-of-Nano-Things (IoNT): microscopic devices that can monitor, process information, and take action in a wide range of medical applications. As the research matures into prototypes, the cybersecurity challenges of molecular networking are now being researched on at both the cryptographic and physical layer level. Due to the limited computation capabilities of IoNT devices, physical layer security (PLS) is of particular interest. As PLS leverages on channel physics and physical signal attributes, the fact that molecular signals differ significantly from radio frequency signals and propagation means new signal processing methods and hardware is needed. Here, we review new vectors of attack and new methods of PLS, focusing on 3 areas: (1) information theoretical secrecy bounds for molecular communications, (2) key-less steering and decentralized key-based PLS methods, and (3) new methods of achieving encoding and encryption through bio-molecular compounds. The review will also include prototype demonstrations from our own lab that will inform future research and related standardization efforts.

Molecular Doping Modulation and Applications of Structure-Sorted Single-Walled Carbon Nanotubes: A Review.

Single-walled carbon nanotubes (SWCNTs) that have a reproducible distribution of chiralities or single chirality are among the most competitive materials for realizing post-silicon electronics. Molecular doping, with its non-destructive and fine-tunable characteristics, is emerging as the primary doping approach for the structure-controlled SWCNTs, enabling their eventual use in various functional devices. This review provides an overview of important advances in the area of molecular doping of structure-controlled SWCNTs and their applications. The first part introduces the underlying physical process of molecular doping, followed by a comprehensive survey of the commonly used dopants for SWCNTs to date. Then, it highlights how the convergence of molecular doping and structure-sorting strategies leads to significantly improved functionality of SWCNT-based field-effect transistor arrays, transparent electrodes in optoelectronics, thermoelectrics, and many emerging devices. At last, several challenges and opportunities in this field are discussed, with the hope of shedding light on promoting the practical application of SWCNTs in future electronics.

Analysis of rotational Doppler shift with multi-ring vortex beams.

Vortex beams (VBs) with orbital angular momentum have shown great potential in the detection of transverse rotational motion of spatial targets which is undetectable in the classical radar scheme. However, most of the reported rotational Doppler measurements based on VBs can only be realized under ideal experimental conditions. The long-range detection is still a challenge. The detection distance based on rotational Doppler effect (RDE) is mainly limited by the scattered signal's signal-to-noise ratio (SNR). In this work, we investigated the influence of multi-ring vortex beams (MVBs) on the rotational Doppler frequency spectrum of scattered light from an object based on RDE and proposed a method of SNR enhancement of RDE signal. Firstly, different types of MVBs composed of a set of single-ring VBs with the same topological charge and different radii are designed, including multi-ring Laguerre Gaussian beam (MLGB), multi-ring perfect vortex beams (MPVB), and high-order Laguerre Gaussian beam (HLGB). Then, the influence of the number of rings and radial radius interval on the intensity profiles of MVBs and rotational Doppler frequency spectra under aligned and misaligned conditions is studied in detail. And the reasons why different types of MVBs lead to different SNR enhancement effectiveness with the increase of rings are also analyzed theoretically. Finally, proof-of-concept experiments were conducted to verify the effectiveness of the SNR enhancement method for RDE signals. The results showed that the amplitudes of the Doppler spectra generated by the MLGB and MPVB are improved substantially with the increase of rings, but the enhancement effect caused by the former is superior to the latter. The gain of HLGB on the RDE signal is the lowest. This study provides a useful reference for the optimization of rotational Doppler detection systems and may be of great application value in telemetry, long-range communication and optical imaging.

Simultaneous measurement of spin and precession based on light's orbital angular momentum.

The rotational Doppler effect of the vortex beam is a recently emerged promising application of the optical vortex with orbital angular momentum. In this paper, we combine the method of the micro-Doppler effect of the traditional radar and the rotational Doppler effect of the vortex beam and propose an approach of rotational micro-Doppler effect, realizing the simultaneous measurement of spin and precession. We firstly analyze the rotational micro-Doppler characteristic introduced by precession under the illuminating of vortex beam and calculate the rotational micro-Doppler parameters related to the spin and precession. Then we conduct an experiment of using the vortex beam to detect a spinning object with precession and the rotational micro-Doppler frequency is successfully observed. By extracting the rotational micro-Doppler parameters, the simultaneous and independent measurement of spin and precession is realized. Both the theoretical analysis and experimental results indicate that the rotational micro-Doppler effect is an effective extension of the rotational Doppler effect and is also a feasible application of the vortex beam detection.

Monolithic three-dimensional integration of RRAM-based hybrid memory architecture for one-shot learning.

In this work, we report the monolithic three-dimensional integration (M3D) of hybrid memory architecture based on resistive random-access memory (RRAM), named M3D-LIME. The chip featured three key functional layers: the first was Si complementary metal-oxide-semiconductor (CMOS) for control logic; the second was computing-in-memory (CIM) layer with HfAlOx-based analog RRAM array to implement neural networks for feature extractions; the third was on-chip buffer and ternary content-addressable memory (TCAM) array for template storing and matching, based on Ta2O5-based binary RRAM and carbon nanotube field-effect transistor (CNTFET). Extensive structural analysis along with array-level electrical measurements and functional demonstrations on the CIM and TCAM arrays was performed. The M3D-LIME chip was further used to implement one-shot learning, where ~96% accuracy was achieved on the Omniglot dataset while exhibiting 18.3× higher energy efficiency than graphics processing unit (GPU). This work demonstrates the tremendous potential of M3D-LIME with RRAM-based hybrid memory architecture for future data-centric applications.

Detection of a spinning object using a superimposed optical vortex array.

The optical vortex (OV) carries unique orbital angular momentum (OAM) and experiences a Doppler frequency shift when backscattered from a spinning object. This rotational Doppler effect (RDE) has provided a solution for the non-contact detection of rotating motion. The reported RDE researches mainly use a single OV that generates frequency shifts proportional to its topological charge and has low robustness to light incidence. Here, we show the distinctive RDE of superimposed optical vortex array (SOVA). We analyze the holistic OAM of SOVA which is represented in terms of a superposition of azimuthal harmonics and displays a unique modal gathering effect. In the experiment of RDE, the frequency shift signals of SOVA show a precise mapping to the OAM modes and the modal gathering effect contributes to enhance the amplitude of signals, which has the potential to enhance robustness against non-coaxial incidence. This finding provides a new aspect of RDE and a pioneered example for introducing various SOVAs into rotation detection.

A case of endobronchial metastasis of colon cancer mimics sarcoidosis, and a review of related literature.

PURPOSE: Endobronchial metastases (EBM) are defined as bronchoscopically visible lesions histopathologically identical to extrapulmonary tumors. We summarized the literature on endobronchial metastasis of colorectal cancer and give a brief review. METHOD: We present a rare case with an episode mistaken for sarcoidosis and unexpectedly identified as colon cancer by bronchoscopic biopsy. A 53-year-old man with dry cough and dyspnea had diffuse micro lung nodules and lymphadenopathy on CT and PET/CT. He was diagnosed with sarcoidosis and took steroid therapy, but the symptoms could not be alleviated. Bronchoscopy was suggested. He was finally identified with colon cancer by bronchoscopic biopsy, which was confirmed by endoscopic biopsy. We summarise the clinical manifestations, imaging, prognosis of EMB of colorectal cancer. RESULT: EBM are rare. Colorectal cancer is common in EBM and the frequency is increasing. CONCLUSION: EBM should be distinguished from primary lung cancer, sarcoidosis.

Dual-branch hybrid encoding embedded network for histopathology image classification.

Objective.Learning-based histopathology image (HI) classification methods serve as important tools for auxiliary diagnosis in the prognosis stage. However, most existing methods are focus on a single target cancer due to inter-domain differences among different cancer types, limiting their applicability to different cancer types. To overcome these limitations, this paper presents a high-performance HI classification method that aims to address inter-domain differences and provide an improved solution for reliable and practical HI classification.Approach.Firstly, we collect a high-quality hepatocellular carcinoma (HCC) dataset with enough data to verify the stability and practicability of the method. Secondly, a novel dual-branch hybrid encoding embedded network is proposed, which integrates the feature extraction capabilities of convolutional neural network and Transformer. This well-designed structure enables the network to extract diverse features while minimizing redundancy from a single complex network. Lastly, we develop a salient area constraint loss function tailored to the unique characteristics of HIs to address inter-domain differences and enhance the robustness and universality of the methods.Main results.Extensive experiments have conducted on the proposed HCC dataset and two other publicly available datasets. The proposed method demonstrates outstanding performance with an impressive accuracy of 99.09% on the HCC dataset and achieves state-of-the-art results on the other two public datasets. These remarkable outcomes underscore the superior performance and versatility of our approach in multiple HI classification.Significance.The advancements presented in this study contribute to the field of HI analysis by providing a reliable and practical solution for multiple cancer classification, potentially improving diagnostic accuracy and patient outcomes. Our code is available athttps://github.com/lms-design/DHEE-net.

Uncertainty quantification of multi-scale resilience in networked systems with nonlinear dynamics using arbitrary polynomial chaos.

Complex systems derive sophisticated behavioral dynamics by connecting individual component dynamics via a complex network. The resilience of complex systems is a critical ability to regain desirable behavior after perturbations. In the past years, our understanding of large-scale networked resilience is largely confined to proprietary agent-based simulations or topological analysis of graphs. However, we know the dynamics and topology both matter and the impact of model uncertainty of the system remains unsolved, especially on individual nodes. In order to quantify the effect of uncertainty on resilience across the network resolutions (from macro-scale network statistics to individual node dynamics), we employ an arbitrary polynomial chaos (aPC) expansion method to identify the probability of a node in losing its resilience and how the different model parameters contribute to this risk on a single node. We test this using both a generic networked bi-stable system and also established ecological and work force commuter network dynamics to demonstrate applicability. This framework will aid practitioners to both understand macro-scale behavior and make micro-scale interventions.

Graphene-like wrapped Ni@C catalyst with magnetic recyclability for selective hydrogenation of nitro-aromatics.

The graphene-like wrapped Ni@C catalysts were facilely synthesized by a modified sol-gel method. Nickel nitrate and citric acid (CA) were adopted as the raw materials to form sol-gel mixture. Under the circumstances, the additive CA were employed not only as a complexing agent but also as a carbon source. It is found that the calcination temperature and the mole ratios between Ni and CA are the key factors affecting the physical property and the catalytic performance of catalysts in the conversion of nitroarenes into corresponding anilines. The results show that the Ni@C-500(1:1) catalyst exhibited the best performance in the hydrogenation ofo-chloronitrobenzenes (o-CNB) too-chloroanilines (o-CAN). The yield ofo-CAN was achieved at 100% wheno-CNB was completely converted at 40.0 °C under 2.0 MPa H2for 2.0 h. Furthermore, the Ni@C-500(1:1) catalyst could be separated and recovered easily after reaction by an external magnetic field. The investigated results indicate that the Ni@C-500(1:1) catalyst remained higher activity after using twelve times. More importantly, this kind of catalyst is also active for the selective hydrogenation of other nitroarenes into the corresponding anilines. This new synthetic method may pave a way for producing low-cost Ni@C catalysts on a large scale, which is attractive for industrial anilines applications.

Diagnostic accuracy of autoverification and guidance system for COVID-19 RT-PCR results.

Background: To date, most countries worldwide have declared that the pandemic of COVID-19 is over, while the WHO has not officially ended the COVID-19 pandemic, and China still insists on the personalized dynamic COVID-free policy. Large-scale nucleic acid testing in Chinese communities and the manual interpretation for SARS-CoV-2 nucleic acid detection results pose a huge challenge for labour, quality and turnaround time (TAT) requirements. To solve this specific issue while increase the efficiency and accuracy of interpretation, we created an autoverification and guidance system (AGS) that can automatically interpret and report the COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR) results relaying on computer-based autoverification procedure and then validated its performance in real-world environments. This would be conductive to transmission risk prediction, COVID-19 prevention and control and timely medical treatment for positive patients in the context of the predictive, preventive and personalized medicine (PPPM). Methods: A diagnostic accuracy test was conducted with 380,693 participants from two COVID-19 test sites in China, the Hong Kong Hybribio Medical Laboratory (n = 266,035) and the mobile medical shelter at a Shanghai airport (n = 114,658). These participants underwent SARS-CoV-2 RT-PCR from March 28 to April 10, 2022. All RT-PCR results were interpreted by laboratorians and by using AGS simultaneously. Considering the manual interpretation as gold standard, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were applied to evaluate the diagnostic value of the AGS on the interpretation of RT-PCR results. Results: Among the 266,035 samples in Hong Kong, there were 16,356 (6.15%) positive, 231,073 (86.86%) negative, 18,606 (6.99%) indefinite, 231,073 (86.86%, negative) no retest required and 34,962 (13.14%, positive and indefinite) retest required; the 114,658 samples in Shanghai consisted of 76 (0.07%) positive, 109,956 (95.90%) negative, 4626 (4.03%) indefinite, 109,956 (95.90%, negative) no retest required and 4702 (4.10%, positive and indefinite) retest required. Compared to the fashioned manual interpretation, the AGS is a procedure of high accuracy [99.96% (95%CI, 99.95-99.97%) in Hong Kong and 100% (95%CI, 100-100%) in Shanghai] with perfect sensitivity [99.98% (95%CI, 99.97-99.98%) in Hong Kong and 100% (95%CI, 100-100%) in Shanghai], specificity [99.87% (95%CI, 99.82-99.90%) in Hong Kong and 100% (95%CI, 99.92-100%) in Shanghai], PPV [99.98% (95%CI, 99.97-99.99%) in Hong Kong and 100% (95%CI, 99.99-100%) in Shanghai] and NPV [99.85% (95%CI, 99.80-99.88%) in Hong Kong and 100% (95%CI, 99.90-100%) in Shanghai]. The need for manual interpretation of total samples was dramatically reduced from 100% to 13.1% and the interpretation time fell from 53 h to 26 min in Hong Kong; while the manual interpretation of total samples was decreased from 100% to 4.1% and the interpretation time dropped from 20 h to 16 min at Shanghai. Conclusions: The AGS is a procedure of high accuracy and significantly relieves both labour and time from the challenge of large-scale screening of SARS-CoV-2 using RT-PCR. It should be recommended as a powerful screening, diagnostic and predictive system for SARS-CoV-2 to contribute timely the ending of the COVID-19 pandemic following the concept of PPPM.

Fragmental optical vortex for the detection of rotating object based on the rotational Doppler effect.

Rotational Doppler effect (RDE), as a counterpart of the conventional linear Doppler effect in the rotating frame, has attracted increasing attention in recent years on rotational object detection. Many previous works have investigated the RDE based on the whole optical vortex field. In this work, we report on the RDE of the partially obstructed optical vortex and the corresponding rotational speed extraction method. Based on the orbital angular momentum (OAM) mode analysis theory, we establish the relationship between the OAM spectrum and the RDE frequency shift of fragmental optical vortex (FOV). The mechanism of the rotational speed extraction is analysed and validated by the numerical simulation and experiments. Further, a dual Fourier transformation method is proposed to accurately obtain the rotational speed which successfully overcomes the problem of the discrete distribution of the RDE signals. Our work may be useful for practical remote sensing based on the optical RDE metrology.

A Learning-Based Vehicle-Cloud Collaboration Approach for Joint Estimation of State-of-Energy and State-of-Health.

The state-of-energy (SOE) and state-of-health (SOH) are two crucial quotas in the battery management systems, whose accurate estimation is facing challenges by electric vehicles' (EVs) complexity and changeable external environment. Although the machine learning algorithm can significantly improve the accuracy of battery estimation, it cannot be performed on the vehicle control unit as it requires a large amount of data and computing power. This paper proposes a joint SOE and SOH prediction algorithm, which combines long short-term memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural networks (CNNs) for EVs based on vehicle-cloud collaboration. Firstly, the indicator of battery performance degradation is extracted for SOH prediction according to the historical data; the Bayesian optimization approach is applied to the SOH prediction combined with Bi-LSTM. Then, the CNN-LSTM is implemented to provide direct and nonlinear mapping models for SOE. These direct mapping models avoid parameter identification and updating, which are applicable in cases with complex operating conditions. Finally, the SOH correction in SOE estimation achieves the joint estimation with different time scales. With the validation of the National Aeronautics and Space Administration battery data set, as well as the established battery platform, the error of the proposed method is kept within 3%. The proposed vehicle-cloud approach performs high-precision joint estimation of battery SOE and SOH. It can not only use the battery historical data of the cloud platform to predict the SOH but also correct the SOE according to the predicted value of the SOH. The feasibility of vehicle-cloud collaboration is promising in future battery management systems.

Spatio-temporal and trade export risk analysis of bluetongue disease in France: A case study of China.

Bluetongue disease (BT) is a viral disease that can be introduced through imported animals and animal products, affecting local animal husbandry. In this study, the spatial and temporal patterns of BT outbreaks (outbreak: a BT infection in cattle, sheep, or goats on a farm, involving at least one infected animal) in France were analyzed and the risk of introducing bluetongue virus (BTV) into countries through trade was assessed. A spatiotemporal analysis of BT reported during the study period (2015-2018) showed that there were clustered outbreaks of BT in France in 2016 and 2017, with outbreaks concentrated from August to December. The outbreak moved eastward from the center of mainland France to surrounding countries. A semi-quantitative risk analysis framework was established by combining the likelihood assessment and consequence analysis of introducing BTV into trading countries through trade. Exemplified by China, the research showed that in the analysis of the likelihood of BTV from France being introduced into trading countries through live cattle trade, China imports a large number of live cattle, bringing high risks. The likelihood of introducing bovine semen into trading countries was similar to that of live cattle, but the harm caused by the trade in live cattle was higher than that caused by the trade in bovine semen. This risk analysis framework can provide a reference for other countries to quickly assess the risk of bluetongue transmission in import and export trade.