Photonic time-delayed reservoir computing based on series-coupled microring resonators with high memory capacity.

On-chip microring resonators (MRRs) have been proposed to construct time-delayed reservoir computing (RC) systems, which offer promising configurations available for computation with high scalability, high-density computing, and easy fabrication. A single MRR, however, is inadequate to provide enough memory for the computation task with diverse memory requirements. Large memory requirements are satisfied by the RC system based on the MRR with optical feedback, but at the expense of its ultralong feedback waveguide. In this paper, a time-delayed RC is proposed by utilizing a silicon-based nonlinear MRR in conjunction with an array of linear MRRs. These linear MRRs possess a high quality factor, providing enough memory capacity for the RC system. We quantitatively analyze and assess the proposed RC structure's performance on three classical tasks with diverse memory requirements, i.e., the Narma 10, Mackey-Glass, and Santa Fe chaotic timeseries prediction tasks. The proposed system exhibits comparable performance to the system based on the MRR with optical feedback, when it comes to handling the Narma 10 task, which requires a significant memory capacity. Nevertheless, the dimension of the former is at least 350 times smaller than the latter. The proposed system lays a good foundation for the scalability and seamless integration of photonic RC.

Novel miniature transendoscopic telerobotic system for endoscopic submucosal dissection (with videos).

BACKGROUND AND AIMS: The lack of tissue traction and instrument dexterity to allow for adequate visualization and effective dissection were the main issues in performing endoscopic submucosal dissection (ESD). Robot-assisted systems may provide advantages. In this study we developed a novel transendoscopic telerobotic system and evaluated its performance in ESD. METHODS: A miniature dual-arm robotic endoscopic assistant for minimally invasive surgery (DREAMS) was developed. The DREAMS system contained the current smallest robotic ESD instruments and was compatible with the commercially available dual-channel endoscope. After the system was established, a prospective randomized controlled study was conducted to validate the performance of the DREAMS-assisted ESD in terms of efficacy, safety, and workload by comparing it with the conventional technique. RESULTS: Two robotic instruments can achieve safe collaboration and provide sufficient visualization and efficient dissection during ESD. Forty ESDs in the stomach and esophagus of 8 pigs were completed by DREAMS-assisted ESD or conventional ESD. Submucosal dissection time was comparable between the 2 techniques, but DREAMS-assisted ESD demonstrated a significantly lower muscular injury rate (15% vs 50%, P = .018) and workload scores (22.30 vs 32.45, P < .001). In the subgroup analysis of esophageal ESD, DREAMS-assisted ESD showed significantly improved submucosal dissection time (6.45 vs 16.37 minutes, P = .002), muscular injury rate (25% vs 87.5%, P = .041), and workload (21.13 vs 40.63, P = .001). CONCLUSIONS: We developed a novel transendoscopic telerobotic system, named DREAMS. The safety profile and technical feasibility of ESD were significantly improved with the assistance of the DREAMS system, especially in the narrower esophageal lumen.

Machine learning-assisted high-accuracy and large dynamic range thermometer in high-Q microbubble resonators.

Whispering gallery mode (WGM) resonators provide an important platform for fine measurement thanks to their small size, high sensitivity, and fast response time. Nevertheless, traditional methods focus on tracking single-mode changes for measurement, and a great deal of information from other resonances is ignored and wasted. Here, we demonstrate that the proposed multimode sensing contains more Fisher information than single mode tracking and has great potential to achieve better performance. Based on a microbubble resonator, a temperature detection system has been built to systematically investigate the proposed multimode sensing method. After the multimode spectral signals are collected by the automated experimental setup, a machine learning algorithm is used to predict the unknown temperature by taking full advantage of multiple resonances. The results show the average error of 3.8 × 10-3°C within the range from 25.00°C to 40.00°C by employing a generalized regression neural network (GRNN). In addition, we have also discussed the influence of the consumed data resource on its predicted performance, such as the amount of training data and the case of different temperate ranges between the training and test data. With high accuracy and large dynamic range, this work paves the way for WGM resonator-based intelligent optical sensing.

Data augmentation using continuous conditional generative adversarial networks for regression and its application to improved spectral sensing.

Machine learning-assisted spectroscopy analysis faces a prominent constraint in the form of insufficient spectral samples, which hinders its effectiveness. Meanwhile, there is a lack of effective algorithms to simulate synthetic spectra from limited samples of real spectra for regression models in continuous scenarios. In this study, we introduced a continuous conditional generative adversarial network (CcGAN) to autonomously generate synthetic spectra. The labels employed for generating the spectral data can be arbitrarily selected from within the range of labels associated with the real spectral data. Our approach effectively produced spectra using a small spectral dataset obtained from a self-interference microring resonator (SIMRR)-based sensor. The generated synthetic spectra were subjected to evaluation using principal component analysis, revealing an inability to discern them from the real spectra. Finally, to enhance the DNN regression model, these synthetic spectra are incorporated into the original training dataset as an augmentation technique. The results demonstrate that the synthetic spectra generated by CcGAN exhibit exceptional quality and significantly enhance the predictive performance of the DNN model. In conclusion, CcGAN exhibits promising potential in generating high-quality synthetic spectra and delivers a superior data augmentation effect for regression tasks.

Actuation technologies for magnetically guided catheters.

Due to their wide range of clinical application possibilities, magnetic actuation technologies have grabbed the attention of researchers worldwide. The design, execution, and analysis of magnetic catheter systems have advanced significantly during the last decade. The review focuses on magnetic actuation for catheter steering and control of the device, which will be explored in detail in the following sections. There is a discussion of future work and the challenges of the review systems, and the conclusions are finally addressed.

Periodic evolution of the out-of-phase dipole and the single-charged vortex solitons in periodic photonic moiré lattice with saturable self-focusing nonlinearity media.

We survey the propagation properties of the out-of-phase (OOP) dipole solitons and the single-charged vortex (SCV) soliton in a periodic photonic moiré lattice with θ=arctan⁡(3/4) under self-focusing nonlinearity media. Since the rotation angle, periodic photonic moiré lattices have peculiar energy band structures, with highly flat bands and the bandgaps being much more extensive, which is very favorable for the realization and stability of the solitons. When exciting a single point on-site with the OOP dipole beam, its evolution shows a periodic rollover around the lattice axis. Whereas, when exciting a single point on-site with the SCV beam, it transmits counterclockwise rotating periodically. Both the OOP dipole solitons and the SVC soliton maintain the local state, but their phase exhibits different variations. The phase of the OOP dipole solitons is flipped, while that of the SCV is rotated counterclockwise. Our work further complements the exploration of solitons in photonic moiré lattice with nonlinearity.

AC field modulated DC magnetic field sensor based on optical whispering gallery mode microcapillary resonator.

A sensitive DC magnetic field sensor is constructed by measuring the signal-to-noise ratio of an AC-modulated magnetic field at a particular frequency from an optical whispering gallery mode microcapillary resonator. The sensing element consists of an optical whispering gallery mode microcapillary resonator bonded to a magnetostrictive material that enables it to respond to external magnetic fields. A DC magnetic field sensitivity of 0.1703dB/Oe and a linear detection range from 4.8Oe to 65.7Oe are realized under an AC modulation field of 168.1kHz in the unshielded environment at room temperature. To our best knowledge, this sensitivity is about 2.3 times of the maximum sensitivity of other DC magnetic field sensors based on magnetic fluid or magnetostrictive material integrated fiber systems that use the dissipative sensing scheme. Furthermore, the sensor can operate at a stable temperature in the range of [-11∼45]°C, as long as the modulation frequency of the AC-modulation field is adjusted according to the ambient temperature. This sensor provides us with a novel DC magnetic field sensing scheme, which may play a role in industrial fields related to current and position detection in the future.

Origami-Inspired Structure with Pneumatic-Induced Variable Stiffness for Multi-DOF Force-Sensing.

With the emerging need for human-machine interactions, multi-modal sensory interaction is gradually pursued rather than satisfying common perception forms (visual or auditory), so developing flexible, adaptive, and stiffness-variable force-sensing devices is the key to further promoting human-machine fusion. However, current sensor sensitivity is fixed and nonadjustable after fabrication, limiting further development. To solve this problem, we propose an origami-inspired structure to achieve multiple degrees of freedom (DoFs) motions with variable stiffness for force-sensing, which combines the ductility and flexibility of origami structures. In combination with the pneumatic actuation, the structure can achieve and adapt the compression, pitch, roll, diagonal, and array motions (five motion modes), which significantly increase the force adaptability and sensing diversity. To achieve closed-loop control and avoid excessive gas injection, the ultra-flexible microfiber sensor is designed and seamlessly embedded with an approximately linear sensitivity of ∼0.35 Ω/kPa at a relative pressure of 0-100 kPa, and an exponential sensitivity at a relative pressure of 100-350 kPa, which can render this device capable of working under various conditions. The final calibration experiment demonstrates that the pre-pressure value can affect the sensor's sensitivity. With the increasing pre-pressure of 65-95 kPa, the average sensitivity curve shifts rightwards around 9 N intervals, which highly increases the force-sensing capability towards the range of 0-2 N. When the pre-pressure is at the relatively extreme air pressure of 100 kPa, the force sensitivity value is around 11.6 Ω/N. Therefore, our proposed design (which has a low fabrication cost, high integration level, and a suitable sensing range) shows great potential for applications in flexible force-sensing development.

Mapping Drug-Induced Neuropathy through In-Situ Motor Protein Tracking and Machine Learning.

Chemotherapy can induce toxicity in the central and peripheral nervous systems and result in chronic adverse reactions that impede continuous treatment and reduce patient quality of life. There is a current lack of research to predict, identify, and offset drug-induced neurotoxicity. Rapid and accurate assessment of potential neuropathy is crucial for cost-effective diagnosis and treatment. Here we report dynamic near-infrared upconversion imaging that allows intraneuronal transport to be traced in real time with millisecond resolution, but without photobleaching or blinking. Drug-induced neurotoxicity can be screened prior to phenotyping, on the basis of subtle abnormalities of kinetic characteristics in intraneuronal transport. Moreover, we demonstrate that combining the upconverting nanoplatform with machine learning offers a powerful tool for mapping chemotherapy-induced peripheral neuropathy and assessing drug-induced neurotoxicity.

Gas identification in high-Q microbubble resonators.

A new, to the best of our knowledge, experimental mechanism is reported to realize the identification of gas by a microcavity sensor. Instead of measuring the change in the environment refractive index or absorption, the gas is detected indirectly and indentified by using the thermo-optics effect of a high-quality-factor microbubble resonator. When passing gas through the microbubble, the pressure induces a geometric deformation and thus an observable frequency shift, and the thermal bistability response varies due to the higher heat dissipation by gas molecules. With the two output parameters, we can unambiguously distinguish gas with different molecular weights, e.g., He, N2, and CO2. Our demonstration opens a new avenue of microcavity sensing by using indirect interaction between light and matter, realizing a multiple-parameter sensing approach for gas or solvent identification.

Time-Domain Blind ICI Compensation in Coherent Optical FBMC/OQAM System.

A blind discrete-cosine-transform-based phase noise compensation (BD-PNC) is proposed to compensate the inter-carrier-interference (ICI) in the coherent optical offset-quadrature amplitude modulation (OQAM)-based filter-bank multicarrier (CO-FBMC/OQAM) transmission system. Since the phase noise sample can be approximated by an expansion of the discrete cosine transform (DCT) in the time-domain, a time-domain compensation model is built for the transmission system. According to the model, phase noise compensation (PNC) depends only on its DCT coefficients. The common phase error (CPE) compensation is firstly performed for the received signal. After that, a pre-decision is made on a part of compensated signals with low decision error probability, and the pre-decision results are used as the estimated values of transmitted signals to calculate the DCT coefficients. Such a partial pre-decision process reduces not only decision error but also the complexity of the BD-PNC method while keeping almost the same performance as in the case of the pre-decision of all compensated signals. Numerical simulations are performed to evaluate the performance of the proposed scheme for a 30 GBaud CO-FBMC/OQAM system. The simulation results show that its bit error rate (BER) performance is improved by more than one order of magnitude through the mitigation of the ICI in comparison with the traditional blind PNC scheme only aiming for CPE compensation.

Multi-Parameter Sensing in a Multimode Self-Interference Micro-Ring Resonator by Machine Learning.

A universal multi-parameter sensing scheme based on a self-interference micro-ring resonator (SIMRR) is proposed. Benefit from the special intensity sensing mechanism, the SIMRR allows multimode sensing in a wide range of wavelengths but immune from frequency noise. To process the multiple mode spectra that are dependent on multiple parameters, we adopt the machine learning algorithm instead of massive asymptotic solutions of resonators. Employing the proposed multi-mode sensing approach, a two-parameter SIMRR sensor is designed. Assuming that two gases have different wavelength dependence of refractive indices, the feasibility and effectiveness of the two-parameter sensing strategy are verified numerically. Moreover, the dependence of parameter estimation accuracy on the laser intensity noises is also investigated. The numerical results indicate that our scheme of multi-parameter sensing in a multimode SIMRR holds great potential for practical high-sensitive sensing platforms compared with the single-mode sensing based on whispering gallery mode (WGM) resonators.

[Different Energy Spectrum CT Findings between Anterior Mediastinal Lymphoma and Thymic Carcinoma].

Objective To investigate the differences in energy spectrum CT findings between anterior mediastinal lymphoma and thymic carcinoma. Methods Twenty-two cases of anterior mediastinal lymphoma and 28 cases of thymic carcinoma confirmed by biopsy in Tangshan People's Hospital were selected.The CT values and changes of iodine content and water content in lesion sites were measured by energy spectrum analysis software.The differences between anterior mediastinal lymphoma and thymic carcinoma were compared. Results The single-energy CT value of 40-80 keV in thymus carcinoma was higher than that in anterior mediastinal lymphoma(P=0.001,P=0.037,P=0.042,P=0.034,P=0.002;P=0.016,P=0.013,P=0.018,P=0.024,P=0.012).The difference in the single-energy CT value of 90-110 keV between anterior mediastinal lymphoma and thymic carcinoma showed no statistical significance(all P>0.05).The concentrations of water in the arterial and venous stages of thymic carcinoma were significantly lower than those in the anterior mediastinal lymphoma(P=0.030,P=0.037),whereas the iodine concentrations were significantly higher(P=0.026,P=0.000). Conclusion Anterior mediastinal lymphoma and thymic carcinoma have remarkably different 40-80 keV single energy CT value and iodine concentration in arterial and venous phases,which may be helpful for the differential diagnosis of these two malignancies.

Multidimensional vector quantization-based fast statistical estimation in compressed digitalized radio-over-fiber systems.

A multidimensional vector quantization-based fast statistical-estimation (VQ-FSE) algorithm is proposed to enhance data compression performance in digitalized radio over fiber (D-RoF) systems. The original samples with Gaussian distribution are first transformed into these with uniform distribution via companding transformation. After the companding transformation operation, the signal vector is constructed by grouping multiple samples in a certain way so that there is little correlation among them. The constructed signal vector may follow approximately multidimensional uniform distribution, and then multidimensional uniform quantization can be easily carried out, where the complex optimized process in nonuniform quantization is not required. For the proposed two-dimensional (2D) VQ-FSE algorithm, the proposed scheme is numerically verified in a 20 km D-RoF system with 2 Gbit/s RF wireless signal. Compared with the scalar-quantization-based FSE algorithm, its compression ratio is significantly enhanced. In comparison to the 2D k-means-clustering-based VQ algorithm, the proposed scheme shares a similar compression ratio and offers lower computational complexity. Therefore, the proposed algorithm has the ability to provide better compression and lower complexity for the digitized D-RoF system when the original sample follows Gaussian distribution.

MR-Conditional SMA-Based Origami Joint.

Foldable origami structures have been implemented into robotics as a way of compacting joints and circuitry into smaller structures. This technique is especially useful in minimally invasive surgical instruments, where the goal is to create slimline devices that can be inserted through small incisions. Origami also has the potential to cut costs by reducing the amount of material required for assembly. Origami devices are especially suitable for MRI-guided procedures, where instruments must be nonmagnetic because origami is more suitable for flexible, non-metallic materials. MR conditional surgical instruments enable intraoperative MRI procedures that provide superior imaging capabilities to physicians to allow for safer procedures. This work presents an MR conditional joint developed using origami techniques that reduces costs by eliminating assembly of various components and has potential applications in endoscopy. The joint is a compliant rolling-contact element that employs curved-folding origami techniques. A chain of these joints can be constructed from a single sheet of material, eliminating assembly of numerous materials to produce a final product, which is specifically advantageous for constructing low-cost, disposable surgical devices. The prototype contains a degree of bending of ±9 degrees per joint, a response time of less than 4 seconds and an actuation force of 0.5 N using a 1.25 A current. The MRI results showed a minimal artifact of less than 1 mm measured from the boundary of the joint chain and a SNR reduction of less than 10%.

Gp91phox (NOX2) in Activated Microglia Exacerbates Neuronal Damage Induced by Oxygen Glucose Deprivation and Hyperglycemia in an in Vitro Model.

BACKGROUND/AIMS: Peri-operative cerebral ischemia reperfusion injury is one of the most serious peri-operative complications that can be aggravated in patients with diabetes. A previous study showed that microglia NOX2 (a NADPH oxidase enzyme) may play an important role in this process. Here, we investigated whether increased microglial derived gp91phox, also known as NOX2, reduced oxygen glucose deprivation (OGD) after induction of hyperglycemia (HG). METHODS: A rat neuronal-microglial in vitro co-culture model was used to determine the effects of gp91phox knockdown on OGD after HG using six treatment groups: A rat microglia and neuron co-culture model was established and divided into the following six groups: high glucose + scrambled siRNA transfection (HG, n = 5); HG + gp91phoxsiRNA transfection (HG-gp91siRNA, n = 5); oxygen glucose deprivation + scrambled siRNA transfection (OGD, n = 5); OGD + gp91phoxsiRNA transfection (OGD-gp91siRNA, n = 5); HG + OGD + scrambled siRNA transfection (HG-OGD, n = 5); and HG + OGD + gp91phoxsiRNA transfection (HG-OGD-gp91siRNA, n = 5). The neuronal survival rate was measured by the MTT assay, while western blotting was used to determine gp91phox expression. Microglial derived ROS and neuronal apoptosis rates were analyzed by flow cytometry. Finally, the secretion of cytokines, including IL-6, IL-8, TNF-α, and 8-iso-PGF2α was determined using an ELISA kit. RESULTS: Neuronal survival rates were significantly decreased by HG and OGD, while knockdown of gp91phox reversed these rates. ROS production and cytokine secretion were also significantly increased by HG and OGD but were significantly inhibited by knockdown of gp91phoxsiRNA. CONCLUSION: Knockdown of gp91phoxsiRNA significantly reduced oxidative stress and the inflammatory response, and alleviated neuronal damage after HG and OGD treatment in a rat neuronal-microglial co-culture model.

Crumpling and Unfolding of Montmorillonite Hybrid Nanocoatings as Stretchable Flame-Retardant Skin.

Flame-retardant coatings are widely used in a variety of personnel or product protection, and many applications would benefit from film stretchability if suitable materials are available. It is challenging to develop flame-retardant coatings that are stretchable, eco-friendly, and capable of being integrated on mechanically dynamic devices. Here, a concept is reported that uses pretextured montmorillonite (MMT) hybrid nanocoatings that can undergo programed unfolding to mimic the stretchability of elastomeric materials. These textured MMT coatings can be transferred onto an elastomeric substrate to achieve an MMT/elastomer bilayer device with high stretchability (225% areal strain) and effective flame retardancy. The bilayer composite is utilized as flame-retardant skin for a soft robotic gripper, and it is demonstrated that the actuated response can manipulate and rescue irregularly shaped objects from a fire scene. Furthermore, by depositing the conformal MMT nanocoatings on nitrile gloves, the firefree gloves can endure direct flame contact without ignition.

Multi-population genetic algorithm for peak-to-average power ratio suppression in an optical OFDM transmission system.

We proposed and experimentally demonstrated the parallel multi-population genetic algorithm (MPGA)-based selected mapping (SLM) technique to reduce the peak-to-average power ratio of the optical orthogonal frequency-division multiplexing system. By introducing the parallel genetic algorithm (GA), the MPGA-SLM can effectively escape the local optimization of the conventional GA and attains fast convergence on the global optimization. Compared with the conventional SLM method, we experimentally achieved 1-dB performance improvement at a 3.8×10-3 bit error ratio by utilizing the MPGA-SLM in the back-to-back case and that of 0.7 dB in the 30-km single-mode fiber transmission case.

A randomized controlled trial on a nurse-led smartphone-based self-management programme for people with poorly controlled type 2 diabetes: A study protocol.

AIM: To develop and compare a nurse-led smartphone-based self-management programme with an existing nurse-led diabetes service on health-related outcomes for people with poorly controlled type 2 diabetes in Singapore. BACKGROUND: Over the past decades, Asia has emerged as the "diabetes epicentre" in the world due to rapid economic development, urbanization and nutrition transition. There is an urgent need to develop more effective care management strategies in response to this rising diabetes epidemic. DESIGN: A randomized controlled trial with pre- and repeated posttests control group design. METHODOLOGY: A total of 128 adults with poorly controlled type 2 diabetes will be recruited from the diabetes clinic of a public acute hospital in Singapore through convenience sampling. Study participants will be randomly allocated either to the experimental group or the control group. Outcome measures will include the 10-item General Self-Efficacy Scale, 11-item Revised Summary of Diabetes Self-care Activities and 19-item Diabetes-Dependent Quality of Life. Data will be collected at three time points: baseline, 3 and 6 months from the baseline. DISCUSSION: It is expected that this programme will be an alternative offered to diabetes patients to master their self-care management skills, in addition to the existing diabetes service provided in diabetes clinics in Singapore hospitals. Furthermore, the self-supporting and less resource-intensive nature of this programme, using a smartphone application as the mode of intervention delivery, will greatly reduce nurses' direct contact time with patients and allow more time to be allocated to those who require more attention. The study has been registered with ClinicalTrials.gov. The trial registration number is NCT03088475.

MRI-powered biomedical devices.

Magnetic resonance imaging (MRI) is beneficial for imaging-guided procedures because it provides higher resolution images and better soft tissue contrast than computed tomography (CT), ultrasound, and X-ray. MRI can be used to streamline diagnostics and treatment because it does not require patients to be repositioned between scans of different areas of the body. It is even possible to use MRI to visualize, power, and control medical devices inside the human body to access remote locations and perform minimally invasive procedures. Therefore, MR conditional medical devices have the potential to improve a wide variety of medical procedures; this potential is explored in terms of practical considerations pertaining to clinical applications and the MRI environment. Recent advancements in this field are introduced with a review of clinically relevant research in the areas of interventional tools, endovascular microbots, and closed-loop controlled MRI robots. Challenges related to technology and clinical feasibility are discussed, including MRI based propulsion and control, navigation of medical devices through the human body, clinical adoptability, and regulatory issues. The development of MRI-powered medical devices is an emerging field, but the potential clinical impact of these devices is promising.