Wearable electrochemical sensors for plant small-molecule detection.

Small molecules in plants - such as metabolites, phytohormones, reactive oxygen species (ROS), and inorganic ions - participate in the processes of plant growth and development, physiological metabolism, and stress response. Wearable electrochemical sensors, known for their fast response, high sensitivity, and minimal plant damage, serve as ideal tools for dynamically tracking these small molecules. Such sensors provide producers or agricultural researchers with noninvasive or minimally invasive means of obtaining plant signals. In this review we explore the applications of wearable electrochemical sensors in detecting plant small molecules, enabling scientific assessment of plant conditions, quantification of environmental stresses, and facilitation of plant health monitoring and disease prediction.

An integrated plant glucose monitoring system based on microneedle-enabled electrochemical sensor.

Real-time monitoring of glucose concentration changes in plants and access to plant physiological information timely are of great significance to the development of precision agriculture. Here, we innovatively present an electrochemical sensing device that combines microneedle sensors and 3D printing technology to achieve real-time monitoring of glucose in plants in a minimally invasive manner. The device consists of two components: the inner part features a highly efficient sensing interface based on platinum wire (MPt-Au-Nafion-GOx-Pu), while the outer part consists of polymer microneedles formed by 3D printing. Additionally, the polymer hollow microneedle features a slender tip diameter of only 300 µm, minimizing plant damage during the detection procedure. The device shows good detection performance, with a limit of detection (LOD) of 33.3 µM and a detection sensitivity of 17 nA/µM·cm2. It can detect glucose concentrations in the range of 100 µM to 100 mM, providing a unique solution for timely agronomic management of crops tool. By performing 12 h real-time monitoring and salt stress treat on tomato and aloe vera, the results verified the feasibility of integrated device applied to real-time glucose detection in plants.

Implantable Electrochemical Microsensors for In Vivo Monitoring of Animal Physiological Information.

In vivo monitoring of animal physiological information plays a crucial role in promptly alerting humans to potential diseases in animals and aiding in the exploration of mechanisms underlying human diseases. Currently, implantable electrochemical microsensors have emerged as a prominent area of research. These microsensors not only fulfill the technical requirements for monitoring animal physiological information but also offer an ideal platform for integration. They have been extensively studied for their ability to monitor animal physiological information in a minimally invasive manner, characterized by their bloodless, painless features, and exceptional performance. The development of implantable electrochemical microsensors for in vivo monitoring of animal physiological information has witnessed significant scientific and technological advancements through dedicated efforts. This review commenced with a comprehensive discussion of the construction of microsensors, including the materials utilized and the methods employed for fabrication. Following this, we proceeded to explore the various implantation technologies employed for electrochemical microsensors. In addition, a comprehensive overview was provided of the various applications of implantable electrochemical microsensors, specifically in the monitoring of diseases and the investigation of disease mechanisms. Lastly, a concise conclusion was conducted on the recent advancements and significant obstacles pertaining to the practical implementation of implantable electrochemical microsensors.

A Short-Term Hybrid TCN-GRU Prediction Model of Bike-Sharing Demand Based on Travel Characteristics Mining.

This paper proposes an accurate short-term prediction model of bike-sharing demand with the hybrid TCN-GRU method. The emergence of shared bicycles has provided people with a low-carbon, green and healthy way of transportation. However, the explosive growth and free-form development of bike-sharing has also brought about a series of problems in the area of urban governance, creating a new opportunity and challenge in the use of a large amount of historical data for regional bike-sharing traffic flow predictions. In this study, we built an accurate short-term prediction model of bike-sharing demand with the bike-sharing dataset from 2015 to 2017 in London. First, we conducted a multidimensional bike-sharing travel characteristics analysis based on explanatory variables such as weather, temperature, and humidity. This will help us to understand the travel characteristics of local people, will facilitate traffic management and, to a certain extent, improve traffic congestion. Then, the explanatory variables that help predict the demand for bike-sharing were obtained using the Granger causality with the entropy theory-based MIC method to verify each other. The Multivariate Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) model were integrated to build the prediction model, and this is abbreviated as the TCN-GRU model. The fitted coefficient of determination R2 and explainable variance score (EVar) of the dataset reached 98.42% and 98.49%, respectively. Meanwhile, the mean absolute error (MAE) and root mean square error (RMSE) were at least 1.98% and 2.4% lower than those in other models. The results show that the TCN-GRU model has strong efficiency and robustness. The model can be used to make short-term accurate predictions of bike-sharing demand in the region, so as to provide decision support for intelligent dispatching and urban traffic safety improvement, which will help to promote the development of green and low-carbon mobility in the future.

Nanocone-Shaped Carbon Nanotubes Field-Emitter Array Fabricated by Laser Ablation.

The nanocone-shaped carbon nanotubes field-emitter array (NCNA) is a near-ideal field-emitter array that combines the advantages of geometry and material. In contrast to previous methods of field-emitter array, laser ablation is a low-cost and clean method that does not require any photolithography or wet chemistry. However, nanocone shapes are hard to achieve through laser ablation due to the micrometer-scale focusing spot. Here, we develop an ultraviolet (UV) laser beam patterning technique that is capable of reliably realizing NCNA with a cone-tip radius of ≈300 nm, utilizing optimized beam focusing and unique carbon nanotube-light interaction properties. The patterned array provided smaller turn-on fields (reduced from 2.6 to 1.6 V/µm) in emitters and supported a higher (increased from 10 to 140 mA/cm2) and more stable emission than their unpatterned counterparts. The present technique may be widely applied in the fabrication of high-performance CNTs field-emitter arrays.

Stable Field Emission from Vertically Oriented SiC Nanoarrays.

Silicon carbide (SiC) nanostructure is a type of promising field emitter due to high breakdown field strength, high thermal conductivity, low electron affinity, and high electron mobility. However, the fabrication of the SiC nanotips array is difficult due to its chemical inertness. Here we report a simple, industry-familiar reactive ion etching to fabricate well-aligned, vertically orientated SiC nanoarrays on 4H-SiC wafers. The as-synthesized nanoarrays had tapered base angles >60°, and were vertically oriented with a high packing density >107 mm-2 and high-aspect ratios of approximately 35. As a result of its high geometry uniformity-5% length variation and 10% diameter variation, the field emitter array showed typical turn-on fields of 4.3 V µm-1 and a high field-enhancement factor of ~1260. The 8 h current emission stability displayed a mean current fluctuation of 1.9 ± 1%, revealing excellent current emission stability. The as-synthesized emitters demonstrate competitive emission performance that highlights their potential in a variety of vacuum electronics applications. This study provides a new route to realizing scalable field electron emitter production.

Optical-Field-Driven Electron Tunneling in Metal-Insulator-Metal Nanojunction.

Optical-field driven electron tunneling in nanojunctions has made demonstrable progress toward the development of ultrafast charge transport devices at subfemtosecond time scales, and have evidenced great potential as a springboard technology for the next generation of on-chip "lightwave electronics." Here, the empirical findings on photocurrent the high nonlinearity in metal-insulator-metal (MIM) nanojunctions driven by ultrafast optical pulses in the strong optical-field regime are reported. In the present MIM device, a 14th power-law scaling is identified, never achieved before in any known solid-state device. This work lays important technological foundations for the development of a new generation of ultracompact and ultrafast electronics devices that operate with suboptical-cycle response times.

A Machine-Learning Method of Predicting Vital Capacity Plateau Value for Ventilatory Pump Failure Based on Data Mining.

Ventilatory pump failure is a common cause of death for patients with neuromuscular diseases. The vital capacity plateau value (VCPLAT) is an important indicator to judge the status of ventilatory pump failure for patients with congenital myopathy, Duchenne muscular dystrophy and spinal muscular atrophy. Due to the complex relationship between VCPLAT and the patient's own condition, it is difficult to predict the VCPLAT for pediatric disease from a medical perspective. We established a VCPLAT prediction model based on data mining and machine learning. We first performed the correlation analysis and recursive feature elimination with cross-validation (RFECV) to provide high-quality feature combinations. Based on this, the Light Gradient Boosting Machine (LightGBM) algorithm was to establish a prediction model with powerful performance. Finally, we verified the validity and superiority of the proposed method via comparison with other prediction models in similar works. After 10-fold cross-validation, the proposed prediction method had the best performance and its explained variance score (EVS), mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), median absolute error (MedAE) and R2 were 0.949, 0.028, 0.002, 0.045, 0.015 and 0.948, respectively. It also performed well on test datasets. Therefore, it can accurately and effectively predict the VCPLAT, thereby determining the severity of the condition to provide auxiliary decision-making for doctors in clinical diagnosis and treatment.

Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data Sets.

Smart transportation is an important part of smart urban areas, and travel characteristics analysis and traffic prediction modeling are the two key technical measures of building smart transportation systems. Although online car-hailing has developed rapidly and has a large number of users, most of the studies on travel characteristics do not focus on online car-hailing, but instead on taxis, buses, metros, and other traditional means of transportation. The traditional univariate variable hybrid time series traffic prediction model based on the autoregressive integrated moving average (ARIMA) ignores other explanatory variables. To fill the research gap on online car-hailing travel characteristics analysis and overcome the shortcomings of the univariate variable hybrid time series traffic prediction model based on ARIMA, based on online car-hailing operational data sets, we analyzed the online car-hailing travel characteristics from multiple dimensions, such as district, time, traffic jams, weather, air quality, and temperature. A traffic prediction method suitable for multivariate variables hybrid time series modeling is proposed in this paper, which uses the maximal information coefficient (MIC) to perform feature selection, and fuses autoregressive integrated moving average with explanatory variable (ARIMAX) and long short-term memory (LSTM) for data regression. The effectiveness of the proposed multivariate variables hybrid time series traffic prediction model was verified on the online car-hailing operational data sets.

Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of Patterns.

The traditional sequential pattern mining method is carried out considering the whole time period and often ignores the sequential patterns that only occur in local time windows, as well as possible periodicity. Therefore, in order to overcome the limitations of traditional methods, this paper proposes status set sequential pattern mining with time windows (SSPMTW). In contrast to traditional methods, the item status is considered, and time windows, minimum confidence, minimum coverage, minimum factor set ratios and other constraints are added to mine more valuable rules in local time windows. The periodicity of these rules is also analyzed. According to the proposed method, this paper improves the Apriori algorithm, proposes the TW-Apriori algorithm, and explains the basic idea of the algorithm. Then, the feasibility, validity and efficiency of the proposed method and algorithm are verified by small-scale and large-scale examples. In a large-scale numerical example solution, the influence of various constraints on the mining results is analyzed. Finally, the solution results of SSPM and SSPMTW are compared and analyzed, and it is suggested that SSPMTW can excavate the laws existing in local time windows and analyze the periodicity of the laws, which solves the problem of SSPM ignoring the laws existing in local time windows and overcomes the limitations of traditional sequential pattern mining algorithms. In addition, the rules mined by SSPMTW reduce the entropy of the system.

Ultrafast Electron Tunneling Devices-From Electric-Field Driven to Optical-Field Driven.

The search for ever higher frequency information processing has become an area of intense research activity within the micro, nano, and optoelectronics communities. Compared to conventional semiconductor-based diffusive transport electron devices, electron tunneling devices provide significantly faster response times due to near-instantaneous tunneling that occurs at sub-femtosecond timescales. As a result, the enhanced performance of electron tunneling devices is demonstrated, time and again, to reimagine a wide variety of traditional electronic devices with a variety of new "lightwave electronics" emerging, each capable of reducing the electron transport channel transit time down to attosecond timescales. In response to unprecedented rapid progress within this field, here the current state-of-the-art in electron tunneling devices is reviewed, current challenges and opportunities are highlighted, and possible future research directions are identified.

Prediction of Hypertension Outcomes Based on Gain Sequence Forward Tabu Search Feature Selection and XGBoost.

For patients with hypertension, serious complications, such as myocardial infarction, a common cause of heart failure, occurs in the late stage of hypertension. Hypertension outcomes can lead to complications, including death. Hypertension outcomes threaten patients' lives and need to be predicted. In our research, we reviewed the hypertension medical data from a tertiary-grade A class hospital in Beijing, and established a hypertension outcome prediction model with the machine learning theory. We first proposed a gain sequence forward tabu search feature selection (GSFTS-FS) method, which can search the optimal combination of medical variables that affect hypertension outcomes. Based on this, the XGBoost algorithm established a prediction model because of its good stability. We verified the proposed method by comparing other commonly used models in similar works. The proposed GSFTS-FS improved the performance by about 10%. The proposed prediction method has the best performance and its AUC value, accuracy, F1 value, and recall of 10-fold cross-validation were 0.96. 0.95, 0.88, and 0.82, respectively. It also performed well on test datasets with 0.92, 0.94, 0.87, and 0.80 for AUC, accuracy, F1, and recall, respectively. Therefore, the XGBoost with GSFTS-FS can accurately and effectively predict the occurrence of outcomes for patients with hypertension, and can provide guidance for doctors in clinical diagnoses and medical decision-making.

Value of a Machine Learning Approach for Predicting Clinical Outcomes in Young Patients With Hypertension.

Risk stratification of young patients with hypertension remains challenging. Generally, machine learning (ML) is considered a promising alternative to traditional methods for clinical predictions because it is capable of processing large amounts of complex data. We, therefore, explored the feasibility of an ML approach for predicting outcomes in young patients with hypertension and compared its performance with that of approaches now commonly used in clinical practice. Baseline clinical data and a composite end point-comprising all-cause death, acute myocardial infarction, coronary artery revascularization, new-onset heart failure, new-onset atrial fibrillation/atrial flutter, sustained ventricular tachycardia/ventricular fibrillation, peripheral artery revascularization, new-onset stroke, end-stage renal disease-were evaluated in 508 young patients with hypertension (30.83±6.17 years) who had been treated at a tertiary hospital. Construction of the ML model, which consisted of recursive feature elimination, extreme gradient boosting, and 10-fold cross-validation, was performed at the 33-month follow-up evaluation, and the model's performance was compared with that of the Cox regression and recalibrated Framingham Risk Score models. An 11-variable combination was considered most valuable for predicting outcomes using the ML approach. The C statistic for identifying patients with composite end points was 0.757 (95% CI, 0.660-0.854) for the ML model, whereas for Cox regression model and the recalibrated Framingham Risk Score model it was 0.723 (95% CI, 0.636-0.810) and 0.529 (95% CI, 0.403-0.655). The ML approach was comparable with Cox regression for determining the clinical prognosis of young patients with hypertension and was better than that of the recalibrated Framingham Risk Score model.

Antenna-coupled vacuum channel nano-diode with high quantum efficiency.

Vacuum channel diodes have the potential to serve as a platform for converting free-space electromagnetic radiation into electronic signals within ultrafast timescales. However, the conversion efficiency is typically very low because conventional vacuum channel diode structures suffer from high surface barriers, especially when using lower energy photon excitation (near-infrared photons or lower). Here, we report on an optical antenna-coupled vacuum channel nano-diode, which demonstrates a greatly improved quantum efficiency up to ∼4% at 800 nm excitation; an efficiency several orders of magnitude higher than any previously reported value. The nano diodes are formed at the cleaved edge of a metal-insulator-semiconductor (MIS) structure, where a gold thin film with nanohole array serves as both the metal electrode and light-harvesting antenna. At the nanoholes-insulator interface, the tunneling barrier is greatly reduced due to the coulombic repulsion induced high local electron density, such that the resonant plasmon induced hot electron population can readily inject into the vacuum channel. The presented vertical tertiary MIS junction enables a new class of high-efficiency, polarization-specific and wavelength- sensitive optical modulated photodetector that has the potential for developing a new generation of opto-electronic systems.

A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data.

The outcomes of hypertension refer to the death or serious complications (such as myocardial infarction or stroke) that may occur in patients with hypertension. The outcomes of hypertension are very concerning for patients and doctors, and are ideally avoided. However, there is no satisfactory method for predicting the outcomes of hypertension. Therefore, this paper proposes a prediction method for outcomes based on physical examination indicators of hypertension patients. In this work, we divide the patients' outcome prediction into two steps. The first step is to extract the key features from the patients' many physical examination indicators. The second step is to use the key features extracted from the first step to predict the patients' outcomes. To this end, we propose a model combining recursive feature elimination with a cross-validation method and classification algorithm. In the first step, we use the recursive feature elimination algorithm to rank the importance of all features, and then extract the optimal features subset using cross-validation. In the second step, we use four classification algorithms (support vector machine (SVM), C4.5 decision tree, random forest (RF), and extreme gradient boosting (XGBoost)) to accurately predict patient outcomes by using their optimal features subset. The selected model prediction performance evaluation metrics are accuracy, F1 measure, and area under receiver operating characteristic curve. The 10-fold cross-validation shows that C4.5, RF, and XGBoost can achieve very good prediction results with a small number of features, and the classifier after recursive feature elimination with cross-validation feature selection has better prediction performance. Among the four classifiers, XGBoost has the best prediction performance, and its accuracy, F1, and area under receiver operating characteristic curve (AUC) values are 94.36%, 0.875, and 0.927, respectively, using the optimal features subset. This article's prediction of hypertension outcomes contributes to the in-depth study of hypertension complications and has strong practical significance.

Ultrafast Field-Emission Electron Sources Based on Nanomaterials.

The search for electron sources with simultaneous optimal spatial and temporal resolution has become an area of intense activity for a wide variety of applications in the emerging fields of lightwave electronics and attosecond science. Most recently, increasing efforts are focused on the investigation of ultrafast field-emission phenomena of nanomaterials, which not only are fascinating from a fundamental scientific point of view, but also are of interest for a range of potential applications. Here, the current state-of-the-art in ultrafast field-emission, particularly sub-optical-cycle field emission, based on various nanostructures (e.g., metallic nanotips, carbon nanotubes) is reviewed. A number of promising nanomaterials and possible future research directions are also established.

Modeling the Capacitated Multi-Level Lot-Sizing Problem under Time-Varying Environments and a Fix-and-Optimize Solution Approach.

In this study, we investigated the time-varying capacitated lot-sizing problem under a fast-changing production environment, where production factors such as the setup costs, inventory-holding costs, production capacities, or even material prices may be subject to continuous changes during the entire planning horizon. Traditional lot-sizing theorems and algorithms, which often assume a constant production environment, are no longer fit for this situation. We analyzed the time-varying environment of today's agile enterprises and modeled the time-varying setup costs and the time-varying production capacities. Based on these, we presented two mixed-integer linear programming models for the time-varying capacitated single-level lot-sizing problem and the time-varying capacitated multi-level lot-sizing problem, respectively, with considerations on the impact of time-varying environments and dynamic capacity constraints. New properties of these models were analyzed on the solution's feasibility and optimality. The solution quality was evaluated in terms of the entropy which indicated that the optimized production system had a lower value than that of the unoptimized one. A number of computational experiments were conducted on well-known benchmark problem instances using the AMPL/CPLEX to verify the proposed models and to test the computational effectiveness and efficiency, which showed that the new models are applicable to the time-varying environment. Two of the benchmark problems were updated with new best-known solutions in the experiments.

Amyotrophic lateral sclerosis in Beijing: Epidemiologic features and prognosis from 2010 to 2015.

OBJECTIVE: To determine the incidence of amyotrophic lateral sclerosis (ALS) in Beijing from 2010 to 2015 and to address the issue of prognosis. METHODS: The number of patients diagnosed with ALS was generated from two aspects, namely, diagnostic hospitals and assisted care institutions. By examining the consistency of the overlapping data in terms of age and gender distributions, the number of ALS patients in Beijing was estimated to analyze the incidence. Finally, a prognosis study was carried out by sorting the clinical data of deceased patients to associate time to death with the demographic characteristics, including gender, age at diagnosis, site of onset, body mass index, and lag from onset to diagnosis. RESULTS: The average yearly incidence was 0.8/100,000 persons, the male-female ratio was 1.63:1, and the mean age at diagnosis was 54.11 years. The mean time from symptom onset to diagnosis was 14.8 months, and the median survival time from diagnosis was 49.4 months. In addition, each of the identified clinical features was related to the survival of the patients with ALS. CONCLUSIONS: The incidence of ALS in Beijing is similar to the rates in Hong Kong and Taiwan but is lower than the rates in Europe and America. In addition, the mean age at onset of the patients in Beijing was early, and overall ALS prognosis appears to be comparable to those reported in recent publications.

A Novel Bearing Multi-Fault Diagnosis Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier.

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available.