High Precision Research Articles

Multi-year soundscape recordings and automated call detection reveals varied impact of moonlight on calling activity of neotropical forest katydids.

Night-time light can have profound ecological effects, even when the source is natural moonlight. The impacts of light can, however, vary substantially by taxon, habitat and geographical region. We used a custom machine learning model built with the Python package Koogu to investigate the in situ effects of moonlight on the calling activity of neotropical forest katydids over multiple years. We prioritised species with calls that were commonly detected in human annotated data, enabling us to evaluate model performance. We focused on eight species of katydids that the model identified with high precision (generally greater than 0.90) and moderate-to-high recall (minimum 0.35), ensuring that detections were generally correct and that many calls were detected. These results suggest that moonlight has modest effects on the amount of calling, with the magnitude and direction of effect varying by species: half of the species showed positive effects of light and half showed negative. These findings emphasize the importance of understanding natural history for anticipating how biological communities respond to moonlight. The methods applied in this project highlight the emerging opportunities for evaluating large quantities of data with machine learning models to address ecological questions over space and time. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Workflow for the unsupervised clustering of sleep stages identifies light and deep sleep in electrophysiological recordings in mice

BackgroundSleep physiology plays a critical role in brain development and aging. Accurate sleep staging, which categorizes different sleep states, is fundamental for sleep physiology studies. Traditional methods for sleep staging rely on manual, rule-based scoring techniques, which limit their accuracy and adaptability. New methodWe describe, test and challenge a workflow for unsupervised clustering of sleep states (WUCSS) in rodents, which uses accelerometer and electrophysiological data to classify different sleep states. WUCSS utilizes unsupervised clustering to identify sleep states using six features, extracted from 4-second epochs. ResultsWe gathered high-quality EEG recordings combined with accelerometer data in diverse transgenic mouse lines (male ApoE3 versus ApoE4 knockin; male CNTNAP2 KO versus wildtype littermates). WUCSS showed high recall, precision, and F1-score against manual scoring on awake, NREM, and REM sleep states. Within NREM, WUCSS consistently identified two additional clusters that qualify as deep and light sleep states. Comparison with existing methodsThe ability of WUCSS to discriminate between deep and light sleep enhanced the precision and comprehensiveness of the current mouse sleep physiology studies. This differentiation led to the discovery of an additional sleep phenotype, notably in CNTNAP2 KO mice, showcasing the method's superiority over traditional scoring methods. ConclusionsWUCSS, with its unsupervised approach and classification of deep and light sleep states, provides an unbiased opportunity for researchers to enhance their understanding of sleep physiology. Its high accuracy, adaptability, and ability to save time and resources make it a valuable tool for improving sleep staging in both clinical and preclinical research.

A thrust vector control test system of large range solid rocket engine utilizing a four-point support piezoelectric dynamometer and a deflected flange structure

The accurate measurement of thrust vector is very important to improve the life and safety of aircraft engine. In order to meet the requirements of thrust vector test of a solid rocket engine and overcome the contradiction between large thrust (>50 kN) and high-frequency response (≫30 Hz)/high precision measurement (<3%), a thrust vector control test system utilizing a four-point support piezoelectric dynamometer (FSPD) and a deflected flange structure was proposed. The test system mainly consists of a four-point support piezoelectric dynamometer and some vector force simulation loading devices. The theoretical mechanical model and the thrust vector control test system was set up to study the performance of solid rocket engine. Firstly, the piezoelectric elements and four-point support piezoelectric dynamometer were calibrated by the three-direction force calibration loading device. Secondly, the rationality and feasibility of the thrust vector control test system were proved by the simulated loading experiment. The research results show that the static and dynamic performance of the test system meet the test requirements, and it has good linearity and repeatability, errors are less than 1.00 %, and interphase interference is less than 2.00 %. The measuring system proposed in this paper provides a new idea for the accurate measurement of vector force with large load and high-frequency response.

HUMAN STRESS LEVEL IDENTIFICATION USING MACHINE LEARNING ALGORITHMS BASED ON SLEEPING PATTERNS

Stress is a mental or emotional state brought on by demanding or unavoidable circumstances, also referred to as stressors. In order to prevent any unfavorable occurrences in life, it is crucial to understand human stress levels. Sleep disturbances are related to a number of physical, mental, and social problems. This study's main objective is to investigate how human stress might be detected using machine learning algorithms based on sleep-related behaviors. The obtained dataset includes various sleep habits and stress levels. Six machine learning techniques, including Multilayer Perception (MLP), Random Forest, Support Vector Machine (SVM), Decision Trees, Naïve Bayes and Logistic Regression were utilized in the classification level after the data had been preprocessed in order to compare and obtain the most accurate results. Based on the experiment results, it can be concluded that the Decision tree algorithm, when used to classify the data, can do so with 93.27% accuracy, high precision, recall, and f1measure values, as well as the lowest mean absolute error (MAE) and root mean squared error rates (RMSE). We can estimate human stress levels using the study's findings, and we can address pertinent problems as soon as possible.

294 Development of a model to predict forage intake and growth of bottle-fed Holstein calves

Abstract Models to predict intake and growth of preweaning calves have received less attention than postweaning calves. The objective was to develop a model to predict forage intake and growth of bottle-fed Holstein calves. Data on milk intake, alfalfa hay intake, and body weight (BW) of 40 Holstein calves from birth to 200 d of age were acquired from Abdelsamei (1989). Milk and forage intake equations were those used by Lancaster et. al. (2021; 10.1093/jas/skaa406). NASEM growth equation evaluation and new equation development were performed using a dataset compiled from the literature by Hildebrand et al. (2022; 10.1093/jas/skac064.321). The mean absolute error to predict milk intake was less for WOOD at peak milk less than 10.88 kg/d, and was less for NASEM at peak milk greater than 10.88 kg/d. A combination of NASEM and WOOD milk yield equations to predict milk intake resulted in CCC of 0.969 and MB of -0.74% and was used to predict milk intake in subsequent analyses. Prediction of forage intake by TED06, BAK76, TED09A, TED09B, and HOL82 had CCC of 0.878, 0.920, 0.729, 0.874, and 0.234 with MB of -17.49, -1.58, -64.40, -35.60, and -341.16%, respectively. Prediction of BW for the same intake equations with the NASEM growth equation had CCC of 0.876, 0.929, 0.819, 0.864, and 0.402 with MB of -21.54, -14.67, -30.10, -25.13, and -99.78%, respectively. With observed BW, prediction of forage intake by TED06, BAK76, TED09A, TED09B, and HOL82 resulted in CCC of 0.935, 0.936, 0.958, 0.964, and 0.640 with MB of -0.55, 6.57, -11.90, -8.83, and -92.26%, respectively, indicating high precision and moderate accuracy. With observed forage intake, prediction of BW by the NASEM growth equation resulted in CCC of 0.926 and MB of -17.08% indicating high precision and poor accuracy. Evaluation of the NASEM growth equation in Holstein cattle resulted in CCC of 0.329 and MB of 61.47%. A new growth equation was developed: EBG, kg/d = 0.78498 ± 0.24035*RE, Mcal/d^(0.78500 ± 0.06033)*EBW, kg^(-0.14323 ± 0.0742) with RMSE = 0.0599 ± 0.0207 and R2 = 0.8795 ± 0.1078. With observed forage intake, prediction of BW with the new growth equation resulted in CCC of 0.992 and MB of -0.11%. With the new growth equation, prediction of forage intake by TED06, BAK76, TED09A, TED09B, and HOL82 resulted in CCC of 0.914, 0.766, 0.881, 0.901, and 0.412 with MB of 15.47, 40.53, 18.11, 17.02, and -172.88%, respectively. Prediction of BW resulted in CCC of 0.978, 0.940, 0.968, 0.969, and 0.701 with MB of 2.83, 8.91, 3.87, 3.92, and -38.73%. In conclusion, prediction of forage intake and BW in Holstein calves were hindered by use of the NASEM growth equation, and a new growth equation more precisely and accurately predicted forage intake and body weight.

Impact behavior of periodic, stochastic, and anisotropic minimal surface-lattice sandwich structures

Recent advancements in 3D printing technologies have made it possible to fabricate intricate lattice architectures with high precision. These lattices can now be utilized to design lightweight sandwich structures that serve multiple functions. To enhance the impact loading performance of these structures, it is crucial to understand how the lattice's topological properties, particularly those with minimal surface attributes like periodic or stochastic Primitive and Gyroid triply periodic minimal surfaces (TPMS) and spinodal-like stochastic cellular materials, associate with the mechanical properties of sandwich structures while keeping the skin thickness fixed. Thus, this paper explores the low-velocity impact behavior of various sheet/shell-based minimal surface-latticed cores of sandwich structures with woven composite skins. The elasto-plastic-damage numerical simulations consider lattice core periodicity, randomness, and anisotropy while keeping the relative density constant. Core lattice randomness and anisotropy are designed using the Gaussian Random Field (GRF) method for spinodal-based stochastic cellular materials and stochastic TPMS. The simulation results showed that the periodic Primitive-lattice core exhibits high out-of-plane shearing strength, enabling the sandwich structure to demonstrate the highest perforation limit. GRF spinodal-based core achieved the highest peak load due to its anisotropic mechanical properties. However, the post-yielding bending of the lattice sheet limited its ability to resist perforation, and absorb and dissipated energy. Interestingly, the stochastic Gyroid TPMS topology, with its inherent densely-distributed microstructure, showed high sensitivity to loading rate, resulting in enhanced energy absorption and dissipation of the sandwich structure. These findings offer valuable insights for optimizing multifunctional sandwich structures with superior impact performance and their design for additive manufacturing.

Freeprotmap: waiting-free prediction method for protein distance map

BackgroundProtein residue–residue distance maps are used for remote homology detection, protein information estimation, and protein structure research. However, existing prediction approaches are time-consuming, and hundreds of millions of proteins are discovered each year, necessitating the development of a rapid and reliable prediction method for protein residue–residue distances. Moreover, because many proteins lack known homologous sequences, a waiting-free and alignment-free deep learning method is needed.ResultIn this study, we propose a learning framework named FreeProtMap. In terms of protein representation processing, the proposed group pooling in FreeProtMap effectively mitigates issues arising from high-dimensional sparseness in protein representation. In terms of model structure, we have made several careful designs. Firstly, it is designed based on the locality of protein structures and triangular inequality distance constraints to improve prediction accuracy. Secondly, inference speed is improved by using additive attention and lightweight design. Besides, the generalization ability is improved by using bottlenecks and a neural network block named local microformer. As a result, FreeProtMap can predict protein residue–residue distances in tens of milliseconds and has higher precision than the best structure prediction method.ConclusionSeveral groups of comparative experiments and ablation experiments verify the effectiveness of the designs. The results demonstrate that FreeProtMap significantly outperforms other state-of-the-art methods in accurate protein residue–residue distance prediction, which is beneficial for lots of protein research works. It is worth mentioning that we could scan all proteins discovered each year based on FreeProtMap to find structurally similar proteins in a short time because the fact that the structure similarity calculation method based on distance maps is much less time-consuming than algorithms based on 3D structures.

SMOTE-based Deep LSTM System with GridSearchCV Optimization for Intelligent Diabetes Diagnosis

Diabetes is a metabolic illness initiated by either inadequate insulin creation by the pancreas or the body's reduced responsiveness to insulin. It is characterised by consistently high levels of blood sugar and symptoms such as frequent urination, thirst, and increased appetite. Untreated diabetes can result in significant problems that impact crucial organs, presenting potentially fatal dangers. In order to address the need for accurate diagnosis, researchers have utilised artificial intelligence to develop the G-LSTM system. This system employs a novel method that combines SMOTE-based deep LSTM and GridSearchCV optimization to classify diabetes. This technique effectively tackles the issue of class imbalance in diabetes datasets, demonstrating an exceptional level of prediction accuracy. When tested on the PIMA dataset, G-LSTM demonstrated exceptional performance with an accuracy of 97.12%. Additionally, it produced high precision, recall, F1-score, AUC, and MCC values of 97.12%, 0.963, 0.954, 0.887, 0.989, and 0.882, respectively. The results highlight the higher performance of the G-LSTM method compared to other techniques, suggesting its use for clinical investigation of diabetes patients. This innovative intelligent diagnostic framework not only demonstrates the potential of artificial intelligence in healthcare, but also highlights its crucial role in enhancing the precision and effectiveness of diabetes diagnosis and treatment.

Predicting associations between CircRNA and diseases through structure-aware graph transformer and path-integral convolution

A series of biological experiments has demonstrated that circular RNAs play a crucial regulatory role in cellular processes and may be potentially associated with diseases. Uncovering these connections helps in understanding potential disease mechanisms and advancing the development of treatment strategies. However, in biology, traditional experiments face limitations in terms of efficiency and cost, especially when enumerating possible associations. To address these limitations, several computational methods have been proposed, but existing methods only measure from a nodal perspective and cannot capture structural similarities between edges. In this study, we introduce an advanced computational method called SATPIC2CD for analyzing potential associations between circular RNAs and diseases. Specifically, we first employ an Structure-Aware Graph Transformer (SAT), which extracts five predefined metapath representations before calculating attention. This adaptive network integrates structural information into the original self-attention by aggregating information within and between paths. Subsequently, we use Path Integral Convolutional Networks (PACN) to integrate feature information for all path weights between two nodes. Afterward, we complement the network node features with feature loss and feature smoothing using Gated Recurrent Units (GRU) and node centrality. Finally, a Multi-Layer Perceptron (MLP) is employed to obtain the ultimate prediction scores for each circular RNA-disease pair. SATPIC2CD performs remarkably well, with an accuracy of up to 0.9715 measured by the Area Under the Curve (AUC) in a 5-fold cross-validation, surpassing other comparative models. Case studies further emphasize the high precision of our method in identifying circular RNA-disease associations, laying a solid foundation for guiding future biological research efforts.

MSFGQ: Design of an efficient Multiparametric model for improving sub-field scheduling performance via novel GA &amp; Q-Learning optimizations

This paper presents the design of a novel multiparametric model aimed at improving sub-field scheduling performance for lithographic processes. The proposed model incorporates various parameters such as sub-field locations, conflict analysis, critical dimensions, delay, current, voltage, dose, and depth of current for optimization of scheduling operations. To achieve this, we have utilized both Genetic Algorithm (GA) and Q-learning algorithms to optimize the scheduling performance in real-time lithographic processes. The need for this work stems from the increasing demand for high precision lithographic processes, which require efficient scheduling operations to achieve optimal results. The proposed model has been tested on real-time lithographic processes, and the results have been evaluated in terms of critical dimensions, scheduling performance, and scheduling efficiency. The results show that the proposed model has reduced critical dimensions by 8.5%, improved scheduling performance by 10.5%, and increased scheduling efficiency by 8.3% . These results demonstrate the efficacy of the proposed model in improving sub-field scheduling performance in lithographic processes. Based on the results it can be observed that this work presents a novel multiparametric model that utilizes GA and Q-learning algorithms to improve sub-field scheduling performance in lithographic processes.

Robotic-assisted free flap harvesting for diverse soft tissue reconstruction: a PRISMA scoping review of clinical outcomes over the last decade.

The aim of our study was to assess the advantages and limitations of robotic technology in diverse reconstructive procedures. A scoping review was conducted in Oct'23, on published studies from 2013 to 2023, focussing on robotic-assisted free flap harvesting. Three databases Ovid-MEDLINE, Scopus, and PubMed were searched. Original research studies reporting robotic-assisted free flap harvest were included. Studies on lesion excision, microvascular anastomosis, local flap harvest, roboticassisted flap inset, review articles, abstract-only studies, non-English documents, and animal studies were excluded from this review. Sixteen studies met the inclusion criteria out of a total of 318, searched initially. These studies included a total of 128 patients, who underwent robotic-assisted free flap harvest for the reconstruction of various defects, with 140 free flaps harvested. The most common flaps harvested by robotic technique were deep inferior epigastric artery perforator (DIEP) flap 120 (85.7%), radial forearm free (RFF) flap 11 (7.9%), latissimus dorsii flap 4 (2.9%), rectus abdominus flap 4 (2.9%), and omental flap 1 (0.7%). Breast reconstruction was the major procedure done i.e. 120 (85.7%) followed by head and neck 11 (7.9%) and limb defects 9 (6.4%) reconstruction procedures. The reported clinical outcomes were acceptable in all the studies with a 99% flap success rate and minimal complications. Variability in operating time was observed depending upon surgical steps undertaken with robotic systems. This scoping review highlights the role of robotic-assisted free flap harvesting in plastic surgery and its potential benefits on clinical outcomes, due to its high precision and minimal invasiveness. However, challenges like cost effectiveness, resource distribution and learning curve are there.

An online fuzzy fraud detection framework for credit card transactions

Credit card transaction fraud is one of the challenging security concerns for financial firms globally. The continuously changing environment of fraud characteristics and the class imbalance and complete separation issues in fraud data create difficulties in accurately and efficiently predicting fraudulent transactions and implementing fraud detection systems in real-time. The study aims to develop a new real-time fraud detection framework that can efficiently be implemented online and address the issues caused by non-stationary changes in transaction and fraud characteristics, class imbalance, and complete separation. We propose a new approach to handle the impact of non-stationary changes in fraud transaction patterns. It enables efficiency in model training, given the sheer size of datasets. By implementing a robust fuzzy logistic regression model against class imbalance and separation problems, we address the challenge of having a very low rate of fraudulent transactions in the dataset and having separation issues due to specific characteristics of transactions. The analysis of the performance versus efficiency nexus of the proposed methodology reveals that the proposed framework shows strong performance results with specificity and sensitivity greater than 0.90 and Matthew’s correlation coefficient greater than 0.80 even on small sample sizes and produces highly accurate results in identifying fraudulent and non-fraudulent transactions with an accuracy greater than 0.99. Benchmarking with machine learning and other fraud detection approaches reveals that the proposed framework provides better detection performance while maintaining a higher rate of identifying non-fraudulent transactions than the alternative approaches. Improved classification performance leads to detecting fraudulent transactions with higher precision while avoiding misclassifying legitimate transfers, resulting in lower financial losses and improved customer satisfaction.

Predicting dental caries outcomes in young adults using machine learning approach

ObjectivesTo predict the dental caries outcomes in young adults from a set of longitudinally-obtained predictor variables and identify the most important predictors using machine learning techniques.MethodsThis study was conducted using the Iowa Fluoride Study dataset. The predictor variables - sex, mother’s education, family income, composite socio-economic status (SES), caries experience at ages 9, 13, and 17, and the cumulative estimates of risk and protective factors, including fluoride, dietary, and behavioral variables from ages 5–9, 9–13, 13–17, and 17–23 were used to predict the age 23 D2+MFS count. The following machine learning models (LASSO regression, generalized boosting machines (GBM), negative binomial (NegGLM), and extreme gradient boosting models (XGBOOST)) were compared under 5-fold cross validation with nested resampling techniques.ResultsThe prevalence of cavitated level caries experience at age 23 (mean D2+MFS count) was 4.75. The predictive analysis found LASSO to be the best performing model (compared to GBM, NegGLM, and XGBOOST), with a root mean square error (RMSE) of 0.70, and coefficient of determination (R2) of 0.44. After dichotomization of the predicted and observed values of the LASSO regression, the classification results showed accuracy, precision, recall, and ROC AUC of 83.7%, 85.9%, 93.1%, 68.2%, respectively. Previous caries experience at age 13 and age 17 and sugar-sweetened beverages intakes at age 13 and age 17 were found to be the four most important predictors of cavitated caries count at age 23.ConclusionOur machine learning model showed high accuracy and precision in the prediction of caries in young adults from a longitudinally-obtained predictor variables. Our model could, in the future, after further development and validation with other diverse population data, be used by public health specialists and policy-makers as a screening tool to identify the risk of caries in young adults and apply more targeted interventions. However, data from a more diverse population are needed to improve the quality and generalizability of caries prediction.

An Origami-enabled Soft Linear Actuator and its Application on a Crawling Robot

Abstract Soft linear actuators have strong deformation ability and good environmental adaptability, which have been widely used in soft robot design. However, little work has focused on designing soft linear actuators with balanced performances, featuring fast driving speed, large output displacement, lightweight, and miniaturization. Herein, we present a novel soft linear actuator design based on the Kresling origami structure. By driving the Kresling tubes with a servo motor, the soft linear actuator has good compliance and strong environmental adaptability and can achieve fast driving speed, large driving force, and high control precision comparable to the traditional electrical motor. The analytical models of the Kresling tubes and the whole actuator are respectively derived to analyze the mechanical properties, determine the optimal geometry of the Kresling tube and evaluate the driving performance of the whole actuator. The actuator prototype is fabricated by 3D printing, and the actual driving performance is tested. It is shown that the prototype can achieve a maximum output displacement of 18.9 mm without payload or 16mm under a payload of 30 N. Finally, as a case study, the soft linear actuator is applied to a crawling robot, where the maximum moving speed of 28 mm/s is reached.

A multi-stage localization framework for accurate and precise docking of autonomous mobile robots (AMRs)

Abstract Autonomous navigation has been a long-standing research topic, and researchers have worked on many challenging problems in indoor and outdoor environments. One application area of navigation solutions is material handling in industrial environments. With Industry 4.0, the simple problem in traditional factories has evolved into the use of autonomous mobile robots within flexible production islands in a self-decision-making structure. Two main stages of such a navigation system are safe transportation of the vehicle from one point to another and reaching destinations at industrial standards. The main concern in the former is roughly determining the vehicle’s pose to follow the route, while the latter aims to reach the target with high accuracy and precision. Often, it may not be possible or require extra effort to satisfy requirements with a single localization method. Therefore, a multi-stage localization approach is proposed in this study. Particle filter-based large-scale localization approaches are utilized during the vehicle’s movement from one point to another, while scan-matching-based methods are used in the docking stage. The localization system enables the appropriate approach based on the vehicle’s status and task through a decision-making mechanism. The decision-making mechanism uses a similarity metric obtained through the correntropy criterion to decide when and how to switch from large-scale localization to precise localization. The feasibility and performance of the developed method are corroborated through field tests. These evaluations demonstrate that the proposed method accomplishes tasks with sub-centimeter and sub-degree accuracy and precision without affecting the operation of the navigation algorithms in real time.

Comparative Analysis of Reverse Truncated Pyramid and Other Nonimaging Concentrators as the Receiver in Laser Wireless Power Transmission System

The global energy landscape faces significant challenges in meeting the demands of modern industries for stable and efficient energy supply. Laser wireless power transmission (LWPT) systems, which utilize the photovoltaic effect to convert laser beam energy, hold great promise due to their long-range transmission capability and high precision. However, the current energy conversion efficiency of these systems requires further improvement to achieve optimal performance. To enhance concentrated photovoltaic (CPV) system performance, the study examines different nonimaging concentrators such as reverse truncated pyramid, cross-compound parabolic concentrator, and square elliptical hyperboloid. Numerical simulations using the finite element method analyze the multifield coupling mechanism of PV modules with various concentration lenses. Three CPV systems with RTP concentrators of different heights were studied to understand the impact of geometry on CPV performance. And the main impact of rotation angle was discussed. The research findings provide essential insights into CPV system performance and the influence of different concentration lenses, contributing valuable knowledge towards improving LWPT technologies.

Design, optimization, manufacture, and tests of CFRP hydraulic cylinder tube without metal liner: A bionic thorn‐tooth connection

AbstractHydraulic cylinders are widely used in rudder surface control, landing gear retraction of aircraft. The aircraft could benefit a lot on weight reduction, by the application of the carbon fiber reinforced polymer (CFPR) on the hydraulic cylinder. The design, optimization, manufacture of the CFRP hydraulic cylinder tube without metal liner is systematically studied in this article. In terms of structure design, inspired by natural thorn plants, a bionic thorn‐tooth connection structure is designed to connect CFRP and the metal end. Then the theoretical design method of thorn‐tooth is proposed. It also provides a new solution for the generic technology of CFRP‐to‐metal connection. In terms of CFRP parameters design method, an accurate mechanical model of CFRP hydraulic tube is established, the influence law of CFRP parameters on mechanical properties is explored, and the optimization method of CFRP parameters adapted to new characteristics is proposed. In terms of the manufacturing process, the split manufacturing‐combined molding process with high precision and low cost is developed to fit the characteristics of CFRP hydraulic tube without metal liner. The prototype manufactured by the proposed method has a weight reduction ratio of 62.37% while maintaining performances compared to the original metal hydraulic cylinder tube. This research will bring significant performance gains and considerable economic and environmental benefits to aircrafts.Highlights The systematic study of CFRP hydraulic tube without metal liner is presented for weight reduction. The bionic thorn‐tooth connection and its parameter design method are proposed. The CFRP parameters design method of CFRP hydraulic tube is established. A manufacturing process adapted to CFRP hydraulic tube without metal liner is developed.

Wireless Sensor System Based on Organohydrogel Ionic Skin for Physiological Activity Monitoring.

Supermolecular hydrogel ionic skin (i-skin) linked with smartphones has attracted widespread attention in physiological activity detection due to its good stability in complex scenarios. However, the low ionic conductivity, inferior mechanical properties, poor contact adhesion, and insufficient freeze resistance of most used hydrogels limit their practical application in flexible electronics. Herein, a novel multifunctional poly(vinyl alcohol)-based conductive organohydrogel (PCEL5.0%) with a supermolecular structure was constructed by innovatively employing sodium carboxymethyl cellulose (CMC-Na) as reinforcement material, ethylene glycol as antifreeze, and lithium chloride as a water retaining agent. Thanks to the synergistic effect of these components, the PCEL5.0% organohydrogel shows excellent performance in terms of ionic conductivity (1.61 S m-1), mechanical properties (tensile strength of 70.38 kPa and elongation at break of 537.84%), interfacial adhesion (1.06 kPa to pig skin), frost resistance (-50.4 °C), water retention (67.1% at 22% relative humidity), and remoldability. The resultant PCEL5.0%-based i-skin delivers satisfactory sensitivity (GF = 1.38) with fast response (348 ms) and high precision under different deformations and low temperature (-25 °C). Significantly, the wireless sensor system based on the PCEL5.0% organohydrogel i-skin can transmit signals from physiological activities and sign language to a smartphone by Bluetooth technology and dynamically displays the status of these movements. The organohydrogel i-skin shows great potential in diverse fields of physiological activity detection, human-computer interaction, and rehabilitation medicine.

Electron wave and quantum optics in graphene.

In the last decade, graphene has become an exciting platform for electron optical experiments, in some aspects superior to conventional two-dimensional electron gases (2DEGs). A major advantage, besides the ultra-large mobilities, is the fine control over the electrostatics,&#xD;which gives the possibility of realising gap-less and compact p-n interfaces with high precision. The latter host non-trivial states, \eg, snake states in moderate magnetic fields, and serve as building blocks of complex electron interferometers. Thanks to the Dirac spectrum and its non-trivial Berry phase, the internal (valley and sublattice) degrees of freedom, and the possibility to tailor the band structure using proximity effects, such interferometers open up a completely new playground based on novel device architectures. In this review, we introduce the theoretical background of graphene electron optics, fabrication methods used to realise electron-optical devices, and techniques for corresponding numerical simulations. Based on this, we give a comprehensive review of ballistic transport experiments and simple building blocks of electron optical devices both in single and bilayer graphene, highlighting the novel physics that is brought in compared to conventional 2DEGs. After describing the different magnetic field regimes in graphene p-n junctions and nanostructures, we conclude by discussing the state of the art in graphene-based Mach-Zender and Fabry-Perot interferometers.

Research on DCT shift strategy for various driving style based on “driver-vehicle-cloud” machine learning

In order to solve the problem that the DCT static shift strategy cannot adapt to the difference in driving style, the driving style identification model based on multi-dimensional data mining and intelligent algorithm heavily depends on vehicle terminal data storage and calculation, an intelligent shift strategy based on “driver-vehicle-cloud” cooperative control is proposed. Firstly, the dynamic model of the DCT vehicle is analyzed, the primary shift schedule is calculated, and a method to adaptively modify the shifting schedule of DCT according to driving style is proposed. Then, many vehicle driving data are collected, cleaned, and reconstructed by wavelet denoising and other methods, and a driving style database with 80-dimensional features is constructed. Five essential features are selected by the ReliefF method, and the driving style recognition model is constructed by combining random forest, support vector machine, naive Bayesian, and other algorithms. Finally, the support vector machine model with the highest precision is selected, and the “driver-vehicle-cloud” collaborative control system is deployed using cloud computing and vehicle-cloud collaborative technology. The experiment car test shows that the system can identify the driver’s driving style in real time and realize the differential shift schedule and driving experience of DCT.