Rare-Earth Metal Complexes Supported by 1,3-Functionalized Indolyl-Based Ligands for Efficient Hydrosilylation of Alkenes.

Two different 1,3-functionalized indolyl-based proligands 1-(2-C4H7O)CH2-3-(2-tBuC6H5N═CH)C8H5N (HL1) and 1-Me2NCH2CH2-3-(2-iPrC6H5N═CH)C8H5N (HL2) were designed, prepared in high yields, and successfully applied to rare-earth metal chemistry showing different reactivities and different bondings with the central metals. The reactions of HL1 with RE(CH2SiMe3)3(THF)2 provided two types of rare-earth metal complexes: the pincer type mononuclear complexes κ3-(L1)RE(CH2SiMe3)2 [L1 = 1-(2-C4H7O)CH2-3-(2-tBuC6H5N═CH)C8H4N, RE = Lu(1), Yb(2)], and the dinuclear rare-earth metal alkyl (per alkyl/per metal) complexes having the ligand in novel coordination modes {(η1:(µ-η2:η1):η1-1-(2-C4H7O)CH2-3-[2-tBuC6H5NCH-(CH2SiMe3)]C8H4N)RECH2SiMe3}2 [RE = Er(3), Y(4), Dy(5), and Gd(6)]. Meanwhile, the reactions of HL2 with RE(CH2SiMe3)3(THF)2 led to the isolation and characterization of only the mononuclear rare-earth metal dialkyl complexes κ3-(L2)RE(CH2SiMe3)2 [L2 = 1-Me2NCH2CH2-3-(2-iPrC6H5N═CH)C8H4N, RE = Lu(7), Gd(8)] bearing the ligand in the pincer chelate form. The mononuclear complexes were formed through the sp2 C-H activation of the 2-indolyl moiety, while the dinuclear complexes were produced unexpectedly through the tandem 2-indolyl sp2 C-H activation and C═N insertion into the RE-CH2SiMe3 bond. These complexes were fully characterized by spectroscopic methods, elemental analyses, and single-crystal X-ray crystallography. The applications of the synthesized complexes as catalysts for the hydrosilylation of terminal alkenes with phenylsilane are described. Anti-Markovnikov addition products were produced by the hydrosilylation of aliphatic olefins, and Markovnikov addition products were isolated with aromatic olefins with high selectivity in the absence of cocatalysts. It is found that the dinuclear rare-earth alkyl complexes exhibited the best catalytic activity with the advantages of mild reaction conditions, short reaction time, low catalyst loading, and wide substrate applicability in comparison with the synthesized mononuclear complexes and the reported catalysts.

A novel hierarchical machine learning model for hospital-acquired venous thromboembolism risk assessment among multiple-departments.

Venous thromboembolism (VTE) is a common vascular disease and potentially fatal complication during hospitalization, and so the early identification of VTE risk is of significant importance. Compared with traditional scale assessments, machine learning methods provide new opportunities for precise early warning of VTE from clinical medical records. This research aimed to propose a two-stage hierarchical machine learning model for VTE risk prediction in patients from multiple departments. First, we built a machine learning prediction model that covered the entire hospital, based on all cohorts and common risk factors. Then, we took the prediction output of the first stage as an initial assessment score and then built specific models for each department. Over the duration of the study, a total of 9213 inpatients, including 1165 VTE-positive samples, were collected from four departments, which were split into developing and test datasets. The proposed model achieved an AUC of 0.879 in the department of oncology, which outperformed the first-stage model (0.730) and the department model (0.787). This was attributed to the fully usage of both the large sample size at the hospital level and variable abundance at the department level. Experimental results show that our model could effectively improve the prediction of hospital-acquired VTE risk before image diagnosis and provide decision support for further nursing and medical intervention.

Novel inverse finite-element formulation for reconstruction of relative local stiffness in heterogeneous extra-cellular matrix and traction forces on active cells.

Accurately resolving the traction forces on active cells in 3D extra-cellular matrix (ECM) is crucial to understanding stress homeostasis in cellularized ECM systems and the resulting collective cellular behavior. The majority of 3D traction force microscopy techniques, which compute the stress distribution in ECM as well as cellular traction forces from experimentally measured deformation field in the ECM using dispersed tracing particles or fluorescently-tagged matrix proteins, have assumed a spatially homogeneous ECM with constant material properties at every location in the system. Recent studies have shown that ECM can exhibit significant heterogeneity due to the disordered nature of collagen network as well as cell remodeling. In this paper, we develop a novel inverse finite-element formulation for accurately resolving the cellular traction forces by explicitly reconstructing the relative local elastic modulus values of the heterogeneous ECM containing an arbitrary shaped cell from a measured displacement field in the ECM. Our formulation does not require any a priori knowledge of the boundary conditions, and simultaneously results in the distribution of the heterogeneous modulus values and stress field in the ECM, as well as the traction forces on the cell, given experimentally measured average modulus of the ECM. We first validate our procedure in artifical model cell-ECM systems, and then employ the procedure to compute the distribution of elastic modulus in a heterogeneous type-I collagen gel as well as the traction force on a rounded breast cancer cell in the gel, based on the deformation field data obtained via 3D reflectance force microscopy. Our results indicate that the majority part of the cell is in a tensile state, while a local region on the cell is in a tri-axial compressive state, indicating the possible development of a local protrusion in this region. This is further verified by tracking the subsequent evolution of the cell morphology.

Absorbing-active transition in a multi-cellular system regulated by a dynamic force network.

Collective cell migration in 3D extracellular matrix (ECM) is crucial to many physiological and pathological processes. Migrating cells can generate active pulling forces via actin filament contraction, which are transmitted to the ECM fibers and lead to a dynamically evolving force network in the system. Here, we elucidate the role of this force network in regulating collective cell behaviors using a minimal active-particle-on-network (APN) model, in which active particles can pull the fibers and hop between neighboring nodes of the network following local durotaxis. Our model reveals a dynamic transition as the particle number density approaches a critical value, from an "absorbing" state containing isolated stationary small particle clusters, to an "active" state containing a single large cluster undergoing constant dynamic reorganization. This reorganization is dominated by a subset of highly dynamic "radical" particles in the cluster, whose number also exhibits a transition at the same critical density. The transition is underlaid by the percolation of "influence spheres" due to the particle pulling forces. Our results suggest a robust mechanism based on ECM-mediated mechanical coupling for collective cell behaviors in 3D ECM.

Continuum percolation-based tortuosity and thermal conductivity of soft superball systems: shape dependence from octahedra via spheres to cubes.

Understanding the effect of particle shape on the percolation threshold, tortuosity and thermal conductivity of soft (geometrical overlapping) particle systems is very crucial for the design and optimization of such materials, including colloids, polymers, and porous and fracture media. In this work, we first combine the excluded-volume theory with the Monte Carlo simulations to determine the percolation threshold for a family of soft superballs, the shape of which interpolates between octahedra and cubes via spheres. Then, we propose two continuum percolation-based models to respectively obtain the tortuosity and effective thermal conductivity of soft superball systems considering their percolation behavior, where monodisperse overlapping superballs are uniformly distributed in a homogeneous solid matrix. Specifically, both models cover the whole feasible range of superball volume fractions, including near the percolation threshold. Comparison with extensive experimental, numerical and theoretical results confirms that the present models are capable of precisely predicting the percolation threshold, tortuosity and thermal conductivity of such systems. Furthermore, we apply the proposed models to probe the influence of particle shape on these important parameters. Our results show that the decreasing percolation threshold and tortuosity as soft particles become more anisotropic is consistent with increasing conductivity. It suggests that the anisotropic-shaped inclusion phase is more conducting than the spherical inclusion phase. The present theoretical strategies and conclusions may provide sound guidance for the synthesis of colloidal and polymer superballs.

The fractal derivative wave equation: Application to clinical amplitude/velocity reconstruction imaging.

This paper proposes a dissipative acoustic wave equation in which the fractal derivative is employed to represent dissipation. The proposed model is derived from the viscoelastic constitutive relationship via the fractal derivative. It is noted that the fractal derivative is a local operator and avoids the expensive computational costs of non-local fractional derivative, which is popular in recent decades to describe frequency-dependent dissipation in acoustic wave propagation in soft materials. The proposed model is tested to simulate the clinical amplitude/velocity reconstruction imaging of breast tumors, where the reflecting plate is imaged as an elevated line in correspondence to tumor. Numerical experiments show that the present model is capable of indicating the size, position and quantity of tumors. The comparative study confirms that the fractal derivative acoustic wave equation has an advantage over the fractional derivative model regarding computational costs.

Insight into interfacial effect on effective physical properties of fibrous materials. I. The volume fraction of soft interfaces around anisotropic fibers.

With advances in interfacial properties characterization technologies, the interfacial volume fraction is a feasible parameter for evaluating effective physical properties of materials. However, there is a need to determine the interfacial volume fraction around anisotropic fibers and a need to assess the influence of such the interfacial property on effective properties of fibrous materials. Either ways, the accurate prediction of interfacial volume fraction is required. Towards this end, we put forward both theoretical and numerical schemes to determine the interfacial volume fraction in fibrous materials, which are considered as a three-phase composite structure consisting of matrix, anisotropic hard spherocylinder fibers, and soft interfacial layers with a constant dimension coated on the surface of each fiber. The interfacial volume fraction actually represents the fraction of space not occupied by all hard fibers and matrix. The theoretical scheme that adopts statistical geometry and stereological theories is essentially an analytic continuation from spherical inclusions. By simulating such three-phase chopped fibrous materials, we numerically derive the interfacial volume fraction. The theoretical and numerical schemes provide a quantitative insight that the interfacial volume fraction depends strongly on the fiber geometries like fiber shape, geometric size factor, and fiber size distribution. As a critical interfacial property, the present contribution can be further drawn into assessing effective physical properties of fibrous materials, which will be demonstrated in another paper (Part II) of this series.

[Detection of p16 by fluorescence in-situ hybridization and immunohistochemistry in malignant mesothelioma].

OBJECTIVE: To study the role of p16 gene mutation status as detected by fluorescence in-situ hybridization (FISH) and p16 protein expression as detected by immunohistochemistry in differential diagnosis of malignant mesothelioma and benign mesothelial hyperplasia. METHODS: p16 gene mutation status and protein expression were detected by FISH and immunohistochemistry respectively in 55 cases of pleural malignant mesothelioma and 30 cases of benign mesothelial hyperplasia. RESULTS: FISH study showed that the rate of p16 deletion in malignant mesothelioma (81.8%,45/55) was higher than that in benign mesothelial hyperplasia (3.3%,1/30). The difference was statistically significant (P<0.05). Immunohistochemical study showed that the rate of p16 protein expression in malignant mesothelioma (23.6%) was lower than that in benign mesothelial hyperplasia (76.7%). The difference was also statistically significant. The sensitivity and specificity of FISH in distinguishing between mesothelioma and reactive mesothelial hyperplasia were higher than those of immunohistochemistry. CONCLUSIONS: In contrast to reactive mesothelial hyperplasia, p16 gene is deleted and p16 protein is not expressed in malignant mesothelioma. The sensitivity and specificity of FISH are higher than those of immunohistochemistry in the distinction.

Prediction of transport behaviors of particulate composites considering microstructures of soft interfacial layers around ellipsoidal aggregate particles.

The effect of microstructures of interfacial layers on transport behaviors of particulate composites has been found to be significant, thus microstructural characteristics of interfacial layers should be considered in the analysis for better prediction of transport properties of particulate composites. However, it is very difficult to determine the volume fraction of soft interfacial layers around polydisperse three-dimensional (3D) ellipsoidal aggregate particles and to practically estimate the influence of such a microstructural characteristic on transport properties of particulate composites by traditional experimental methods and simple models proposed so far. In this article, an approximate analytical model for the volume fraction of soft interfacial layers is proposed on the basis of a theory of the nearest-surface distribution functions and geometric characteristics of polydisperse ellipsoidal particle systems. A theoretical model that adopts a three-phase composite ellipsoid structure by a generalized self-consistent scheme is further presented to predict the effective transport properties of particulate composites containing such soft interfacial layers. To test the developed models, numerical results of the soft interfacial volume fraction from the previous work, experimental data in the literature, the Hashin-Shtrikman bounds model and the Maxwell-Garnett model for the effective electrical conductivity are compared respectively. Finally, by virtue of the present models, the effects of key factors on the effective electrical conductivity of particulate composites are investigated in a quantitative manner.

Modeling of soft interfacial volume fraction in composite materials with complex convex particles.

The influence of the soft interfacial volume fraction on physical properties of composite materials has been found to be significant. However, the soft interfacial volume fraction is difficultly determined by traditional experimental methods and simple models proposed so far. This article addresses the problem by means of theoretical and numerical approaches that start at a microscopic scale of composite materials, which are regarded as a three-phase composite structure with polydisperse convex particles, soft interfaces, and a matrix. A theoretical scheme for the soft interfacial volume fraction is proposed by a theory of the nearest-surface distribution functions and geometrical configurations of polydisperse convex particles. The theoretical scheme represents a generalized model for the soft interfacial volume fraction in that it cannot only determine the interfacial volume fraction around convex polyhedral particles but also to derive that around ellipsoidal and spherical particles. In order to test the theoretical scheme, a numerical model that adopts the three-phase composite structure and a numerical Monte Carlo integration scheme is presented. Also, theoretical and numerical results of the soft interfacial volume fraction around ellipsoidal and spherical particles in the literature are further compared. By way of application, it is shown that the developed model provides a quantitative means to evaluate the dependence of the soft interfacial volume fraction on various factors, such as geometrical configurations of particles and the interfacial thickness.

Development and validation of machine learning models for venous thromboembolism risk assessment at admission: a retrospective study.

Introduction: Venous thromboembolism (VTE) risk assessment at admission is of great importance for early screening and timely prophylaxis and management during hospitalization. The purpose of this study is to develop and validate novel risk assessment models at admission based on machine learning (ML) methods. Methods: In this retrospective study, a total of 3078 individuals were included with their Caprini variables within 24 hours at admission. Then several ML models were built, including logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB). The prediction performance of ML models and the Caprini risk score (CRS) was then validated and compared through a series of evaluation metrics. Results: The values of AUROC and AUPRC were 0.798 and 0.303 for LR, 0.804 and 0.360 for RF, and 0.796 and 0.352 for XGB, respectively, which outperformed CRS significantly (0.714 and 0.180, P < 0.001). When prediction scores were stratified into three risk levels for application, RF could obtain more reasonable results than CRS, including smaller false positive alerts and larger lower-risk proportions. The boosting results of stratification were further verified by the net-reclassification-improvement (NRI) analysis. Discussion: This study indicated that machine learning models could improve VTE risk prediction at admission compared with CRS. Among the ML models, RF was found to have superior performance and great potential in clinical practice.

Aggressive driving behavior prediction considering driver's intention based on multivariate-temporal feature data.

Aggressive driving behavior is mainly motivated by the intention of the driver; therefore, the underlying intention of behavior should be considered in investigating aggressive driving behavior. However, existing aggressive driving behavior prediction methods are not advanced in a compelling of characterizing the driver's intention among a large set of attributes and describing the random process among time-varying transversion. To address this, this paper proposes a prediction method, which is structured with a Hidden Markov Model (HMM) and attention-based Long Short-Term Memory (LSTM) Network. HMM is applied to extract the driver's intention which leads to aggressive driving behavior; attention-based LSTM networks are applied in the multivariate-temporal aggressive driving behavior prediction. The method input uses panel data which contains observations about different cross-sections across time. In the case study, the model was trained based on the Shanghai Naturalistic Driving Study data. After comparing with other deep learning methods and normal LSTM, results show the proposed method provides good performance for aggressive driving behavior prediction (Mean of Accuracy = 80%), especially with the 2-sec time interval applied (Training Accuracy = 82% and Validation Accuracy = 84%). Also, the result shows that the attention mechanism can improve the result's interpretability, and using the driver's intentions as input can enhance the model accuracy. This method for predicting aggressive driving behavior that combines driver's intention, variable contribution sorting, and time-series processing. This method can be used in real-world applications for improving driving safety with the applications in the Advanced Driver Assistance Systems.

Novel 3D Printing Phase Change Aggregate Concrete: Mechanical and Thermal Properties Analysis.

The use of phase change materials (PCMs) in concrete is a double-edged sword that improves the thermal inertia but degrades the mechanical properties of concrete. It has been an essential but unsolved issue to enhance the thermal capacity of PCMs while non-decreasing their mechanical strength. To this end, this work designs a novel 3D printing phase change aggregate to prepare concrete with prominent thermal capacity and ductility. The work investigated the effects of 3D printing phase change aggregate on the compressive strength and splitting tensile strength of concrete. The compressive strength of phase change aggregate concrete is 21.18 MPa, but the ductility of concrete improves. The splitting tensile strength was 1.45 MPa. The peak strain is 11.69 × 10-3, nearly 13 times that of basalt aggregate concrete. Moreover, using 3D printing phase change aggregate reduced concrete's early peak hydration temperature by 7.1%. The thermal insulation capacity of the experiment cube model with phase change concrete has been improved. The results show that the novel 3D printing change aggregate concrete has good mechanical properties and latent heat storage, providing a guideline for applying PCMs in building materials.

Optimal Trajectory Planning of the Variable-Stiffness Flexible Manipulator Based on CADE Algorithm for Vibration Reduction Control.

Robotic manipulators are widely used for precise operation in the medical field. Vibration suppression control of robotic manipulators has become a key issue affecting work stability and safety. In this paper an optimal trajectory planning control method to suppress the vibration of a variable-stiffness flexible manipulator considering the rigid-flexible coupling is proposed. Through analyzing the elastic deformation of the variable-stiffness flexible manipulator, a distributed dynamic physical model of the flexible manipulator is constructed based on the Hamilton theory. Based on the mathematical model of the system, the design of the vibration damping controller of the flexible manipulator is proposed, and the control system with nonlinear input is considered for numerical analysis. According to the boundary conditions, the vibration suppression effect of the conventional and the variable-stiffness flexible manipulator is compared. The motion trajectory of the variable-stiffness flexible manipulator and compare the vibration response from different trajectories. Then, with minimum vibration displacement, minimum energy consumption and minimum trajectory tracking deviation as performance goals, the trajectory planning of the variable-stiffness flexible manipulator movement is carried out based on the cloud adaptive differential evolution (CADE) optimization algorithm. The validity of the proposed trajectory planning method is verified by numerical simulation.

Probing information content of hierarchical n-point polytope functions for quantifying and reconstructing disordered systems.

Disordered systems are ubiquitous in physical, biological, and material sciences. Examples include liquid and glassy states of condensed matter, colloids, granular materials, porous media, composites, alloys, packings of cells in avian retina, and tumor spheroids, to name but a few. A comprehensive understanding of such disordered systems requires, as the first step, systematic quantification, modeling, and representation of the underlying complex configurations and microstructure, which is generally very challenging to achieve. Recently, we introduced a set of hierarchical statistical microstructural descriptors, i.e., the "n-point polytope functions" P_{n}, which are derived from the standard n-point correlation functions S_{n}, and successively included higher-order n-point statistics of the morphological features of interest in a concise, explainable, and expressive manner. Here we investigate the information content of the P_{n} functions via optimization-based realization rendering. This is achieved by successively incorporating higher-order P_{n} functions up to n=8 and quantitatively assessing the accuracy of the reconstructed systems via unconstrained statistical morphological descriptors (e.g., the lineal-path function). We examine a wide spectrum of representative random systems with distinct geometrical and topological features. We find that, generally, successively incorporating higher-order P_{n} functions and, thus, the higher-order morphological information encoded in these descriptors leads to superior accuracy of the reconstructions. However, incorporating more P_{n} functions into the reconstruction also significantly increases the complexity and roughness of the associated energy landscape for the underlying stochastic optimization, making it difficult to convergence numerically.

Distinct miRNA Gene Expression Profiles Among the Nodule Tissues of Lung Sarcoidosis, Tuberculous Lymphadenitis and Normal Healthy Control Individuals.

Background: Sarcoidosis and tuberculosis share similarities in clinical manifestations and histopathological features. We aimed to identify the microRNA (miRNA) profiles of the lymph nodes of individuals with sarcoidosis and of those with tuberculous lymphadenitis to investigate the value of miRNAs in the differential diagnosis of sarcoidosis and tuberculous lymphadenitis. Methods: The miRNA profiles of the lymph nodes of individuals with sarcoidosis, those with tuberculous lymphadenitis (TBLN) and controls were detected by miRNA microarray analysis in the age- and sex-matched development group of the controls (n = 3), patients with TBLN (n = 3) and patients with sarcoidosis (n = 3), and the results were validated by quantitative real-time polymerase chain reaction in the validation group of the controls (n = 30), TBLN (n = 30) and patients with sarcoidosis (n = 31). The relationship between miRNA expression and the clinical parameters of sarcoidosis was analyzed. Results: miR-145, miR-185-5p, miR-301, miR-425-5P, miR-449b and miR-885-5P were differentially expressed between individuals with sarcoidosis and controls (P < 0.0001, P < 0.0001, P = 0.0008, P = 0.0002, P = 0.0018, and P < 0.0001, respectively), and the same six miRNAs were differentially expressed between individuals with tuberculous lymphadenitis and controls (P = 0.0002, P = 0.0004, P = 0.0238, P = 0.0006, P = 0.0149, and P = 0.0045, respectively). miR-185-5p was differentially expressed between individuals with tuberculous lymphadenitis and those with sarcoidosis (P = 0.0101). The area under the receiver operating characteristic curve calculated for miR-185-5p was 0.6860, and the sensitivity and specificity of miR-185-5p for the differential diagnosis of sarcoidosis from TBLN were 61 and 80%, respectively. The levels of miR-145, miR-301, miR-425-5P, and miR-885-5P were positively correlated with CD4+/CD8+ T lymphocytes in bronchoalveolar lavage fluid. Conclusions: miRNAs in lymph nodes show similar expression patterns between individuals with sarcoidosis and those with tuberculous lymphadenitis, which were experimentally selected. miR-185-5p in the lymph nodes can be used as an auxiliary marker for the differential diagnosis of sarcoidosis and tuberculous lymphadenitis.

Disordered hyperuniformity in two-dimensional amorphous silica.

Disordered hyperuniformity (DHU) is a recently proposed new state of matter, which has been observed in a variety of classical and quantum many-body systems. DHU systems are characterized by vanishing infinite-wavelength normalized density fluctuations and are endowed with unique novel physical properties. Here, we report the discovery of disordered hyperuniformity in atomic-scale two-dimensional materials, i.e., amorphous silica composed of a single layer of atoms, based on spectral-density analysis of high-resolution transmission electron microscopy images. Moreover, we show via large-scale density functional theory calculations that DHU leads to almost complete closure of the electronic bandgap compared to the crystalline counterpart, making the material effectively a metal. This is in contrast to the conventional wisdom that disorder generally diminishes electronic transport and is due to the unique electron wave localization induced by the topological defects in the DHU state.

Continuum percolation of congruent overlapping polyhedral particles: Finite-size-scaling analysis and renormalization-group method.

The continuum percolation of randomly orientated overlapping polyhedral particles, including tetrahedron, cube, octahedron, dodecahedron, and icosahedron, was analyzed by Monte Carlo simulations. Two numerical strategies, (1) a Monte Carlo finite-size-scaling analysis and (2) a real-space Monte Carlo renormalization-group method, were, respectively, presented in order to determine the percolation threshold (e.g., the critical volume fraction ϕ_{c} or the critical reduced number density η_{c}), percolation transition width Δ, and correlation-length exponent ν of the polyhedral particles. The results showed that ϕ_{c} (or η_{c}) and Δ increase in the following order: tetrahedron < cube < octahedron < dodecahedron < icosahedron. In other words, both the percolation threshold and percolation transition width increase with the number of faces of the polyhedral particles as the shape becomes more "spherical." We obtained the statistical values of ν for the five polyhedral shapes and analyzed possible errors resulting in the present numerical values ν deviated from the universal value of ν=0.88 reported in literature. To validate the simulations, the corresponding excluded-volume bounds on the percolation threshold were obtained and compared with the numerical results. This paper has practical applications in predicting effective transport and mechanical properties of porous media and composites.

Effects of connected vehicle-based variable speed limit under different foggy conditions based on simulated driving.

In response to developing and/or diminishing foggy conditions, the variable speed limit application in a connected vehicle environment (CV-VSL) can estimate and deliver recommended travel speeds to individual drivers, which can help to reduce crashes when visibility conditions change. This study aims to quantify the effectiveness of the CV-VSL application by exploring drivers' reactions to warnings (e.g., recommended travel speeds). In order to analyze the effectiveness of the CV-VSL application, a connected vehicle testing platform was established based on a driving simulator, and characteristics of the drivers' speed adjustments after receiving warnings were analyzed with respect to different levels of visibility (i.e., no fog, slight fog, and heavy fog). This study also examined the effect of warnings on drivers in different impact zones (i.e., clear zone, transition zone, and fog zone). Three indicators were identified: 1) speed at the end of the clear zone, 2) maximum deceleration rate in the transition zone, and 3) average speed reduction in the fog zone. Throughout the experiment, the relationship between speed adjustments and the level of visibility was explored. The results indicated that the CV-VSL application is effective in making drivers reduce travel speeds in all three types of zones. Furthermore, it appeared that the CV-VSL application could help manage travel speeds prior to vehicles entering the transition zone, and influence drivers' braking decisions upon encountering reduced visibility. It was also found that the CV-VSL application was more effective in heavy fog conditions than in light fog conditions. The connected vehicle testing platform based on the driving simulator provided a new method for evaluating the effectiveness of in-vehicle messaging generated by connected vehicle applications.

An inner filter effect-based near-infrared probe for the ultrasensitive detection of tetracyclines and quinolones.

Antibiotics play important roles in the treatment and prevention of bacterial infections. However, the over-use of antibiotics can lead to a lot of adverse side-effects like nerve damage, anaphylaxis and drug tolerance. Herein, we report an inner filter effect (IFE)-based near-infrared (NIR) fluorescent probe for highly sensitive and selective detection of tetracyclines (TCs) and quinolones (QNs), of which being the most frequently applied antibiotics. NIR emissive carbon-dots (NIR-CDs) that displayed broad excitation range, stable and strong emission were selected as label-free fluorophores. In the presence of TCs or QNs, efficient IFE from CDs to the two types of antibiotics occurred because of the good spectrum overlap between the ultraviolet absorption of TCs/QNs and the excitation of NIR-CDs. As a result, fluorescence emission of the NIR-CDs was obviously quenched, and this builds the foundation of TCs and QNs quantification. The probe provides linear detection ranges, i.e. 1-80 nM and 0.01-0.2 µM for oxytetracycline (OTC, an example for TCs) and norfloxacin (NOR, an example for QNs) with detection limits of 0.5 nM and 6.3 nM, respectively. To the best of our knowledge, this is the first report on NIR fluorescent probe for antibiotics assay. Moreover, the probe has been demonstrated to be very robust, and was successfully adopted for the determination of OTC and NOR in milk samples.