Characterization of Fiber-Optic Vector Magnetic Field Sensors Based on the Magneto-Strictive Effect.

Fiber-optic magnetic field sensors have garnered considerable attention in the field of marine monitoring due to their compact size, robust anti-electromagnetic interference capabilities, corrosion resistance, high sensitivity, ease of multiplexing and integration, and potential for large-scale sensing networks. To enable the detection of marine magnetic field vector information, we propose an optical fiber vector magnetic field sensor that integrates three single-axis sensors in an orthogonal configuration. Theoretical analysis and experimental verification are conducted to investigate its magnetic field and temperature sensing characteristics, and a sensitivity matrix is established to address the cross-sensitivity between the magnetic field and temperature; experimental tests were conducted to assess the vector response of the three-dimensional (3D) vector sensor across the three orthogonal axes; the obtained experimental results illustrate the commendable magnetic field vector response exhibited by the sensor in the orthogonal axes, enabling precise demodulation of vector magnetic field information. This sensor presents several advantages, including cost-effectiveness, easy integration, and reliability vectorially. Consequently, it holds immense potential for critical applications in marine magnetic field network detection.

Analgesia and stress attenuation of ultrasound-guided modified pectoral nerve block type-II with different volumes of 0.3% ropivacaine in patients undergoing modified radical mastectomy for breast cancer: A prospective parallel randomized double-blind controlled clinical trial.

WHAT IS KNOWN AND OBJECTIVE: A previous randomized clinical trial concluded that an optimal concentration of 0.3% ropivacaine could provide satisfactory analgesia for breast cancer patients undergoing modified radical mastectomy. We wondered if a smaller volume (30 ml vs. 40 ml) of 0.3% ropivacaine could still provide adequate analgesia in an ultrasound-guided PECS II block in modified radical mastectomy. METHODS: We performed a prospective parallel randomized double-blind controlled clinical trial. Eligible patients were assigned to either the P30 or P40 group (30 or 40 ml of 0.3% ropivacaine, respectively). The skin area of hypoesthesia, anaesthetic plane determined with ultrasound, pain visual analogue scale (VAS), anaesthetic dosages, and complications were recorded. Serum levels of interleukin-1ß and interleukin-6 were measured postoperatively. RESULTS AND DISCUSSION: A total of 40 patients completed the trials, with 20 patients in each group. Although the skin area of hypoesthesia and the anaesthetic planes were significantly larger in the P40 group compared with the P30 group (p < 0.05), the VAS, analgesic and opioid doses, serum cytokine levels, anaesthetic toxicity, and complications had no significant differences between the two groups. WHAT IS NEW AND CONCLUSION: Compared with 40 ml, 30 ml of 0.3% ropivacaine could provide adequate analgesia and reduce surgical stress in patients undergoing modified radical mastectomy for breast cancer.

High-Availability Computing Platform with Sensor Fault Resilience.

Modern computing platforms usually use multiple sensors to report system information. In order to achieve high availability (HA) for the platform, the sensors can be used to efficiently detect system faults that make a cloud service not live. However, a sensor may fail and disable HA protection. In this case, human intervention is needed, either to change the original fault model or to fix the sensor fault. Therefore, this study proposes an HA mechanism that can continuously provide HA to a cloud system based on dynamic fault model reconstruction. We have implemented the proposed HA mechanism on a four-layer OpenStack cloud system and tested the performance of the proposed mechanism for all possible sets of sensor faults. For each fault model, we inject possible system faults and measure the average fault detection time. The experimental result shows that the proposed mechanism can accurately detect and recover an injected system fault with disabled sensors. In addition, the system fault detection time increases as the number of sensor faults increases, until the HA mechanism is degraded to a one-system-fault model, which is the worst case as the system layer heartbeating.

Rehabilitative post-acute care for stroke patients delivered by per-diem payment system in different hospitalization paths: A Taiwan pilot study.

OBJECTIVE: To explore how post-acute care (PAC) for stroke patients delivered by per-diem payment system in varying hospitalization paths affects medical care utilization and functional status. DESIGN, SETTING AND PATIENTS: A longitudinal prospective cohort study of 181 acute stroke patients in a southern Taiwan hospital and patients were separated into two groups: patients transferred from regional hospitals (group 1) and patients referred from medical centers (group 2). INTERVENTION: The intervention was a hospital based, function oriented, 3- to 12-weeks rehabilitative PAC intervention for patients with cerebrovascular diseases. MEASUREMENTS: Barthal Index, Functional Oral Intake Scale, Instrumental Activities of Daily Living Scale, EuroQoL Quality of Life Scale, and Berg Balance Scale. RESULTS: The average duration between day of stroke onset and day of admission to PAC ward was significantly (P < 0.001) shorter in group 1 (9.88 days) compared to group 2 (17.11 days). The average duration of PAC was also significantly (P < 0.01) shorter in group 1 (25.51 days) compared to group 2 (34.11 days). Finally, the average cost of PAC under per-diem payment was significantly lower (P < 0.01) in group 1 (US$2637) compared to group 2 (US$3450). Functional status significantly (P < 0.05) improved in patients who had received rehabilitative PAC. However, functional status did not significantly differ between the two groups. CONCLUSIONS: The most effective way to reduce the costs of PAC for stroke patients is to minimize the duration of their hospital stay before transfer to rehabilitative PAC. Because it substantially reduces medical costs, rehabilitative PAC should be considered standard care for stroke patients.

Crystal structures of Staphylococcal SaeR reveal possible DNA-binding modes.

Two-component system SaeRS of Staphylococcus regulates virulence factor expression through phosphorylation of the DNA-binding regulator SaeR by the sensor histidine kinase SaeS. Here crystal structures of the DNA-binding domain (DBD) of SaeR from two Staphylococcal species Staphylococcus epidermidis and Staphylococcus aureus were determined and showed similar folds. Analyzing the DNA binding activity of three mutants of SeSaeR, we observed that Thr217 is important in binding to the phosphate group of DNA and Trp219 may interact with the base pairs. Additionally, the tandem arrangement of DBD may represent a possible way for SaeR oligomerization on DNA.

Mueller matrix polarimetry approach for noninvasive glucose sensing with absorbance and anisotropic parameters on fingertips.

A Mueller matrix polarimetry system at 532 nm wavelength is developed for noninvasive glucose sensing in turbid media such as human's fingertip. The system extracts mean absorbance and anisotropic properties, demonstrated numerically and experimentally with phantom glucose samples. It is found that mean absorbance ( A e $$ {A}_e $$ ), depolarization index (Δ), and linear dichroism (LD) show linear variation with glucose concentration 100-500 mg/dL. In addition, LightTools simulations indicate proportional scaling of scattering effects with A e $$ {A}_e $$ , Δ, and LD. Real-world tests on fingertip show a strong correlation between these properties and blood glucose levels with a mean absolute relative deviation (MARD) of 12.56% and a correlation coefficient (R2) of 0.875 in prediction by a neural network (NN) model, highlighting the advantages of Mueller matrix in extracting more parameters related to blood glucose.

A novel collision warning system based on the visual road environment schema: An examination from vehicle and driver characteristics.

Drivers pay unequal attention to different road environmental elements and visual fields, which greatly influences their driving behavior. However, existing collision warning systems ignore these visual characteristics of drivers, which limits the performance of collision warning systems. Therefore, this study proposes a novel collision warning system based on the visual road environment schema, in order to enhance the support for avoiding potential dangers in objects and areas that are easily overlooked by the drivers' vision. To capture the above visual characteristics of drivers, the visual road environment schema that consists of the semantic layer, the scene depth layer, the sensitive layer, and the visual field layer is established by using several different deep neural networks, which realizes the recognition, quantization, and analysis of the road environment from the drivers' visual perspective. The effectiveness of the novel collision warning system is verified by the driving simulation experiment from six indicators, including warning distance, maximum lateral acceleration, maximum longitudinal deceleration, minimum collision time, reaction time, and heart rate. Additionally, a grey target decision-making model is built to comprehensively evaluate the system. The results show that compared with the traditional collision warning system, the novel collision warning system proposed in this study performs significantly better and can discover potential dangers earlier, give timely warnings, enhance the vehicles' lateral stability and driving comfort, shorten reaction time, and relieve the drivers' nervousness. By integrating the drivers' visual characteristics into the collision warning system, this study could help to optimize the existing collision warning system and promote the mutual understanding between intelligent vehicles and human drivers.

An interpretable prediction model of illegal running into the opposite lane on curve sections of two-lane rural roads from drivers' visual perceptions.

Illegal running into the opposite lane (IROL) on curve sections of two-lane rural roads is a frequently hazardous behavior and highly prone to fatal crashes. Although driving behaviors are always determined by the information from drivers' visual perceptions, current studies do not consider visual perceptions in predicting the occurrence of IROL. In addition, most machine learning methods belong to black-box algorithms and lack the interpretation of prediction results. Therefore, this study aims to propose an interpretable prediction model of IROL on curve sections of two-lane rural roads from drivers' visual perceptions. A new visual road environment model, consisting of five different visual layers, was established to better quantify drivers' visual perceptions by using deep neural networks. In this study, naturalistic driving data was collected on curve sections of typical two-lane rural roads in Tibet, China. There were 25 input variables extracted from the visual road environment, vehicle kinematics, and driver characteristics. Then, XGBoost (eXtreme Gradient Boosting) and SHAP (SHapley Additive exPlanation) methods were combined to build a prediction model. The results showed that our prediction model performed well, with an accuracy of 86.2% and an AUC value of 0.921. The average lead time of this prediction model was 4.4 s, sufficient for drivers to respond. Due to the advantages of SHAP, this study interpreted the impacting factors on this illegal behavior from three aspects, including relative importance, specific impacts, and variable dependency. After offering more quantitative information on the visual road environment, the findings of this study could improve the current prediction model and optimize road environment design, thereby reducing IROL on curve sections of two-lane rural roads.

Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach.

Influencing factors on crash severity involved with autonomous vehicles (AVs) have been paid increasing attention. However, there is a lack of comparative analyses of those factors between AVs and human-driven vehicles. To fill this research gap, the study aims to explore the divergent effects of factors on crash severity under autonomous and conventional (i.e., human-driven) driving modes. This study obtained 180 publicly available autonomous vehicle crash data, and 39 explanatory variables were extracted from three categories, including environment, roads, and vehicles. Then, a hierarchical Bayesian approach was applied to analyze the impacting factors on crash severity (i.e., injury or no injury) under both driving modes with considering unobserved heterogeneities. The results showed that some influencing factors affected both driving modes, but their degrees were different. For example, daily visitors' flowrate had a greater impact on the crash severity under the conventional driving mode. More influencing factors only had significant impacts on one of the driving modes. For example, in the autonomous driving mode, mixed land use increased the severity of crashes, while daytime had the opposite effects. This study could contribute to specifying more appropriate policies to reduce the crash severity of both autonomous and human-driven vehicles especially in mixed traffic conditions.

Comparative Analysis of Influencing Factors on Crash Severity between Super Multi-Lane and Traditional Multi-Lane Freeways Considering Spatial Heterogeneity.

With the growth of traffic demand, the number of newly built and renovated super multi-lane freeways (i.e., equal to or more than a two-way ten-lane) is increasing. Compared with traditional multi-lane freeways (i.e., a two-way six-lane or eight-lane), super multi-lane freeways have higher design speeds and more vehicle interweaving movements, which may lead to higher traffic risks. However, current studies mostly focus on the factors that affect crash severity on traditional multi-lane freeways, while little attention is paid to those on super multi-lane freeways. Therefore, this study aims to explore the impacting factors of crash severity on two kinds of freeways and make a comparison with traditional multi-lane freeways. The crash data of the Guangzhou-Shenzhen freeway in China from 2016 to 2019 is used in the study. This freeway contains both super multi-lane and traditional multi-lane road sections, and data on 2455 crashes on two-way ten-lane sections and 13,367 crashes on two-way six-lane sections were obtained for further analysis. Considering the effects of unobserved spatial heterogeneity, a hierarchical Bayesian approach is applied. The results show significant differences that influence the factors of serious crashes between these two kinds of freeways. On both two types of freeways, heavy-vehicle, two-vehicle, and multi-vehicle involvements are more likely to lead to serious crashes. Still, their impact on super multi-lane freeways is much stronger. In addition, for super multi-lane freeways, vehicle-to-facility collisions and rainy weather can result in a high possibility of serious crashes, but their impact on traditional multi-lane freeways are not significant. This study will contribute to understanding the impacting factors of crash severity on super multi-lane freeways and help the future design and safety management of super multi-lane freeways.

A three-dimensional mixotrophic model of Karlodinium veneficum blooms for a eutrophic estuary.

Blooms of dinoflagellate Karlodinium veneficum are widely distributed in estuarine and coastal waters and have been found to cause fish kills worldwide. K. veneficum has a mixed nutritional mode and relies on both photosynthesis and phagotrophy for growth; it is a mixotroph. Here, a model of mixotrophic growth of K. veneficum (MIXO) was developed, calibrated with previously-reported laboratory physiological data, and subsequently embedded in a 3D-coupled hydrodynamic (ROMS)-biogeochemical (RCA) model of eutrophic Chesapeake Bay, USA. The resulting ROMS-RCA-MIXO model was applied in hindcast mode to investigate seasonal and spatial distributions. Simulations showed that K. veneficum blooms occurred during June-August and were confined to the upper and middle Bay, consistent with long-term field observations. Autotrophic growth dominated in spring but heterotrophic growth dominated during the summer. The number of prey ingested by K. veneficum varied from 0.1 to 0.6 day-1 and the food vacuole content reached up to 50% of the core mixotroph biomass. The ingestion rate increased with prey density and also when P:N ratio fell below ∼0.03 (N:P ∼ 33), indicating that K. veneficum only switched to mixotrophic feeding in P-deficient waters when sufficient prey were available; this occurred during the summer months. The digestion rate increased with both the food vacuole content and temperature. The modeling analysis affirms K. veneficum as a phagotrophic 'alga' which is primarily photosynthetic but switches to mixotrophic feeding under nutrient deficient conditions.

Field-Free Magnetoplasmon-Induced Ultraviolet Circular Dichroism Switching in Premagnetized Magnetic Nanowires.

Broadband modulation of magnetic circular dichroism (MCD) using a relatively low magnetic field or by producing a field-free magnetoplasmonic effect in the remnant magnetic state was achieved by the integration of the noble metals (NMs) Au and Ag and the perpendicular magnetic anisotropy of Co with ZnO nanowires (NWs) used as the template. The samples containing NMs revealed MCD sign reversals and enhancements when compared with the original Co/ZnO NWs. The magnetoplasmonic effect of Au close to the visible light spectrum could induce the CD change in the visible region. Notably, the ultraviolet (UV) CD in Ag/Co/ZnO NWs is 12.5 times larger under a magnetic field (∼0.2 T) and 10 times greater in the remnant state (field-free) than those of the original Co/ZnO NWs because of the magnetoplasmonic effect of Ag in the UV spectrum. These results are attributable to the coupling of the remnant magnetic state of Co magnetization, the magnetoplasmons of the NMs, and the excitons of the ZnO NWs. The findings are potentially applicable in magneto-optical recording, biosensing, and energy contexts involving magnetoplasmonic functionalization.

Impact of preoperative carbohydrate loading on gastric volume in patients with type 2 diabetes.

BACKGROUND: Enhanced recovery after surgery advocates that consuming carbohydrates two hours before anesthesia is beneficial to the patient's recovery. Patients with diabetes are prone to delayed gastric emptying. Different guidelines for preoperative carbohydrate consumption in patients with diabetes remain controversial due to concerns about the risk of regurgitation, aspiration and hyperglycemia. Ultrasonic gastric volume (GV) assessment and blood glucose monitoring can comprehensively evaluate the safety and feasibility of preoperative carbohydrate intake in type 2 diabetes (T2D) patients. AIM: To evaluate the impact of preoperative carbohydrate loading on GV before anesthesia induction in T2D patients. METHODS: Patients with T2D receiving surgery under general anesthesia from December 2019 to December 2020 were included. A total of 78 patients were randomly allocated to 4 groups receiving 0, 100, 200, or 300 mL of carbohydrate loading 2 h before anesthesia induction. Gastric volume per unit weight (GV/W), Perlas grade, changes in blood glucose level, and risk of reflux and aspiration were evaluated before anesthesia induction. RESULTS: No significant difference was found in GV/W among the groups before anesthesia induction (P > 0.05). The number of patients with Perlas grade II and GV/W > 1.5 mL/kg did not differ among the groups (P > 0.05). Blood glucose level increased by > 2 mmol/L in patients receiving 300 mL carbohydrate drink, which was significantly higher than that in groups 1 and 2 (P < 0.05). CONCLUSION: Preoperative carbohydrate loading < 300 mL 2 h before induction of anesthesia in patients with T2D did not affect GV or increase the risk of reflux and aspiration. Blood glucose levels did not change significantly with preoperative carbohydrate loading of < 200 mL. However, 300 mL carbohydrate loading may increase blood glucose levels in patients with T2D before induction of anesthesia.

Oncogenesis induced by combined Phf6 and Idh2 mutations through increased oncometabolites and impaired DNA repair.

The pathogenesis of acute leukemia involves interaction among genetic alterations. Mutations of IDH1/2 and PHF6 are common and co-exist in some patients of hematopoietic malignancies, but their cooperative effects remain unexplored. In this study, we addressed the question by characterizing the hematopoietic phenotypes of mice harboring neither, Phf6 knockout, Idh2 R172K, or combined mutations. We found that the combined Phf6KOIdh2R172K mice showed biased hematopoietic differentiation toward myeloid lineages and reduced long-term hematopoietic stem cells. They rapidly developed neoplasms of myeloid and lymphoid lineages, with much shorter survival compared with single mutated and wild-type mice. The marrow and spleen cells of the combined mutated mice produced a drastically increased amount of 2-hydroxyglutarate compared with mice harboring Idh2 R172K. Single-cell RNA sequencing revealed distinct patterns of transcriptome of the hematopoietic stem/progenitor cells from the combined mutated mice, including aberrant expression of metabolic enzymes, increased expression of several oncogenes, and impairment of DNA repairs, as confirmed by the enhanced γH2AX expression in the marrow and spleen cells. We conclude that Idh2 and Phf6 mutations are synergistic in leukemogenesis, at least through overproduction of 2-hydroxyglutarate and impairment of DNA repairs.

Electrophysiologic evidence of spinal accessory neuropathy in patients with cervical myofascial pain syndrome.

OBJECTIVE: To evaluate whether or not spinal accessory neuropathy exists in patients with cervical myofascial pain syndrome (MFPS). DESIGN: Prospective study. SETTING: A neurophysiologic laboratory in a university hospital. PARTICIPANTS: Patients with cervical MFPS (n=25) and healthy controls (n=20). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: We performed nerve conduction studies (NCSs) in bilateral spinal accessory nerves, and electromyography and stimulated single-fiber electromyography in the trapezius muscles of all patients and controls. Parameters including nerve conduction velocities (NCVs), amplitudes and areas of compound muscle action potentials (CMAPs), and mean consecutive differences (MCDs) in single-fiber electromyography were measured, analyzed, and compared with the disease durations of the patients. RESULTS: Spinal accessory NCSs showed normative NCVs but with prominently reduced CMAP amplitude in the patients with cervical MFPS, which is recognized as an axonal neuropathy of the spinal accessory nerves. Electromyography showed prominent evidence of denervation and reinnervation patterns in 48% of the MFPS patients. The abnormal MCDs in single-fiber electromyography indicated a synaptic delay of motor endplates in the motor units, and may signify evolving instability of neuromuscular transmission in the spinal accessory nerves innervating trapezius muscles of the patients. CONCLUSIONS: This study demonstrates electrophysiologic evidence of neuroaxonal degeneration and neuromuscular transmission disorder in a significant proportion of patients with cervical MFPS. We suggest that spinal accessory neuropathy may be associated with cervical MFPS.

Effects of Energy Parameters on Dimensional Accuracy When Joining Stainless-Steel Powders with Heterogeneous Metal Substrates.

This work presents some breakthroughs for obtaining high dimensional accuracy and reliable geometrical tolerance in the joining of stainless-steel powders with heterogeneous substrates. In the laser melting process, the interfacial energy fractions and forces acting at the solid-liquid surface of the melting powders can effectively vary their geometrical shapes and positions before they turn into the liquid phase. When the interfacial free energy is low, the melting powders are near molten, thus the successive volumetric changes can alter the layered geometry and positions. This assumption was validated by a powder-bedding additive manufacturing process to consolidate stainless-steel 316L powders (SLM 316L) on a thin heterogeneous stainless-steel substrate. Experiments were carried out to reveal the effects of the process parameters, such as laser power (100-200 W), exposure duration (50-100 µs) and point distance (35-70 µm) on the resulting material density and porosity and the corresponding dimensional variations. A fractional factorial design of experiment was proposed and the results of which were analyzed statistically using analysis of variances (ANOVA) to identify the influence of each operating factor. High energy densities are required to achieve materials of high density (7.71 g/cm3) or low porosity (3.15%), whereas low energy densities are preferable when the objective is dimensional accuracy (0.016 mm). Thermally induced deflections (~0.108 mm) in the heterogeneous metal substrate were analyzed using curvature plots. Thermally induced deformations can be attributed to volumetric energy density, scanning strategy, and the lay-up orientation. The parametric optimizations for increasing in dimensional accuracy (Z1: ~0.105 mm), or in material density (~7.71 g/cm3) were proven with high conversion rates of 88.2% and 96.4%, respectively, in validation runs.

Effects of an integrated collision warning system on risk compensation behavior: An examination under naturalistic driving conditions.

Collision warning systems can improve traffic safety, while their safety benefit may be lessened due to improper risk compensation or system misuse. There are limited studies of advanced safety systems increasing unexpected risky driving behavior, especially with adolescent drivers. This study is designed to address this research gap in two main areas: 1) it seeks to examine whether and how the introduction of advanced driver-assistance systems influences drivers' risk compensation behavior (e.g., increase of hard braking frequency), and 2) it investigates key factors (e.g., distraction) that contribute to changes in hard braking frequency during driving for both teen and adult drivers. Naturalistic driving data from two previous studies were analyzed in this study with two methods: a hierarchical logistic regression model was used to evaluate the effects of an integrated collision warning system on hard braking behavior, while a Random forests algorithm was applied to model hard braking behavior and to rank the contributing factors by calculating the importance scores. No statistical evidence was observed that the integrated collision warning system significantly changed the likelihood of hard braking for teen or adult drivers. Other factors like distraction, especially visual-manual distraction, had the largest impact on the hard braking behavior, followed by speeding and roadway segments (i.e., at intersections or not). Short time-headways and driving in high-density traffic significantly increased the likelihood of hard braking. Furthermore, the rate of hard braking behavior on surface roads was much higher than on highways, as expected. Compared with straight road segments, hard braking behavior was less likely to occur on curve roads. This study applied an analytical strategy by using both machine learning and statistical analysis methods to achieve high model accuracy and facilitate inference concerning the relationships among variables. Findings in this study can help to improve the design of integrated collision warning systems and the use of autonomous braking systems, and to apply appropriate analysis methods in understanding teen drivers' behavior changes with those safety systems.

Quantifying the Effects of Visual Road Information on Drivers' Speed Choices to Promote Self-Explaining Roads.

Roads should deliver appropriate information to drivers and thus induce safer driving behavior. This concept is also known as "self-explaining roads" (SERs). Previous studies have demonstrated that understanding how road characteristics affect drivers' speed choices is the key to SERs. Thus, in order to reduce traffic casualties via engineering methods, this study aimed to establish a speed decision model based on visual road information and to propose an innovative method of SER design. It was assumed that driving speed is determined by road geometry and modified by the environment. Lane fitting and image semantic segmentation techniques were used to extract road features. Field experiments were conducted in Tibet, China, and 1375 typical road scenarios were picked out. By controlling variables, the driving speed stimulated by each piece of information was evaluated. Prediction models for geometry-determined speed and environment-modified speed were built using the random forest algorithm and convolutional neural network. Results showed that the curvature of the right boundary in "near scene" and "middle scene", and the density of roadside greenery and residences play an important role in regulating driving speed. The findings of this research could provide qualitative and quantitative suggestions for the optimization of road design that would guide drivers to choose more reasonable driving speeds.

Quantifying visual road environment to establish a speeding prediction model: An examination using naturalistic driving data.

Speeding is one of the major contributors to traffic crashes. To solve this problem, speeding prediction is recognized as a critical step in a pre-warning system. While previous studies have shown that speeding is affected by road environmental design, research in predicting speeding behavior through road environment features has not yet been conducted. Furthermore, there is a large discrepancy between actual and perceived road environmental information given that a driver's visual perception plays a crucial role as the dominant source of information in determining driver's behavior. Thus, this paper aims to establish a speeding prediction model based on quantifying the visual road environment to improve the design of pre-waring systems, which can predict whether drivers are going to speed and provide them with visual or/and audio warnings about their current driving speed and the speed limit prior to the occurrence of speeding behavior. Twenty input variables derived from three categories including visual road environment parameters, vehicle kinematic features, and driver characteristics were considered in the proposed speeding prediction model. Especially, the road environmental design factors consisting of the visual road geometry and visual roadside environment as perceived by the driver's eyes were quantified using a visual road environment model. Field experiments were conducted to collect naturalistic driving data concerning speeding behavior on the typical two-lane mountainous rural highways in five provinces of China. Random Forests, an ensemble learning method for regression and classification, were applied to build the speeding prediction model and variable importance was calculated. Additionally, logistic regression was used as a supplement to further investigate factors impacting on speeding behavior. A speeding criterion was defined with two levels in this study: a lower level (exceeding the posted speed limit) and a higher level (10% above the posted speed limit). Under both levels of the speeding criterion, the speeding prediction model performed well with high accuracy (over 85%). This model could use the value of the variables obtained from the current position to predict drivers' speeding behavior at the future position located a sighting distance away. This interval was sufficient for a pre-warning system to give a speeding warning that a driver with normal perception-reaction time (around 2.5 s) could respond to. Findings in this study can be used to effectively predict speeding in advance and help to reduce speeding-related traffic accidents.

Middle-aged Drivers' subjective categorization for combined alignments on mountainous freeways and their speed choices.

Road geometric design is a fundamental factor that impacts driving speed. Previous research generally paid attentions to the influences of specific road characteristics (e.g. curvature) on driving behaviors. Limited studies have focused on how drivers identify different alignments and how they further take the varying speed choices. This study aims at filling the gap by investigating the subjective categorization of road alignments based on middle-aged driver groups. A total of sixteen participants with ages ranging from 23 to 40 years were recruited. Participants were first asked to undertake naturalistic driving tests on a four-lane divided mountainous freeway while photos of the road and the driving speed were collected. Participants were then asked to subjectively sort the photos of the road into piles, within each pile we considered their driving behaviors would be similar. Finally, questionnaire survey was conducted in terms of comfort, safety, speed choice and sight distance. The picture grouping revealed three distinct and non-overlapping subjective categories of road alignment. And driver's ratings about comfort and safety were significantly different between these categories. The category with the largest sight distance and highest speed choice turned out to have the lowest rating in comfort and safety (note that the rating scales for comfort and safety had reversed polarity such that low numbers indicated high comfort and high safety). Statistical evidences indicated that the drivers have developed underlying mental schema about road alignment. Therefore, their speed choices on combined alignment were further investigated. The difference between actual driving speed and driver's expected speed showed close relation to the ratings and significant difference between two of the categories. Road with large absolute value in speed difference informed inconsistency between geometric design and driver's expectation from the aspects of drivers' perception and expectation of the road. The findings provided insight into how middle-aged driver views and categorizes road alignment. And it was found that the drivers relied on visual characteristics of the alignment to distinguish the categories instead of separate horizontal and vertical geometric parameters. It was implied that more considerations should be taken into driver's perception of road during alignment design to improve road safety.