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The formation of solid electrolyte interphase (SEI) and lithium ion intercalation are two crucial processes in lithium ion batteries. Given the complexity and challenges involved in investigating real batteries, a combination of model batteries comprising well-defined ultrathin graphite electrodes with surface-sensitive techniques can provide valuable information on these processes. Here, a comparative study of the performance of batteries using regular- and high-concentration electrolytes (RCE and HCE) is presented, aided by a myriad of correlative surface science techniques. It is found that the SEI formed in HCE possesses a thinner organic layer and more inorganic substances. Such an effective SEI protects the graphite electrode and facilitates the processes of Li+ intercalation/deintercalation, thereby improving the reversibility and cycling stability of the battery, superior to those with RCE. Moreover, the battery performance can be readily tuned by switching the electrolytes. The good cycling stability of the battery can still be maintained in RCE after the preformation of a robust SEI on the ultrathin graphite electrode in HCE. This work highlights the importance of electrolyte-modulated SEI formation to battery performance and provides powerful platforms to comprehensively study the SEI formation and ion intercalation processes. [ABSTRACT FROM AUTHOR]

In our prior investigation, we discerned loss-of-function variants within the gene encoding glutamine-rich protein 2 (QRICH2) in two consanguineous families, leading to various morphological abnormalities in sperm flagella and male infertility. The Qrich2 knockout (KO) in mice also exhibits multiple morphological abnormalities of the flagella (MMAF) phenotype with a significantly decreased sperm motility. However, how ORICH2 regulates the formation of sperm flagella remains unclear. Abnormal glutamylation levels of tubulin cause dysplastic microtubules and flagella, eventually resulting in the decline of sperm motility and male infertility. In the current study, by further analyzing the Qrich2 KO mouse sperm, we found a reduced glutamylation level and instability of tubulin in Qrich2 KO mouse sperm flagella. In addition, we found that the amino acid metabolism was dysregulated in both testes and sperm, leading to the accumulated glutamine (Gln) and reduced glutamate (Glu) concentrations, and disorderly expressed genes responsible for Gln/Glu metabolism. Interestingly, mice fed with diets devoid of Gln/Glu phenocopied the Qrich2 KO mice. Furthermore, we identified several mitochondrial marker proteins that could not be correctly localized in sperm flagella, which might be responsible for the reduced mitochondrial function contributing to the reduced sperm motility in Qrich2 KO mice. Our study reveals a crucial role of a normal Gln/Glu metabolism in maintaining the structural stability of the microtubules in sperm flagella by regulating the glutamylation levels of the tubulin and identifies Qrich2 as a possible novel Gln sensor that regulates microtubule glutamylation and mitochondrial function in mouse sperm. [ABSTRACT FROM AUTHOR]

The mechanistic understanding of electrochemical CO2 reduction reaction (CO2RR) requires a rapid and accurate characterisation of product distribution to unravel the activity and selectivity, which is yet hampered by the lack of advanced correlative approaches. Here, we present the time‐resolved identification of CO2RR products by using the synchronised electrochemistry‐mass spectrometry (sEC‐MS). Transients in product formation can be readily captured in relation to electrochemical conditions. Moreover, a soft ionisation (SI) strategy is developed in MS for the direct observation of CO, immune to the interference of CO2 fragments. With the sEC‐MS‐SI, the kinetic information, such as Tafel slopes and onset potentials, for a myriad of CO2RR products are revealed and we show the hysteresis seen for the evolution of some species may originate from the potential‐driven changes in surface coverage of intermediates. This work provides a real‐time picture of the dynamic formation of CO2RR products. [ABSTRACT FROM AUTHOR]

The mechanistic understanding of electrochemical CO2 reduction reaction (CO2RR) requires a rapid and accurate characterisation of product distribution to unravel the activity and selectivity, which is yet hampered by the lack of advanced correlative approaches. Here, we present the time‐resolved identification of CO2RR products by using the synchronised electrochemistry‐mass spectrometry (sEC‐MS). Transients in product formation can be readily captured in relation to electrochemical conditions. Moreover, a soft ionisation (SI) strategy is developed in MS for the direct observation of CO, immune to the interference of CO2 fragments. With the sEC‐MS‐SI, the kinetic information, such as Tafel slopes and onset potentials, for a myriad of CO2RR products are revealed and we show the hysteresis seen for the evolution of some species may originate from the potential‐driven changes in surface coverage of intermediates. This work provides a real‐time picture of the dynamic formation of CO2RR products. [ABSTRACT FROM AUTHOR]

Machine learning has played transformative roles in numerous chemical and biophysical problems such as protein folding where large amount of data exists. Nonetheless, many important problems remain challenging for data-driven machine learning approaches due to the limitation of data scarcity. One approach to overcome data scarcity is to incorporate physical principles such as through molecular modeling and simulation. Here, we focus on the big potassium (BK) channels that play important roles in cardiovascular and neural systems. Many mutants of BK channel are associated with various neurological and cardiovascular diseases, but the molecular effects are unknown. The voltage gating properties of BK channels have been characterized for 473 site-specific mutations experimentally over the last three decades; yet, these functional data by themselves remain far too sparse to derive a predictive model of BK channel voltage gating. Using physics-based modeling, we quantify the energetic effects of all single mutations on both open and closed states of the channel. Together with dynamic properties derived from atomistic simulations, these physical descriptors allow the training of random forest models that could reproduce unseen experimentally measured shifts in gating voltage, ∆V1/2, with a RMSE ~ 32 mV and correlation coefficient of R ~ 0.7. Importantly, the model appears capable of uncovering nontrivial physical principles underlying the gating of the channel, including a central role of hydrophobic gating. The model was further evaluated using four novel mutations of L235 and V236 on the S5 helix, mutations of which are predicted to have opposing effects on V1/2 and suggest a key role of S5 in mediating voltage sensor-pore coupling. The measured ∆V1/2 agree quantitatively with prediction for all four mutations, with a high correlation of R = 0.92 and RMSE = 18 mV. Therefore, the model can capture nontrivial voltage gating properties in regions where few mutations are known. The success of predictive modeling of BK voltage gating demonstrates the potential of combining physics and statistical learning for overcoming data scarcity in nontrivial protein function prediction. Author summary: Deep machine learning has brought many exciting breakthroughs in chemistry, physics and biology. These models require large amount of training data and struggle when the data is scarce. The latter is true for predictive modeling of the function of complex proteins such as ion channels, where only hundreds of mutational data may be available. Using the big potassium (BK) channel as a biologically important model system, we demonstrate that a reliable predictive model of its voltage gating property could be derived from only 473 mutational data by incorporating physics-derived features, which include dynamic properties from molecular dynamics simulations and energetic quantities from Rosetta mutation calculations. We show that the final random forest model captures key trends and hotspots in mutational effects of BK voltage gating, such as the important role of pore hydrophobicity. A particularly curious prediction is that mutations of two adjacent residues on the S5 helix would always have opposite effects on the gating voltage, which was confirmed by experimental characterization of four novel mutations. The current work demonstrates the importance and effectiveness of incorporating physics in predictive modeling of protein function with scarce data. [ABSTRACT FROM AUTHOR]

An organic fluorescent probe, citric acid-1,3-Propanediamine-Rhodamine B (CPR) was synthesized to determine pH of ordinary Portland cement requiring only a small sample size (less than 500 μL cement leachate). The SEM, XRD, and FTIR investigations demonstrate that citric acid-1,3-Propanediamine are polymer dots with a fusiform structure. Ratio pH probe is constructed by rhodamine B with polymer dots, which exhibits a linear response in high alkaline range. A 6-fold increase in fluorescence intensity (455 nm) is achieved at pH from 12.00 to 13.25. Combined with measurements of the isothermal calorimeter, mineral composition, and microscopic morphology, variation of pH is used to evaluate the changes of components during hydration. Furthermore, CPR can be applied to measure pH of high-dose pulverized fuel ash blending systems, the non-pure cement with slightly lower alkalinity. [ABSTRACT FROM AUTHOR]

Flexible job-shop scheduling is considerably important in the modern intelligent manufacturing factory. In a real job shop, transportation and setup times account for a large percentage of the total processing flow, and with today's companies demanding higher delivery times, the feasibility and punctuality of scheduling will be considerably reduced if these time constraints are ignored. Recently, several companies have become green in their manufacturing processes. However, transportation, setup, and delivery times have rarely been combined with energy efficiency. To solve this problem, we employed an integer programming approach to develop a complete mathematical model of the problem and simultaneously optimized four objectives: maximum completion time, total energy consumption, workload of critical machines, and penalties for earliness/tardiness. Subsequently, an evolutionary algorithm incorporating reinforcement learning was proposed to solve the model. The algorithm had the following features: (1) four initialization strategies were designed to obtain high-quality populations; (2) a reinforcement learning-based parameter-adaptive strategy was proposed to guide the population to select the best parameters; (3) a critical path-based neighborhood structure with transportation and setup times was designed, and according to the objectives of this study, four additional neighborhood structures were designed; (4) a reference point-based non-dominated sorting selection was presented to guide the solution toward the Pareto-optimal front; and (5) an external archive was proposed to enhance the utilization of abandoned historical solutions. Finally, the effectiveness of this algorithm was demonstrated using 33 benchmark instances of variants and comparison experiments. [ABSTRACT FROM AUTHOR]

Reductive homocoupling transformations mediated by bases and TMSCN have been used as a strategy for synthesis of 1,2‐disubstituted ethanones. This protocol provides a method for synthesizing functionalized ethanones from aldehydes in one pot without transition metals. [ABSTRACT FROM AUTHOR]

Moisture has a significant effect on the properties of concrete, and drying concrete in an oven is a common method to obtain specimens with different moisture content. In this paper, C30 concrete specimens with different sizes were oven-dried at constant temperatures of 60, 80, 105, 120, and 150 °C, respectively. The change of specimen mass, ultrasonic testing, and compressive and splitting tensile strength were tested before and after drying. The size effect and the size effect law (SEL) of concrete were analysed. The results indicated that the maximum drying rate and water loss ratio of the specimens with different sizes under the same drying temperature are basically the same. The larger the size, the longer the drying time. After drying, the concrete compressive and splitting tensile strength have increased to varying degrees, and the smaller the size, the greater the increase. For C30 concrete with different sizes, the drying temperature that has the minimal influence is 105 °C. The SEL was modified by introducing drying temperature, and the formulas were obtained after drying. [ABSTRACT FROM AUTHOR]

Abrus cantoniensis Hance (ACH), known as Jigucao (Chinese: 鸡骨草) has been used in ethnopharmacology for a long history with therapeutic effects for clearing heat, soothing the liver, especially in treating acute and chronic hepatitis which was very effective. In southern China, such as Guangdong and Guangxi, people often use ACH in soup or herbal tea as dietetic therapy. This paper aims to review ACH's ethnopharmacology, phytochemistry, and pharmacological activity systematically, at the same time, we also hope to provide more research avenues between traditional uses and pharmacological properties. Through PubMed, Wan Fang Database, CNKI, Web of Science, EBSCO Database, and Google Scholar search for relevant literature in both Chinese and English, the keywords " Abrus cantoniensis , Abrus cantoniensis Hance, Jigucao, pharmacology, chemical constituents, clinical application, network pharmacology" were used alone or combination. Traditionally, ACH was believed to have the effect of soothing the liver, clearing heat, and detoxifying, often used to treat diseases of the liver and inflammation. Modern pharmacological research indicates that ACH has liver protection, anti-inflammation, anti-oxidant, immunomodulation, anti-tumor effects and so on. Whether it was a single chemical compound or an extract from ACH, studies have found that it has abundant pharmacological activities, these were the fundamental sources of traditional uses, like liver protection and anti-inflammation. A systematic review found that modern phytochemistry and pharmacodynamic research reports on ACH are closely related to its traditional uses, especially its hepatoprotective and anti-inflammatory effects. Modern research has also further explored and expanded the effects of ACH, such as its anti-tumor effect. And all these efforts are gradually filling the gap between traditional uses and modern pharmacology. In general, the current research on the pharmacodynamic mechanism of ACH still needs further in-depth research, and the strategies adopted must also be further strengthened. [Display omitted] [ABSTRACT FROM AUTHOR]

Aphrocallistes vastus lectin (AVL) is a Ca2+ dependent C-type lectin produced by sponges. Previous studies have demonstrated that oncolytic vaccinia virus harboring AVL (oncoVV-AVL) effectively triggers cell death in various tumors. However, the effects of oncoVV-AVL on human ovarian cancer (OV) remain unknown. This study aims to investigate the mechanism-of-action of oncoVV-AVL in human OV cell lines and in tumor-bearing nude mice. We found that oncoVV-AVL could directly induce apoptosis and autophagy in ovarian cancer cells. Additionally, our results showed that oncoVV-AVL increased the serum levels of mouse IFN-γ (mIFN-γ), leading to the activation of M1-polarized macrophages. Conversely, NADPH, a reducing agent by providing reducing equivalents, reduced the production of mIFN-γ, and suppressed M1-polarization of macrophage. Based on these findings, we propose that oncoVV-AVL not only contributes to direct cytolysis, but also enhances host immune response by promoting ROS levels. [Display omitted] • OncoVV-AVL initiates apoptosis and autophagy in ovarian cancer cells. • OncoVV-AVL elevates the serum level of IFN-γ (mIFN-γ) in mice and stimulates the activation of M1 macrophages. • The function of oncoVV-AVL is associated with the level of ROS. What's new : In this study, we found that oncoVV-AVL has the ability to directly induce cell apoptosis and autophagy in human ovarian cancer cells. Furthermore, oncoVV-AVL was found to elevate the levels of mouse IFN-γ (mIFN-γ) in serum, resulting in the activation of M1-polarized macrophages. Conversely, NADPH was observed to decrease mIFN-γ levels and inhibit the differentiation of M1-polarization. These findings suggest that oncoVV-AVL not only facilitates direct oncolysis, but also boosts the host immune response by enhancing ROS levels. [ABSTRACT FROM AUTHOR]

Satellite mission planning is the basis and top-level work of space missions and the beginning of each space mission. Therefore, the scientific research of satellite mission planning is very important. By analyzing the existing research results, we can know that the research on task planning mainly focuses on three aspects: research objects, established model, and solution algorithm. Starting from these three aspects vertically and then horizontally, this paper comprehensively discusses the theoretical basis, application, and advantages and disadvantages of related technologies in the research literature in recent years. Finally, based on the research on satellite mission planning, this paper puts forward its own views on the future development direction and research focus. [ABSTRACT FROM AUTHOR]

In the practical production, the transportation of jobs is existed between different machines. These transportation operations directly affect the production cycle and the production efficiency. In this study, an improved memetic algorithm is proposed to solve the flexible job shop scheduling problem with transportation times, and the optimization objective is minimizing the makespan. In the improved memetic algorithm, an effective simulated annealing algorithm is adopted in the local search process, which combines the elite library and mutation operation. All the feasible solutions are divided into general solutions and local optimal solutions according to the elite library. The general solutions are executed by the simulated annealing algorithm to improve the quality, and the local optimal solutions are executed by the mutation operation to increase the diversity of the solution set. Comparison experiments with the improved genetic algorithm show that the improved memetic algorithm has better search performance and stability. [ABSTRACT FROM AUTHOR]

As a new swarm intelligence algorithm, sparrow search algorithm (SSA) has the advantages of fewer parameters, simplicity, strong global and local search capability, and has been successfully applied in continuous problem and its engineering applications. Meanwhile, SSA for job shop scheduling problem (JSP) is studied rarely and would arise new problems such as conversion from continuous space to discrete space, falling into local optimum, etc. To address these issues, considering the features of SSA and JSP, the multi-strategy improved sparrow search algorithm (MISSA) is devised to solve minimum makespan of JSP. First, the operation sort based encoding transformation method of SSA for discrete problems is devised. Second, tent chaotic mapping is instead of random generation to initialize sparrow population to expand space of solution. Third, the crossover operation of genetic algorithm is introduced in producers and scroungers positions updating to maintain the population diversity and improve the algorithm search ability. Fourth, the mutation operation of genetic algorithm is adopted in the position update of the vigilance to enhance the local searching capability. Fifth, the simulated annealing algorithm was adopted to avoid the local optimal solution and reach the global best solution. In the end, using 10 classical examples of JSP and one practical scheduling example, comparisons of MISSA with other algorithms are simulated, and the results show that MISSA effectively solves JSP. [ABSTRACT FROM AUTHOR]

In order to find natural active substance alternative to the chemical fungicides currently used against the devastating Curvularia leaf spot of rice, the bioactivity of aloesin and aloe gel against Curvularia lunata was determined. The filter paper method showed that 300 mg/mL aloesin led to an obvious inhibitory band (3.37 mm) at 28 h, and a inhibitory zone (Φ = 51.27 mm) against spores (3 × 104 cfu/mL), and these inhibitory effects remained until 6 d. The mycelial inhibition rate by aloe gel at 1, 2, 4, 8, 16, 32 and 64 mg/mL ranged from 9.25% to 56.21% at 5 d, and it increased with increased concentration. Additionally, the inhibition rate of 64 mg/mL aloe gel increased by 21.87% from 2 to 5 d. The mycelial growth of C. lunata treated with 160 mg/mL aloesin was weaker than control, and no appressorium produced. The germ tube of C. lunata treated with aloe gel was much slower than control, and the inhibition rate was 75.39%-96.58%. This study was first reported that aloesin effectively inhibited mycelia growth and conidia germination of C. lunata, and aloe gel effectively inhibited mycelia growth of C. lunata. [ABSTRACT FROM AUTHOR]

Background: The mutations of KCNMA1 BK‐type K+ channel have been identified in patients with various movement disorders. The underlying pathophysiology and corresponding therapeutics are lacking. Objectives: To report our clinical and biophysical characterizations of a novel de novo KCNMA1 variant, as well as an effective therapy for the patient's dystonia‐atonia spells. Methods: Combination of phenotypic characterization, therapy, and biophysical characterization of the patient and her mutation. Results: The patient had >100 dystonia‐atonia spells per day with mild cerebellar atrophy. She also had autism spectrum disorder, intellectual disability, and attention deficit hyperactivity disorder. Whole‐exome sequencing identified a heterozygous de novo BK N536H mutation. Our biophysical characterization demonstrates that N536H is a gain‐of‐function mutation with markedly enhanced voltage‐dependent activation. Remarkably, administration of dextroamphetamine completely suppressed the dystonia‐atonia spells. Conclusions: BK N536H is a gain‐of‐function that causes dystonia and other neurological symptoms. Our stimulant therapy opens a new avenue to mitigate KCNMA1‐linked movement disorders. © 2020 International Parkinson and Movement Disorder Society [ABSTRACT FROM AUTHOR]

In the practical production, the transportation of jobs is existed between different machines. These transportation operations directly affect the production cycle and the production efficiency. In this study, an improved memetic algorithm is proposed to solve the flexible job shop scheduling problem with transportation times, and the optimization objective is minimizing the makespan. In the improved memetic algorithm, an effective simulated annealing algorithm is adopted in the local search process, which combines the elite library and mutation operation. All the feasible solutions are divided into general solutions and local optimal solutions according to the elite library. The general solutions are executed by the simulated annealing algorithm to improve the quality, and the local optimal solutions are executed by the mutation operation to increase the diversity of the solution set. Comparison experiments with the improved genetic algorithm show that the improved memetic algorithm has better search performance and stability. [ABSTRACT FROM AUTHOR]

With the discrete element method, studies of the influence of friction coefficients on the granular segregation in a 2000 particle system have been performed. Segregation phenomena reported previously such as convection cells [Phys. Rev. Lett. 70, 3728 (1993), Exp. Fluids 51:795–800 (2011)] and intermediate phase [Phys. Rev. E 85, 061302 (2012)] were discovered in our simulations. Higher friction coefficients (between particles as well as between particles and container walls) generally speed up and strengthen the segregation process. Three stages of the segregation process were discovered, i.e. the initial segregation, the subsequent sinking of the large particles, and the long-time height fluctuation of the large particles. Furthermore, by systematically analyzing the variations of the scaling parameters (phase parameter λ and Lacey index M Lacey) with respect to different friction coefficients, we found that the initial segregation is completed by both mechanisms of particle arching and convection. In addition, both the friction drag from the container walls and the gravity forces contribute to the subsequent sinking of the large particles. Moreover, the height fluctuation of the large particles is proved to have a relation with restitution coefficients. Unlabelled Image • A typical BNE segregation process can be divided into three stages. • Higher friction coefficients lead to a faster and more complete segregation. • Both arching and convection contribute to the initial BNE segregation. • Both friction drag and gravity contribute to the sinking of the large particles. [ABSTRACT FROM AUTHOR]

Aims: Acetaminophen (APAP) overdose leads to acute liver injury by inducing hepatic mitochondrial oxidative stress and inflammation. However, the molecular mechanisms involved are still unclear. Farnesoid X receptor (FXR) serves as a therapeutic target for the treatment of liver disorders, whose activation has been proved to protect APAP-induced hepatotoxicity. In this study, we examined whether FXR activation by schaftoside (SS), a naturally occurring flavonoid from Desmodium styracifolium, could protect mice against APAP-induced hepatotoxicity via regulation of oxidative stress and inflammation. Results: We first found that SS exhibited potent protective effects against APAP-induced hepatotoxicity in mice. The study reveals that SS is a potential agonist of FXR, which protects mice from hepatotoxicity mostly via regulation of oxidative stress and inflammation. Mechanistically, the hepatoprotective SS is associated with the induction of the genes of phase II detoxifying enzymes (e.g., UGT1A1, GSTα1), phase III drug efflux transporters (e.g., bile salt export pump, organic solvent transporter protein β), and glutathione metabolism-related enzymes (e.g., glutamate-cysteine ligase modifier subunit [Gclm], glutamate-cysteine ligase catalytic subunit [Gclc]). More importantly, SS-mediated FXR activation could fine-tune the pro- and anti-inflammatory eicosanoids generation via altering eicosanoids metabolic pathway, thereby resulting in decrease of hepatic inflammation. In contrast, FXR deficiency can abrogate the above effects. Innovation and Conclusion: Our results provided the direct evidence that FXR activation by SS could attenuate APAP-induced hepatotoxicity via inhibition of nuclear factor kappa-B signaling and fine-tuning the generation of proinflammatory mediators' eicosanoids. Our findings indicate that strategies to activate FXR signaling in hepatocytes may provide a promising therapeutic approach to alleviate liver injury induced by APAP overdose. [ABSTRACT FROM AUTHOR]

The training workflow of neural networks can be quite complex, potentially time‐consuming, and require specific hardware to accomplish operation needs. This study presents a novel analytical video‐based approach for vehicle tracking and vehicle volume estimation at nighttime using a monocular traffic surveillance camera installed over the road. To build this approach, we employ computer vision‐based algorithms to detect vehicle objects, perform vehicle tracking, and vehicle counting in a predefined detection zone. To address low‐illumination conditions, we adapt and employ image noise reduction techniques, image binary conversion, image projective transformation, and a set of heuristic reasoning rules to extract the headlights of each vehicle, pair them belonging to the same vehicle, and track moving candidate vehicle objects continuously across a sequence of video frames. The robustness of the proposed method was tested in various scenarios and environmental conditions using a publicly available vehicle dataset as well as own labeled video data. [ABSTRACT FROM AUTHOR]

Production scheduling problems are typically combinational optimization problems named bases on the processing routes of jobs on different machines. In this paper, the flexible job shop scheduling problem aimed to minimize the maximum completion times of operations or makespan is considered. To solve such an NP-hard problem, variable neighborhood search (VNS) based on genetic algorithm is proposed to enhance the search ability and to balance the intensification and diversification. VNS algorithm has shown excellent capability of local search with systematic neighborhood search structures. External library is improved to save the optimal or near optimal solutions during the iterative process, and when the objective value of the optimal solutions are the same, the scheduling Gantt charts need to be considered. To evaluate the performance of our proposed algorithm, benchmark instances in different sizes are optimized. Consequently, the computational results and comparisons illustrate that the proposed algorithm is efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

• Design and optimisation on refrigerants, crucial components and innovative systems of VRFS are reviewed. • Control strategies of VRFS including energy efficiency, improved humidity, and defrosting operation are summarized. • Investigations on inherent energy performance, energy use prediction, and energy saving potential of VRFS are presented. • Advances in the fault detect and diagnosis of VRFS are reported. • Overlooks on VRFS are recommended for the future research works. Variable refrigerant flow systems (VRFS) are gradually gaining popularity in commercial and residential buildings owing to their satisfactory part-load performance, flexible control, and ease of installation and maintenance. However, the characteristic complex system structure of VRFS makes it difficult to understand their performance. This study presents a comprehensive review of VRFS concerning their design and optimisation, control strategies, modelling and simulation, and fault detection and diagnosis (FDD). This review begins with the developments in design and optimisation of VRFS in terms of alternative refrigerants, crucial components, and innovative systems. Subsequently, the control strategies of VRFS including energy efficiency, improved humidity, and defrosting operation are reviewed. Meanwhile, the research objectives on modelling and simulation of VRFS could be divided into three aspects: investigation of the inherent energy performance, prediction of energy consumption in buildings, and comparisons of energy saving potential. In addition, the advances in FDD for VRFS, based on the rapid development of data mining technologies, are presented. The main conclusions drawn from this detailed review provide a comprehensive overview of VRFS and inspire new insights and feasible solutions for relevant researchers, product developers, practitioners, and policymakers in this field. [ABSTRACT FROM AUTHOR]

• Superhydrophobic PCL/MSO/SiO 2 membranes are prepared via one-step technique. • PCL microsphere-fiber structure constructed superhydrophobic membranes. • The membrane can separate the oil-water mixture with high separation efficiency. • The PCL/MSO/SiO 2 membrane has excellent anti-corrosive ability. Hydrophobic polycaprolactone (PCL) membranes with hierarchical structure were fabricated via one-step electrospinning technique. Meanwhile, the addition of the liquid methyl silicone oil (MSO) and hydrophobic silica (SiO 2) nanoparticles in the PCL membrane were expected to improve the hydrophobicity of PCL membranes. FE-SEM images showed the microsphere-fiber interlinked hierarchical morphology of PCL/MSO/SiO 2 composite fiber. FT-IR confirmed the formation of PCL/MSO/SiO 2 composite fibers. XPS approved its hierarchical structure of the composition. By comparing with pure PCL membrane which the water contact angle (WCA) was 132.2 ± 1.2°, the WCAs of PCL/MSO and PCL/SiO 2 membrane increased to 143.3 ± 1.2° and 141.7 ± 1.9°, respectively. It was worth noting the WCA of PCL/MSO/SiO 2 composite mat could reach 151.6 ± 1.7° and the sliding angle was 7.4 ± 1.2°. The excellent surface roughness and air-pocket capacity on the hierarchical membranes were believed to contribute to the superhydrophobicity of PCL/MSO/SiO 2 membrane. The unique microsphere-fiber interlinked hierarchical structure and superhydrophobicity of membrane could provide adequate support for oil adsorption and oil-water separation. The maximum n-hexane adsorption capacity of PCL/MSO/SiO 2 membrane was 41.37 g g−1. Separation efficiency for n-hexane-water mixture of PCL/MSO/SiO 2 membrane was as high as 99.92%. The PCL/MSO/SiO 2 hierarchical superhydrophobic mats indicate the potential applications of environmental-friendly biopolymers as separator. [ABSTRACT FROM AUTHOR]

Superhydrophobic polycaprolactone (PCL) membranes with hierarchical structure were fabricated via alternate electrospinning/electrospraying techniques. Electrospun PCL/methyl silicone oil (PCL/MSO) nanofibers were employed as substrate. PCL/MSO‐PCL microspheres (PCL/MSO‐PCLMS) hierarchical membrane was prepared via electrosprayed PCLMS as an additional layer on the substrate. Field emission scanning electron microscopy images showed the formation of hierarchical PCL/MSO‐PCLMS membranes. Compared to pure PCL fibers substrate (120 ± 1.3°), the water contact angle (WCA) of MSO‐modified PCL membrane was 142 ± 0.7°. The most interesting observation was that the WCA of PCLMS without any modification could be achieved to 146 ± 2.8°. On this basis, PCL/MSO‐PCLMS hierarchical membrane possessed superhydrophobic surface with 150 ± 0.6° of WCA. The excellent surface roughness and air‐pocket capacity of hierarchical membranes would make the membranes more hydrophobic. The maximum oil (n‐hexane) adsorption capacity of PCL/MSO‐PCLMS membrane was 32.53 g g−1. Oil–water separation efficiencies of the superhydrophobic membranes were all higher than 99.93% after 10 cycles. The hierarchically structured PCL superhydrophobic membranes indicate the potential applications of environmentally friendly biopolymers as separation membranes. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2019, 57, 421–430 The combination of polycaprolactone (PCL) microspheres and PCL/methyl silicone oil fiber via alternate electrospinning/electrospraying techniques which imitate lotus‐leaf‐like papillae is used to construct the superhydrophobic hierarchical membranes. The superhydrophobicity is achieved by the modification of morphological structures of PCL and incorporation of small quantities of additives. The excellent adsorption capacity for n‐hexane suggests its potential applications as an environmentally friendly biopolymer in the field of separation membranes. [ABSTRACT FROM AUTHOR]

Clinical studies showed that intrauterine growth retardation (IUGR) neonatus had lower cholesterol concentrations in cord blood, which might be associated with increased risk of metabolic syndrome and cardiovascular diseases in adulthood. We previously observed lower blood cholesterol levels in prenatal nicotine exposure (PNE)-induced IUGR fetal rats, and this study aimed to elucidate the placental mechanism. Pregnant Wistar rats were subcutaneously injected with nicotine (2.0 mg/kg⋅d) on gestational day 9–20. In vivo , PNE increased levels of total cholesterol (TCH), high-density lipoprotein-cholesterol (HDL-C) and low-density lipoprotein-cholesterol (LDL-C) in maternal serum, while decreased levels of TCH and LDL-C in female fetal serum. Meanwhile, the expression of scavenger receptor class B type 1 (SR-B1), ATP-binding cassette transporter A1 (ABCA1) and ATP-binding cassette transporter G1 (ABCG1) were decreased, and the expression of liver X receptor (LXR) α and β were also decreased in female placentas. In vitro , nicotine (0.1–10 μM) reduced the expression of LXRα, LXRβ, SR-B1, ABCA1 and ABCG1 in a concentration dependent manner, which could be annulled by nAChR antagonist and LXR agonist. Taken together, nicotine could inhibit the expression of SR-B1, ABCA1 and ABCG1 via nAChR and LXR α/β in female placentas, finally leading to reduced blood cholesterol levels in fetal rats. [ABSTRACT FROM AUTHOR]

Background and Purpose: Activation of the human pregnane X receptor (PXR; NR1I2) has potential therapeutic uses for inflammatory bowel disease (IBD). Imperatorin (IMP), a naturally occurring coumarin, is the main bioactive ingredient of Angelica dahurica Radix, which is regularly used to treat the common cold and intestinal disorders. However, there are no data on the protective effects of IMP against IBD.Experimental Approach: The effects of IMP on PXR-modulated cytochrome P450 3A4 (CYP3A4) expression were assessed using a PXR transactivation assay, a mammalian two-hybrid assay, a competitive ligand-binding assay, analysis of CYP3A4 mRNA and protein expression levels and measurement of CYP3A4 activity using a cell-based reporter gene assay and in vitro model. The inhibitory effects of IMP on NF-κB activity were evaluated by a reporter assay and NF-κB p65 nuclear translocation. The anti-IBD effects of IMP were investigated in a dextran sulphate sodium (DSS)-induced colitis mouse model. Colon inflammatory cytokines were assessed by elisa.Key Results: IMP activated CYP3A4 promoter activity, recruited steroid receptor coactivator 1 to the ligand-binding domain of PXR and increased the expression and activity of CYP3A4. PXR knockdown substantially reduced IMP-induced increase in CYP3A4 expression. Furthermore, IMP-mediated PXR activation suppressed the nuclear translocation of NF-κB and down-regulated LPS-induced expression of pro-inflammatory genes. Nevertheless, PXR knockdown partially reduced the IMP-mediated inhibition of NF-κB. IMP ameliorated DSS-induced colitis by PXR/NF-κB signalling.Conclusions and Implications: IMP acts as a PXR agonist to attenuate DSS-induced colitis by suppression of the NF-κB-mediated pro-inflammatory response in a PXR/NF-κB-dependent manner. [ABSTRACT FROM AUTHOR]

In this paper, the strong laws of large numbers for maximum value of weighted sums of extended negatively dependent random variables are obtained, which improve and extend the corresponding ones for independent random variables and some dependent random variables. [ABSTRACT FROM AUTHOR]

In order to obtain the drying control condition with no or minimal effect on concrete strength, various drying temperatures conditions are used as 150 °C, 120 °C, 105 °C, 85 °C and 60 °C, and the drying procedure of continuous and intermittent cyclic are presented independently in this study. The change of specimen mass is recorded in the drying process, and the compressive strength and splitting tensile strength are measured after the natural cooling. The results show that the concrete compressive strength initially decreases and then increases with the increase in drying temperature, while the splitting tensile strength always decreases. The relative compressive and splitting tensile strength of concrete in dry state are 1 and 0.99 respectively after 115.5 h of continuous drying at 105 °C, which denotes the optimal drying control condition has minimal effect on the strength, and efficiency is suitable. Finally, the change in components and defects structure of the concrete after minimal damage drying is analyzed through the thermo-gravimetric analysis, electron microscope scanning and industrial CT scanning. [ABSTRACT FROM AUTHOR]

Periplakin (PPL), a member of the plakin protein family, has been reported to be down-expressed in urothelial carcinoma. The role of PPL in human colorectal cancer, however, remains largely unknown. Also little is known about the contribution of PPL to the malignant property of colorectal cancer and the intracellular function of PPL. In this study, we demonstrated that PPL was apparently down-expressed in colon carcinomas compared with normal and para-carcinoma tissues, which was correlated with the tumor size. Enforced expression of PPL in HT29 cells inhibited its proliferation evidenced by decreased expression of phosphorylated ERK and PCNA. Furthermore, PPL overexpression could reduce metastasis and epithelial-mesenchymal transition (EMT) of HT29 cells, with decreased expression of N-cadherin, Snail, Slug and α-SMA while increased expression of E-cadherin. On the contrary, the PPL knockdown could promote the cell proliferation, migratory, invasive and EMT ability of HT29 cells. Moreover, enforced expression of PPL induced G1/G0 cell cycle arrest, with decreased cyclin D1, p-Rb and increased expression of p27 kib , which could be reversed by PPL knockdown. In addition, PPL overexpression inhibited the growth of colon cancer allograft in vivo. Taken together, acted as a tumor suppressor in colon cancer progression, PPL could be a new biomarker or potential therapeutic target in colon cancer. [ABSTRACT FROM AUTHOR]

In order to solve the unclear problems of structure and composition for 2,4-dihydroxybenzoic acid (2,4-DHBA) copper salt (β -Cu), a new insoluble ternary copper (Ⅱ) complex of 2,4-DHBA ([Cu(C 12 H 8 N 2) 2 (C 7 H 5 O 4)](C 7 H 5 O 4)) was designed and synthesized in water. The ternary complex crystallizes in the monoclinic system with the space group P2 1 /n. The central Cu2+ is coordinated with four nitrogen atoms from two 1,10-phenanthroline ligands and one carboxyl oxygen atom from a 2,4-DHBA anion. The other 2,4-DHBA anion does not participate in coordination and only provides the action of balance charge. In addition, compared with β -Cu, no water molecules exist in the ternary complex. The thermal stability of the ternary complex was investigated, as well as its catalytic decomposition activity on AP and RDX. With the addition of the ternary complex, the exothermic decomposition peak temperatures of AP and RDX were reduced from 406.5 to 353.9 ​°C, and 242.3 to 181.4 ​°C respectively, and the apparent activation energies were reduced from 293.1 to 207.4 ​kJ ​mol−1, and 239.6 to 177.5 ​kJ ​mol−1, respectively. Moreover, the addition of the ternary complex has greatly improved the heat release of AP and RDX. The catalytic decomposition performance of the new ternary complex on AP and RDX is much better than that of the traditional β -Cu. Moreover, the new copper (Ⅱ) complex completely changed the thermal decomposition process of RDX from melting decomposition to direct solid phase decomposition, and such catalytic effect is uncommon. Therefore, the new ternary copper (Ⅱ) complex presents a better potential application for modified double-base propellant of RDX. A new insoluble ternary copper complex of 2,4-DHBA was designed, synthesized, structurally characterized, and its properties tested. The catalytic decomposition performance of the ternary copper complex on AP and RDX is much better than that of the traditional β -Cu. [Display omitted] • A new ternary copper (Ⅱ) complex of 2,4-dihydroxybenzoic acid was designed and synthesized in water. • The central Cu2+ is coordinated with four nitrogen atoms of two 1,10-phenanthroline ligands and one carboxyl oxygen atom of a 2,4-DHBA anion. The other 2,4-DHBA anion does not participate in coordination and only provides the action of balance charge. • The ternary complex exhibits outstanding catalytic effect on AP and RDX, and its catalytic performance is better than that of traditional β -Cu. • The new ternary copper complex presents a better potential application for modified double-base propellant of RDX. [ABSTRACT FROM AUTHOR]

There is a high potential of severe injury outcomes in traffic crashes on rural interstate highways due to the significant amount of high speed traffic on these corridors. Hierarchical Bayesian models are capable of incorporating between-crash variance and within-crash correlations into traffic crash data analysis and are increasingly utilized in traffic crash severity analysis. This paper applies a hierarchical Bayesian logistic model to examine the significant factors at crash and vehicle/driver levels and their heterogeneous impacts on driver injury severity in rural interstate highway crashes. Analysis results indicate that the majority of the total variance is induced by the between-crash variance, showing the appropriateness of the utilized hierarchical modeling approach. Three crash-level variables and six vehicle/driver-level variables are found significant in predicting driver injury severities: road curve, maximum vehicle damage in a crash, number of vehicles in a crash, wet road surface, vehicle type, driver age, driver gender, driver seatbelt use and driver alcohol or drug involvement. Among these variables, road curve, functional and disabled vehicle damage in crash, single-vehicle crashes, female drivers, senior drivers, motorcycles and driver alcohol or drug involvement tend to increase the odds of drivers being incapably injured or killed in rural interstate crashes, while wet road surface, male drivers and driver seatbelt use are more likely to decrease the probability of severe driver injuries. The developed methodology and estimation results provide insightful understanding of the internal mechanism of rural interstate crashes and beneficial references for developing effective countermeasures for rural interstate crash prevention. [ABSTRACT FROM AUTHOR]

Rural non-interstate crashes induce a significant amount of severe injuries and fatalities. Examination of such injury patterns and the associated contributing factors is of practical importance. Taking into account the ordinal nature of injury severity levels and the hierarchical feature of crash data, this study employs a hierarchical ordered logit model to examine the significant factors in predicting driver injury severities in rural non-interstate crashes based on two-year New Mexico crash records. Bayesian inference is utilized in model estimation procedure and 95% Bayesian Credible Interval (BCI) is applied to testing variable significance. An ordinary ordered logit model omitting the between-crash variance effect is evaluated as well for model performance comparison. Results indicate that the model employed in this study outperforms ordinary ordered logit model in model fit and parameter estimation. Variables regarding crash features, environment conditions, and driver and vehicle characteristics are found to have significant influence on the predictions of driver injury severities in rural non-interstate crashes. Factors such as road segments far from intersection, wet road surface condition, collision with animals, heavy vehicle drivers, male drivers and driver seatbelt used tend to induce less severe driver injury outcomes than the factors such as multiple-vehicle crashes, severe vehicle damage in a crash, motorcyclists, females, senior drivers, driver with alcohol or drug impairment, and other major collision types. Research limitations regarding crash data and model assumptions are also discussed. Overall, this research provides reasonable results and insight in developing effective road safety measures for crash injury severity reduction and prevention. [ABSTRACT FROM AUTHOR]

• Dynamic flexible job shop scheduling problem with machine breakdown is studied. • A two-stage algorithm based on convolutional neural network is proposed. • The improved imperialist competition algorithm is proposed to generate schedules. • A predictive model is proposed to predict the robustness of scheduling. In the actual manufacturing process, the environment of the job shop is complex. There will be many kinds of uncertainties such as random job arrivals, machine breakdowns, order cancellations and other dynamic events. In this paper, an effective two-stage algorithm based on convolutional neural network is proposed to solve the flexible job shop scheduling problem (FJSP) with machine breakdown. A bi-objective dynamic flexible job shop scheduling problem (DFJSP) model with the objective of maximum completion time and robustness is established. In the two-stage algorithm, the first stage is to train the prediction model by convolutional neural network (CNN). The second stage is to predict the robustness of scheduling through the model trained in the first stage. First, an improved imperialist competition algorithm (ICA) is proposed to generate training data. Then, a predictive model constructed by CNN was proposed, and an alternative metric called RMn was developed to evaluate robustness. RMn evaluates that the float time has an effect on the robustness through the information of machine breakdown, workload and float time of the operation. The experimental results show that the proposed two-stage algorithm is effective for solving DFJSP, and RMn can evaluate the robustness of scheduling more quickly, efficiently and accurately. [ABSTRACT FROM AUTHOR]

In this study, a mixed logit model is developed to identify the heterogeneous impacts of gender-interpreted contributing factors on driver injury severities in single-vehicle rollover crashes. The random parameter of the variables in the mixed logit model, the heterogeneous mean, is elaborated by driver gender-based linear regression models. The model is estimated using crash data in New Mexico from 2010 to 2012. The percentage changes of factors’ predicted probabilities are calculated in order to better understand the model specifications. Female drivers are found more likely to experience severe or fatal injuries in rollover crashes than male drivers. However, the probability of male drivers being severely injured is higher than female drivers when the road surface is unpaved. Two other factors with fixed parameters are also found to significantly increase driver injury severities, including Wet and Alcohol Influenced . This study provides a better understanding of contributing factors influencing driver injury severities in rollover crashes as well as their heterogeneous impacts in terms of driver gender. Those results are also helpful to develop appropriate countermeasures and policies to reduce driver injury severities in single-vehicle rollover crashes. [ABSTRACT FROM AUTHOR]

This study analyzes driver injury severities for single-vehicle crashes occurring in rural and urban areas using data collected in New Mexico from 2010 to 2011. Nested logit models and mixed logit models are developed in order to account for the correlation between severity categories ( No injury , Possible injury , Visible injury , Incapacitating injury and fatality ) and individual heterogeneity among drivers. Various factors, such as crash and environment characteristics, geometric features, and driver behavior are examined in this study. Nested logit model and mixed logit model reveal similar results in terms of identifying contributing factors for driver injury severities. In the analysis of urban crashes, only the nested logit model is presented since no random parameter is found in the mixed logit model. The results indicate that significant differences exist between factors contributing to driver injury severity in single-vehicle crashes in rural and urban areas. There are 5 variables found only significant in the rural model and six significant variables identified only in the urban crash model. These findings can help transportation agencies develop effective policies or appropriate strategies to reduce injury severity resulting from single-vehicle crashes. [ABSTRACT FROM AUTHOR]

Variance reduction methods in Monte Carlo calculations are essential especially for the design and analysis of some real-world nuclear applications with complex and large geometry. As the most popular variance reduction method, weight window technique has demonstrated its great capability and been implemented in most Monte Carlo particle transport codes. In this work, we propose two new variance reduction methods based on weight window. One is automatic inner iterative fixed source calculation method, which makes it more convenient and efficient to use weight window technique. The other is called adaptive weight window method, which solves one of the most common frustrations when using weight window that the output of zero weight windows and difficulties to obtain a set of variance reduction parameters in some deep penetration problems. These two new methods are verified and compared. The efficiency of newly proposed adaptive weight window method and automatic inner iterative fixed source calculation method was found to be 32,966 and 25,793 times higher than that of an analogue run. [ABSTRACT FROM AUTHOR]

Rollover crash is one of the major types of traffic crashes that induce fatal injuries. It is important to investigate the factors that affect rollover crashes and their influence on driver injury severity outcomes. This study employs support vector machine (SVM) models to investigate driver injury severity patterns in rollover crashes based on two-year crash data gathered in New Mexico. The impacts of various explanatory variables are examined in terms of crash and environmental information, vehicle features, and driver demographics and behavior characteristics. A classification and regression tree (CART) model is utilized to identify significant variables and SVM models with polynomial and Gaussian radius basis function (RBF) kernels are used for model performance evaluation. It is shown that the SVM models produce reasonable prediction performance and the polynomial kernel outperforms the Gaussian RBF kernel. Variable impact analysis reveals that factors including comfortable driving environment conditions, driver alcohol or drug involvement, seatbelt use, number of travel lanes, driver demographic features, maximum vehicle damages in crashes, crash time, and crash location are significantly associated with driver incapacitating injuries and fatalities. These findings provide insights for better understanding rollover crash causes and the impacts of various explanatory factors on driver injury severity patterns. [ABSTRACT FROM AUTHOR]

Rear-end crashes are a major type of traffic crashes in the U.S. Of practical necessity is a comprehensive examination of its mechanism that results in injuries and fatalities. Decision table (DT) and Naïve Bayes (NB) methods have both been used widely but separately for solving classification problems in multiple areas except for traffic safety research. Based on a two-year rear-end crash dataset, this paper applies a decision table/Naïve Bayes (DTNB) hybrid classifier to select the deterministic attributes and predict driver injury outcomes in rear-end crashes. The test results show that the hybrid classifier performs reasonably well, which was indicated by several performance evaluation measurements, such as accuracy, F-measure, ROC, and AUC. Fifteen significant attributes were found to be significant in predicting driver injury severities, including weather, lighting conditions, road geometry characteristics, driver behavior information, etc. The extracted decision rules demonstrate that heavy vehicle involvement, a comfortable traffic environment, inferior lighting conditions, two-lane rural roadways, vehicle disabled damage, and two-vehicle crashes would increase the likelihood of drivers sustaining fatal injuries. The research limitations on data size, data structure, and result presentation are also summarized. The applied methodology and estimation results provide insights for developing effective countermeasures to alleviate rear-end crash injury severities and improve traffic system safety performance. [ABSTRACT FROM AUTHOR]

Traffic crashes occurring on rural roadways induce more severe injuries and fatalities than those in urban areas, especially when there are trucks involved. Truck drivers are found to suffer higher potential of crash injuries compared with other occupational labors. Besides, unobserved heterogeneity in crash data analysis is a critical issue that needs to be carefully addressed. In this study, a hierarchical Bayesian random intercept model decomposing cross-level interaction effects as unobserved heterogeneity is developed to examine the posterior probabilities of truck driver injuries in rural truck-involved crashes. The interaction effects contributing to truck driver injury outcomes are investigated based on two-year rural truck-involved crashes in New Mexico from 2010 to 2011. The analysis results indicate that the cross-level interaction effects play an important role in predicting truck driver injury severities, and the proposed model produces comparable performance with the traditional random intercept model and the mixed logit model even after penalization by high model complexity. It is revealed that factors including road grade, number of vehicles involved in a crash, maximum vehicle damage in a crash, vehicle actions, driver age, seatbelt use, and driver under alcohol or drug influence, as well as a portion of their cross-level interaction effects with other variables are significantly associated with truck driver incapacitating injuries and fatalities. These findings are helpful to understand the respective or joint impacts of these attributes on truck driver injury patterns in rural truck-involved crashes. [ABSTRACT FROM AUTHOR]

Congestion pricing has been proposed and investigated as an effective means of optimizing traffic assignment, alleviating congestion, and enhancing traffic operation efficiencies. Meanwhile, advanced traffic information dissemination systems, such as Advanced Traveler Information System (ATIS), have been developed and deployed to provide real-time, accurate, and complete network-wide traffic information to facilitate travelers’ trip plans and routing selections. Recent advances in ATIS technologies, especially telecommunication technology, allow dynamic, personalized, and multimodal traffic information to be disseminated and impact travelers’ choices of departure times, alternative routes, and travel modes in the context of congestion pricing. However, few studies were conducted to determine the impact of traffic information dissemination on toll road utilizations. In this study, the effects of the provisions of traffic information on toll road usage are investigated and analyzed based on a stated preference survey conducted in Texas. A Bayesian Network (BN)-based approach is developed to discover travelers’ opinions and preferences for toll road utilization supported by network-wide traffic information provisions. The probabilistic interdependencies among various attributes, including routing choice, departure time, traffic information dissemination mode, content, coverage, commuter demographic information, and travel patterns, are identified and their impacts on toll road usage are quantified. The results indicate that the BN model performs reasonably well in travelers’ preference classifications for toll road utilization and knowledge extraction. The BN Most Probable Explanation (MPE) measurement, probability inference and variable influence analysis results illustrate travelers using highway advisory radio and internet as their primary mode of receiving traffic information are more likely to comply with routing recommendations and use toll roads. Traffic information regarding congested roads, road hazard warnings, and accident locations is of great interest to travelers, who tend to acquire such information and use toll roads more frequently. Travel time formation for home-based trips can considerably enhance travelers’ preferences for toll road usage. Female travelers tend to seek traffic information and utilize toll roads more frequently. As expected, the information provided at both pre-trip and en-route stages can positively influence travelers’ preferences for toll road usage. The proposed methodology and research findings advance our previous study and provide insight into travelers’ behavioral tendencies concerning toll road utilization in support of traffic information dissemination. [ABSTRACT FROM AUTHOR]

Rear-end crash is one of the most common types of traffic crashes in the U.S. A good understanding of its characteristics and contributing factors is of practical importance. Previously, both multinomial Logit models and Bayesian network methods have been used in crash modeling and analysis, respectively, although each of them has its own application restrictions and limitations. In this study, a hybrid approach is developed to combine multinomial logit models and Bayesian network methods for comprehensively analyzing driver injury severities in rear-end crashes based on state-wide crash data collected in New Mexico from 2010 to 2011. A multinomial logit model is developed to investigate and identify significant contributing factors for rear-end crash driver injury severities classified into three categories: no injury, injury, and fatality. Then, the identified significant factors are utilized to establish a Bayesian network to explicitly formulate statistical associations between injury severity outcomes and explanatory attributes, including driver behavior, demographic features, vehicle factors, geometric and environmental characteristics, etc. The test results demonstrate that the proposed hybrid approach performs reasonably well. The Bayesian network reference analyses indicate that the factors including truck-involvement, inferior lighting conditions, windy weather conditions, the number of vehicles involved, etc. could significantly increase driver injury severities in rear-end crashes. The developed methodology and estimation results provide insights for developing effective countermeasures to reduce rear-end crash injury severities and improve traffic system safety performance. [ABSTRACT FROM AUTHOR]

Although various innovative traffic sensing technologies have been widely employed, incomplete sensor data is one of the most major problems to significantly degrade traffic data quality and integrity. In this study, a hybrid approach integrating the Fuzzy C-Means (FCM)-based imputation method with the Genetic Algorithm (GA) is develop for missing traffic volume data estimation based on inductance loop detector outputs. By utilizing the weekly similarity among data, the conventional vector-based data structure is firstly transformed into the matrix-based data pattern. Then, the GA is applied to optimize the membership functions and centroids in the FCM model. The experimental tests are conducted to verify the effectiveness of the proposed approach. The traffic volume data collected at different temporal scales were used as the testing dataset, and three different indicators, including root mean square error, correlation coefficient, and relative accuracy, are utilized to quantify the imputation performance compared with some conventional methods (Historical method, Double Exponential Smoothing, and Autoregressive Integrated Moving Average model). The results show the proposed approach outperforms the conventional methods under prevailing traffic conditions. [ABSTRACT FROM AUTHOR]

Highlights: [•] A generalized nonlinear model-based approach is formulated for crash analyses. [•] Nonlinear relationships among variables can be better quantified and elaborated. [•] Truck percentage and grade have parabolic impacts on rear-end crash occurrences. [•] This research is helpful to evaluate rear-end crash safety improvement plans. [ABSTRACT FROM AUTHOR]

This article describes a coordinated ramp metering algorithm for systematically mitigating freeway congestion. A preemptive hierarchical control scheme with a three-priority-layer structure is employed in this algorithm. Ramp metering is formulated as a multiobjective optimization problem to enhance system performance. These optimization objectives include promptly tackling freeway congestion, sufficiently utilizing on-ramp storage capacities, and preventing on-ramp vehicles from overflowing to local streets, balancing on-ramp vehicle equity, and maximizing traffic throughputs for the entire system. Instead of relying heavily on accurate estimates of freeway traffic flow evolvement, this new approach models ramp meter control as a linear program and uses real-time traffic sensor measurements for minimizing the indeterminate impacts from the mainstream flow capacities. VISSIM-based simulation experiments are performed to examine its practicality and effectiveness using geometric and traffic demand data from one real-world freeway segment. The simulation test results show that the proposed ramp metering approach performed well in optimizing overall freeway system operations under various traffic conditions. The system-wide optimal control performance can be achieved to quickly mitigate freeway congestion, prevent traffic from overflowing to local streets, and maximize overall traffic throughputs. The proposed ramp metering approach can dynamically assemble relevant ramp meters to work together and effectively coordinate the individual meter rates to leverage their response strengths for minimizing time to clear the congestion. This study demonstrates that utilization of existing freeway infrastructure can be optimized through the proposed algorithm. [ABSTRACT FROM AUTHOR]

Travel speed ties directly to travel time, so it is an important measure for quantifying arterial performance. However, accurately estimating link travel speed for urban arterials is difficult because of traffic fluctuations and stop-and-go conditions caused by signal control. This research proposes a two-step empirical approach to effectively estimate the link-journey speeds using only advance loop detector outputs. The first step is to estimate the spot speed on the basis of advance loop measurements using Athol's algorithm. The robust regression technique can be used to calibrate the speed estimation parameter (or -factor) in Athol's algorithm. The second step is to use the proposed simplified speed estimation model to estimate the link speed using only the calculated loop-spot speed without any knowledge of signal timing plans. Traffic operations in the central business district of the City of Bellevue, Washington, are simulated in the VISSIM traffic simulation model. The test results show that only 50 cycles of data are needed to calibrate the -factor in loop-speed estimation and the same datasets can be used to calibrate the proposed link-speed model. Using this model, the average mean absolute error over the study links is reduced from 4.24 to 1.51 mph. With proper calibration, this average error can be further reduced to 0.91 mph. The results are encouraging and satisfactory. The results also show that the accuracy of speed estimation may be further increased when more data are applied for calibration. [ABSTRACT FROM AUTHOR]

Traffic speed and length-based vehicle classification data are critical inputs for traffic operations, pavement design and maintenance, and transportation planning. However, they cannot be measured directly by single-loop detectors, the most widely deployed type of traffic sensor in the existing roadway infrastructure. In this study, a Gaussian mixture model (GMM)-based approach is developed to estimate more accurate traffic speeds and classified vehicle volumes using single-loop outputs. The estimation procedure consists of multiple iterations of parameter correction and validation. After the GMM is established to empirically model vehicle on-times measured by single-loop detectors, the optimal solution can be initially sought to separate length-based vehicle volume data. Based on the on-time of the separated short vehicles from the GMM, an iterative process will be conducted to improve traffic speed and classified volume estimation until the estimation results become statistically stable and converge. This method is straightforward and computationally efficient. The effectiveness of the proposed approach was examined using data collected from several loop stations on Interstate 90 in the Seattle area. The traffic volume data for three vehicle classes are categorized based on the proposed method. The test results show the proposed GMM approach outperforms the previous models, including conventional constant g-factor method, sequence method, and moving median method, and produces more reliable, accurate estimates of traffic speeds and classified vehicle volumes under various traffic conditions. [ABSTRACT FROM PUBLISHER]

This study is the first attempt to integrate flood resilience into the electric vehicle (EV) charging station planning process. Instead of fully avoiding flood‐prone areas, an optimized placement considering the magnitude of flood inundations can minimize the impact of flood hazards and simultaneously maximize the socio‐economic benefit of EV charging station networks. In this study, an integrated framework of the non‐dominated sorting genetic algorithm‐III (NSGA‐III) and the technique for order of preference by similarity to ideal solution (TOPSIS) is proposed to optimize the charging station locations by maximizing the charging convenience, minimizing the impact of flood hazards, and minimizing the impact of existing charging stations. The NSGA‐III is applied to solve the multi‐objective location optimization of charging stations. TOPSIS is subsequently used to determine the best solution from the feasible candidates generated by the NSGA‐III. A case study conducted in the Waikiki area demonstrates that the proposed optimization framework can effectively deal with the trade‐off between the impact of flood hazards and the charging service of a charging station network. This study provides new insights into best practices for dealing with multiple conflicting objectives in EV charging station planning under climate change. [ABSTRACT FROM AUTHOR]

• High resolution traffic speed temporospatial distribution and propagation dynamics prediction • Deep learning framework integrating traffic flow dynamics and topological dependency • Dynamic programming capturing traffic flow interdependency The information of network-wide future traffic speed distribution and its propagation is beneficial to develop proactive traffic congestion management strategies. However, predicting network-wide traffic speed propagation is non-trivial. This study develops a t raffic flow d ependency and d ynamics based deep learning aided approach (TD2-DL), which predict network-wide high resolution traffic speed propagation by explicitly integrating temporal-spatial flow dependency, traffic flow dynamics with deep learning method techniques. Specifically, we first develop a graph theory-based method to identify the local temporal-spatial traffic dependency of each road among neighboring roads adaptive to the prediction horizon and traffic delay. Then, traffic speed propagation on every road is mathematically described by v -CTM based on traffic initial and boundary conditions. Next, the long short-term memory (LSTM) model is employed to predict boundary conditions factoring the traffic temporal-spatial dependency and historical data predicted by v -CTM. In this way, we well couple the physical models (traffic dependency and v -CTM) with the deep learning approach, and further make them coevolution under this framework. Last, an EKF is used to assimilate predicted traffic speed predicted by v -CTM coupled with the LSTMs and the field traffic data; an FNN is introduced to impute missing and corrupted data for improving the traffic speed prediction accuracy. The numerical experiments indicated that the TD2-DL predicted the network-wide traffic speed propagation in 30 minutes with accuracy varying from 85%-98%. It outperformed the tested models recently developed in literature. The ablation experimental results confirmed the significance of factoring traffic dependency and integrating data imputation and assimilation techniques for improving the prediction accuracy. [ABSTRACT FROM AUTHOR]

Abstract: Using two 238U samples placed in a gridded ionization chamber and a parallel-plate fission chamber, fluence of monoenergetic fast neutrons was determined. Four runs of measurements were performed. Analysis showed that although the neutron fluences for the two 238U samples differ by 20–33 times in the present work, the fluences at the position of the sample in the gridded ionization chamber determined by the two ways are in agreement within experimental uncertainties. [Copyright &y& Elsevier]