Search

Dialogue state tracking (DST) is a significant part of prevalent task-oriented dialogue systems, which monitor the user's goals based on current and previous dialogues for effective dialogue management. However, most existing approaches train DST on a single domain, ignoring the information across domains, and thus relevant slots in each domain must learn to cooperate. This paper deals this challenge by introducing a multi-view graph convolution and multi-agent reinforcement learning (MGCRL) method to help each domain-specific slot to learn to cooperate. Specifically, first, a multi-view graph is presented to provide more related information to transfer structured features among domain-specific slots across various domains. Compared with a single-view graph, multi-view has better complementarity, incorporating additional graphs for learning multiple types of association, thus improving the cross-domain information-sharing capability of DST models. On this basis, a multi-agent reinforcement learning method is presented to train DST models. Compared to current reinforcement learning methods used in DST models, the proposed multi-agent reinforcement learning utilizing multi-view graph convolutional networks allows each agent to learn to cooperate in multi-agent environments. Experimental results on the widely-used datasets MultiWOZ (Multi-Domain Wizard-of-Oz) 2.0/2.1 demonstrate that the proposed MGCRL method achieves a higher joint goal accuracy than the existing state-of-the-art DST models. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the relationship between platelet count (PC) and mortality in patients with hemorrhagic stroke (HS). The research reviewed data from 10,466 patients hospitalized in 208 hospitals in the United States from January 1, 2014, to December 31, 2015. Of these, 3262 HS patients were included in the primary analysis for those admitted to the intensive care unit (ICU). The average age of these patients was 67.05 years, with 52.79% being male. The median PC was (221.67 ± 73.78) × 109/L. Multivariate logistic regression analysis revealed that PC was a protective factor for mortality in HS patients (OR = 0.98, 95% CI 0.97–1.00, P < 0.05). Additionally, a non-linear association between PC and mortality in HS patients was found using a generalized additive model (GAM) and smooth curve fitting (penalty spline method). For the first time, a recursive algorithm identified the inflection point of platelet count as 194 × 109/L. On the left side of the inflection point, for every increase of 10 units in platelet count, the mortality rate of HS patients decreases by 10%. The study demonstrates a non-linear relationship between PC and the risk of mortality in HS patients. A platelet counts higher than the inflection point (194 × 109/L) may be a significant intervention to reduce mortality in HS patients. [ABSTRACT FROM AUTHOR]

Knowledge distillation (KD) is a widely adopted model compression technique for improving the performance of compact student models, by utilizing the "dark knowledge" of a large teacher model. However, previous studies have not adequately investigated the effectiveness of supervision from the teacher model, and overconfident predictions in the student model may degrade its performance. In this work, we propose a novel framework, Teacher–Student Complementary Sample Contrastive Distillation (TSCSCD), that alleviate these challenges. TSCSCD consists of three key components: Contrastive Sample Hardness (CSH), Supervision Signal Correction (SSC), and Student Self-Learning (SSL). Specifically, CSH evaluates the teacher's supervision for each sample by comparing the predictions of two compact models, one distilled from the teacher and the other trained from scratch. SSC corrects weak supervision according to CSH, while SSL employs integrated learning among multi-classifiers to regularize overconfident predictions. Extensive experiments on four real-world datasets demonstrate that TSCSCD outperforms recent state-of-the-art knowledge distillation techniques. • We present a supervision correction component for knowledge distillation (KD). • We propose a self-learning component that constrains the overconfident prediction. • The effectiveness of framework is evaluated on four real-world datasets. [ABSTRACT FROM AUTHOR]

Impurities 1,2-propylene glycol (PG), 1,2-butylene glycol (BG), 2,3-butylene glycol (23BG), 1,2-pentanediol (PDO), and 1,2-hexanediol (HDO) were isolated from ethylene glycol (EG) via biphasic-solvent extraction, which involves immiscible solvents saline water (S1) and ethyl acetate (S2). After adding S1 to the diol-S2 system, the partition coefficients were almost unaltered, while the selectivity coefficients were doubled or tripled. The larger the carbon-atom number of diol was, the higher the removal rate was. The removal rates of PG, BG, 23BG, PDO, and HDO reached 68%, 95.3%, 90.1%, 100%, and 100%, respectively. The EG content was increased from 72.7 to 91.2 wt%, and EG recovery reached 80.1%. The phase diagrams showed that the S1-EG-S2, S1-PDO-S2, and S1-HDO-S2 systems exhibited Treybal's type-II behaviors, while the S1-PG-S2 and S1-BG-S2 systems showed type-I characteristics. In addition, the low-carbon-number diols were miscible with S1 and rejected S2, while the high-carbon-number diols exhibited opposite characteristics. The intense endotherm during the dissolution of EG in ester implied that the increase of enthalpy of system is an important reason for the high chemical potential of EG. The obvious Tyndall phenomena of C 3–6 diols dispersed in saline water indicated that these diols formed into ordered micelles with low entropy, resulting in their high chemical potentials. The chemical potentials of diols in the biphasic solvents synergistically contributed to the separation selectivity. [Display omitted] • The selectivity coefficients were doubled or even tripled by using this novel strategy of biphasic-solvent extraction. • Removal rates of 1,2-butylene glycol, 1,2-pentanediol, and 1,2-hexanediol reached 95.3%, 100%, and 100%, respectively. • The purity and total recovery of EG reached 91.2 wt% and 80.1%, respectively. • The changes in enthalpy and entropy were measured by isothermal titration calorimetry and Tyndall-phenomenon experiments. • The different partition behaviors of diols were correlated with their chemical potentials. [ABSTRACT FROM AUTHOR]

The study of oscillating water column (OWC)-type wave energy converters (WEC) has primarily focused on applications in the nearshore environment with an end use in residential power grids. This study examines the power performance of a new OWC geometry relative to blue economy energy objectives that focus on providing power in the intermediate-water-depth environment. The method examines power performance through the performance indicators of extraction efficiency, coefficient of amplification, and coefficient of pressure. The study discusses the implications of these coefficients relative to a new end use and examines if an analytical relationship between each indicator exists. The power performance is evaluated through experimental testing on a fixed-geometry OWC that consists of a cylindrical OWC chamber affixed above a heave plate with V-shaped channels. In evaluating power performance, the impact of different representative power take-off (PTO) damping values and directional dependence is investigated. It was found that incident wave angle has a minimal impact on extraction efficiency for this geometry and that a relationship exists between extraction efficiency, wave frequency, and PTO damping. It can be concluded that extraction efficiency can be expressed as a function of each coefficient, that an emphasis on extraction efficiency may be misguided for at-sea power applications, and that performance indicators have functionality in OWC design outside of power performance. [ABSTRACT FROM AUTHOR]

A three-phase (air, water, and sediment) flow computational-fluid-dynamics model based on OpenFOAM was used in the present work to simulate the beach erosion induced by breaking solitary waves. The simulation results were first validated by comparing them against experimental measurements. The simulated time series of the surface elevation and pore pressure, as well as the simulated beach profile after erosion, compared fairly well with the experiment. The simulation results showed that the wave runup and rundown processes were accompanied by distinct hydrodynamics and sediment transport characteristics. The hydrodynamics in the runup stage is featured by a weaker flow before the wave breaking and a stronger one afterward, which then gradually weakens toward the runup limit. The hydrodynamics in the rundown stage is featured by a generally weaker flow compared to the runup stage. The flow is also stronger before the hydraulic jump and weaker afterward. A flow reversal and the exfiltration process are captured by the numerical model at the end of the rundown stage. The sediment transport in the runup stage is featured by an intense rate but a short duration, while the sediment transport in the rundown stage is much slower but has a longer duration. As a result, the total amounts of the transported sediment in the runup and rundown stages were comparable to each other. Finally, a discussion on the limitations of the numerical model and possible modifications to further improve the model in the future is presented. [ABSTRACT FROM AUTHOR]

Abstract The safety and integrity of the global food system is in a constant state of flux with persistent chemical and microbial risks. While chemical risks are being managed systematically, microbial risks pose extra challenges. Antimicrobial resistant microorganism and persistence of related antibiotic resistance genes (ARGs) in the food chain adds an extra dimension to the management of microbial risks. Because the food chain microbiome is a key interface in the global health system, these microbes can affect health in many ways. In this review, we systematically summarize the distribution of ARGs in foods, describe the potential transmission pathway and transfer mechanism of ARGs from farm to fork, and discuss potential food safety problems and challenges. Modulating antimicrobial resistomes in the food chain facilitates a sustainable global food production system. [ABSTRACT FROM AUTHOR]

In recent years, with the development of green environmental protection, starch film has become of interest due to the wide availability of sources, low price, and biodegradability. Amylose/polyvinyl alcohol (PVA) blend films crosslinked with different amounts of glutaraldehyde (GLU) were prepared by a solution casting method. The cross-linking degree, water sorption, tensile property, crystallization and section morphology of the films were examined. With the increase in glutaraldehyde concentration, the cross-linking degree of the blend film was improved. The wide-angle X-ray scattering (WAXS) result indicated that cross-linking hindered the crystallization of film. The section morphology of films was examined by scanning electron microscope (SEM). The results showed that the cross-linking degree of amylose film improved while the crystallinity decreased with the increase in glutaraldehyde content. Cross-linking had no obvious effect on the water sorption property of the blend films. The cross-linking modification significantly enhanced the tensile strength and Young's modulus, while it reduced the elongation at break of the blend films. It was found that the film with 0.5 wt % glutaraldehyde possessed the best performance: the tensile strength increased by 115%, while the elongation at break decreased by 18% even at high relative humidity (RH) of 90% compared to non-crosslinked films. The developed amylose/PVA blend films have promising application prospects as agricultural mulch films and packaging materials. [ABSTRACT FROM AUTHOR]

Advanced oxidation processes (AOPs) based on peroxymonosulfate (PMS) activation have been developed as an ideal pathway for completely eradication of recalcitrant organic pollutants from water environment. Herein, the V-doped graphitic carbon nitride (g-C3N4) is rationally fabricated by one-step thermal polymerization method to activate PMS for contamination decontamination. The results demonstrate the V atoms are successfully integrated into the framework of g-C3N4, which can effectively improve light absorption intensity and enhance charge separation. The V-doped g-C3N4 displays superior catalytic performance for PMS activation. Moreover, the doping content has a great influence on the activation performances. The radical quenching experiments confirm •O2−, SO4•−, and h+ are the significant species in the catalytic reaction. This work would provide a feasible strategy to exploit efficient g-C3N4-based material for PMS activation. [ABSTRACT FROM AUTHOR]

Group recommendation has recently drawn a lot of attention to the recommender system community. Currently, several deep learning-based approaches are leveraged to learn preferences of groups for items and predict next items in which groups may be interested. Yet, their recommendation performance is still unsatisfactory due to sparse group–item interactions. To address this challenge, this study presents a novel model, called group recommendation model with two-stage deep learning (GRMTDL), which encompasses two sequential stages: 1) group representation learning (GRL) and 2) group preference learning (GPL). In GRL, we first construct an undirected tripartite graph over group–user–item interactions, and then employ it to accurately learn group semantic features through a spatial-based variational graph autoencoder network. While in GPL, we first introduce a dual PL-network that contains two structure-sharing subnetworks: 1) group PL-network employed for GPL and 2) user PL-network utilized for user preference learning. Then, we design a novel layered transfer learning (LTL) method to learn group preferences by alternately optimizing these two subnetworks. In particular, it can effectively absorb knowledge of user preferences into the process of GPL. Furthermore, extensive experiments on four real-world datasets demonstrate that the proposed GRMTDL model outperforms the state-of-the-art baselines for group recommendation. [ABSTRACT FROM AUTHOR]

Cyanobacterial blooms, which not only threaten the health and stability of aquatic ecosystems but also influence the microbial community within, emerges as one of the most concerning problems in China. However, how cyanobacterial blooms affect the spatiotemporal variation of aquatic microbial communities remains relatively unclear. In this study, we used high-throughput sequencing to investigate how the cyanobacterial and bacterial community spatiotemporally vary along with main cyanobacterial bloom phases in upstream rivers of a eutrophicated water source reservoir. Both cyanobacterial and bacterial diversities in each river were significantly lower (P < 0.05) during the bloom outbreak phase, showing the apparent influence of cyanobacterial bloom. Dominant cyanobacterial taxa included Cyanobacteriales and Synechococcales, and dominant bacterial taxa comprised Acinetobacter, CL500-29, hgcI clade, Limnohabitans, Flavobacterium, Rhodoluna, Porphyrobacter, Rhodobacter, Pseudomonas, and Rhizobiales, whose changes of relative abundance along with the bloom indicated distinct community composition. Non-metric multidimensional scaling analysis proved that community composition had significant difference amongst bloom phases. Linear discriminant analysis (LDA) with LDA effect size analysis (LEfSe) identified unique dominant cyanobacterial and bacterial OTUs at different phases in each river, indicating spatiotemporal variations of communities. Canonical correlation analysis or redundancy analysis revealed that at different bloom phases communities of each river had distinct correlation patterns with the environmental parameters (temperature, ammonium, nitrate, and total phosphorus etc.), implying the spatial variations of microbial communities. Overall, these results expand current understanding on the spatiotemporal variations of microbial communities due to cyanobacterial blooms. Microbial interactions during the bloom may shed light on controlling cyanobacterial blooms in the similar aquatic ecosystems. [ABSTRACT FROM AUTHOR]

Integrating oscillating water columns (OWCs) into a row of closely-spaced hollow piles can form a wave farm, leading to cost reductions through cost-sharing with other structures and thus making the wave energy conversion more economically viable. It is important to understand the effects of the distance between the OWCs in the row on the performance of the wave farm. Using an air-water two-phase flow model, validated by a set of wave-flume test results, the hydrodynamic performance of the wave farm was investigated, with a focus on effects of the distance between OWCs on capture efficiency as well as wave transmission and reflection. The power takeoff (PTO) was represented by an orifice whose effects were parameterized by a porous media in the simulation. Sidewall effects that may be encountered in wave flume tests were also discussed. [ABSTRACT FROM AUTHOR]

[Display omitted] • EG was efficiently and instantly isolated from PG via adsorption with basic resin. • Moisture improved the intraparticle diffusion rate and adsorption performance. • The apparent EG adsorption capacity reached 250.6 mg/g. • Acid-base interaction between EG and resin contributed to the adsorption effect. Ethylene glycol (EG) is a common impurity in bio-derived 1,2-propylene glycol (PG). EG was isolated from PG via selective adsorption on basic ion-exchange resin. A small amount of moisture improved the porosity of resin, thereby reinforcing the intraparticle diffusion and adsorption performance. The adsorption equilibrium could be reached within 1 min, indicating that EG can be instantly removed. The real adsorption capacity for PG was much lower than that for EG, and the selectivity was up to 5.5 at 60 °C due to the susceptibility of PG adsorption to temperature. The PG content was increased from 90 to 95.7 wt% in a continuous-flow process with low bed pressure drop. The EG adsorption process agreed with the Langmuir isotherm model, and the apparent EG adsorption capacity reached 250.6 mg/g at 60 °C, while real PG adsorption capacity was negligible. The different interactions between diols and OH− ions were proven by UV and surface-tension measurements, and the stronger acidity of hydroxyl group in EG contributed to its stronger acid-base interaction with basic materials. The resin adsorbent could be reused and completely regenerated without leaching via elution with 20 wt% NaOH. Based on the acidity of EG, this work presents an unprecedented cost-effective strategy to separate diols. [ABSTRACT FROM AUTHOR]

• Self-Restoring cryogels with chitosan and hydroxyapatite nanowires was reported. • CSHAn2 cryogels exhibited an impressive absorption capacity of water/blood. • CSHAn2 cryogels demonstrate superior blood-clotting ability, enhanced blood cell and platelet adhesion. • CSHAn2 significantly promote osteogenesis by upregulating the formation of alkaline phosphatase and calcium nodules. Bleeding from severe traumatic bone defects poses a significant threat to patient survival, and conventional hemostatic agents prove inadequate for addressing bone defects when pressure application is not feasible. Therefore, the development of a bio-multifunctional material with outstanding hemostatic and osteogenic performances is highly desirable. In this study, chitosan/hydroxyapatite nanowires cryogels (CSHAn) were fabricated via schiff base reaction and electrostatic interaction. These CSHAn cryogels demonstrate an impressive absorption capacity, reaching up to 5387 %, and possess considerable mechanical strength, quantified at 167.92 kPa, along with significant compressive recovery capabilities. CSHAn cryogels demonstrate superior blood-clotting ability, enhanced blood cell and platelet adhesion, and activation compared to gelatin sponges. Moreover, in vitro cellular assessments indicate that the developed CSHAn2 exhibits favorable cytocompatibility and significantly promote osteogenesis by upregulating the formation of alkaline phosphatase and calcium nodules. Taken together, this multi-functional cryogels is potentially valuable for clinical applications towards hemorrhagic bone defect repair. [ABSTRACT FROM AUTHOR]

Intelligent video surveillance is an important computer vision application in natural environments. Since detected objects under surveillance are usually low-resolution and noisy, their accurate recognition represents a huge challenge. Knowledge distillation is an effective method to deal with it, but existing related work usually focuses on reducing the channel count of a student network, not feature map size. As a result, they cannot transfer “privilege information” hidden in feature maps of a wide and deep teacher network into a thin and shallow student one, leading to the latter’s poor performance. To address this issue, we propose a Feature Map Distillation (FMD) framework under which the feature map size of teacher and student networks is different. FMD consists of two main components: Feature Decoder Distillation (FDD) and Feature Map Consistency-enforcement (FMC). FDD reconstructs the shallow texture features of a thin student network to approximate the corresponding samples in a teacher network, which allows the high-resolution ones to directly guide the learning of the shallow features of the student network. FMC makes the size and direction of each deep feature map consistent between student and teacher networks, which constrains each pair of feature maps to produce the same feature distribution. FDD and FMC allow a thin student network to learn rich “privilege information” in feature maps of a wide teacher network. The overall performance of FMD is verified in multiple recognition tasks by comparing it with state-of-the-art knowledge distillation methods on low-resolution and noisy objects. [ABSTRACT FROM AUTHOR]

Subsidy incentive programs for accelerating the replacement of construction equipment are widely considered effective for reducing emissions from construction equipment. However, there are no known effective models for determining the optimal subsidy levels for voluntary early replacement of in-use high-emission construction equipment. In this study, a novel quantitative model is developed to address this problem by integrating emission reduction targets and life cycle cost of construction equipment to calculate the reduced service life of an item of construction for early replacement. The economic life of construction equipment and its corresponding equivalent uniform annual cost (EUAC) can be determined by using a traditional life cycle cost analysis model. Optimal subsidy levels are obtained on the basis that the subsidized EUAC of a piece of construction equipment over its shortened service life is not more than that over its normal economic life. The applicability of the proposed model is demonstrated through a case study of a crawler crane used in Hong Kong, whereby the subsidy levels for accelerating its replacement are determined. An interview with the contractor's equipment manager responsible for the crawler crane is conducted to validate the findings of the model. It is found that the optimal subsidy levels determined by the model can accelerate the replacement of construction equipment and achieve the goal of reducing emissions from construction equipment. Replacing construction equipment earlier than its economic life is a plausible strategy for emissions reduction but places a heavy financial burden on contractors if there is no financial compensation. This study shows that the subsidy levels determined by the proposed model can adequately compensate for the extra costs incurred from early replacement and would therefore not financially discourage contractors. This model also shows that the cost-effectiveness of subsidies increases as emission reduction targets are set at higher levels. [ABSTRACT FROM AUTHOR]

The dissolution of microcrystalline cellulose (MCC) in a urea-NaOH system is beneficial for its mechanical processing. The apparent MCC solubility was greatly improved to 14 wt% under a slow-cooling condition with a cooling rate of −0.3 °C/min. The cooling curve or thermal history played a crucial role in the dissolution process. An exotherm (−54.7 ± 3 J/g MCC) was detected by DSC only under the slow-cooling condition, and the cryogenic dissolution of MCC was attributed to the exothermic interaction between MCC and solvent. More importantly, the low cooling rate promoted the dissolution of MCC by providing enough time for the diffusion of OH− and urea into MCC granules at higher temperatures. The Raman spectral data showed that the intramolecularly and intermolecularly hydrogen bonds in cellulose were cleaved by NaOH and urea, respectively. XPS and solid-state 13C NMR results showed that hydrogen bonds were generated after dissolution, and a dual-hydrogen-bond binding mode between urea and cellulose was confirmed by DFT calculations. Both the decrease of enthalpy and increase of entropy dominated the spontaneity of MCC dissolution, and that is the reason for the indispensability of cryogenic environment. The high apparent solubility of MCC in the slow-cooling process and the dissolution mechanism are beneficial for the studies on cellulose modification and mechanical processing. [Display omitted] • Apparent MCC solubility was improved to 14 wt% by a new slow-cooling method. • Intramolecularly and intermolecularly hydrogen bonds were cleaved by NaOH and urea. • The spontaneity depends on both the decrease of enthalpy and increase of entropy. [ABSTRACT FROM AUTHOR]

Subaerial landslides falling into a large body of water can generate large impulse waves (tsunamis). The wave generation by subaerial landslides involves sediment, water and air, and thus multi-phase flow models are most suitable for simulating the processes involved. This paper reports a laboratory and numerical study of impulse waves generated by subaerial landslides, with a focus on grain size effects. Four different grain sizes were examined in this study, ranging from fine sand to very coarse sand. A multi-phase flow model was used to perform the numerical simulations of the laboratory experiment. Both measured and simulated results have shown that the speed of the granular front and the height of the impulse wave increase with decreasing grains size. Possible explanations for the effects of grain size on the landslide process and the resulting waves are discussed. [ABSTRACT FROM AUTHOR]

Path tracking control of intelligent vehicles has to deal with the difficulties of model uncertainties and nonlinearities. As a class of adaptive optimal control methods, reinforcement learning (RL) has received increasing attention in solving difficult control problems. However, feature representation and online learning ability are two major problems to be solved for learning control of uncertain dynamic systems. In this article, we propose a multi-kernel online RL approach for path tracking control of intelligent vehicles. In the proposed approach, a multiple kernel feature learning framework is designed for online learning control based on dual heuristic programming (DHP) and the new online learning control algorithm is called multi-kernel DHP (MKDHP). In MKDHP, instead of the expert knowledge for selecting and fine-tuning of a suitable kernel function, only a set of basic kernel functions is required to be predefined and the multi-kernel features can be learned for value function approximation in the critic. The simulation studies on path tracking control for intelligent vehicles have been conducted under $S$ -curve and urban road conditions. The results demonstrated that compared with other typical path tracking controllers for intelligent vehicles, such as the linear quadratic regulator (LQR), the pure pursuit controller and the ribbon-based controller, the proposed multi-kernel learning controller can achieve better performance in terms of tracking precision and smoothness. [ABSTRACT FROM AUTHOR]

Contribution: A systematic interdisciplinary engineering and technology instructional model using cutting-edge technologies is proposed in this research. This model consists of four key components: 1) interdisciplinary collaboration; 2) hands-on projects; 3) real-world simulations; and 4) cutting-edge technologies. The model was designed to engage students in the real-world engineering projects and experience through the following activities: 1) performing the design, fabrication, and lab testing; 2) modeling and analyzing testing samples; 3) conducting on-site experiments; and 4) performing data processing and analysis. Background: Engineering programs are challenged by the rapid development of innovative technologies. To address this challenge, it is required to provide engineering graduates with “soft skills” for the capability of project management, especially the communication and collaboration among disciplines through university education. Therefore, it is desired to develop a systematic interdisciplinary engineering and technology educational strategy. Research questions: 1) is the proposed systematic interdisciplinary instructional model appropriate and feasible to be integrated into the engineering curriculum? 2) what were the students’ perceptions and experiences about the interdisciplinary model? 3) does the interdisciplinary model facilitate engineering students’ soft skills, such as interdisciplinary communication and collaboration? and 4) does the interdisciplinary model help to enhance engineering students’ real-world professional experience, develop design thinking and problem-solving skills, which will help them to transfer their learning experience from the classroom setting to the real-world setting? Methodology: A systematic interdisciplinary engineering and technology model was developed, implemented, and evaluated. Findings: The feasibility and effectiveness of the proposed systematic instructional model were validated by various data sources. The tested model is recommended for broader adaptation in various STEM disciplines. [ABSTRACT FROM AUTHOR]

To investigate the effect of infrastructure traits on resilience after an exploration, a blast case (West Fertilizer Plant in West, Texas, 2013) was studied, in which all the buildings' damage data (damage pictures, damage scales, and building locations) and resilience information (recovery decision, recovery time, and recovery cost) were collected by authors through site visits, interviews, and appraisal data collections. The novel analysis methods and machine learning algorithms (logistical/linear regression, neural networks, k-nearest neighbor, support vector machine, and gradient boosting) were applied to analyze the West Fertilizer Plant explosion resilience. This study is unique because it implements a resilience analysis for an explosion hazard, although there are some reports discussing the resilience after natural hazards, such as earthquakes, tsunamis, hurricanes, and tornados. Additionally, using machine learning for resilience analysis is also unique. The results can assist decision-makers, civil engineers, and building designers in designing the most resilient structures and/or materials for buildings. The findings in this study can help to develop the most resilient buildings, communities, and cities by considering the impact of explosion hazards. [ABSTRACT FROM AUTHOR]

This study uses a multi-phase flow model to examine the impulsive waves generated by collapse of steep slopes at a water body. Possible effects of slope's erodibility on the motion of the landslide and the resulting waves are investigated by comparing a sub-aerial landslide on a rigid slope to that on an erodible slope. Key parameters that are relevant to the generation of the impulsive waves are examined, including the time series of the elevation and location of first wave crest, the location and thickness of granular flow front, submerged granular volume, and potential and kinetic energy for water and granular landslide. Before it is free from the influence of the granular front, the generated wave is a forced wave traveling at the speed of the granular flow front. The first wave crest is about to leave the granular flow front when the speed of the granular front is close to the celerity at which a free long wave can propagate in the water above the granular front, and the maximum height of the first wave crest is reached at the time when the first wave has completed its transition from a forced wave to a free wave. It appears that the erodible slope can increase (1) the volume of the granular material plunged into water, (2) the thickness of granular flow front, and (3) the height of the first wave generated by the landslide. However, the total volume of the granular material plunged into water is not directly related to the height of the first wave. [ABSTRACT FROM AUTHOR]

• The H-B model is more suitable for describing the rheological behavior of TPB and CPB. • Variation of TPB rheological properties with temperature as a result of competition between zeta potential and water activity. • The hydration reaction of the cementing agent causes the rheological parameters of CPB to change exponentially. • Higher temperatures are not conducive to conveying work in complex pipelines. This study focuses on investigating the rheological properties and micro-mechanisms of paste filling materials under varying temperatures, which is crucial for addressing the issue of transporting paste materials through pipelines at different temperatures. The rheological properties of Pure Tailing sand Paste Backfill (TPB) with a mass concentration set at 66% and Cement Paste Backfill (CPB) with a 1:4 ratio of the cementing agent were analyzed at different temperatures were compared and analyzed at different temperatures using rheometer, Zeta potential, and low-field nuclear magnetic resonance measurement technology. The study aims to identify the macroscopic trends in the dynamic yield stress and viscosity coefficient of paste slurry under temperature influence, and to determine the microscopic mechanisms of the competition between water activity and electrostatic interaction between particles. The study not only examines the characteristics of paste slurry at macro and micro levels but also provides a model of rheological parameters and temperature for the optimization of paste filling technology. Studies have shown that temperature has the opposite effect on dynamic yield stress and consistency factor, and both should be considered in practical applications. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNNs) have demonstrated great success in graph processing. However, current message-passing-based GNNs have limitations in terms of feature aggregations and update mechanisms that rely on a fixed mode, resulting in inadequate representations of the neighborhood structure's richness. Furthermore, the convolution layer in most GNNs lacks flexibility and multiple channels compared to convolutional neural networks (CNNs), which employ multiple channels, where different kernels capture shared patterns of images. To address these limitations, we propose a novel scalable graph convolution network (SGCN) to enhance structural information learning by introducing an expressive feature aggregation mechanism. Drawing inspiration from the design of CNNs, the SGCN includes graph-shaped kernels that perform multichannel convolutions on the subgraph structure. A sampler based on degree centrality is employed to simplify the computation costs, and extensive experiments demonstrate that the SGCN achieved state-of-the-art performances on graph datasets with various scales (with accuracy improvements of up to 5.38%). • We introduce a novel GNN architecture that utilizes flexible graph-shaped convolution kernels and multi-channels. • Our model outperforms existing GNNs in node classification across multiple graph datasets. • The proposed model is capable of handling graphs of different scales. [ABSTRACT FROM AUTHOR]

Out-of-hospital cardiac arrest (OHCA) due to drowning carries high morbidity and mortality. There are a few studies on drowning-related out-of-hospital cardiac arrest (OHCA), in which patients are followed from the scene to hospital discharge. This study aims to compare patient characteristics between the survival group and mortality group of OHCA due to drowning. OHCA due to drowning cases were selected from the North America Termination of Resuscitation Association database between 2011 and 2015. The retrospective analysis of epidemiological characteristics and clinical features of all OHCA patients were performed. Of the 17,094 OHCA cases in the registry, 54 cases of OHCA due to drowning were included in this study. Among the 54 OHCAs due to drowning, 7 (13.0%) survived, while 47 (87.0%) died. Compared to the mortality group, the survival group had a higher bystander witness rate (57.1% versus 17.0%, p < 0.05), higher asystole rate (42.9% versus 78.7%, p < 0.05), and higher mild therapeutic hypothermia rate (28.6% versus 2.1%, p < 0.05). In addition, a large proportion of survivors were children (71.4%) and males (71.4%). Survival among OHCA's due to drowning was found to be improved with a higher bystander rate, higher asystole rate, and higher mild hypothermia rate. In addition, children and males comprised the majority of survivors. [ABSTRACT FROM AUTHOR]

It has been proven that vitamin D was decreased and function of circulating endothelial progenitor cells (EPCs) was injured in systemic lupus erythematosus (SLE) patients. However, the effect of vitamin D on the function of EPCs in vitro and its mechanism need further study. Therefore, we investigated whether vitamin D improved the function of EPCs in vitro. The peripheral blood mononuclear cells of the participants were isolated from SLE patients and control subjects and cultured to EPCs. After the EPCs were treated with vitamin D (1,25-(OH)2D3), we evaluated the number, migratory and proliferative activities, and nitric oxide (NO) production of EPCs in vitro and detected vascular endothelial function by flow-mediated dilatation (FMD). We found that vitamin D in a dose-dependent manner improved number and migratory and proliferative activities of EPCs from SLE patients. Additionally, vitamin D upregulated NO production from EPCs in vitro. A significant correlation between the FMD and plasma NO level was found. There was also a correlation between number, migration, and proliferation of EPCs and NO production. Thus, the present findings indicated that vitamin D improved the function of EPCs from SLE patients via NO secretion. [ABSTRACT FROM AUTHOR]

This essay reviews three books by Xu Yong that examine three critical historical processes of political transformation in rural China: the politicization of society due to urban–rural disparity, the government's efforts to encourage political integration in rural areas, and the development of rural grassroots democracy. Urban–rural disparity has been a structural characteristic of China's politicized society since the establishment of the monarchy. The analysis of this inequality focuses on uncovering grassroots society (as opposed to Chinese society's upper echelons) and examining its evolutionary logic. Since 1949, China has faced the historical task of building a modern state. The government aggressively entered the countryside through large-scale political mobilization and social integration with the Chinese Communist Party as the driving force. The goal was to create a strongly integrated communist nation. Since the 1980s, the Chinese countryside has not only experienced economic liberalization but also received an opportunity for political democratization through the creation and practice of village autonomy. Self-government in autonomous villages has provided Chinese peasants with a wide range of democratic rights. In addition, calls for transformation and promotion of the democratic paradigm have increased. Xu's three books effectively present many critical aspects of China's rural political transformations. However, questions remain concerning the consistency of the theory and the accuracy of the analysis, which leaves room for further research and discussion. [ABSTRACT FROM AUTHOR]

Liu, C. and Huang, Z., 2020. A flexible membrane breakwater with a piezoelectric layer for providing harborage and wave-energy conversion. Journal of Coastal Research, 36(1), 148–156. Coconut Creek (Florida), ISSN 0749-0208. Integration of wave-energy converters with coastal structures provides a way to make wave-energy utilization economically viable. This theoretical study examines a piezoelectric membrane system that serves as a breakwater and a wave-energy harvester. The system is in the form of a piezoelectric layer built into a flexible membrane wave barrier. Series resistor-inductor-capacitor electric circuits are used to obtain electrical output power from ocean waves through the piezoelectric layer. This type of piezoelectric membrane system is proposed for providing harborage and wave-energy extraction at marinas and small boat harbors. Use of an eigenfunction expansion method, solutions to the wave-scattering problem, the calculation of the deflection of the piezoelectric membrane, and an analytical expression for the electric-power output per unit surface area of the piezoelectric layer (output power density) are described. The output power density can reach its maximum value at an optimum resistance, which increases with the thickness and surface area of the piezoelectric layer and decreases with wave frequency. At the optimum resistance, the maximum power output density and the minimum transmission coefficient occur at the same critical frequency, which is controlled by the dimensions and initial tension of the piezoelectric membrane. The surface area and the inductance of the piezoelectric layer have little effect on the output power density, which increases with the thickness of piezoelectric layer nearly linearly. [ABSTRACT FROM AUTHOR]

Farnesoid X receptor (FXR), a member of the nuclear receptor superfamily that controls bile acid (BA) homeostasis, has also been proposed as a tumor suppressor for breast and liver cancer. However, its role in pancreatic ductal adenocarcinoma (PDAC) tumorigenesis remains controversial. We recently found that FXR attenuates acinar cell autophagy in chronic pancreatitis resulting in reduced autophagy and promotion of pancreatic carcinogenesis. Feeding Kras-p48-Cre (KC) mice with the BA chenodeoxycholic acid (CDCA), an FXR agonist, attenuated pancreatic intraepithelial neoplasia (PanIN) progression, reduced cell proliferation, neoplastic cells and autophagic activity, and increased acinar cells, elevated pro-inflammatory cytokines and chemokines, with a compensatory increase in the anti-inflammatory response. Surprisingly, FXR-deficient KC mice did not show any response to CDCA, suggesting that CDCA attenuates PanIN progression and decelerate tumorigenesis in KC mice through activating pancreatic FXR. FXR is activated in pancreatic cancer cell lines in response to CDCA in vitro. FXR levels were highly increased in adjuvant and neoadjuvant PDAC tissue compared to healthy pancreatic tissue, indicating that FXR is expressed and potentially activated in human PDAC. These results suggest that BA exposure activates inflammation and suppresses autophagy in KC mice, resulting in reduced PanIN lesion progression. These data suggest that activation of pancreatic FXR has a protective role by reducing the growth of pre-cancerous PDAC lesions in response to CDCA and possibly other FXR agonists. • Bile acids have been correlated with poor survival in metastatic PDAC patients. • Bile acids activate pancreatic FXR in vitro and in vivo. • FXR is highly activated in chemo-naïve and neoadjuvant human PDAC tissue. • FXR activation by BAs reduce pancreatic precancerous lesions in the KC mouse model. • Loss of FXR eliminates the reduction of pancreatic precancerous lesions by BAs • FXR is a potential tumor suppressor in PDAC. [ABSTRACT FROM AUTHOR]

Group recommendation has garnered significant attention recently, aiming to suggest items of interest to groups. Most deep learning-based approaches for group recommendation focus on learning group representation through group–user interactions or by aggregating user preferences. However, these approaches face challenges due to the complexity of group representation learning and the limited availability of group–item interactions. To address these difficulties, we propose a multi-graph neural group recommendation model with meta-learning and multi-teacher distillation. Our model consists of three stages: multiple graphs representation learning (MGRL), meta-learning-based knowledge transfer (MLKT), and multi-teacher distillation (MTD). In the MGRL stage, we construct two bipartite graphs and an undirected weighted graph based on group–user interactions, group–item interactions, and similarity between groups, respectively. We then utilize a linear variational graph auto-encoder network to learn the semantic features of groups, users, and items. In the MLKT stage, we introduce a multilayer perceptron (MLP) network for the recommendation task and design a meta-learning-based knowledge transfer algorithm to train an initialization parameter that incorporates user preference knowledge. In the MTD stage, we use the trained parameters as initialization parameters for all teacher networks. We evenly divide the group–item interactions among the teachers and sequentially train the teacher networks. Finally, we employ a multi-teacher distillation approach to train student networks with superior performance. Furthermore, we conduct extensive experiments on six real-world datasets to evaluate the effectiveness of our proposed model. The experimental findings demonstrate that our model, in comparison to state-of-the-art approaches, achieves superior performance in terms of group recommendation. • A novel multi-graph neural group recommendation model is proposed. • A multiple graphs representation learning method is designed. • A meta-learning-based knowledge transfer method is introduced. • A multi-teacher distillation method is presented. [ABSTRACT FROM AUTHOR]

A three-dimensional (3D) numerical model that solves the Reynolds-Averaged Navier–Stokes (RANS) equations coupled with k − ω turbulence closure and VOF method is developed with the open source CFD tool OpenFOAM to simulate the hydrodynamics in an idealized reef-lagoon-channel coastal system. Comparison with experimental measurements shows satisfactory agreements in terms of the wave transformation, wave-induced setup and the mean current. The validated simulation result is further analyzed to investigate the detailed wave transformation, mass and momentum transport processes in the reef-lagoon-channel system, where both the differential and integral form of the depth-integrated time-averaged momentum equations are applied. The mass balance shows that the cross-shore flow on the reef flat is almost uniform on cross-shore transects away from the channel, while the lagoonal flow gradually increases in strength as it approaches the channel. The cross-shore momentum balance shows that the excess momentum flux and hydrostatic pressure forces dominate in the shoaling and breaker zones as expected, while the bottom friction force plays a more important role in the breaker zone than previous studies assumed. On the reef flat where the wave forces become weak, the integral form of the momentum balance shows advantages over the differential form due to its higher accuracy, which enables us to identify that the bottom friction and hydrostatic pressure forces dominate the momentum balance on the reef flat. Finally, the reliability of the differential and integral form of momentum equations and the effect of a depth non-uniform current on the excess momentum flux are discussed. • A 3D CFD simulation of wave transformation and the resulting nearshore circulation is reported. • The nearshore circulation is initiated by the strong current generated by wave breaking on the reef flat. • Water mass on the reef flat is transported into the lagoon in the form of a nears-surface jet flow. • The strong shear in mean flow may affect the validity of depth-integrated equations. [ABSTRACT FROM AUTHOR]

Abstract This paper introduces a time-domain potential flow method for simulating fully nonlinear wave interaction with a fluid-filled membrane mounted on the seabed. The numerical method is based on a mixed Eulerian-Lagrangian method, which tracks the free water surface and the membrane using a Lagrangian method and solves the flow fields inside and outside the membrane using an Eulerian method. To remove the numerical instability, a method that satisfies the no-flux condition on the membrane at any location and any time was developed to calculate the radial displacement of the membrane at every time step and a Fourier series expression for the radial displacement of the membrane was used to calculate the local curvature of the membrane accurately. Experimental results from an existing experimental study were used for model validation and verification. The response of the membrane and the nonlinear wave scattering were simulated. The simulation results show that the transmission coefficients of the fundamental waves, second harmonic free and locked waves approach zero when a resonant response of the membrane to waves occurs. Sample results of the simulated tension in the membrane and interior pressure are also included in the discussion. [ABSTRACT FROM AUTHOR]

This paper presents a parameterized batch reinforcement learning algorithm for near-optimal longitudinal control of autonomous land vehicles (ALVs). The proposed approach uses an actor-critic architecture, where parameterized feature vectors based on kernels are learned from collected samples for approximating the value functions and policies. One difference between the parameterized batch actor-critic (PBAC) algorithm and previous actor-critic learning approaches is that the critic and actor in PBAC share the same linear features, which has been theoretically proved to be a beneficial property for the convergence of actor-critic learning approaches. In order to obtain better learning efficiency, least-squares-based batch updating rules are designed for the critic and actor, respectively. Based on the PBAC learning algorithm, a data-driven longitudinal control method is presented for ALVs to obtain near-optimal control policies which adaptively tune the fuel/brake control signals to track different speeds. A multiobjective reward function is designed so that both tracking precision and driving smoothness are considered. Extensive experiments were conducted on a real ALV platform while driving on flat, slippery, sloping, and bumpy roads. The experimental results illustrate the superiority of the PBAC-based self-learning controller over conventional longitudinal control methods such as proportional-integral (PI) control and learning-based PI control. [ABSTRACT FROM AUTHOR]

Abstract This study presents a three-dimensional numerical simulation of a circular bottom-sitting OWC device for wave energy extraction. The numerical model is based on Reynolds-Averaged Navier-Stokes equations with a modified k- ω turbulence model and a Volume-of-Fluid method to track the air-water surface. The numerical model and setup are validated against a set of wave-flume experimental results. The numerical simulation provides detailed flow-field information, which allows for an analysis of the spatial non-uniformity inside the OWC chamber and the measurement error associated with it. New numerical results show that vortex shedding at the sharp edge of the OWC chamber enhances the spatial non-uniformity inside the OWC chamber through a resonant sloshing mechanism. The enhanced spatial non-uniformity can affect the accuracy of the experimentally-determined quantities such as the extraction efficiency of the OWC chamber and the characteristics of the power take-off device; Suggestions are also provided to improve the accuracy of the experimentally-determined quantities that may be affected by the spatial non-uniformity inside the OWC chamber. [ABSTRACT FROM AUTHOR]

In general, the stock prices of the same industry have a similar trend, but those of different industries do not. When investing in stocks of different industries, one should select the optimal model from lots of trading models for each industry because any model may not be suitable for capturing the stock trends of all industries. However, the study has not been carried out at present. In this paper, firstly we select 424 S&P 500 index component stocks (SPICS) and 185 CSI 300 index component stocks (CSICS) as the research objects from 2010 to 2017, divide them into 9 industries such as finance and energy respectively. Secondly, we apply 12 widely used machine learning algorithms to generate stock trading signals in different industries and execute the back-testing based on the trading signals. Thirdly, we use a non-parametric statistical test to evaluate whether there are significant differences among the trading performance evaluation indicators (PEI) of different models in the same industry. Finally, we propose a series of rules to select the optimal models for stock investment of every industry. The analytical results on SPICS and CSICS show that we can find the optimal trading models for each industry based on the statistical tests and the rules. Most importantly, the PEI of the best algorithms can be significantly better than that of the benchmark index and “Buy and Hold” strategy. Therefore, the algorithms can be used for making profits from industry stock trading. [ABSTRACT FROM AUTHOR]

Discovering hidden knowledge patterns in trajectory data can help to hunt passengers for taxi drivers. And it is an important issue in the intelligent transportation domain. However, the existing approaches are inaccurate in real applications. Hence in this paper, by using the GPS trajectory big data of taxis, we innovatively present an efficient and effective recommendation system (TRec) for hunting passengers with deep neural structures. This proposed recommendation system is mainly based on the wide & deep model, which is trained wide linear frameworks and deep neural networks together and can simultaneously have the benefits of memorization and generalization to hunt passengers. Meanwhile, in order to improve the accuracy of hunt passengers, our proposed recommendation system uses experienced taxi drivers as learning objects, while considering the prediction of hunting passengers, the prediction of road condition and the evaluation of earnings simultaneously. A performance study using the real GPS trajectory dataset is conducted to evaluate our proposed recommendation system. The experimental evaluation shows that the proposed recommendation system is both efficient and effective. This work strides forward a first step toward building a recommendation system for hunting passengers based on the wide & deep model. [ABSTRACT FROM AUTHOR]

Highlights • Characteristics of 3D scour at a pile breakwater were studied experimentally. • Solitary waves were used to represent flows in extreme events such as tsunami. • Maximum scour depth occurred at the gap between two adjacent piles. • Sand bar formed on the down-wave side of the pile breakwater. • An empirical formula was proposed to describe the development of the scour. Abstract Understanding the sediment transport and the resulting scour around coastal structures such as pile breakwaters under local extreme wave conditions is important for the foundation safety of various coastal structures. This study reports a wave-flume experiment investigating the scour induced by solitary waves at a pile breakwater, which consists of a row of closely spaced large piles. A wave blacking gate with a simple operation procedure in the experiment was designed to eliminate possible multiple reflections of the solitary wave inside the flume. An underwater laser scanner and a point probe were used in combination to provide high-resolution data of the bed profile around the pile breakwater. Effects of incident wave height and local water depth on the maximum scour depth, the maximum deposition height and the total scour and deposition volumes were examined. An existing empirical formula describing the evolution of the scour at a single pile in current or waves was extended to describe the scour at the pile breakwater under the action of multiple solitary waves, and new empirical coefficients were obtained by fitting the formula to the new experimental data to estimate the equilibrium scour depth. It appears that the maximum scour depth and the total scour volume are two reliable quantities for validation of numerical models developed for the scour around pile breakwaters under highly nonlinear wave conditions. [ABSTRACT FROM AUTHOR]

Highlights • Conversion into a route choice problem enables consideration of traffic variability. • Choice probability model can easily capture effects of non-recurring traffic events. • The model calibrated by massive trip data identifies trip anomalies dynamically. • Unintentional trip anomalies can be separated from the intentional ones effectively. Abstract Taxi service is an essential complement to public transport systems due to its convenience and availability. It often provides hundreds of millions of rides for urban travelers every year in cities across the world. At the same time, the number of trip-induced passenger complaints about trip anomalies (trips with anomalous trip length, time, fare, etc.) is also considerable. Hence, the taxi regulators impose harsh penalties on verified trip anomalies. The existing anomaly verification process is labor-intensive, and it does not consider the traffic variability as well as the passengers' perception of trip anomalies. Quite often the imprecise and unfair outputs are generated as a result. To tackle this issue, we propose a choice probability-based taxi trip anomaly detection model (ProbDetect) that considers the taxi drivers' route choice behavior as well as the traffic variability. We first generate a route choice set for each OD pair based on the massive taxi GPS trajectory data. Second, we assign each route with a choice probability derived from a cumulative multivariate probability over differences of generalized costs. Third, we distinguish the unintentional anomalies from the intentional anomalies using the expected and the realized choice probability. Lastly, the model is tested on 5000 OD pairs using 180 days of taxi GPS data in Shanghai, China. Three types of anomalies are detected as a result. Insights into the driver's route choice behavior are derived as well. [ABSTRACT FROM AUTHOR]

This study examines, both experimentally and numerically, the collapse process of a submerged granular column in loose packing, with an emphasis on the effects of the grain size and the initial aspect ratio of the granular column. A two-phase flow continuum model, developed based on the rheological characteristics of solid-water mixtures, is used to perform the numerical simulations. Two particle sizes representing very fine particles and coarse particles are examined in this study. The two-phase flow model is validated and verified by comparing with the measured characteristics of the collapse process. Compared to the collapse of a granular column of coarse particles, the runout distance of a granular column with very fine particles is longer and the spread velocity is higher. Based on the two-phase flow simulation results supported by the experimental results, critical initial aspect ratios are identified to classify the types of the initial collapse process and the final deposit morphology, and the empirical expressions are provided to describe the changes of the runout distance, collapse duration, final deposition height, and spread velocity with the initial aspect ratio. A phenomenon similar to the so-called Kelvin-Helmholtz instability can be observed in both the experiment and the simulation at the later stage of the collapse, which causes the formation of several vortexes in the two-phase flow during the collapse process. The presence of these vortexes is responsible for the pressure drop on the bed after the passage of the flow front and the wavy feature on the final deposit morphology. [ABSTRACT FROM AUTHOR]

Highlights • A dual functional wave-power plant addresses the high cost issue in wave energy utilization. • The dual functional wave-power plant integrates OWCs into a pile breakwater. • The dual functional wave-power plant can effectively extract wave energy and protect shore. • The cost-sharing can make wave-powered electricity economically competitive. Abstract The fundamental roadblock toward commercial-scale wave power operations is cost. The main objective of this work was to address the cost challenge facing wave energy commercialization through cost-sharing with pile breakwaters to be built for shore protection. This was achieved in this study through a dual-functional wave-power plant for generation of wave-power electricity and protection against coastal erosion for sustainable coastal development. The dual-functional wave-power plant was formed by integrating oscillating-water-column (OWC) devices into a pile breakwater, with each pile being an OWC-pile equipped with a power take-off device. The power extraction efficiency and hydrodynamic characteristics of the dual-functional wave-power plant were measured in a wave flume under various wave conditions. An orifice was used at the top of the pneumatic chamber of each OWC-pile to simulate the power take-off device. To evaluate the performance of the power plant in wave power extraction and shore protection, the surface elevation and pressure inside the OWC chamber, as well as the scattered waves, were measured. It was found that comparing to a standalone OWC-pile device with an identical design and geometric characteristics, an OWC-pile in the dual-functional wave-power plant could achieve significantly larger power-extraction efficiency. Comparing to a pile breakwater with the same dimensions, the wave transmission and reflection of the dual-functional wave-power plant were both weaker, especially the wave reflection, which is beneficial for structure safety and shore protection. Based on the Froude's law of similarity and an estimation of the effect of air compressibility at full scale, an evaluation of the performance of a dual-functional wave-power plant at full scale was also provided. The findings of this work promote close collaboration between wave-energy utilization community and the shore-protection community for commercial-scale deployment of wave energy converters and contribute to making wave energy economically competitive. [ABSTRACT FROM AUTHOR]

Development of smart city has been increasingly accepted as a new technology-based solution to mitigate urban diseases. The Chinese government has been devoting good efforts to the promotion of smart city through introducing a series of policies. However, policies may have limited effectiveness in application if they do not respond to the practice. There is little study examining what results have been achieved in practice by applying policy measures. This study presents a holistic evaluation of smart city performance in the context of China. The evaluation indicators in this study are selected by applying a hybrid research methodology including literature review and semi-structured interviews. Indicator data are collected from 44 sample smart cities. The evaluation was conducted by applying Entropy method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique collectively. This study highlights that the overall smart city performance in China is at a relatively low level. There is also a significant unbalance in performance between five smart city dimensions including smart infrastructure, governance, people, economy and environment. The smart performance between cities varies significantly since cities implement smart city programs in different ways. These differences impede experience sharing between cities. Actions have been recommended in this study for promoting further development of smart city in the context of China, such as increasing the investment on smart infrastructure, providing training programs, and establishing evaluation mechanism. [ABSTRACT FROM AUTHOR]

The primary action of near-shore waves at many field reef sites is to produce a wave-induced setup and 2DH reef-lagoon-channel circulation cells. Instead of using wave basin to study the behaviors of the wave-induced setup and wave-driven flow in such systems, an experimental technique was introduced to achieve the same objective through wave-flume tests. A pipe with a valve was installed underneath the wave flume to connect the lagoon and the ocean side, which makes it possible to simulate a rip channel connecting the lagoon and the offshore side. Experimental results for two reef systems with a fore-reef slope (1V:6H) were obtained under a series of monochromatic wave conditions: one simulating a closed lagoon and the other simulating an open lagoon. Compared with the reef system with a closed lagoon, connecting the lagoon to the ocean side was found to have the following effects: (i) producing a wave-driven current on the reef flat, (ii) reducing the maximum wave setup on the reef flat by 10%–40%, and (iii) causing the wave setup to decrease continuously along the reef flat all the way to the lagoon. For the reef system with an open lagoon, the wave-driven current was found to increase with increasing wave height and wave period, and there appeared to be a peak value of the current within the tested range of reef-flat submergences. The experimental results for the two reef systems were compared with a set of existing wave-basin results for similar reef systems, but with a much steeper fore-reef slope (1V:1H). The dimensionless parameters used in the literature were found to be insufficient to describe both the wave setup and wave-driven current on the reef flat for the reef system with an open lagoon. A semi-analytical quasi-2DH model, which was devised according to the balances of momentum and mass, was also included to understand how the reef and channel morphology and the bottom roughness affect the wave setup and wave-driven current in a reef-lagoon-channel system. The semi-analytical model was validated by our laboratory data as well as field observations reported in the literature. [ABSTRACT FROM AUTHOR]

Endothelial progenitor cells (EPCs) have endogenous endothelium-reparative potential, but obesity impairs EPCs. Overweight premenopausal women have a normal number of circulating EPCs with functional activity, but whether EPCs in overweight postmenopausal women can repair obesity-related endothelial damage requires further investigation. For this purpose, we examined the function and number of circulating EPCs, evaluated vascular endothelial function, and explored the underlying mechanism. Compared with normal weight or overweight age-matched men, postmenopausal women (overweight or normal weight) had a diminished number of circulating EPCs and impaired vascular endothelial function, as detected by flow-mediated dilatation. Moreover, GTCPH I expression and the nitric oxide level in overweight postmenopausal women and men were significantly decreased. Together, our findings demonstrate that the number or function of circulating EPCs and endothelial function, which is partially regulated by the GTCPH I/BH4 signaling pathway, is not preserved in overweight postmenopausal women. The GTCPH I/BH4 pathway in circulating EPCs may be a potential therapeutic target for endothelial injury in overweight postmenopausal women. [ABSTRACT FROM AUTHOR]

ABSTRACT Yao, Y.; Huang, Z., and Lo, E.Y.M., 2018. Experimental Study on Floating Debris Generated by Solitary Waves Running up a Composite Beach. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 851–860. Coconut Creek (Florida), ISSN 0749-0208. Tsunami induced debris-laden flow is a destructive process, since the high speed and energetic tsunami waves could destroy and carry any items they could move. A key limitation in understanding such flows is the lack of quantitative experimental data. A laboratory-based study on a debris-laden flow under tsunami waves, modelled as solitary waves in a 2-D wave flume is reported here with the waves being incident on a composite 3-slope setup. Here, two rows of debris with different separation distances were tested. The debris reduced the maximum inundation with the reduction increased with the additional row of debris. The maximum inundation, maximum and final debris positions were shown to be more sensitive to the incident wave conditions. Ensemble measures and normalized kernel density functions were used to characterize the spread in the maximum and final debris positions. A newly defined dimensionless mass loading is further developed to characterize the inundation reduction. The obtained experimental results should find use in calibration or validation of numerical models and aid in field estimations of tsunami wave conditions via post-event surveys of the inundation and debris extent. [ABSTRACT FROM AUTHOR]

The collapse process of a submerged granular column is strongly affected by its initial packing. Previous models for particle response time, which is used to quantify the drag force between the solid and liquid phases in rheology-based two-phase flow models, have difficulty in simulating the collapse process of granular columns with different initial concentrations (initial packing conditions). This study introduces a new model for particle response time, which enables us to satisfactorily model the drag force between the two phases for a wide range of volume concentration. The present model can give satisfactory results for both loose and dense packing conditions. The numerical results have shown that (i) the initial packing affects the occurrence of contractancy/diltancy behavior during the collapse process, (ii) the general buoyancy and drag force are strongly affected by the initial packing through contractancy and diltancy, and (iii) the general buoyancy and drag force can destabilize the granular material in loose packing condition but stabilize the granular material in dense packing condition. The results have shown that the collapse process of a densely-packed granular column is more sensitive to particle response time than that of a loosely-packed granular column. [ABSTRACT FROM AUTHOR]