Hesitant Triangular Fuzzy Information Aggregation Operators Based on Bonferroni Means and Their Application to Multiple Attribute Decision Making

We investigate the multiple attribute decision-making (MADM) problems with hesitant triangular fuzzy information. Firstly, definition and some operational laws of hesitant triangular fuzzy elements are introduced. Then, we develop some hesitant triangular fuzzy aggregation operators based on Bonferroni means and discuss their basic properties. Some existing operators can be viewed as their special cases. Next, we apply the proposed operators to deal with multiple attribute decision-making problems under hesitant triangular fuzzy environment. Finally, an illustrative example is given to show the developed method and demonstrate its practicality and effectiveness

ENVIRONMENTAL SURROUNDINGS AND PERSONAL WELL-BEING IN URBAN CHINA

We examine the relationship between atmospheric pollution, water pollution, traffic congestion, access to parkland and personal well-being using a survey administered across six Chinese cities in 2007. In contrast to existing studies of the determinants of well-being by economists, which have typically employed single item indicators to measure well-being, we use the Personal Well-Being Index (PWI). We also employ the Job Satisfaction Survey (JSS) to measure job satisfaction, which is one of the variables for which we control when examining the relationship between environmental surroundings and personal well-being. Previous research by psychologists has shown the PWI and JSS to have good psychometric properties in western and Chinese samples. A robust finding is that in cities with higher levels of atmospheric pollution and traffic congestion, respondents report lower levels of personal well-being ceteris paribus. Specifically, we find that a one standard deviation increase in suspended particles or sulphur dioxide emissions is roughly equivalent to a 12-13 per cent reduction in average monthly income in the six cities.China, Environment, Pollution, Personal Well-Being.

Single-mode tunable laser emission in the single-exciton regime from colloidal nanocrystals

Whispering-gallery-mode resonators have been extensively used in conjunction with different materials for the development of a variety of photonic devices. Among the latter, hybrid structures, consisting of dielectric microspheres and colloidal core/shell semiconductor nanocrystals as gain media, have attracted interest for the development of microlasers and studies of cavity quantum electrodynamic effects. Here we demonstrate single-exciton, single-mode, spectrally tuned lasing from ensembles of optical antenna-designed, colloidal core/shell CdSe/CdS quantum rods deposited on silica microspheres. We obtain single-exciton emission by capitalizing on the band structure of the specific core/shell architecture that strongly localizes holes in the core, and the two-dimensional quantum confinement of electrons across the elongated shell. This creates a type-II conduction band alignment driven by coulombic repulsion that eliminates non-radiative multi-exciton Auger recombination processes, thereby inducing a large exciton–bi-exciton energy shift. Their ultra-low thresholds and single-mode, single-exciton emission make these hybrid lasers appealing for various applications, including quantum information processing

The effect of fog on the probability density distribution of the ranging data of imaging laser radar

This paper outlines theoretically investigations of the probability density distribution (PDD) of ranging data for the imaging laser radar (ILR) system operating at a wavelength of 905 nm under the fog condition. Based on the physical model of the reflected laser pulses from a standard Lambertian target, a theoretical approximate model of PDD of the ranging data is developed under different fog concentrations, which offer improved precision target ranging and imaging. An experimental test bed for the ILR system is developed and its performance is evaluated using a dedicated indoor atmospheric chamber under homogeneously controlled fog conditions. We show that the measured results are in good agreement with both the accurate and approximate models within a given margin of error of less than 1%

Fabrication and Properties of Carbon- Encapsulated Cobalt Nanoparticles over NaCl by CVD

Carbon-encapsulated cobalt (Co@C) nanoparticles, with a tunable structure, were synthesized by chemical vapor deposition using Co nanoparticles as the catalyst and supported on a water-soluble substrate (sodium chloride), which was easily removed by washing and centrifugation. The influences of growth temperature and time on the structure and magnetic properties of the Co@C nanoparticles were systematically investigated. For different growth temperatures, the magnetic Co nanoparticles were encapsulated by different types of carbon layers, including amorphous carbon layers, graphitic layers, and carbon nanofibers. This inferred a close relationship between the structure of the carbon-encapsulated metal nanoparticles and the growth temperature. At a fixed growth temperature of 400 °C, prolonged growth time caused an increase in thickness of the carbon layers. The magnetic characterization indicated that the magnetic properties of the obtained Co@C nanoparticles depend not only on the graphitization but also on the thickness of the encapsulated carbon layer, which were easily controlled by the growth temperatures and times. Optimization of the synthesis process allowed achieving relatively high coercivity of the synthesized Co@C nanoparticles and enhancement of its ferromagnetic properties, which make this system promising as a magnetic material, particularly for high-density magnetic recording applications

Comparison of outcomes between immediate implant-based and autologous reconstruction: 15-year, single-center experience in a propensity score-matched Chinese cohort

Objective: The number of immediate breast reconstruction (IBR) procedures has been increasing in China. This study aimed to investigate the oncological safety of IBR, and to compare the survival and surgical outcomes between implant-based and autologous reconstruction. Methods: Data from patients diagnosed with invasive breast cancer who underwent immediate total breast reconstruction between 2001 and 2016 were retrospectively reviewed. Long-term breast cancer-specific survival (BCSS), disease-free survival (DFS), and locoregional recurrence-free survival (LRFS) were evaluated. Patient satisfaction with the breast was compared between the implant-based and autologous groups. BCSS, DFS, and LRFS were compared between groups after propensity score matching (PSM). Results: A total of 784 IBR procedures were identified, of which 584 were performed on patients with invasive breast cancer (implant-based, n = 288; autologous, n = 296). With a median follow-up of 71.3 months, the 10-year estimates of BCSS, DFS, and LRFS were 88.9% [95% confidence interval (CI) (85.1%–93.0%)], 79.6% [95% CI (74.7%–84.8%)], and 94.0% [95% CI (90.3%–97.8%)], respectively. A total of 124 patients completed the Breast-Q questionnaire, and no statistically significant differences were noted between groups (P = 0.823). After PSM with 27 variables, no statistically significant differences in BCSS, DFS, and LRFS were found between the implant-based (n = 177) and autologous (n = 177) groups. Further stratification according to staging, histological grade, lymph node status, and lymph-venous invasion status revealed no significant survival differences between groups. Conclusions: Both immediate implant-based and autologous reconstruction were reasonable choices with similar long-term oncological outcomes and patient-reported satisfaction among patients with invasive breast cancer in China

FusionFormer: A Multi-sensory Fusion in Bird's-Eye-View and Temporal Consistent Transformer for 3D Objection

Multi-sensor modal fusion has demonstrated strong advantages in 3D object detection tasks. However, existing methods that fuse multi-modal features through a simple channel concatenation require transformation features into bird's eye view space and may lose the information on Z-axis thus leads to inferior performance. To this end, we propose FusionFormer, an end-to-end multi-modal fusion framework that leverages transformers to fuse multi-modal features and obtain fused BEV features. And based on the flexible adaptability of FusionFormer to the input modality representation, we propose a depth prediction branch that can be added to the framework to improve detection performance in camera-based detection tasks. In addition, we propose a plug-and-play temporal fusion module based on transformers that can fuse historical frame BEV features for more stable and reliable detection results. We evaluate our method on the nuScenes dataset and achieve 72.6% mAP and 75.1% NDS for 3D object detection tasks, outperforming state-of-the-art methods

FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving

Building a multi-modality multi-task neural network toward accurate and robust performance is a de-facto standard in perception task of autonomous driving. However, leveraging such data from multiple sensors to jointly optimize the prediction and planning tasks remains largely unexplored. In this paper, we present FusionAD, to the best of our knowledge, the first unified framework that fuse the information from two most critical sensors, camera and LiDAR, goes beyond perception task. Concretely, we first build a transformer based multi-modality fusion network to effectively produce fusion based features. In constrast to camera-based end-to-end method UniAD, we then establish a fusion aided modality-aware prediction and status-aware planning modules, dubbed FMSPnP that take advantages of multi-modality features. We conduct extensive experiments on commonly used benchmark nuScenes dataset, our FusionAD achieves state-of-the-art performance and surpassing baselines on average 15% on perception tasks like detection and tracking, 10% on occupancy prediction accuracy, reducing prediction error from 0.708 to 0.389 in ADE score and reduces the collision rate from 0.31% to only 0.12%