455 research outputs found
Mineralogical Characteristics and Genesis of Trapiche-like Sapphire in Changle, Eastern North China Craton
No full text“Trapiche-like” texture is distinct from “trapiche” texture as typically observed in emeralds, amethysts, and aquamarines. It is also occasionally encountered in sapphires from Changle, eastern North China Craton. The advent of the trapiche-like texture has enhanced the ornamental value of sapphire, although its origin is still unclear. In this study, techniques, such as Fourier transform infrared (FTIR) spectroscopy, ultraviolet–visible (UV-Vis) spectroscopy, Raman spectroscopy, electron probe microanalysis (EPMA), and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), have been applied to test the spectroscopic data of the cores, arms, and blue sectors of trapiche-like sapphires from Changle and explore the mineralogical characteristics of different domains. The main component of the core, arms, and blue sectors of trapiche-like sapphire is corundum (Al2O3), with trace elements including Fe, Ti, Mg, Cr, V, Ga, etc. From arms to cores to sectors, trace elements show a trend of increasing and then decreasing. Nb and Ta elements are more enriched in the arms than in the sectors, indicating the existence of rutile. With changes in physicochemical conditions during magma evolution, rutile melted, and related voids were filled with glassy inclusions, which formed the arms of trapiche-like sapphires. Field observations of primary deposits, as well as petrological and geochemical analyses, reveal that the trapiche-like sapphire of Changle belongs to magmatic sapphire
Additional file 1 of Diet communication on the early Silk Road in ancient China: multi-analytical analysis of food remains from the Changle Cemetery
No full textAdditional file 1: Table S1. Identified proteins and specific peptides in the archaeological food remains from the Changle Cemetery
Architectural Complex VI of Changle Palace, Han Chang’an City in Xi’an
No full textAbstractFrom November 2005 to January 2006, Han Chang’an Archaeological Team of the Institute of Archaeology, Chinese Academy of Social Sciences excavated the northern periphery of Architectural Complex VI located in the northwest of Changle Palace site. The primary result was bringing to light a pair of auxiliary compounds on the east and west wings of the main hall of Complex VI. The auxiliary compounds comprised of structures of halls, verandas, rain aprons and open-air courtyards. The hall structures were indicated by raised rammed-earth foundations, rows of stone pillar bases on the perimeter, and a set of semi-subterranean features in the west compound. The floors of the verandas were lined with square bricks, flanked by rows of stone pillar bases with occasional traces of wooden pillars. The rain aprons were paved with pebbles and rectangular bricks. The courtyards featured water systems comprised of wells, saturation pools, pipelines, runoff ditches and drainage. The assemblage of material remains comprised mainly of architectural components of semi-cylindrical tiles, flat tiles, tile-ends, bricks, pipes, well curbs, etc., and a small amount of pottery, iron and bronze artifacts, as well as bronze coins. The chronology of the material remains and their context suggested that the complex was built in the early years of Western Han and continued to be occupied till the Wang Mang Interregnum. Combined with previous archaeological findings and historic documents, complex VI was most likely the front hall of Changle Palace.</jats:p
Seroprevalence of cytotoxin-associated gene A positive Helicobacter pylori strains in Changle, an area with very high prevalence of gastric cancel in South China
No full textBackground: Helicobacter pylori, especially the CagA-positive strains, are closely associated with peptic ulcers and gastric cancers. We performed a large scale gastric cancer screening project and examined the prevalence of H. pylori and CagA-positive strains in Changle, China, an area with one of the World's highest gastric cancer mortality. We also compared the prevalence with that in Hong Kong which has one-tenth of the gastric cancer mortality of that in Changle. Methods: A total of 2424 subjects in Changle and 523 subjects in Hong Kong had endoscopic examination and venesection. Sera were tested for anti-H. pylori antibody and anti-CagA antibody and correlated with endoscopic findings. Results: In Changle, 80.9% of the subjects were H. pylori carriers. Out of 551 carriers, 408 (74%) were positive for anti-CagA antibody. A total of 76% and 87% of the asymptomatic and gastric cancer patients were positive for anti-CagA antibody, respectively (P > 0.05). Compared to Hong Kong, there was a significantly (P < 0.0001) higher prevalence of CagA-positive strains in asymptomatic subjects in Changle (76%) than in Hong Kong (28%), but not in peptic ulcers or gastric cancers. Conclusions: Subjects in Changle had a high prevalence of H. pylori infection and a high prevalence of the CagA-positive strains. The contrast in the prevalence of CagA-positive strains, in asymptomatic subjects in two areas with differing gastric cancer mortality, supports the pathogenic role of CagA-positive strains in gastric carcinogenesis.link_to_OA_fulltex
DataSheet_1_Community structure of benthic molluscs shaped by environmental and ecological variables in the coastal waters of Changle, Fujian Province, China.docx
No full textTo understand the community structure of benthic molluscs and their relationship under varying environmental and ecological conditions, monthly samplings in April−September 2019 were conducted at 27 stations in an approximate sea area of 20,600 ha (Changle District, Fujian Province, China). Forty-five species were identified, most as food; six dominant species, all bivalves and commercially important, were determined by the index of relative importance > 500. The average abundance and biomass were 308.32 × 103 ± 1,156.24 × 103 ind./km2 and 1,423.71 ± 2,272.37 kg/km2, respectively. Three spatial community groups were identified, named Min River Estuary, Nearshore, and Offshore, with significant differences in species composition and abundance (ANOSIM, p < 0.01). Results of the canonical correlation analysis indicated that the community structure of benthic molluscs was significantly related to water depth, pH, salinity, temperature, phytoplankton abundance and zooplankton abundance (p < 0.1). As the important habitat for benthic molluscs, long-term monitoring in the coastal waters of Changle is needed for sustainable harvest.</p
Optimisation par essaim de particules application au clustering des données de grandes dimensions
No full textLe clustering est une tâche difficile de forage de données dans des
applications où les données impliquées sont de grandes dimensions.
Dans les applications réelles, chaque objet de données est souvent
représenté par un vecteur des caractéristiques ( ou attributs) dont le
nombre peutêtre très élevé. Par exemple, pour représenter un texte
on utilise un vecteur de grande taille dont les éléments représentent
les fréquences des mots. Les algorithmes traditionnels de clustering
ont beaucoup de difficulté quand la dimensionnahté est grande,
leurs résultats détériorent rapidement à mesure qμe le nombre de caractéristiques
augmente. Le phénomène s'appelle la malédiction de là
dimensionnalité (curse of dimensionality). En effet, quand le nombre
de caractéristiques devient grand, les données deviennent très
clairsemées et les mesures de distance dans l'espace entier deviennent
non significatives. Dans ces cas, certaines caractéristiques peuvent
être non pertinentes ou superflues pour certains clusters, et de
différents sous-ensembles de caractéristiques peuvent être appropriés
pour différents clusters. Ainsi, des clusters se trouvent dans différents
sous-espaces de caractéristiques plutôt que dans l'espace de toutes
les caractéristiques. Les méthodes visant à trouver des clusters dans
différents sous-espaces de caractéristiques sont appelées clustering de
sous-espace ou clustering projectif. Cependant, la performance des
algorithmes de clustering de sous-espace ou projectif diminue rapidement
avec )a taille (dimension) des sous-espaces dans lequel les clusters
se trouvent. Aussi, beaucoup d'entre eux nécessitent des informations a priori, fournies pars l'usager, pour les aider à déterminer les valeurs
de leurs paramètres. Ces informations incluent la distance maximale
entre les valeurs d'une dimension, les seuils tels que la densité minimale
et la moyenne des dimensions à retenir pour les clusters, etc.,
qui sont en général difficiles à estimer.
Le but principal de cette thèse est de développer une nouvelle
méthode, en se basant sur l'optimisation par Essaim de Particules
(PSO pour Particle Swarm Optimization), pour le clustering des données
de grandes dimensions. Premièrement, nous avons étudié les principales
causes de la convergence prématurée de· PSO et proposé une
nouvelle version de l'algorithme PSO améliorée que l'on appelle InformPSO.
InformPSO est basée sur des principes de diffusion adaptative
et de mutation hybride. En s'inspirant de la physique de diffusion
d'information, nous avons conçu une fonction pour obtenir une
meilleure diversité de particules en tenant compte de leurs distributions
et de leur nombre de générations évolutives et en ajustant
leurs " habilités cognitives sociales ". En nous basant sur l'auto organisation
génétique et l'évolution de chaos, nous avons intégré la
sélection clonale dans InformPSO pour implanter l'évolution locale
du meilleur candidat particule, gBest, et fait usage d'une séquence de
logistique pour contrôler la dérive aléatoire du gBest. Ces techniques
contribuent grandement à éviter des optimums locaux. La convergence
globale de l'algorithme est prouvée en utilisant le théorème de
chaîne de Markov. Nos expériences sur l'optimisation des fonctions
d'étalonnage unimodales et multimodales démontrent que, comparé
aux autres variantes de PSO, InformPSO converge plus rapidement
et résulte en de meilleurs optimums. Il est plus robuste et plus effectif
à empêcher la convergence prématurée.
Par la suite, nous avons étudié deux des principaux problèmes du clustering des données de grandes dimensions, à savoir le problème de
pondération de variables dans ce qu'on appelle le " soft " clustering
projectif avec un nombre fixé ( ou connu) de clusters et le problème
même de détermination du nombre de clusters. Nous avons proposé
des fonctions objectives spéciales et des schémas de codage adaptés
pour permettre d'utiliser le PSO dans la résolution de ces problèmes.
Plus précisément, le premier problème, avec le nombre de clusters
préfixé et qui vise à trouver un ensemble de poids pour chaque cluster,
est formulé comme un problème d'optimisation non linéaire avec
des variables continues, sous contraintes de limites. Un nouvel algorithme,
appelé PSOVW, est proposé pour chercher les valeurs de
poids optimales pour les clusters. Dans PSOVW, nous avons conçu
une fonction d'objectif de type k-moyenne impliquant les poids dont
les variations sont exponentiellement reflétées. Nous transformons
également les contraintes d'égalité initiales en des contraintes de limites
en utilisant une représentation non normalisée des poids variables.
Nous utilisons ensuite un optimisateur PSO pour minimiser
la fonction objective. Nos résultats expérimentaux sur des données
synthétiques et des données réelles démontrent que notre algorithme
améliore significativement la qualité des clusters trouvés. De plus, les
résultats du nouvel algorithme dépendent beaucoup moins des centres
initiaux des clusters.
Le deuxième problème vise à déterminer automatiquement le nombre
de clusters k et de trouver les clusters en même temps. Ce
problème est aussi formulé comme un problème d'optimisation non
linéaire avec des contraintes de limites. Pour ce problème de la détermination
automatique de k, qui est problématique pour la plupart des algorithmes
existants, ·nous avons proposé un nouvel algorithme de PSO
appelé l'autoPSO. Un codage spécial des particules est introduit dans l'autoPSO pour représenter des partitions avec dÜférents nombres de
clusters dans la même population. L'index de DB est utilisé comme
fonction objective pour mesurer la qualité des partitions avec des nombres
semblables ou différents de clusters. L'algorithme autoPSO est
testé sur des ensembles de données synthétiques de grandes dimensions
et des ensembles de données artificielles de petites dimensions.
Ses performances ont été comparées à celles d'autres techniques de
clustering. Les résultats expérimentaux indiquent que l'algorithme
autoPSO a un potentiel intéressant pour résoudre le problème de
clustering des données de grandes dimensions sans le préréglage du
nombre de clusters.Clustering high-dimensional data is an important but difficult task in various data mining applications. A fundamental starting point for data mining is the assumption that a data object, such as text document, can be represented as a high-dimensional feature vector. Traditional clustering algorithms struggle with high-dimensional data because the quality of results deteriorates due to the curse of dimensionality. As the number of features increases, data becomes very sparse and distance measures in the whole feature space become meaningless. Usually, in a high-dimensional data set, some features may be irrelevant or redundant for clusters and different sets of features may be relevant for different clusters. Thus, clusters can often be found in different feature subsets rather than the whole feature space. Clustering for such data sets is called subspace clustering or projected clustering, aimed at finding clusters from different feature subspaces. On the other hand, the performance of many subspace/projected clustering algorithms drops quickly with the size of the subspaces in which the clusters are found. Also, many of them require domain knowledge provided by the user to help select and tune their settings, like the maximum distance between dimensional values, the threshold of input parameters and the minimum density, which are difficult to set. Developing effective particle swarm optimization (PSO) for clustering high-dimensional data is the main focus of this thesis. First, in order to improve the performance of the conventional PSO algorithm, we analyze the main causes of the premature convergence and propose a novel PSO algorithm, call InformPSO, based on principles of adaptive diffusion and hybrid mutation. Inspired by the physics of information diffusion, we design a function to achieve a better particle diversity, by taking into account their distribution and the number of evolutionary generations and by adjusting their"social cognitive" abilities. Based on genetic self-organization and chaos evolution, we build clonal selection into InformPSO to implement local evolution of the best particle candidate, gBest, and make use of a Logistic sequence to control the random drift of gBest. These techniques greatly contribute to breaking away from local optima. The global convergence of the algorithm is proved using the theorem of Markov chain. Experiments on optimization of unimodal and multimodal benchmark functions show that, comparing with some other PSO variants, InformPSO converges faster, results in better optima, is more robust, and prevents more effectively the premature convergence. Then, special treatments of objective functions and encoding schemes are proposed to tailor PSO for two problems commonly encountered in studies related to high-dimensional data clustering. The first problem is the variable weighting problem in soft projected clustering with known the number of clusters k . With presetting the number of clusters k, the problem aims at finding a set of variable weights for each cluster and is formulated as a nonlinear continuous optimization problem subjected to bound. constraints. A new algorithm, called PSOVW, is proposed to achieve optimal variable weights for clusters. In PSOVW, we design a suitable k -means objective weighting function, in which a change of variable weights is exponentially reflected. We also transform the original constrained variable weighting problem into a problem with bound constraints, using a non-normalized representation of variable weights, and we utilize a particle swarm optimizer to minimize the objective function in order to obtain global optima to the variable weighting problem in clustering. Our experimental results on both synthetic and real data show that the proposed algorithm greatly improves cluster quality. In addition, the results of the new algorithm are much less dependent on the initial cluster centroids. The latter problem aims at automatically determining the number of clusters k as well as identifying clusters. Also, it is formulated as a nonlinear optimization problem with bound constraints. For the problem of automatical determination of k , which is troublesome to most clustering algorithms, a PSO algorithm called autoPSO is proposed. A special coding of particles is introduced into autoPSO to represent partitions with different numbers of clusters in the same population. The DB index is employed as the objective function to measure the quality of partitions with similar or different numbers of clusters. autoPSO is carried out on both synthetic high-dimensional datasets and handcrafted low-dimensional datasets and its performance is compared to other selected clustering techniques. Experimental results indicate that the promising potential pertaining to autoPSO applicability to clustering high-dimensional data without the preset number of clusters k
Theoretical and experimental analysis of excessively tilted fiber gratings
No full textWe have theoretically and experimentally investigated the dual-peak feature of tilted fiber gratings with excessively tilted structure (named as Ex-TFGs). We have explained the dual-peak feature by solving eigenvalue equations for TM0m and TE0m of a circular waveguide, in which the TE (transverse electric) and TM (transverse magnetic) core modes are coupled into TE and TM cladding modes, respectively. Meanwhile, in the experiment, we have verified that one of the dual peaks at the shorter wavelength is due to the TM mode coupling whereas the other one at the longer wavelength arises from TE mode coupling when a linearly polarized light launched into the Ex-TFG. We have also investigated the peak separation of TE and TM cladding mode for different surrounding medium refractive indexes (SRI), revealed that the dual peaks separation is decreasing as increasing of SRI, which agrees very well with the theoretical analysis results. (C) 2016 Optical Society of Americ
Temperature-calibrated high-precision refractometer using a tilted fiber Bragg grating
No full textWe present a refractometer with main- and vernier-scale to measure the refractive index (RI) of liquids with high precision by using the fine spectrum structure of a tilted fiber Bragg grating (TFBG). The absolute RI values are determined by the accurate wavelength of cut-off mode resonances. The main- and vernier-scale are calibrated by measuring large groups of fine spectra at different cut-off mode resonances in a small RI range, and the use of vernier-scale certainly reduces the RI measurement uncertainty resulted from the discrete cladding mode resonances. The performance of the TFBG-based vernier refractometer is experimentally verified by exploring the temperature dependence of RI of anhydrous ethanol in a near infrared region, showing an enhanced accuracy to the order of 10−4, high repeatability and temperature self-calibration capability
Comb-locked cavity-assisted double resonance spectroscopy (CLCA-DR)
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Previous issue date: 2019-06-17Double resonance spectroscopy has been frequently applied in state-selective excitation and to reach energy levels forbidden to single-photon transition. Due to the low cross section of two-photon transitions, usually high-power pulsed lasers are needed, which prevent high-precision measurements. Optical resonant cavity can be used to enhance the effective path length and also the laser power inside the cavity. By simultaneously locking two continuous-wave lasers with one high-finesse cavity, we established comb-locked cavity-assisted double resonance (CLCA-DR) spectroscopy of molecules. Doppler-free two-photon CLCA-DR spectroscopy of the monoxide carbon molecule were recorded by using two diode lasers with milli-watts power in the 1.5m region. Three different types of double resonance: -, V- and ladder-type, were demonstrated. By comparison to the energy difference obtained in previous high-precision single-photon spectroscopy using comb-locked cavity ring-down spectroscopy [1], we confirmed that the CLCA-DR measurement can also achieve an accuracy of kHz level. The energy of the highly excited state of CO, V=6, J=9 was determined to be kHz level (E/E is about 10) by a “ladder-type” double resonance measurement using the V=3, J=10 level as the intermediate state.
Keywords: cavity assisted; double resonance; Doppler free;
Reference:
[1] Wang, J. et al., J. Chem. Phys. 2017, 147: 091103
Small-period long-period fiber grating with improved refractive index sensitivity and dual-parameter sensing ability
No full textWe UV inscribe and characterize a long-period fiber grating with a period of 25 μm. A series of polarization-dependent dual-peak pairs can be seen in the transmission spectrum, even though only the symmetrical refractive index modification is introduced. The fabricated grating exhibits a lower temperature sensitivity compared with standard long-period gratings and an enhanced refractive index sensitivity of ∼312.5 nm?RIU averaged from 1.315 to 1.395, which is more than four-fold higher than standard long-period gratings in this range. The full width at half-maximum of the fabricated grating is only about 0.6 nm, allowing for high-resolution sensing. Moreover, the grating period is so small that the attenuation dip corresponding to a high-order Bragg resonance can also be seen, which can act as a monitor of the unwanted perturbation to realize dual-parameter sensing
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