双圈连通图的L(2，1)-labelling(英文)

给定图G,G的一个L(2,1)-labelling是指一个映射f:V(G)→{0,1,2,…},满足:当dG(u,v)=1时,|f(u)-f(v)|≥2;当dG(u,v)=2时,|f(u)-f(v)|≥1。如果G的一个L(2,1)-labelling的像集合中没有元素超过k,则称之为一个k-L(2,1)- labelling.G的L(2,1)-labelling数记作l(G),是指使得G存在k-L(2,1)-labelling的最小整数k.如果G的一个L(2,1)-labelling中的像元素是连续的,则称之为一个no-hole L(2,1)-labelling.本文证明了对每个双圈连通图G,l(G)=△+1或△+2.这个工作推广了[1]中的一个结果.此外,我们还给出了双圈连通图的no-hole L(2,1)-labelling的存在性.

<i>L</i>(0, 1)-Labelling of Cactus Graphs

An L(0,1)-labelling of a graph G is an assignment of nonnegative integers to the vertices of G such that the difference between the labels assigned to any two adjacent vertices is at least zero and the difference between the labels assigned to any two vertices which are at distance two is at least one. The span of an L(0,1)-labelling is the maximum label number assigned to any vertex of G. The L(0,1)-labelling number of a graph G, denoted by λ0.1(G) is the least integer k such that G has an L(0,1)-labelling of span k. This labelling has an application to a computer code assignment problem. The task is to assign integer control codes to a network of computer stations with distance restrictions. A cactus graph is a connected graph in which every block is either an edge or a cycle. In this paper, we label the vertices of a cactus graph by L(0,1)-labelling and have shown that, △-1≤λ0.1(G)≤△ for a cactus graph, where △ is the degree of the graph G.

Informing Food Consumption Choices: Innovations in Measuring and Labelling

The need to inform consumers about the health impact of their food choices is ever more pressing in a world where obesity is a growing problem. Concerns over food safety, its origins and its environmental impacts are also growing, as frequently reported in the popular press in many parts of the world. Nutritional and health information on food labels is quite well developed, but the complex nature of the information presented may hinder widespread use of the existing labels. In comparison, there has been little widespread success of carbon labels on food, and their usefulness in reducing carbon emissions from consumption is uncertain. In an attempt to address the need for clearer information on health and environmental impacts of food purchases, we present a novel dual-purpose food labelling system which provides information on both health and environmental impacts of food items. This paper presents results from a pilot study introducing a novel approach to food labelling: a simplified, combined carbon and health label to inform consumers simultaneously about the environmental and health impacts of their choices. Environmental impacts of various food categories were calculated on the basis of their relative energy use along the supply chain by using a newly designed Food Energy Index. Health impacts were based on the NuVal system developed in the USA in 2010. As part of the Norfolk Island Carbon and Health Evaluation study (NICHE), labels were designed, tested and displayed on 25 food categories for a 3-month period in the main supermarket on Norfolk Island (Australia). The in-store labelling trial was followed by a consumer survey on their attitudes to the labels. The results from this pilot trial indicate that consumers were supportive of food labelling including both environmental and health impacts, but the information provided in the dual labels was not sufficient to induce changes in consumption between food categories. We conclude that simple label design is clearly essential, and our findings warrant further investigation, including a broader study using a larger population and a wider range of food categories.

Hyperperfusion of Multiple Sclerosis Plaques Characterized by 3D FSE Arterial Spin Labelling

MULTIPLE sclerosis （MS） is a common inflammatory demyelinating disorder of central nervous system, and the disease burder could be well evaluated by conven-tional magnetic resonance imaging （MRI）,1 including T2-weighted, fluid-attenuatd inversion recovery, and postcontrast Tl-weighted sequences. 2 We investigated the perfusion state of MS plaques using brain perfusion imaging in a 12-year-old boy with MS.

The Gap Labelling Integrated Density of States for a Quasi Crystal Universe Is Identical to the Observed 4.5 Percent Ordinary Energy Density of the Cosmos

Condense matter methods and mathematical models used in solving problems in solid state physics are transformed to high energy quantum cosmology in order to estimate the magnitude of the missing dark energy of the universe. Looking at the problem from this novel viewpoint was rewarded by a rather unexpected result, namely that the gap labelling method of integrated density of states for three dimensional icosahedral quasicrystals is identical to the previously measured and theoretically concluded ordinary energy density of the universe, namely a mere 4.5 percent of Einstein’s energy density, i.e. E(O) = mc2/22 where E is the energy, m is the mass and c is the speed of light. Consequently we conclude that the missing dark energy density must be E(D) = 1 - E(O) = mc2(21/22) in agreement with all known cosmological measurements and observations. This result could also be interpreted as a strong evidence for the self similarity of the geometry of spacetime, which is an expression of its basic fractal nature.

Felicitous Labellings of Some Network Models

Building up graph models to simulate scale-free networks is an important method since graphs have been used in researching scale-free networks. One use labelled graphs for distinguishing objects of communication and information networks. In this paper some methods are given for constructing larger felicitous graphs from smaller graphs having special felicitous labellings, and some network models are shown to be felicitous.

Automated labelling of radiology reports using natural language processing:Comparison of traditional and newer methods

Automated labelling of radiology reports using natural language processing allows for the labelling of ground truth for large datasets of radiological studies that are required for training of computer vision models.This paper explains the necessary data preprocessing steps,reviews the main methods for automated labelling and compares their performance.There are four main methods of automated labelling,namely:(1)rules-based text-matching algorithms,(2)conventional machine learning models,(3)neural network models and(4)Bidirectional Encoder Representations from Transformers(BERT)models.Rules-based labellers perform a brute force search against manually curated keywords and are able to achieve high F1 scores.However,they require proper handling of negative words.Machine learning models require preprocessing that involves tokenization and vectorization of text into numerical vectors.Multilabel classification approaches are required in labelling radiology reports and conventional models can achieve good performance if they have large enough training sets.Deep learning models make use of connected neural networks,often a long short-term memory network,and are similarly able to achieve good performance if trained on a large data set.BERT is a transformer-based model that utilizes attention.Pretrained BERT models only require fine-tuning with small data sets.In particular,domain-specific BERT models can achieve superior performance compared with the other methods for automated labelling.

CATALYTIC LABELLING AND ~3H NMR ANALYSES OF SOME AROMATIC AND HETEROCYCLIC COMPOUNDS

This paper described the ~3H NMR determination of the positions and content of 24 tritium labeled aromatic and heterocyclic compounds prepared by catalytic tritiation derivatives, pyrrole, 1,2,4-triazole, thiazole, benzothiazole, purine and derivatives, indole, indanol, piperidine and derivative, diphenylamine, N- phenylanthranilic acid, 8- OH- quinoline, naphthol, diphenylether have been tritiated by catalytic exchange method using tritiated water and platinum catalyst (from the dioxide and sodium borohydride), and the pattern of labelling has been assigned by ~3H NMR spectroscopy. The results show that this exchange process can give general labelling compounds at various time and temperatures. For 4-Br-diphenylether, Raney nickel was used instead of Pt as a catalyst.

APOPTOSIS OF LEFT VENTRICULAR WALL OF SPONTANEOUSLY HYPERTENSIVE RATS STUDIED BY IN SITU END LABELLING

Objective To study the relationship between apoptosis and hypertrophy of the left ventricie (LV) before and after its regression with antihypertensive treatment. Methods Forty 12- week old spontaneously. hypertensive rats(SHR were randomly divided into 2 groups, one given enalapril (SHR - d grouP) and the other without treatment (SHR group). Twenty normotensive Wistar Kyoto rats of the same age served as controls (WKY group). The changes of body weight and blood pressure were observed for 3 months. The rats were killed at the 24th week of age. The weight of LV, the body weight (B W), and the ratio of LV/BW were compared between the groups. Then by in situ specific labelling of nuclear DNA fragmentation, the apoptosis in the wall of the left ventricle was compared between them. RcsuIts The index of apoptosis was SHR - d>SHR> WKY The dofference was significant. ConcIusion The hypertrophy of the LV and its regression is very’likely related with the imbalance between the proloferation and death of cells of the muscle in the LV.

RGDC Peptide Modified Quantum Dots Labelling and Imaging of Tumor Cells

The labelling and imaging of tumor cells were investigated via arginine-glycine-aspartic acidcysteine（RGDC） peptide-labelled quantum dots（QDs）. The results show that RGDC modified QDs can label SMMC-7721 tumor cells and adhere to cellular membrane. In constrast, the unmodified QDs are mainly dispersed around the cell. We also found that the RGDC-QDs can penetrate into the cell at 2 h of incubation. After 6 h of incubation, RGDC-QDs can accumulate in a unique intracellular region.

Active Learning Strategies for Textual Dataset-Automatic Labelling

The Internet revolution has resulted in abundant data from various sources,including social media,traditional media,etcetera.Although the availability of data is no longer an issue,data labelling for exploiting it in supervised machine learning is still an expensive process and involves tedious human efforts.The overall purpose of this study is to propose a strategy to automatically label the unlabeled textual data with the support of active learning in combination with deep learning.More specifically,this study assesses the performance of different active learning strategies in automatic labelling of the textual dataset at sentence and document levels.To achieve this objective,different experiments have been performed on the publicly available dataset.In first set of experiments,we randomly choose a subset of instances from training dataset and train a deep neural network to assess performance on test set.In the second set of experiments,we replace the random selection with different active learning strategies to choose a subset of the training dataset to train the same model and reassess its performance on test set.The experimental results suggest that different active learning strategies yield performance improvement of 7% on document level datasets and 3%on sentence level datasets for auto labelling.

OPTIMAL LABELLING OF UNIT IN TERVAL GRAPHS

This paper shows that, for every unit interval graph, there is a labelling which is simultaneously optimal for the following seven graph labelling problems: bandwidth, cyclic bandwidth, profile, fill-in, cutwidth, modified cutwidth, and bandwidth sum(linear arrangement).

Importance of labelling and patient knowledge to ensure proper care during drug dispensing: A case study from a tertiary hospital in Ethiopia

Correct drug labelling is central for ensuring proper drug dispensing and thus for patient safety. Labelling errors may result in adverse health outcomes. The objective of this study was, therefore, to assess the effect of labelling on the quality of drug dispensing and patient knowledge about dispensed drugs in Jimma University model and specialized hospital outpatient Pharmacies. Individual packages with prescribed drugs were examined using pretested questionnaire and observational check lists during the dispensing process. Patients’ knowledge about drugs dispensed to them was assessed at the exit interview using a pretested questionnaire. Out of 743 prescribed drugs, 682 (91.8%) were dispensed to 426 patients. The average labelling score (range from 0 to 6) of dispensed drugs in Model and Outpatient pharmacy was 2.00 (95% CI 1.97 to 2.04) and 1.73 (95% CI 1.6 to 1.8) respectively, with overall average labelling score of 1.90 (95% CI 1.84 to 1.91). The average patient knowledge score (range from 0 t0 5) was 3.45 (95% CI 3.31 to 3.59) and 3.5 (95% CI 3.35 to 3.64) for model and outpatient pharmacy, respectively, while the overall average knowledge score was 3.46 (95% CI 3.37 to 3.57). Major labelling problems were absence of patient’s name and dose followed by frequency of administration, duration of treatment, and the reason for prescription. Literacy status of patients had a significant effect on their knowledge (p 0.05). We recommend that corrective measures targeting both, labelling and patients’ knowledge should be implemented to improve the patients’ safety and drug therapy adherence.

Labelling of the Energetic Efficiency on a Case Study in Passo Fundo, RS, Brazil, in Accordance with the Mentioned Methods under the Respective Brazilian Regulation-RTQ-C

Having as a starting point of the problematic energy consumption of buildings, it studied the panorama of energy efficiency, emphasizing the Brazilian program of the INMETRO （Instituto Nacional de Metrologia, Normalizacao e Qualidade Industrial）, PROCEL “Edifica” （national program for energy efficiency in buildings）, contained in the norm RTQ-C （technical requirements of the quality for the level of energetic efficiency of commercial, service, and public buildings）. For a better understanding of the regulation, it elected a building, emblematic in the city of Passo Fundo, RS, Brazil, illustrating the perspective of the program. The building was classified in accordance with the two methodologies presented by the program： prescriptive and simulation, so it was possible to investigate the peculiarities of each methodology and the own regulation. After the application of the methodologies, we came to the conclusion that the prescriptive method is less accurate and requires more dedication by the evaluator to do all the calculations and surveys required, however, it is the fastest tool and accessible to the field professionals. The simulation on the other hand, is a more accurate methodology and reaches levels of analysis that the prescriptive method does not reach, but still is a tool that needs large initial financial contribution and prior knowledge.

Control of Ewe and Goat Cheeses Labelling on the Croatian Market

The aim of the research was to examine ewe and goat cheese labelling on the market of the Republic of Croatia. During the four years (2012-2016) of the research, 133 cheeses from retail supermarket chains were selected by the random-choice method by the responsible inspection of the Republic of Croatia and analysed at the Reference Laboratory for Milk and Dairy Products of the Department of Dairy Science at the Faculty of Agriculture University of Zagreb. To check the correct labelling of cheeses, the chemical composition of cheese was examined: the fat content, the dry matter content, the fat in dry matter content, and the water content in the fat-free cheese compound were calculated according to the Ordinance on Cheeses and Cheese Products, which refers to cheeses manufactured in the Republic of Croatia, and Regulation EU (2011) of the European Parliament and the Council on providing information to consumers about food products, which covers foreign cheeses. The presence of cow milk in cheeses labelled as products manufactured exclusively from ewe or goat milk was tested by the reference method using iso-electric focusing to determine any adulteration according to Commission Regulation (2008). The results of analysis in this research show the condition of the quality and correct labelling of ewe and goat cheeses on the market of the Republic of Croatia. Of the 133 cheeses analysed, 67 cheeses (50%) did not correspond to the values stated on the declaration or to legal regulations, while the presence of cow milk was found in 30% of the cheeses analysed. The research results indicate the need to carry out continuous systematic control in the interest of protecting consumers and those manufacturers who are following good manufacturing practices.

Investigation of Nitrite Production Pathway in Integrated Partial Denitrification/Anammox Process via Isotope Labelling Technique and the Relevant Microbial Communities

In this study,the nitrogen removal performance of partial denitrificaiton/anammox(PDA)process was investigated by using an UASB reactor.High total nitrogen(TN)removal efficiency(91.97%)was achieved at an influent nitrogen loading rate of 0.64 kg/(m3·d).Anammox bacteria did execute the function of converting nitrate to nitrite in PDA system according to ^(15)N isotope labeling experiments and the contribution was approximately 36.3%.Candidatus_Brocadia,Candidatus_Kuenenia and Thauera were functional strains for anammox and denitrification process,respectively.Thauera and Candidatus_Brocadia were more important for TN removal at high loading rates(0.64 kg/(m3·d)).This result can provide a theoretical and technical foundation for the application of the PDA process.

Some Sequence of Wrapped Δ-Labellings for the Complete Bipartite Graph

The design of large disk array architectures leads to interesting combinatorial problems. Minimizing the number of disk operations when writing to consecutive disks leads to the concept of “cluttered orderings” which were introduced for the complete graph by Cohen et al. (2001). Mueller et al. (2005) adapted the concept of wrapped Δ-labellings to the complete bipartite case. In this paper, we give some sequence in order to generate wrapped Δ-labellings as cluttered orderings for the complete bipartite graph. New sequence we give is different from the sequences Mueller et al. gave, though the same graphs in which these sequences are labeled.

Induced Total Labellings of Models as Scale-free Networks

A recently discovered approach including de Brujin graphs and Eulerian circuits are used to DNA sequencing and fragment assembly, and to simplify DNA graphs through a series of transformations on graphs and digraphs in the field of bioinformatics. Since numbered graphs provide underlying mathematical models in studying the wide variety of seemingly unrelated practical applications, so graph colorings often are used to divide large systems into subsystems. A new graph labeling has been introduced and investigated.

Highly efficient labelling of extracellular vesicles for enhanced detection on a microfluidic platform

Developing precise extracellular vesicles(EVs)labelling techniques with minimal disturbance is of great importance to the follow-up EVs detection and analysis.However,currently available methods such as using probes to conjugate phospholipids or membrane proteins have certain limitations due to EV steric hindrance,dye aggregation,etc.Here,we present a microfluidic platform to enhance EVs’labelling efficiency and improve their detection.This platform provides excellent sample throughput and high-efficiency EV labelling at lower label concentrations with an optimized flowing rate.Flow cytometry analysis(FCM)and cellular uptake results show that EV labelling by utilizing this platform possesses the merits of a higher labelling efficiency with 64.1%relative improvement than conventional co-incubation method and a lower background noise.Moreover,this technique maintains EVs’size,morphology and biological activities.After the recipient cells uptake the EVs treated by the microfluidic platform,the spatial and temporal distribution of EVs in the cells are clearly observed.These results demonstrate that our method holds great potential in efficient labelling of EVs,which is essential to subsequent EV quantification and analysis.

A Note on L(2,1)-labelling of Trees

An L（2, 1）-labelling of a graph G is a function from the vertex set V（G） to the set of all nonnegative integers such that │f（u） - f（v）│≥2 if dG（u, v） = 1 and │f（u） - f（v）│ ≥ 1 if dG（u, v） = 2. The L（2, 1）-labelling problem is to find the smallest number, denoted by A（G）, such that there exists an L（2, 1）-labelling function with no label greater than it. In this paper, we study this problem for trees. Our results improve the result of Wang [The L（2, 1）-labelling of trees, Discrete Appl. Math. 154 （2006） 598-603].