To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
A family of nutrient-rich food (NRF) indices was validated against the mean adequacy ratio (MAR) and their associations with obesity were tested.
Cross-sectional study. NRF indices include nutrients to encourage ranging from 6–11 (protein; fibre; vitamin A, vitamin C, vitamin E and vitamin B12; Ca; Fe; K; Mg; Zn) and two nutrients to limit (saturated fat and Na), described as NRFn.2 (where n 6–11), based on reference amount of 100 g or 100 kcal using the NRF index family of algorithms. The percentage of variation in MAR (R2) was the criteria of index performance. Logistic regression models were applied to predict the association between NRF index and obesity.
Three communities in Zhengzhou city, Henan province, China.
A total of 656 adults were recruited from Henan as the subjects.
The NRF9·2 index, based on nine beneficial nutrients and two nutrients to limit, using the algorithm based on sums and 100 kcal, had the higher R2 values (R2 = 0·232). The OR for overweight (defined by BMI) in the 4th quartile (Q4) v. the 1st quartile (Q1) of the NRF9·2 index was 0·61 (95 % CI = 0·37, 0·98) after multiple adjustments.
NRF9·2 index using the algorithm based on sums and 100 kcal gave the best predicted model for diet quality. NRF9·2 index score was associated with overweight defined by BMI, but it was not associated with central obesity. The NRF9·2 index is a valid tool to assess the overall diet quality among adults in Henan province of China.
The Lochkovian (Lower Devonian) conodont biostratigraphy in China is poorly known, and conodont-based subdivision schemes for the Lochkovian in peri-Gondwana (the Spanish Central Pyrenees, the Prague Synform, Sardinia, and the Carnic Alps) have not been tested in China. Therefore, we studied conodonts from the lower part (Bed 9 to Bed 13) of the Shanjiang Formation at the Alengchu section of Lijiang, western Yunnan to test the application of established subdivision schemes. The conodont fauna is assignable to 12 taxa belonging to eight genera (Ancyrodelloides, Flajsella, Lanea, Wurmiella, Zieglerodina, Caudicriodus, Pelekysgnathus, and Pseudooneotodus), and enables recognition of two chronostratigraphical intervals from the lower part of the Shanjiang Formation. The interval ranging from the uppermost part of Bed 9 to the upper part of Bed 10 belongs to the lower Lochkovian; whereas an interval covering the uppermost part of Bed 11 to the upper part of Bed 13 is correlated with the upper half of the middle Lochkovian. The Silurian-Devonian boundary is probably located within Bed 9, in the basal part of the Shanjiang Formation. However, the scarcity of specimens precludes definitive identification of bases of the lower, middle, and upper Lochkovian as well as other conodont zones recognized in peri-Gondwana.
The unprecedented disruption brought about by the global coronavirus disease 2019 (COVID-19) pandemic had produced tremendous influence on the practice of pharmacy. Sufficient knowledge of pharmacists was needed to deal with the epidemic situation; however, outbreak also aggravated psychological distress among health-care professionals. Therefore, this study aimed to determine knowledge about the pandemic and related factors, prevalence and factors associated with psychological distress among hospital pharmacists of Xinjiang Province, China.
An anonymous online questionnaire-based cross-sectional study was conducted by means of WeChat, a popular social media platform in China, February 23-27, 2020, during the COVID-19 outbreak. The survey questionnaire consisted of 4 parts, including informed consent section, demographic section, knowledge about COVID-19, and assessment of overall mental health through World Health Organization’s Self-Reporting Questionnaire (SRQ-20). A score of 8 or above on SRQ-20 was used as cutoff to classify the participant as in psychological distress. SRQ-20 score and related knowledge score were used as dependent variables, demographic characteristics (such as gender, age, monthly income, etc.) were used as independent variables, and univariate binary logistic regression was used to screen out the variables with P < 0.05. Then, the filtered variables were used as independent variables, and multivariate logistic regression models were used to analyze associations with sufficient knowledge of COVID-19 and psychological distress.
A total of 365 pharmacists participated in the survey, fewer than half (35.1%; n = 128) of pharmacists attained a score of 6 or greater (out of 10) in overall disease knowledge, and most were able to select effective disinfectants and isolation or discharge criteria. In the multivariable model, age ages 31-40 (odds ratio [OR] = 3.25; P < 0.05), ages 41-50 (OR = 2.96; P < 0.05) versus >50 (referent); primary place of practice in hospitals: drug supply (OR = 4.00; P < 0.01), inpatient pharmacy (OR = 2.06, P < 0.01), clinical pharmacy (OR = 2.17, P < 0.05) versus outpatient pharmacy (referent); monthly income Renminbi (RMB, China’s legal currency) 5000-10,000 (OR = 1.77; P < 0.05) versus < 5000 (referent); contact with COVID-19 patients or suspected cases (OR = 2.27; P < 0.01); access to COVID-19 knowledge remote work+ on-site work (OR = 6.07; P < 0.05), single on-site work (OR = 6.90; P < 0.01) versus remote work (referent) were related to better knowledge of COVID-19. Research found that 18.4% of pharmacists surveyed met the SRQ-20 threshold for distress. Self-reported history of mental illness (OR = 3.56; P < 0.05) and working and living in hospital versus delay in work resumption (OR = 2.87; P < 0.01) were found to be risk factors of psychological distress.
Further training of COVID-19 knowledge was required for pharmacists. As specific pharmacist groups were prone to psychological distress, it was important for individual hospitals and government to consider and identify pharmacists’ needs and take steps to meet their needs with regard to pandemic and other work-related distress.
Sarcopenic obesity is regarded as a risk factor for the progression and development of non-alcoholic fatty liver disease (NAFLD). Since male sex is a risk factor for NAFLD and skeletal muscle mass markedly varies between the sexes, we examined whether sex influences the association between appendicular skeletal muscle mass to visceral fat area ratio (SVR), that is, an index of skeletal muscle mass combined with abdominal obesity, and the histological severity of NAFLD. The SVR was measured by bioelectrical impedance in a cohort of 613 (M/F = 443/170) Chinese middle-aged individuals with biopsy-proven NAFLD. Multivariable logistic regression and subgroup analyses were used to test the association between SVR and the severity of NAFLD (i.e. non-alcoholic steatohepatitis (NASH) or NASH with the presence of any stage of liver fibrosis). NASH was identified by a NAFLD activity score ≥5, with a minimum score of 1 for each of its categories. The presence of fibrosis was classified as having a histological stage ≥1. The SVR was inversely associated with NASH in men (adjusted OR 0·62; 95 % CI 0·42, 0·92, P = 0·017 for NASH, adjusted OR 0·65; 95 % CI 0·43, 0·99, P = 0·043 for NASH with the presence of fibrosis), but not in women (1·47 (95 % CI 0·76, 2·83), P = 0·25 for NASH, and 1·45 (95 % CI 0·74, 2·83), P = 0·28 for NASH with the presence of fibrosis). There was a significant interaction for sex and SVR (Pinteraction = 0·017 for NASH and Pinteraction = 0·033 for NASH with the presence of fibrosis). Our findings show that lower skeletal muscle mass combined with abdominal obesity is strongly associated with the presence of NASH only in men.
Because the ARMA–GARCH model can generate data with some important properties such as skewness, heavy tails, and volatility persistence, it has become a benchmark model in analyzing financial and economic data. The commonly employed quasi maximum likelihood estimation (QMLE) requires a finite fourth moment for both errors and the sequence itself to ensure a normal limit. The self-weighted quasi maximum exponential likelihood estimation (SWQMELE) reduces the moment constraints by assuming that the errors and their absolute values have median zero and mean one, respectively. Therefore, it is necessary to test zero median of errors before applying the SWQMELE, as changing zero mean to zero median destroys the ARMA–GARCH structure. This paper develops an efficient empirical likelihood test without estimating the GARCH model but using the GARCH structure to reduce the moment effect. A simulation study confirms the effectiveness of the proposed test. The data analysis shows that some financial returns do not have zero median of errors, which cautions the use of the SWQMELE.
This chapter covers other canonical applications of network tomography that have been studied in the literature but fallen out of the scope of the previous chapters. This includes the inference of network routing topology (network topology tomography) and the inference of traffic demands (traffic matrix or origin-destination tomography). It also covers miscellaneous techniques used in network tomography that are not covered in the previous chapters (e.g., network coding). The chapter then concludes the book with discussions on practical issues in the deployment of tomography-based monitoring systems and future directions in addressing these issues.
Additive network tomography, which addresses the inference of link/node performance metrics (e.g., delays) that are additive from the sum metrics on measurement paths, represents the most well-studied branch in the realm of network tomography, upon which a rich body of seminal works have been conducted. This chapter focuses on the case in which the metrics of interest are additive and constant, which allows the network tomography problem to be cast as a linear system inversion problem. After introducing the abstract definitions of link identifiability and network identifiability using linear algebraic conditions, the chapter presents a series of graph-theoretic conditions that establish the necessary and sufficient requirements to achieve identifiability in terms of the number of monitors, the locations of monitors, the connectivity of the network topology, and the routing mechanism. It also contains extended conditions that allow the evaluation of robust link identifiability under failures and partial link identifiability when the network-wide identifiability condition is not satisfied.
This chapter completes the topic of measurement design for additive network tomography, started in Chapter 3, by discussing how to construct suitable measurement paths to identify additive link metrics using a given set of monitors. As in Chapter 3, the focus is on the design of efficient path construction algorithms that make novel use of certain graph algorithms (specifically, algorithms for constructing independent spanning trees) to find a set of paths that form a basis of the link space without enumerating all possible paths. The chapter also discusses a variation of the path construction problem when the number of measurement paths is constrained and each measurement path may fail with certain probability.
Chapters 7 and 8 are designated for network tomography for stochastic link metrics, which is a more fine-grained model than the models of deterministic additive/Boolean metrics, capturing the inherent randomness in link performances at a small time scale. Referred to as stochastic network tomography, these problems are typically cast as parameter estimation problems, which model each link metric as a random variable with a (partially) unknown distribution and aim at inferring the parameters of these distributions from end-to-end measurements. Chapter 7 focuses on one branch of stochastic network tomography that is based on unicast measurements. It introduces a framework based on concepts from estimation theory (e.g., maximum likelihood estimation, Fisher information matrix, Cramér–Rao bound), within which probing experiments and parameter estimators are designed to estimate link parameters from unicast measurements with minimum errors. Closed-form solutions are given for inferring parameters of packet losses (i.e., loss tomography) and packet delay variations (i.e., packet delay variation tomography).
Boolean network tomography is another well-studied branch of network tomography, which addresses the inference of binary performance indicators (e.g., normal vs. failed, or uncongested vs. congested) of internal network elements from the corresponding binary performance indicators on measurement paths. Boolean network tomography fundamentally differs from additive network tomography in that it is a Boolean linear system inversion problem in which each measurement path only provides one bit of information and hence deserves a separate discussion. This chapter introduces a series of identifiability measures (e.g., k-identifiability, maximum identifiability index) to quantify the capability of Boolean network tomography in uniquely detecting and localizing failed/congested network elements. As the definitions of these identifiability measures are combinatorial in nature and hard to verify for large networks, the discussion focuses on polynomial-time verifiable conditions and computable bounds, as well as the associated algorithms.
Based on the conditions for identifying additive link metrics presented in Chapter 2, this chapter addresses two network design questions: (1) Given an unbounded number of monitors, where should they be placed in the network to identify the metrics of all the links using a minimum number of monitors? (2) Given a bounded number of monitors, where should they be placed in the network to identify the metrics of the largest subset of links? The focus here is on the design of intelligent algorithms that can efficiently compute the optimal monitor locations without enumerating all possible monitor placements, achieved through strategic decomposition of the network topology based on the required identifiability conditions. Variations of these algorithms are also given to address cases with predictable or unpredictable topology changes and limited links of interest. In addition to theoretical analysis, empirical results are given to demonstrate the capability of selected algorithms for which such results are available.
In contrast to unicast measurements considered in Chapter 7, this chapter focuses on stochastic network tomography based on multicast measurements, where each probe is sent along a multicast tree from one source to multiple destinations, duplicated at each intermediate node with at least two outgoing links. Using loss tomography as an example, the chapter details how the correlations between the measurements at different destinations sharing links in the multicast tree can be utilized to infer link loss rates, while briefly discussing how this approach applies to other performance metrics. Moreover, this chapter further illustrates how correlated loss observations obtained from multicast probes can be used to reliably infer the topology of the multicast tree, which belongs to another branch of network tomography (network topology tomography) that will be formally introduced in Chapter 9, and complements the high-level discussions there.
This chapter covers preliminary materials required to understand the presentation in the following chapters, including selected definitions from graph theory, linear algebra, and parameter estimation. We also introduce a classification of routing mechanisms based on the controllability of the routing of probes by monitors generating the probes, which will facilitate the discussion in the following chapters.
Based on the identifiability measures for Boolean network tomography presented in Chapter 5, this chapter addresses the follow-up question of how to design the measurement system to optimize the identifiability measure of interest, with a focus on the placement of monitoring nodes. Depending on the mechanism to collect measurements, the problem is divided into (1) monitor placement, (2) beacon placement, and (3) monitoring-aware service placement, where the first approach requires monitoring nodes at both endpoints of each measurement path, the second approach requires a monitoring node only at one of the endpoints of each measurement path, and the third approach requires each measurement path to be the default routing path between a client and a server. As many of such problems are NP-hard, the focus is put on establishing the hardness of the optimal solution and developing polynomial-time suboptimal algorithms with performance guarantees. The chapter also covers a suite of path construction problems addressing how to construct or select measurement paths to optimize the tradeoff between identifiability and probing cost.