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Journal of Imaging Sep 2020Recently, our world witnessed major events that attracted a lot of attention towards the importance of automatic crowd scene analysis. For example, the COVID-19 breakout... (Review)
Review
Recently, our world witnessed major events that attracted a lot of attention towards the importance of automatic crowd scene analysis. For example, the COVID-19 breakout and public events require an automatic system to manage, count, secure, and track a crowd that shares the same area. However, analyzing crowd scenes is very challenging due to heavy occlusion, complex behaviors, and posture changes. This paper surveys deep learning-based methods for analyzing crowded scenes. The reviewed methods are categorized as (1) crowd counting and (2) crowd actions recognition. Moreover, crowd scene datasets are surveyed. In additional to the above surveys, this paper proposes an evaluation metric for crowd scene analysis methods. This metric estimates the difference between calculated crowed count and actual count in crowd scene videos.
PubMed: 34460752
DOI: 10.3390/jimaging6090095 -
PLoS Medicine Sep 2014A major challenge in monitoring universal health coverage (UHC) is identifying an indicator that can adequately capture the multiple components underlying the UHC... (Review)
Review
A major challenge in monitoring universal health coverage (UHC) is identifying an indicator that can adequately capture the multiple components underlying the UHC initiative. Effective coverage, which unites individual and intervention characteristics into a single metric, offers a direct and flexible means to measure health system performance at different levels. We view effective coverage as a relevant and actionable metric for tracking progress towards achieving UHC. In this paper, we review the concept of effective coverage and delineate the three components of the metric - need, use, and quality - using several examples. Further, we explain how the metric can be used for monitoring interventions at both local and global levels. We also discuss the ways that current health information systems can support generating estimates of effective coverage. We conclude by recognizing some of the challenges associated with producing estimates of effective coverage. Despite these challenges, effective coverage is a powerful metric that can provide a more nuanced understanding of whether, and how well, a health system is delivering services to its populations.
Topics: Delivery of Health Care; Health Services Accessibility; Health Services Needs and Demand; Humans; Universal Health Insurance
PubMed: 25243780
DOI: 10.1371/journal.pmed.1001730 -
The Southern Medical Record Jan 1886
PubMed: 36024379
DOI: No ID Found -
Applied Network Science 2022Network analysis is a useful tool to analyse the interactions and structure of graphs that represent the relationships among entities, such as sectors within an urban... (Review)
Review
UNLABELLED
Network analysis is a useful tool to analyse the interactions and structure of graphs that represent the relationships among entities, such as sectors within an urban system. Connecting entities in this way is vital in understanding the complexity of the modern world, and how to navigate these complexities during an event. However, the field of network analysis has grown rapidly since the 1970s to produce a vast array of available metrics that describe different graph properties. This diversity allows network analysis to be applied across myriad research domains and contexts, however widespread applications have produced polysemic metrics. Challenges arise in identifying which method of network analysis to adopt, which metrics to choose, and how many are suitable. This paper undertakes a structured review of literature to provide clarity on behind metric selection and suggests a way forward for applied network analysis. It is essential that future studies explicitly report the rationale behind metric choice and describe how the mathematics relates to target concepts and themes. An exploratory metric analysis is an important step in identifying the most important metrics and understanding redundant ones. Finally, where applicable, one should select an optimal number of metrics that describe the network both locally and globally, so as to understand the interactions and structure as holistically as possible.
SUPPLEMENTARY INFORMATION
The online version contains supplementary material available at 10.1007/s41109-022-00476-w.
PubMed: 35854964
DOI: 10.1007/s41109-022-00476-w -
Entropy (Basel, Switzerland) May 2022We consider real-time timely tracking of infection status (e.g., COVID-19) of individuals in a population. In this work, a health care provider wants to detect both...
We consider real-time timely tracking of infection status (e.g., COVID-19) of individuals in a population. In this work, a health care provider wants to detect both infected people and people who have recovered from the disease as quickly as possible. In order to measure the timeliness of the tracking process, we use the long-term average difference between the actual infection status of the people and their real-time estimate by the health care provider based on the most recent test results. We first find an analytical expression for this average difference for given test rates, infection rates and recovery rates of people. Next, we propose an alternating minimization-based algorithm to find the test rates that minimize the average difference. We observe that if the total test rate is limited, instead of testing all members of the population equally, only a portion of the population may be tested in unequal rates calculated based on their infection and recovery rates. Next, we characterize the average difference when the test measurements are erroneous (i.e., noisy). Further, we consider the case where the infection status of individuals may be dependent, which occurs when an infected person spreads the disease to another person if they are not detected and isolated by the health care provider. In addition, we consider an age of incorrect information-based error metric where the staleness metric increases linearly over time as long as the health care provider does not detect the changes in the infection status of the people. Through extensive numerical results, we observe that increasing the total test rate helps track the infection status better. In addition, an increased population size increases diversity of people with different infection and recovery rates, which may be exploited to spend testing capacity more efficiently, thereby improving the system performance. Depending on the health care provider's preferences, test rate allocation can be adjusted to detect either the infected people or the recovered people more quickly. In order to combat any errors in the test, it may be more advantageous for the health care provider to not test everyone, and instead, apply additional tests to a selected portion of the population. In the case of people with dependent infection status, as we increase the total test rate, the health care provider detects the infected people more quickly, and thus, the average time that a person stays infected decreases. Finally, the error metric needs to be chosen carefully to meet the priorities of the health care provider, as the error metric used greatly influences who will be tested and at what test rate.
PubMed: 35741500
DOI: 10.3390/e24060779 -
Chemical Communications (Cambridge,... Feb 2023Effective assessment of catalytic performance is the foundation for the rational design and development of new catalysts with superior performance. The ubiquitous... (Review)
Review
Effective assessment of catalytic performance is the foundation for the rational design and development of new catalysts with superior performance. The ubiquitous screening/optimization studies use reaction yields as the sole performance metric in an approach that often neglects the complexity of the catalytic system and intrinsic reactivities of the catalysts. Using an example of hydrogenation catalysis, we examine the transient behavior of catalysts that are often encountered in activation, deactivation and catalytic turnover processes. Each of these processes and the reaction environment in which they take place are gradually shown to determine the real-time catalyst speciation and the resulting kinetics of the overall catalytic reaction. As a result, the catalyst performance becomes a complex and time-dependent metric defined by multiple descriptors apart from the reaction yield. This behaviour is not limited to hydrogenation catalysis and affects various catalytic transformations. In this feature article, we discuss these catalytically relevant descriptors in an attempt to arrive at a comprehensive depiction of catalytic performance.
PubMed: 36683401
DOI: 10.1039/d2cc05625a -
HardwareX Apr 2022Current battery data sheets focus on battery energy and power density, neglecting thermal performance. This leads to reduced system level efficiency since cells with...
Current battery data sheets focus on battery energy and power density, neglecting thermal performance. This leads to reduced system level efficiency since cells with poor thermal performance require larger, heavier cooling systems to maintain cell temperatures in a suitable range. To address this a new metric, the Cell Cooling Coefficient (CCC), has been developed and it's use as a tool for appropriate cell selection has been demonstrated. It also allows the pack designer to calculate which cooling direction method is most suitable by comparing CCC values for tab and surface cooling. The metric is the ratio between the heat rejected from the cell and the temperature difference between the hottest and coolest point. It therefore has units and allows a pack designer to easily calculate the required amount of cooling power for the cell given a maximum acceptable temperature rise. In this paper we describe a system and method for the accurate determination of the CCC with the aim of facilitating wider adoption of the metric. The system is able to reliably quantify the surface and tab cooling CCC of any pouch cell.
PubMed: 35509912
DOI: 10.1016/j.ohx.2022.e00257 -
The Chicago Medical Journal and Examiner Jul 1878
PubMed: 37617241
DOI: No ID Found -
Global Challenges (Hoboken, NJ) Oct 2021Global health and global economies are predicted to be severely affected by antimicrobial resistance (AMR). The three organizations World Health Organization/World...
Global health and global economies are predicted to be severely affected by antimicrobial resistance (AMR). The three organizations World Health Organization/World Organisation for Animal Health/Food and Agriculture Organization (WHO/OIE/FAO) are working in their domains to prevent any future AMR crisis. Antimicrobial use (AMU), especially in food animals, is contributing to the development and dissemination of AMR bacteria and genes. AMU monitoring is a strategic objective of the global and national action plans on AMR. However, the AMU reporting metrics at different levels are not harmonized yet, posing difficulties in comparisons among AMU data from different sources. A tripartite WHO/OIE/FAO collaboration is urgently required to develop and implement a globally accepted AMU metric system to ensure reliable comparisons among various data sets.
PubMed: 34631149
DOI: 10.1002/gch2.202100017 -
PloS One 2022When keystroke dynamics are used for authentication, users tend to get different levels of security due to differences in the quality of their templates. This paper...
When keystroke dynamics are used for authentication, users tend to get different levels of security due to differences in the quality of their templates. This paper addresses this issue by proposing a metric to quantify the quality of keystroke dynamic templates. That is, in behavioral biometric verification, the user's templates are generally constructed using multiple enrolled samples to capture intra-user variation. This variation is then used to normalize the distance between a set of enrolled samples and a test sample. Then a normalized distance is compared against a predefined threshold value to derive a verification decision. As a result, the coverage area for accepted samples in the original space of vector representation is discrete. Therefore, users with the higher intra-user variation suffer higher false acceptance rates (FAR). This paper proposes a metric that can be used to reflect the verification performance of individual keystroke dynamic templates in terms of FAR. Specifically, the metric is derived from statistical information of user-specific feature variations, and it has a non-decreasing property when a new feature is added to a template. The experiments are performed based on two public keystroke dynamic datasets comprising of two main types of keystroke dynamics: constrained-text and free-text, namely the CMU keystroke dynamics dataset and the Web-Based Benchmark for keystroke dynamics dataset. Experimental results based on multiple classifiers demonstrate that the proposed metric can be a good indicator of the template's false acceptance rate. Thus, it can be used to enhance the security of the user authentication system based on keystroke dynamics.
Topics: Biometric Identification
PubMed: 35061684
DOI: 10.1371/journal.pone.0261291