scholarly journals Demand Forecasting for Liquified Natural Gas Bunkering by Country and Region Using Meta-Analysis and Artificial Intelligence

2021 ◽  
Vol 13 (16) ◽  
pp. 9058
Author(s):  
Gi-Young Chae ◽  
Seung-Hyun An ◽  
Chul-Yong Lee
Keyword(s):  
Artificial Intelligence ◽  
Natural Gas ◽  
Meta Analysis ◽  
Demand Forecasting ◽  
Premature Death ◽  
Exhaust Emission ◽  
Demand Prediction ◽  
Global Demand ◽  
Meta Regression Analysis ◽  
Change Concerns

Ship exhaust emission is the main cause of coastal air pollution, leading to premature death from cardiovascular cancer and lung cancer. In light of public health and climate change concerns, the International Maritime Organization (IMO) and several governments are reinforcing policies to use clean ship fuels. In January 2020, the IMO reduced the acceptable sulfur content in ship fuel to 0.5% m/m (mass/mass) for sustainability. The use of liquified natural gas (LNG) as a ship fuel is currently the most likely measure to meet this regulation, and LNG bunkering infrastructure investment and network planning are underway worldwide. Therefore, the aim of this study is to predict the LNG bunkering demand for investment and planning. So far, however, there has been little quantitative analysis of LNG bunkering demand prediction. In this study, first, the global LNG bunkering demand was predicted using meta-regression analysis. Global demand for LNG bunkering is forecast to increase from 16.6 million tons in 2025 to 53.2 million tons in 2040. Second, LNG bunkering prediction by country and region was performed through analogy and artificial intelligence methods. The information and insights gained from this study may facilitate policy implementation and investments.

The effects of centralizing cancer surgery on postoperative mortality: A systematic review and meta-analysis

2021 ◽  
pp. 135581962110089
Author(s):  
Roberto Grilli ◽  
Federica Violi ◽  
Maria Chiara Bassi ◽  
Massimiliano Marino
Keyword(s):  
Evaluation Studies ◽  
Meta Analysis ◽  
Postoperative Mortality ◽  
Interrupted Time Series ◽  
High Volume ◽  
Cancer Surgery ◽  
Interrupted Time Series Analysis ◽  
Meta Regression ◽  
Meta Regression Analysis ◽  
After Cancer

Objectives To review the evidence of the effects of centralization of cancer surgery on postoperative mortality. Methods We searched Medline, Embase, Cinahl, Cochrane and Scopus (up to November 2019) for studies that (i) assessed the effects of centralization of cancer surgery policies on in-hospital or 30-day mortality, or (ii) described changes in both postoperative mortality for a surgical intervention and degree of centralization using reduction in the number of hospitals or increases in the proportion of patients undergoing cancer surgery at high volume hospitals as proxy. PRISMA guidelines were followed. We estimated pooled odds ratios (OR) and conducted meta-regression to assess the relationship between degree of centralization and mortality. Results A total of 41 studies met our inclusion criteria of which 15 evaluated the effect of centralization policies on postoperative mortality after cancer surgery and 26 described concurrent changes in the degree of centralization and postoperative mortality. Policy evaluation studies mainly used before-after designs (n = 13) or interrupted time series analysis (n = 2), mainly focusing on pancreatic, oesophageal and gastric cancer. All but one showed some degree of reduction in postoperative mortality, with statistically significant effects demonstrated by six studies. The pooled odds ratio for centralization policy effect was 0.68 (95% Confidence interval: 0.54–0.85; I2 = 80%). Meta-regression analysis of the 26 descriptive studies found that an increase of the proportion of patients treated at high volume hospitals was associated with greater reduction in postoperative mortality. Conclusions Centralization of cancer surgery is associated with reduced postoperative mortality. However, existing evidence tends to be of low quality and estimates of the effect size are likely inflated. There is a need for prospective studies using more robust approaches, and for centralization efforts to be accompanied by well-designed evaluations of their effectiveness.

Trust in Artificial Intelligence: Meta-Analytic Findings

2021 ◽  
pp. 001872082110139
Author(s):  
Alexandra D. Kaplan ◽  
Theresa T. Kessler ◽  
J. Christopher Brill ◽  
P. A. Hancock
Keyword(s):  
Artificial Intelligence ◽  
Empirical Research ◽  
Meta Analysis ◽  
Automated Vehicles ◽  
Factors Influencing ◽  
Moderating Factors ◽  
Shared Context ◽  
Build Systems ◽  
Significant Factors

Objective The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. Background There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. Method Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. Results Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. Conclusion Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. Application Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

scholarly journals A systematic review and meta-analysis of prevalence of insomnia in the third trimester of pregnancy

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Nader Salari ◽  
Niloofar Darvishi ◽  
Behnam Khaledi-Paveh ◽  
Aliakbar Vaisi-Raygani ◽  
Rostam Jalali ◽  
...  
Keyword(s):  
Pregnant Women ◽  
Sleep Disorders ◽  
Sleep Disturbances ◽  
Meta Analysis ◽  
Physical And Mental Health ◽  
Third Trimester ◽  
Cross Sectional ◽  
The Third ◽  
Physical Problems ◽  
Meta Regression Analysis

Abstract Background Sleep disorders, which are among the foremost important medical care issues, are prevalent in pregnancy. The present study is a meta-analysis of the prevalence of insomnia in the third trimester of pregnancy. This study aims to systematically review the overall prevalence of insomnia in the third trimester of pregnancy through conducting a meta-analysis. Method The literature used in this meta-analysis for the topic discussed above were obtained through searching several databases, including SID, MagIran, IranDoc, Scopus, Embase, Web of Science (WoS), PubMed Science Direct and Google Scholar databases without time limitation until December 2020. Articles developed based on cross-sectional studies were included in the study. The heterogeneity of studies was investigated using the I2 index. Also, the possible effects of heterogeneity in the studied studies are investigated using meta-regression analysis. Result In 10 articles and 8798 participants aged between11–40, the overall prevalence of insomnia in the third trimester of pregnancy based on meta-analysis was 42.4% (95% CI: 32.9–52.5%). It was reported that as the sample size increases, the prevalence of insomnia in the third trimester of pregnancy increases. Conversely, as the year of research increases, the prevalence of insomnia in the third trimester of pregnancy decreases. Both of these differences were statistically significant (P < 0.05). Conclusion Insomnia was highly prevalent in the last trimester of pregnancy. Sleep disorders are neglected among pregnant women, and they are considered natural. While sleep disturbances can cause mental and physical problems in pregnant women, they can consequently cause problems for the fetus. As a result, maintaining the physical and mental health of pregnant mothers is very important. It is thus recommended that in addition to having regular visits during pregnancy, pregnant women should also be continuously monitored for sleep-related disorders.

The Influence of Breastfeeding Educational Interventions on Breast Engorgement and Exclusive Breastfeeding: A Systematic Review and Meta-Analysis

2021 ◽  
pp. 089033442110292
Author(s):  
Mega Hasanul Huda ◽  
Roselyn Chipojola ◽  
Yen Miao Lin ◽  
Gabrielle T. Lee ◽  
Meei-Ling Shyu ◽  
...  
Keyword(s):  
Exclusive Breastfeeding ◽  
Meta Analysis ◽  
Educational Interventions ◽  
Random Effects Model ◽  
Breast Pain ◽  
Randomized Controlled ◽  
Effective Components ◽  
Breastfeeding Education ◽  
Meta Regression ◽  
Meta Regression Analysis

Background Breast engorgement and breast pain are the most common reasons for the early cessation of exclusive breastfeeding by mothers. Research Aims (1) To examine the influence of breastfeeding educational interventions on breast engorgement, breast pain, and exclusive breastfeeding; and (2) to identify effective components for implementing breastfeeding programs. Methods Randomized controlled trials of breastfeeding educational interventions were searched using five English and five Chinese databases. Eligible studies were independently evaluated for methodological quality, and data were extracted by two investigators. In total, 22 trials were identified, and 3,681 participants were included. A random-effects model was used to pool the results, and a subgroup analysis and meta-regression analysis were conducted. Results Breastfeeding education had a significant influence on reducing breast engorgement at postpartum 3 days (odds ratio [OR]: 0.27, 95% CI [0.15, 0.48] p < .001), 4 days (OR: 0.16, 95% CI [0.11, 0.22], p < .001), and 5–7 days (OR: 0.24, 95% CI [0.08, 0.74], p = .013) and breast pain (standardized mean difference: −1.33, 95% CI [−2.26, −0.40]) at postpartum 4–14 days. Participants who received interventions had higher odds of exclusive breastfeeding. Breastfeeding educational interventions provided through lecture combined with skills practical effectively reduced breast engorgement (OR: 0.21; 95% CI [0.15, 0.28]; p = .001) and improved exclusive breastfeeding at postpartum 1–6 weeks (OR: 2.16; 95% CI [1.65, 2.83]; p = .001). Conclusions Breastfeeding educational interventions have been effective in reducing breast engorgement, breast pain, and improved exclusive breastfeeding. A combination of knowledge and skill-based education has been beneficial for sustaining exclusive breastfeeding by mothers.

scholarly journals Systematic review protocol to assess artificial intelligence diagnostic accuracy performance in detecting acute ischaemic stroke and large-vessel occlusions on CT and MR medical imaging

BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e043665
Author(s):  
Srinivasa Rao Kundeti ◽  
Manikanda Krishnan Vaidyanathan ◽  
Bharath Shivashankar ◽  
Sankar Prasad Gorthi
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Diagnostic Accuracy ◽  
Ischaemic Stroke ◽  
Study Protocol ◽  
Systematic Reviews ◽  
Acute Ischaemic Stroke ◽  
Meta Analysis ◽  
Large Vessel ◽  
Sufficient Data

IntroductionThe use of artificial intelligence (AI) to support the diagnosis of acute ischaemic stroke (AIS) could improve patient outcomes and facilitate accurate tissue and vessel assessment. However, the evidence in published AI studies is inadequate and difficult to interpret which reduces the accountability of the diagnostic results in clinical settings. This study protocol describes a rigorous systematic review of the accuracy of AI in the diagnosis of AIS and detection of large-vessel occlusions (LVOs).Methods and analysisWe will perform a systematic review and meta-analysis of the performance of AI models for diagnosing AIS and detecting LVOs. We will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols guidelines. Literature searches will be conducted in eight databases. For data screening and extraction, two reviewers will use a modified Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. We will assess the included studies using the Quality Assessment of Diagnostic Accuracy Studies guidelines. We will conduct a meta-analysis if sufficient data are available. We will use hierarchical summary receiver operating characteristic curves to estimate the summary operating points, including the pooled sensitivity and specificity, with 95% CIs, if pooling is appropriate. Furthermore, if sufficient data are available, we will use Grading of Recommendations, Assessment, Development and Evaluations profiler software to summarise the main findings of the systematic review, as a summary of results.Ethics and disseminationThere are no ethical considerations associated with this study protocol, as the systematic review focuses on the examination of secondary data. The systematic review results will be used to report on the accuracy, completeness and standard procedures of the included studies. We will disseminate our findings by publishing our analysis in a peer-reviewed journal and, if required, we will communicate with the stakeholders of the studies and bibliographic databases.PROSPERO registration numberCRD42020179652.

scholarly journals Serum albumin concentrations are associated with disease severity and outcomes in coronavirus 19 disease (COVID-19): a systematic review and meta-analysis

2021 ◽  
Author(s):  
Panagiotis Paliogiannis ◽  
Arduino Aleksander Mangoni ◽  
Michela Cangemi ◽  
Alessandro Giuseppe Fois ◽  
Ciriaco Carru ◽  
...  
Keyword(s):  
Systematic Review ◽  
Serum Albumin ◽  
Disease Severity ◽  
Meta Analysis ◽  
Severe Disease ◽  
Geographic Area ◽  
Adverse Outcomes ◽  
Poor Outcome ◽  
Standard Mean Difference ◽  
Meta Regression Analysis

AbstractCoronavirus disease 2019 (COVID-19), an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is responsible for the most threatening pandemic in modern history. The aim of this systematic review and meta-analysis was to investigate the associations between serum albumin concentrations and COVID-19 disease severity and adverse outcomes. A systematic literature search was conducted in PubMed, from inception to October 30, 2020. Sixty-seven studies in 19,760 COVID-19 patients (6141 with severe disease or poor outcome) were selected for analysis. Pooled results showed that serum albumin concentrations were significantly lower in patients with severe disease or poor outcome (standard mean difference, SMD: − 0.99 g/L; 95% CI, − 1.11 to − 0.88, p < 0.001). In multivariate meta-regression analysis, age (t =  − 2.13, p = 0.043), publication geographic area (t = 2.16, p = 0.040), white blood cell count (t =  − 2.77, p = 0.008) and C-reactive protein (t =  − 2.43, p = 0.019) were significant contributors of between-study variance. Therefore, lower serum albumin concentrations are significantly associated with disease severity and adverse outcomes in COVID-19 patients. The assessment of serum albumin concentrations might assist with early risk stratification and selection of appropriate care pathways in this group.

scholarly journals The reliability of maternal audit instruments to assign cause of death in maternal deaths review process: a systematic review and meta-analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ratnasari D. Cahyanti ◽  
Widyawati Widyawati ◽  
Mohammad Hakimi
Keyword(s):  
Systematic Review ◽  
Maternal Death ◽  
Meta Analysis ◽  
Diagnostic Reliability ◽  
Maternal Deaths ◽  
Measurement Tools ◽  
Study Population ◽  
Underlying Causes ◽  
Meta Regression Analysis

Abstract Background Maternal Death Reviews (MDR) can assist in formulating prevention strategies to reduce maternal mortality. To support MDR, an adequate MDR instrument is required to accurately identify the underlying causes of maternal deaths. We conducted a systematic review and meta-analysis to determine the reliability of maternal death instruments for conducting the MDR process. Method Three databases: PubMed, ProQuest and EBSCO were systematically searched to identify related research articles published between January 2004 and July 2019. The review and meta-analysis involved identification of measurement tools to conduct MDR in all or part of maternal audit. Eligibiliy and quality of studies were evaluated using the Modified Quality Appraisal of Diagnostic Reliability (QAREL) Checklist: Reliability Studies. Results Overall, 242 articles were identified. Six articles examining the instrument used for MDR in 4 countries (4 articles on verbal autopsy (VA) and 2 articles on facility-based MDR) were included. None of studies identified reliability in evaluation instruments assessing maternal audit cycle as a comprehensive approach. The pooled kappa for the MDR instruments was 0.72 (95%CI:0.43–0.99; p < 0.001) with considerable heterogeneity (I2 = 96.19%; p < 0.001). Subgroup analysis of MDR instruments showed pooled kappa in VA of 0.89 (95%CI:0.52–1.25) and facility-based MDR of 0.48 (95%CI:0.15–0.82). Meta-regression analysis tended to show the high heterogeneity was likely associated with sample sizes, regions, and year of publications. Conclusions The MDR instruments appear feasible. Variation of the instruments suggest the need for judicious selection of MDR instruments by considering the study population and assessment during the target periods.

Rate of Death and Complications in Laparoscopic and Open Roux-en-Y Gastric Bypass. A Meta-analysis and Meta-regression Analysis on 69,494 Patients

2016 ◽  
Vol 26 (8) ◽  
pp. 1956-1963 ◽  
Author(s):  
Emanuele Rausa ◽  
Luigi Bonavina ◽  
Emanuele Asti ◽  
Maddalena Gaeta ◽  
Cristian Ricci
Keyword(s):  
Gastric Bypass ◽  
Regression Analysis ◽  
Meta Analysis ◽  
Meta Regression ◽  
Meta Regression Analysis
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